EXTERNAL SCIENTIFIC REPORT

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1 EXTERNAL SCIENTIFIC REPORT APPROVED 31 May 2017 doi: /sp.efsa.2017.en-1252 Closing gaps for performing a risk assessment on Listeria monocytogenes in ready-to-eat (RTE) foods: activity 2, a quantitative risk characterization on L. monocytogenes in RTE foods; starting from the retail stage Fernando Pérez-Rodríguez, 1 Elena Carrasco, 1 Sara Bover-Cid, 2 Anna Jofré, 2 and Antonio Valero 1 1 Departamento de Bromatología y Tecnología de los Alimentos, University of Cordoba (UCO), Córdoba, Spain, 2 Institut de Recerca i Tecnologia Agroalimentàries (IRTA), Food Safety Programme, Monells, Spain Abstract A quantitative risk characterization of Listeria monocytogenes in various ready-to-eat (RTE) food categories (heat-treated meat; smoked and gravad fish; and soft and semi-soft cheeses) in the European Union (EU) was performed; starting from the retail stage. For prevalence and concentration, data from the EU-wide baseline survey was complemented with EU monitoring data and data from other sources. Food serving size and the number of servings per year were estimated from the European food consumption database. Demographical data from Eurostat were also used. Growth of L. monocytogenes considering interaction with lactic acid bacteria was modelled from retail to consumption using temperature-time profiles during transport and storage. This information was combined with the Pouillot dose-response models to estimate the number of listeriosis cases per 10 6 servings as well as the annual number of listeriosis cases in the EU associated with the consumption of the RTE foods. The total number of cases was estimated as 2,318 (95 confidence interval (CI): 1,450-3,612). Cooked meat and sausage presented most cases (median of 863 and 541, respectively). Sliced pâté packaged in normal atmosphere presented the highest listeriosis risk per million servings. With respect to the estimation of the total number of cases per population group, considering each food subcategory separately, the higher risk population group corresponded to elderly, followed, in most cases, by pregnant and healthy, with the exceptions of cooked meat and hot smoked fish in which pregnant presented higher risk than elderly. In the light of results, it seems necessary that educative programs and specific recommendations are specially oriented the most susceptible population groups so as to mitigate the risk. Uncertainty sources for some variables such as initial MAY prevalence should be further elucidated as well as variability in Listeria growth when types of product and populations are compared. European Food Safety Authority, 2017 Key words: quantitative risk characterization, Listeria monocytogenes, ready-to-eat food, doseresponse model, growth parameter, number of listeriosis cases Requestor: European Food Safety Authority Question number: EFSA-Q Correspondence: biocontam@efsa.europa.eu EFSA Supporting publication 2017:EN-1252

2 Disclaimer: The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors. Acknowledgements: The authors would like to thank the EFSA staff members: Winy Messens, Marios Georgiadis and Davide Arcella as well as the members of the Working Group on Listeria monocytogenes contamination of ready-to-eat foods: Kostas Koutsoumanis, Roland Lindqvist, Moez Sanaa, Panagiotis Skandamis, Niko Speybroeck, Johanna Takkinen and Martin Wagner for the support, revisions and suggestions provided during the development of the present procurement activity and report. Suggested citation: Fernando Pérez-Rodríguez, Elena Carrasco, Sara Bover-Cid, Anna Jofré, and Antonio Valero, Closing gaps for performing a risk assessment on Listeria monocytogenes in ready-to-eat (RTE) foods: activity 2, a quantitative risk characterization on L. monocytogenes in RTE foods; starting from the retail stage. EFSA supporting publication 2017:EN pp. doi: /sp.efsa.2017.en-1252 ISSN: European Food Safety Authority, 2017 Reproduction is authorised provided the source is acknowledged. 2 EFSA Supporting publication 2017:EN-1252

3 Summary The project Closing gaps for performing a risk assessment on Listeria monocytogenes in ready-to-eat (RTE) foods: activity 2, a quantitative risk characterization on L. monocytogenes in RTE foods; starting from the retail stage was awarded by EFSA to a Spanish consortium which comprises the Universidad de Córdoba (UCO, project coordinator) and the Institut de Recerca i Tecnologia Agroalimentàries (IRTA). The overall objective of this project was to provide EFSA with a quantitative risk characterization of L. monocytogenes in various RTE food categories in the European Union (EU); starting from the retail stage. The four objectives as stated in the Tender specifications were: - to carry out a search and critically review data and existing microbial risk assessments on listeriosis and L. monocytogenes in RTE foods (hazard identification; objective 1); - to determine the exposure of humans in the EU to L. monocytogenes from consumption of various RTE food categories (exposure assessment; objective 2); - to assess the potential for L. monocytogenes to cause illness in human populations (hazard characterisation/dose-response; objective 3); and - to apply an appropriate model, integrating exposure and DR models, in order to estimate the public health risks from consumption of various RTE food categories contaminated with L. monocytogenes (risk characterisation; objective 4). This objective also foresees the provision of a training session to EFSA staff and other potential users on the risk characterisation model. RTE food categories were studied, specifically those falling, at least, into these categories: packaged (not-frozen) gravad fish, packaged heat-treated meat products and soft or semi-soft cheese (excluding fresh cheeses). The well-known four-step scheme for risk assessment was followed: hazard identification, exposure assessment, hazard characterization and risk characterization. In this report, a comprehensive quantitative risk assessment model of L. monocytogenes in RTE foods in the EU population is presented. For hazard identification, a systematic review on microbial risk assessments and models on listeriosis and L. monocytogenes in RTE foods was carried out. The review was based on selection of questions and eligibility criteria, searching in specialized bibliographic databases for study selection and final characterization of information and data collection. Bibliometric information based on the pre-established fields was generated to provide a global overview of the characteristics of the risk assessment models available for L. monocytogenes/listeriosis in RTE foods. Basically, the information was grouped into: (i) the scope of the study; (ii) the approach and technical aspects of the risk assessment; (iii) hazard characterization / dose-response features; (iv) exposure assessment features; and (v) risk characterisation features. The outcome from this revision corresponded mostly to research studies aiming to perform a global risk assessment and/or being a case study with a specific aim. Among the food categories presented, 25 studies (53%) included meat products, and specifically, 38% cooked meat (deli meat). Fish, dairy and produce were included in 14, 17 and 11 risk assessment studies, respectively. However, it should be noted that for RTE food categories, sub-categorization yielded a variety of definitions for specific products, such as RTE cheese (i.e. fresh cheese, soft cheese, cheese in general etc.). Risk assessment studies including produce mainly considered leafy greens. Finally, the risk was mostly described per annum, risk per serving or per habitant. Regarding hazard characterization, selection of the most appropriate dose-response (DR) relationships information coming from the hazard identification was further processed and a literature 3 EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is without prejudice to the rights of the author(s).

4 review of risk assessments and other studies dealing with L. monocytogenes DR models was achieved. Application of the so-called Numerical Unit Spread Assessment Pedigree (NUSAP) system was used as a scoring system for model selection. It was concluded to select the DR model of Pouillot et al. (2015). The FAO/WHO model was also selected as an alternative model. For exposure assessment, four main quantitative components were analyzed, i.e. prevalence/concentration of L. monocytogenes; food serving size and number of serving per year; stochastic growth models of the pathogen; and temperature-time profiles during food transport and storage. As regard the type of product, i.e. type of package atmosphere and slicing/non-slicing, a definitive and general conclusion could not be drawn. Overall, it appears that slicing and normal atmosphere are more often related to higher listeriosis, although, this was not a general rule and, for example, for sausage, the riskiest combination corresponded to sliced and ROP packaged sausage. It is likely that the combined effect of prevalence and the shelf-life associated with each product could play a relevant role in these differences. For risk characterization, estimation of yearly number of listeriosis cases for food category evidenced that the higher risk population group corresponded to pregnant, followed by elderly, and finally healthy population, in heat-treated meat, while for RTE fish and soft and semi-soft cheeses, elderly population presented the highest risk. The total number of cases estimated by the risk model corresponded to 2,318 (95CI: 1,450-3,612). Results showed that cooked meat and sausage presented most cases (median of 863 and 541, respectively) followed by gravad and cold-smoked fish (median of 370 and 358, respectively). The lowest risk was associated with hot-smoked fish with seven annual cases (median). According to the scenario simulation outcomes (cases per 10 6 servings), pregnant population presented the highest listeriosis risk per million servings. In turn, by considering total annual cases per food subcategory, the higher risk population group corresponded to elderly, followed, in most subcategories, by pregnant and healthy, with the exceptions of cooked meat and hot smoked fish in which pregnant presented higher risk than elderly. The riskiest scenarios corresponded to sliced pâté packaged in normal atmosphere (95CI: cases per 10 6 servings) together with sliced gravad fish under ROP and normal atmospheres (medians of 1.13 and 1.09 cases per 10 6 servings, respectively). Uncertainty sources associated to public health risk of L. monocytogenes were identified as: Expected prevalence and concentration of L. monocytogenes at retail. This was related to the shape and the parameters of the distributions used to account for both variability and uncertainty; The assessment of growth of L. monocytogenes from retail up to consumption. In this case, uncertainty is related to the choice of environmental factors considered in the risk model and how they affect the microbiological growth/survival process; and Consumption patterns among the EU population (i.e., serving size, frequency of consumption). Model convergence and stability of the outcomes. This affected the selection of the RNG seed together with the number of iterations. Calculated Coefficients of Variation using different seeds and number of iterations indicated that RTE fish models had the greatest influence on the variation in the number of cases. It should be mentioned that these variations are substantially reduced for RTE meat and soft and semi-soft cheese models. According to previous literature, it can be concluded that model variations due to RGN seed, although relatively high in some cases, can be appraised acceptable taking into account the high variability found in reported outbreaks in real world. Scenario analysis demonstrated that the effect of selecting abuse temperature conditions, together with increasing maximum values of the initial concentration distribution of L. monocytogenes and increasing time to consumtion led to a substantial increase in the number of listeriosis cases. On the 4 EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is without prejudice to the rights of the author(s).

5 contrary, the effect of inclusion of lag time, produced a different effect depending on the RTE food. While for heat-treated meat the effect of lag time was relevant, its effect on the estimated number of cases in RTE fish and cheese was not significant. Results obtained from the risk model indicated that the growth component was less determinant than expected, which can be due to the selected time-temperature profiles, the level of interaction with lactic acid bacteria, food spoilage and percentage of servings supporting growth. Uncertainty on these elements and predictive models should be considered if results are used to derive specific measurement or mitigation strategies. Further analyses should be performed as long as more knowledge or data become available. The higher incidence detected in specific population groups (elderly population and pregnant women) signals that educative programs and specific recommendations are needed to mitigate the risk in these more susceptible populations. 5 EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is without prejudice to the rights of the author(s).

6 Appendix A Quantitative risk characterization on Listeria monocytogenes in ready-to-eat foods Table of contents Abstract... 1 Summary Introduction Background and Terms of Reference (ToR) as provided by the requestor Interpretation of the ToR Approach to answer the ToR Data and Methodologies Data The EU-wide baseline survey (BLS) on the prevalence of Listeria monocytogenes in certain ready-to-eat (RTE) foods in the EU, Monitoring data of food-borne zoonotic infections in the EU Listeria Growth data bases based on Combase and literature review The EFSA Comprehensive European Food Consumption Database Mintel database Eurostat: EU population data Fishstat: EU production data for fish products Scientific literature and other data sources Use of data from NP/EFSA/BIOCONTAM/2015/04 (activity 1) Methodologies Hazard identification Hazard characterization Exposure Assessment Risk characterization Assessment/Results Hazard identification Search for studies Scope of the risk assessment, approach and technical aspects Hazard characterization/dose-response Exposure assessment Risk characterisation Hazard characterization Introduction General review on dose-response models of L. monocytogenes NUSAP assessment Discussion and final selection of the dose-response models of L. monocytogenes Exposure assessment Prevalence-based subcategorization Growth-based subcategorization Risk modelling process structure Prevalence distributions for L. monocytogenes in different RTE products in the EU Concentration distribution for L. monocytogenes in different RTE products in the EU Consumption patterns Growth modelling Risk characterization Description of the dose-response model and r parameter Assessment of the effect of different subcategories and scenarios of heat treatment products on the listeriosis risk in the EU Estimated number of annual cases of listeriosis associated with the consumption of RTE foods in the EU Uncertainty Scenario analysis EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is without prejudice to the rights of the author(s).

7 4. Conclusions Recommendations References Abbreviations Appendix A Growth databases Appendix B Representativeness of the formulation recorded in the growth databases Appendix C Protocol for literature/systematic review Appendix D Bibliometric information from the reviewed Risk Assessment studies Appendix E Characteristics of the Listeria monocytogenes risk assessment studies Appendix F NUSAP system Appendix G Time-temperature profiles retrieved from Frisbee database ( Appendix H Organic acids as preservatives in commercial RTE products Appendix I Growth information and lag time modelling of Listeria monocytogenes Appendix J Growth predictive models Appendix K Maximum Population Density Modelling Appendix L Initial population Lactic Acid Bacteria in different RTE categories Appendix M Risk models and simulation settings Appendix N Assumptions and modelling approach for the Baseline model Appendix O Statistical analysis for growth data Appendix P Concentration distributions of L. monocytogenes in different scenarios Appendix Q Stochastic model for L. monocytogenes growth Appendix R Simulation results to evaluate risk model stability EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is without prejudice to the rights of the author(s).

8 1. Introduction 1.1. Background and Terms of Reference (ToR) as provided by the requestor In the European Union (EU), listeriosis continues to be a serious food-borne illness, with high morbidity, hospitalisation and mortality in vulnerable populations such as pregnant women and the elderly and chronically ill, who are more susceptible to invasive listeriosis. For example, in 2012 and 2013, 1,642 and 2,161 confirmed human cases of listeriosis were reported including 198 and 210 deaths. 1 The trend in reported human listeriosis cases has been gradually increasing over the past four years. The main route of transmission to humans is through consumption of contaminated food. The bacterium can be found in raw foods and in processed foods that are contaminated during and/or after processing. Because L. monocytogenes is able to multiply at low temperatures (2 to 4 C), readyto-eat (RTE) foods with a relatively long shelf-life (such as fishery products, meat products and cheese) are of particular public health concern. An EU-wide baseline survey (BLS) was conducted in 2010 and 2011 to estimate the prevalence and contamination levels in three RTE foods at retail in accordance with Decision 2010/678/EU: packaged (not frozen) smoked or gravad fish (3,053 samples), packaged heat-treated meat products (3,530 samples) and soft or semi-soft cheeses, excluding fresh cheeses (3,452 samples). The foods were randomly selected from the customer display in the outlet. One sample was taken from the selected batch, stored refrigerated in the laboratory and then analysed at the end of shelf-life. For fish, an additional sample from the same batch was analysed on arrival at the laboratory (at the time of sampling). The Part A report on the prevalence estimates and analysis of the qualitative and quantitative survey test results derived from this BLS was published in The EU prevalence of fish samples at the time of sampling was 10.4% and at the end of shelf-life 10.3%, while for meat and cheese samples at the end of shelf-life these prevalence levels were 2.07% and 0.47%, respectively. An External Scientific Report from a contractor submitted to EFSA 5 reports on EU level prevalence analyses and on MS specific data and descriptive statistics. The Terms of Reference of the subsequent Part B report are (a) the analysis of factors related to the prevalence of contaminated foods, (b) the development of predictive models for the microbial growth of L. monocytogenes under various storage conditions, and (c) the development of predictive models for compliance with L. monocytogenes food safety criteria in RTE foods. Although the BLS was designed to target L. monocytogenes in a selection of RTE foods to be at risk of contamination, it did not intend to consider the consumption patterns of the surveyed foods. Prevalence estimates and bacterial counts in the surveyed RTE foods (i.e. exposure assessment) combined with the consumption patterns and dose-response relationship (i.e. hazard characterisation), would enable to estimate the risk to consumers (i.e. risk assessment). The aim of this procurement procedure is to conclude a direct contract for the execution of specific tasks over a clearly defined period as defined in these tender specifications. The overall objective is to provide EFSA with a quantitative risk characterization of L. monocytogenes in various RTE food categories in the EU; starting from the retail stage. 1 EFSA and ECDC, The European Union Summary Report on Trends and Sources of Zoonoses, Zoonotic Agents and Foodborne Outbreaks in EFSA Journal, 12(2):3547, 312 pp. 8 EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is without prejudice to the rights of the author(s).

9 Objectives: The objectives of the contract resulting from the present procurement procedure follow the framework recommended by the Codex Alimentarius Commission 2 and are as follows: objective 1: to carry out a search and critically review data and existing microbial risk assessments on listeriosis and L. monocytogenes in RTE foods (hazard identification) The successful contractor is expected to collect and critically review existing microbial risk assessments and models on listeriosis and L. monocytogenes in RTE foods. The contractor is also requested to assess the availability of consumption data of relevant RTE foods at risk of causing human listeriosis in the EU. objective 2: to determine the exposure of humans in the EU to L. monocytogenes from consumption of various RTE food categories (exposure assessment) The successful contractor is expected to determine the exposure for humans to L. monocytogenes from consumption of foods belonging to various RTE food categories in the EU. The food categories should at least be focussed on the three RTE food categories (packaged (not frozen) smoked or gravad fish, packaged heat-treated meat products and soft or semi-soft cheeses, excluding fresh cheeses) sampled during the BLS. The contractor will be provided by EFSA at the start of the project with the sample-based data (epidemiological data, detection and enumeration) from this EU-wide baseline survey. 3 Details of the foods sampled can be found in the report part A and in the External Scientific Report. The contractor is expected to split the RTE food categories into a selection of subcategories to be agreed with EFSA and informed by the previously mentioned reports. The data dictionary in the report part A lists the variables included in the dataset. The RTE food category packaged (not frozen) smoked or gravad fish could, for example, be split into the subtypes of the RTE fish that was sampled: cold smoked fish, hot smoked fish, unknown smoked fish and gravad fish. The food category packaged heat-treated meat products could, for example, be split into the type of the meat product: sausage, pâté, or cold, cooked meat product. The food category soft or semi-soft cheeses, excluding fresh cheeses could, for example, be split into the subtypes of the RTE food that was sampled (smear-ripened, mould-ripened, brine-matured, otherwise ripened) and/or the type of milk treatment (pasteurised milk, raw milk, and thermised milk). As indicated above, the BLS was conducted at retail level. The successful contractor is expected to investigate the subsequent public health impact at the time of consumption. The potential impact of post-retail factors that could influence the risk to the consumer, such as temperature and duration of refrigerated storage, should be at least described. The successful contractor will be provided during the project with summary statistics related to the consumption of fish and other seafood; meat and meat products; and milk and dairy products in different EU countries. These data will be extracted from the EFSA Comprehensive European Food Consumption Database 4 and provided according to the FoodEx food classification system 5 or, if needed, according to different groups based on the national food descriptors (including more information in certain circumstances). Due to the possible limitations of this Database, the contractor is also expected to retrieve consumption data from other sources (such as food frequency questionnaires). objective 3: to assess the potential for L. monocytogenes to cause illness in human populations (hazard characterisation/dose-response) The successful contractor is expected to describe the health outcome of listeriosis and the sources of outbreaks due to listeriosis based on published studies. 2 CAC (Codex Alimentarius Commission), Procedural Manual, Twentieth Edition. Joint FAO/WHO Food Standards Programme. Available at; ftp://ftp.fao.org/codex/publications/procmanuals/manual_20e.pdf 3 No objections were raised at the Meeting of the SCoFCAH (Section Biological Safety of the Food Chain), held in Brussels on 16 October 2013 for using this EU-wide baseline survey data for this activity EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is without prejudice to the rights of the author(s).

10 The successful contractor is also expected to review and summarise the information on the doseresponse (DR) relationship for L. monocytogenes available in the scientific literature and potential other sources. The review should at least cover the description of the data employed to develop the various DR models (e.g. outbreak data), the endpoint of the model (e.g. infection, illness), the population under study (e.g. general and/or susceptible population). In addition, the advantages and disadvantages of each of these DR models are to be described in the report by the contractor. Based on this information, the contractor is expected to propose the most relevant DR modelling approach with the reasons for choosing this approach and then develop a model (unless the model is already available) to carry out the tasks described under objective 4. objective 4: to apply an appropriate model, integrating exposure and DR models, in order to estimate the public health risks from consumption of various RTE food categories contaminated with L. monocytogenes (risk characterisation). This objective also foresees the provision of a training session to EFSA staff and other potential users on the risk characterisation model. To perform this activity, the successful contractor is expected to employ a stochastic quantitative model to estimate the risks per serving and the yearly risk to the EU population from the consumption of the selected previously described RTE food subcategories contaminated with L. monocytogenes. Also an overall risk estimate should also be provided. The outcome is to be expressed the predicted risk for listeriosis per million servings from consumption of a meal containing each of the RTE food subcategories. The risk estimate is then to be provided for the three RTE food categories as a whole considering the consumption frequencies. The contractor is also expected to estimate the yearly number of listeriosis cases in the EU by each of the (sub)categories. The outcomes are to be provided both for the normal and susceptible population. The principles described in EFSA s guidance document 6 are to be implemented by the successful contractor. For example, all assumptions should be documented and explained. It is important to identify and describe the most influential contributors to variability in risk, preferably by statistical analysis of the underlying data. The types of uncertainties encountered should be described and considered during the different risk assessment steps, and their relative importance and influence on the assessment outcome expressed to the extent that is scientifically achievable. The software through which the model runs should be limited to standard software used by EFSA: R (preferred software) or SAS or Winbugs or excel add-in. EFSA staff and other potential users as indicated by EFSA should be given a training session at the EFSA premises in Parma, Italy, so they are able to use the model independently and generate the results that are described in the Contractors report. The training should also explore the ways in which the model can be utilised in future assessments. The training may last from 1 up to 1.5 working days, and may be attended by up to 12 participants. The successful contractor will be expected to work closely with EFSA and any potential ad hoc Working Group of the BIOHAZ Panel throughout the course of the project to discuss key scientific decisions (e.g. via tele/web conference and via ) to allow timely communication of progress made and results. Participation in four physical meetings in EFSA is envisaged. This contract was awarded by EFSA to: Contractor: Consortium with the University of Cordoba (UCO as leader) and Institut de Recerca i Tecnologia Agroalimentàries (IRTA as partner) 6 Guidance of the Scientific Committee on transparency in the scientific aspects of risk assessment carried out by EFSA. Part 2: general principles. The EFSA Journal (2009) 1051, EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is without prejudice to the rights of the author(s).

11 Contract title: Closing gaps for performing a risk assessment on Listeria monocytogenes in ready-toeat (RTE) foods: activity 2, a quantitative risk characterization on L. monocytogenes in RTE foods; starting from the retail stage Contract number: OC/EFSA/BIOCONTAM/2014/02 CT Interpretation of the ToR The overall objective of the present project is to perform a quantitative risk characterization of L. monocytogenes) in various RTE food categories in the EU; starting from the retail stage, in order to estimate the risk for listeriosis, in different population groups, per million servings from consumption of a meal containing each of the RTE food sub-categories and the yearly number of listeriosis cases in the EU by each of the (sub-) categories. To achieve this goal, four objectives (OBJ) were defined by EFSA which are summarised in Table 1. Table 1: Objectives and brief description of the present project Objective Title Brief description OBJ 1 Literature review Hazard identification To search and critically review data and existing microbial risk assessments and models on listeriosis and L. monocytogenes in RTE foods (hazard identification). Assess availability of consumption data of relevant RTE foods. OBJ 2 Exposure Assessment To determine the exposure of humans in the EU to L. monocytogenes from consumption of various RTE food categories OBJ 3 Hazard characterization/ dose-response To assess the potential for L. monocytogenes to cause illness in human population. Review and summarize available data on DR relationship. Propose the most relevant DR modelling approach and develop a model. OBJ 4 Risk characterization To apply an appropriate model integrating exposure and DR models to estimate the public health risks. Training session. DR: dose reponse; RTE: ready-to-eat. The proposed quantitative risk assessment addresses the specifications of the procurement, in terms of: Food chain: from retail to consumption; Selected RTE food categories: packaged smoked or gravad fish, packaged heat-treated meat products and soft or semi-soft cheeses (excluding fresh cheeses), spit down in subcategories of these foods; Population: The population groups targeted corresponds to healthy, elderly and pregnant woman populations in EU (i.e., 28 EU MSs). These groups were built on the factor age category, thus the healthy population encompassed individuals ranging from 10 to 64 years, elderly from 65 years onwards and pregnant population was estimated based on the number of birth, assuming a constant birth rate between years. Three main activities of different nature were envisaged, which conform the Working Packages (WP) outlined in Table 2 together with a brief description of the nature of the work. To fulfil objective 1, UCO-IRTA consortium has carried out the review activity A1. Risk assessments and models on listeriosis / L. monocytogenes in RTE located within the WP1. Moreover, objectives 2, 3 and 4 were accomplished based on the development of several review and data collection activities (WP1) as well as modelling activities (WP2). The information provided in the present report will be structured by following the same scheme of objectives proposed in the present project, providing detail how they were accomplished and the main outcome obtained in each case EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is without prejudice to the rights of the author(s).

12 Table 2: Working Packages and brief description of activities Work Title WP Objectives Package WP 0 Project coordination and management Coordination between partners, establishment of communication strategy, follow-up of the on-going work and revision of the finished work the WPs, implementation of the contingency plan WP 1 Review and data collection Systematic review, literature search, data collection, quality assessment, analysis and reporting WP 2 WP 3 Model development and framework Report and output communication and training Selection of predictive models and variables to describe input data and connections, implementation of algorithms, stochastic modelling by integrating information, addressing transparency of results by means of uncertainty and variability inclusion in the risk assessment models Output review, writing reports and documentation, generating deliverables Training material and session organization 1.3. Approach to answer the ToR To develop the four objectives, different approaches were proposed. In objective 1, a systematic review was applied based on the systematic review methodology proposed by EFSA for food and feed safety assessments (EFSA, 2010). The objective 2 was based on the application of stochastic and deterministic mathematical models aimed at describing the fate of the pathogen along the food steps included in the scope of risk assessment. For objective 3, an adaptation of Numeral Unit Spread Assessment Pedigree (NUSAP) system was applied to select the most appropriate dose-response models (Boone et al., 2009). Finally, objective 4 was accomplished by developing an easy-to-use risk assessment model built (Palisade, UK) and Excel Microsoft (Redmon, USA) to simulate the number of cases in a probabilistic environment, considering variability and uncertainty in the model variables. 2. Data and Methodologies 2.1. Data The EU-wide baseline survey (BLS) on the prevalence of Listeria monocytogenes in certain ready-to-eat (RTE) foods in the EU, Data obtained in the BLS conducted in 2010 and 2011 and reported in Part A report (EFSA, 2013) were used to estimate the L. monocytogenes prevalence and concentration in RTE food categories (in combination with data taken scientific literature). In this survey, a total of 3,053 batches of packaged (not frozen) hot or cold smoked or gravad fish, 3,530 packaged heat-treated meat products and 3,452 soft or semi-soft cheeses were sampled from 3,632 retail outlets in 26 EU MSs and one country not belonging to the EU (Norway). Samples were analysed at the end of shelf-life, while for fish these were also analysed on arrival at the laboratory (at the time of sampling). The consortium was provided by EFSA with an excel file containing detection and enumeration data for each analysed sample together with the corresponding metadata (food description, testing date, temperature, sampling date, use by date, type of atmosphere, etc.). The prevalence data provided by the BLS corresponding to the values found at the end of the shelflife were used to represent prevalence at time of consumption, assuming that no cross contamination takes place at home. The same reasoning as in the BLS was considered in such a way that a sample was identified as positive if L. monocytogenes was detected by at least the detection or the 12 EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is without prejudice to the rights of the author(s).

13 enumeration method. The data from Norway were excluded from the analysis. The consortium used the information given by EFSA with the excel file containing prevalence data for each analysed sample. The corresponding metadata regarding food description, and slicing procedure were subsequently used for setting the prevalence scenarios described in Section 2.2. In the case of concentration, values at the end of the shelf-life for heat-treated meat and soft and semi-soft cheese were deemed not to be adequate for the modelling purposes and were discarded since the scope of the risk model covers the L. monocytogenes growth from retail to consumption. For these food categories, concentration was modelled mainly based on other data sources (i.e. scientific literature and monitoring data). A description of the followed methodology is further described in Section On the contrary, for smoked and gravad fish, BLS data of L. monocytogenes concentration just after sampling at retail were used for estimating retail concentration. The subsequent Part B report (EFSA, 2014), where factors related to prevalence and concentration were statistically analysed, were utilized to support food sub-categorization in the modelling process. A brief description of BLS part A and B (EFSA, 2013, 2014) is given in Section Monitoring data of food-borne zoonotic infections in the EU EU monitoring data were specifically applied to: Calculate prevalence estimates to assess the effect of the data source on prevalence (alternatively to prevalence calculated with BLS data). Prevalence using EU monitoring data together with BLS data was defined as global prevalence ; Model initial concentration of L. monocytogenes at time of sampling. The raw database was examined to select those records belonging to the selected RTE food categories. The original file included several variables, which are described in an EFSA report published in 2016 (EFSA, 2016). A selection protocol was followed as agreed with EFSA. This protocol was based on the selection of representative data from 2011 to 2014, both inclusive. EU monitoring data for L. monocytogenes from in different food products were provided by EFSA. The original file included following variables which are described by EFSA (EFSA, 2016): General information and identification of the isolate/result: result code; reporting year; reporting country; language); Information about the type and source of samples and isolates: zoonotic agent (Listeria) (three levels L1 L3), matrix (four levels L1 L4); Information about sampling plans and conditions: total units tested, total units positive, total samples tested, total samples positive, sampling unit type, sampling stage (retail), sample origin, sample type, sampling context, sampler, programme code, sampling strategy, sampling details, area of sampling, analytical method code; Qualitative information (number of positive samples and units below a certain limit of detection) and quantitative information (number of positive samples and units between 10 and 100 CFU/g and above 100 CFU/g); Additional information when a specific sampling method was followed. The following sub-categories were considered for data selection: Cold smoked fish; Hot smoked fish; Gravad fish; Cold, cooked meat; Pâté; 13 EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is without prejudice to the rights of the author(s).

14 Soft and semi-soft cheese. For the selection of prevalence data, the fields corresponding to totunitstested and totunitspositive were used. Besides, according to the analytical methods performed, the criteria stated in EFSA (2016) were followed. Sausage was not used to calculate global prevalence estimates since the information reported in the monitoring data was only referred to heat-treated meat products. This definition was associated to cold, cooked meat instead. It was agreed with EFSA that this sub-category would be included as cooked meat since there was not further detailed information about the product type. On the other hand, the procedure followed for data selection for initial concentration was: For RTE soft and semi-soft cheese: repyear: Matrix_L1: cheeses made from cows milk, cheeses made from goats milk, cheeses made from sheep s milk, Cheeses, made from mixed milk from cows, sheep and/or goats, Cheeses, made from unspecified milk or other animal milk matrix_l2: "soft and semi-soft" sampstage: "retail" samptype: "food sample " sampcontext: "surveillance" and "Survey- National survey" sampler: "Official sampling", "Official and industry sampling", "Industry sampling" progsampstrategy: "Objective sampling" (527 remain) samparea: all to select (no regional data left (e.g. from Ireland, else these data are duplicated sampunit: "Single" (394 remain) For RTE fish products: repyear: matrix_l1: "Fish", "Fishery products, unspecified", "Fish (food)" matrix_l2: "smoked" (only for smoked fish) or Gravad/slightly salted (only for gravid fish) matrix_l3: "hot-smoked" or cold-smoked matrix_l4: "blanks" sampstage: "retail" samptype: "food sample" sampcontext: "surveillance"; "Survey" sampler: "Official sampling" progsampstrategy: "Objective sampling" (12 remain) samparea: all to select (no regional data left (e.g. from Ireland, else these data are duplicated) (12 remain) sampunit: "Single" (12 remain) For RTE meat products: repyear: "2011, 2012, 2013 and 2014" 14 EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is without prejudice to the rights of the author(s).

15 matrix_l2: "Meat products" matrix_l3: "Cooked ham" or Pâté matrix_l4: "non-sliced", "sliced" or "blanks" sampstage: "retail" samptype: "food sample"; "food sample: meat" sampcontext: "surveillance"; "Survey- National survey" sampler: "Official sampling" progsampstrategy: "Objective sampling" samparea: to exclude regional data (e.g. from Ireland, else these data are duplicated) sampunit: "Single EU monitoring data was used to calculate the global prevalence as well as the initial concentration for the following sub-categories: Cold smoked fish; Hot smoked fish; Gravad fish; Cold, cooked meat; Pâté; Soft and semi-soft cheese Listeria Growth data bases based on Combase and literature review The exponential growth rate (EGR, 1/h), maximum population density (MPD) and lag time values used to model Listeria growth in the targeted RTE products were directly retrieved from Combase database ( for each target food category during the period between 7 and 28 September, The first step consisted of setting the searching criteria in Combase to: Organisms: Listeria monocytogenes/innocua ; Matrix: Sausage, Pork, Beef, Poultry, Cheese, Seafood/fish. Then, the obtained records were examined one by one, recording type of product, food characteristics (ph, water activity (a w ), temperature, etc.), specific preservatives (lactate, acetate, etc.) and source. Specific excluding and including criteria were developed to select those records matching the food categories and sub-categories targeted in the risk assessment. As a general exclusion criterion, records containing negative growth rates or showing no growth were discarded for being integrated into a data base. For the criteria related to the type of product, definitions given in BLS were considered (EFSA, 2013). In the case of heat-treated meat, those records containing terms referred to pâté, cooked meat and (cooked) sausage were included while those containing terms referred to dry fermented meat were excluded. For fish, the records referring to fishery products submitted to a smoking or gravad process were included, discarding those that considered freezing conditions. Finally, for cheese, the type of cheese referred in the information reported in combase database was checked against the those contained in FoodEx2 classification provided by EFSA in order to select those records dealing with soft and semi-soft ripened cheeses, which is the cheese category targeted in the present risk assessment. Those records on cheeses matching with the ones considered in FoodEx2 for soft-ripened cheese were maintained in the DB, while the rest were excluded. Particularly, fresh soft cheeses were excluded as they are not considered in the risk assessment EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is without prejudice to the rights of the author(s).

16 This database was updated using scientific data obtained from a review carried out on Listeria growth values published in scientific papers between 2004 and This review of more than 200 scientific articles resulted in a collection of 221 datasets for RTE heat-treated meat products, 52 for RTE smoked and gravad fish products and 26 for soft and semi-soft cheeses. For those studies that did not report growth parameters, the figures including the growth curves were digitalized by using Digitizelt software (Digitizelt 2.2, Bruanschweig, Germany). Appendix A shows the studies from which data were extracted. The extracted data points were then used to fit the Baranyi primary growth model with DMFit Excel Add-In software (Institute of Food Research, Norwich, UK) to derive the lag phase durations (h), growth rates (1/h) and maximum population density (log 10 CFU/g). The growth data and metadata were standardized and tabulated in independent Excel files (Microsoft, Redmond) for each food category for further use. Scientific studies are usually intended to analyse the effect of preservatives in a broad range regardless other additional aspects such as legal limits or changes in organoleptic attributes; hence, in many cases, values can be out of the industrial ranges. In such a way, the representativeness of the growth data included in the growth database (DB containing combase and literature data) was assessed based on specific criteria in order to discard those where the RTE food has been submitted to no real commercial formulation. These criteria can be summarised as type of ingredient and concentration (ppm, %, ) (see the section Effect of lactate formulation on Listeria EGR). For example, in some cases there are RTE foods containing not allowed preservatives (such as nisin, EDTA) or above the legal limit (nitrite>150 ppm, assuming that it will be added, not residual amount). As result of the representativeness analysis, a qualitative factor (0,1) named as representativeness index was derived and included in the database where food products were categorized as 0: nonrepresentative; or 1: representative. Then, EGR 5ºC values labelled with 0 was filtered out and discarded for modelling. Further details can be consulted in Appendix B The EFSA Comprehensive European Food Consumption Database The EFSA food consumption database (EFSA, 2011c) contains data on food consumption patterns across the EU. This database provides with basic statistics of consumption levels/frequencies for the main food categories/types and specific population groups. The database can be freely accessed at The database was used to estimate serving size and numbers of servings in the target food categories since it is the only data source providing detailed and updated information on consumption patterns for the EU MSs. The information in the EFSA s food consumption data base is built on the food classification and description system FoodEx 1 (EFSA, 2011b), a hierarchical system that allows for unique and universal identification of food items. Recently, a new and updated version of this classification system has been developed, so-called FoodEx2, including an expanded hierarchical structure (i.e. high number of food classification levels) and providing more detailed information on categories (EFSA, 2011a). Since FoodEx1 lacks a suitable food classification level specifying smoked and gravad fish and semisoft and soft cheese, the expanded version of the standard, FoodEx 2, was chosen for these food categories. For fish, available consumption data in FoodEx 2 were quite scatter, with few countries reporting data (i.e. northern countries). Some countries only reported for the smoked fish consumption in specific population groups except for Germany that included a more representative sample of the different population groups. Only one country included data for gravad fish (i.e., Sweden). Due to these limitations, EFSA data base was deemed not to provide representative data for this food subcategory. In turn, for heat-treated meat, FoodEx 1 could be utilized. As the public database only reports summary statistics, an updated and unpublished database containing raw data on the amount consumed (i.e. acute intake) per individual and related metadata were requested and provided by EFSA in csv files where consumption information was provided at 4 th classification level. The EFSA database provided by EFSA included the following variables: Country; Survey; 16 EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is without prejudice to the rights of the author(s).

17 Population: infant (0-1 year old, yo), toddlers (1-3 yo), other children (3-10 yo), adolescents (10-18 yo), adults (18-65 yo), elderly (65-75), very elderly (>75 yo), pregnant women, lactating women; Number of days included in the survey ( ndays ); Day of the record ( day ): e.g. in a survey of two days, a record can correspond to day 1 or 2; Frequency ( freq ): number of eating events of a surveyed individual in one day (i.e., per record); Amount ( amount ): amount consumed by an individual in one day (i.e., per record). Based on these variables, new variables were derived. They were intended to facilitate calculation of serving size and number of servings for 28 EU MSs as a whole. The tailored variables corresponded to the Adjusted amount, which is calculated as amount / freq, and represents the amount consumed per individual and eating event (i.e. serving size) and the Adjusted frequency, that is estimated as freq / ndays, representing the number of eating events per day in a specific survey. The database with the new variables was filtered for those available products included in the databse that match within the considered sub-categories by using Power Pivot Add-in for Microsoft Excel (Microsoft, Redmond). The finally included food products are shown in Table 3. The adjusted (consumption) frequency (per day) and the adjusted (consumed) amount (per eating event) in the filtered database were pooled according to the three population groups considered in the risk assessment. As decribed above, these population groups are mainly built on age range. In such a way, the consumption for healthy population group was derived from data from both Adult and Adolescent, while that the elderly population group was obtained by pooling the categories Elderly and Very Elderly. There were no data available for all countries since in several cases, depending on the survey and country, some population groups were not included. This limitation was also considered when data were extrapolated to the whole EU though this, undoubtedly, confered an additional source of uncertainty in the model. For pregnant population, data were extrapolated from Adult, since information on this specific group was limited to data from Latvia. Filtered excel files were further used to derive exposure assessment inputs for each food sub-category (i.e. serving size and number of servings) Mintel database Mintel-Global New Products Database 7 provides information from ingredients and packaging about new products launched to the market worldwide. The database compiles information present on the labels of food products. This information was used to search for RTE cooked meat products (sliced meat products, pâté and sausages), smoked and gravad/marinated fish products and soft and semisoft cheeses put on the market in the EU during the last 10 years. Information regarding product formulation (i.e., the presence of specific preservatives, such as lactate, acetate/diacetate, nitrite) and packaging was extracted Eurostat: EU population data Eurostat is the statistical office of the EU situated whose mission is to provide high quality statistics for Europe. Eurostat provides with a comprehensive database and data tables, organized in different themes that can be freely accessed. 8 The population size per age group was obtained for each EU MS by accessing theme population data and Population on 1 January by age and sex (demo_pjan). This information was used to extrapolate the estimates of serving size and number of servings from surveys to specific EU populations, i.e. adult and elderly ( 65 years). The number of births for each EU MS was also obtained from Eurostat by accessing theme births and fertility data and used to EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is without prejudice to the rights of the author(s).

18 extrapolate the estimates for the population of pregnant. Data were retrieved from the most recent available year which corresponded to Fishstat: EU production data for fish products Fishstat 9 plus is a free access database powered by Food and Agriculture Organization (FAO) that can be download. The system consists of a main module and the datasets. Each dataset can be installed and uninstalled separately. Also, specific data tables can be accessed on-line and required information can be retrieved and downloaded. The system provides users with access to Fishery Statistics of various sorts. The most recent data (i.e. 2011) on the production, export and import of smoked fish for the different European countries was retrieved from this database and stored in csv files. This information was further used to estimate the apparent consumption of smoked fish products in the EU Scientific literature and other data sources Model selections and input definitions are built on data and information collected from available scientific literature (web of knowledge, Pubmed_NCBI, etc.), grey literature (e.g. WHO/FAO reports, governmental risk assessment studies, etc.), data/model repositories such as foodrisk.org and open predictive microbiology model repository. 10 A systematic review (Appendix C) was carried out focused on risk assessment studies of L. monocytogenes in foods. Results and collected biography were further used as basis to develop the model structure and define the model inputs. In addition, when needed, expert opinion and elicitation were deployed as tools to determine most probable values and ranges for specific inputs (e.g. food formulation, dose-response selection) Use of data from NP/EFSA/BIOCONTAM/2015/04 (activity 1) This information source corresponded to an extensive literature search concerning L. monocytogenes in different RTE foods. The database (in Excel Microsoft file) resulting from this activity was accessed and studies reporting levels of the pathogen in the food category addressed in this work were retrieved at retail (Jofré et al., 2016). The prevalence and concentration values extracted from the selected studies were further treated to populate probability distributions. This information source is referred further to activity 1 report. Among the information reported in the activity 1 report, the following subcategories were selected: soft/semi-soft cheese; cured/salted fish; hot-smoked fish; cold-smoked fish; cooked meat; pâté and sausage. For each subcategory, the number of samples and positives reported in the retrieved studies were considered. To further select studies, those containing all information regarding the microbial concentrations reported were included to populate prevalence and concentration data. Studies where absence of positives samples was reported were included for both prevalence and concentration data (absence in 10 g or 25 g). On the other hand, those studies non-reporting the number of positives, and/or including concentration in other units different from CFU/g were excluded. For prevalence, data regarding cured/salted fish and soft/semi-soft cheese were used to complement previous retrieved data from the BLS. Regarding concentration, all subcategories abovementioned were used EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is without prejudice to the rights of the author(s).

19 Quantitative characterization on Listeria monocytogenes in ready-toeat food Table 3: Ready-to-eat food products selected from the EFSA consumption database matching the RTE food (sub-)categories targeted in the risk assessment Soft and semi-soft cheese Smoked and Cooked meat Sausage Pâté gravad fish Soft-ripened cheese Smoked fish Bacon Berliner-Style, sausage Meat paste Soft-brine cheese (feta type) Beef loaf Blood and tongue sausage Pastes, pâtés and terrines Corned beef Blood sausage Pâté, chicken liver Corned pork Bockwurst Pâté, goose liver Ham and cheese loaf Bologna, sausage Pâté, pork liver Ham, beef Boterhamworst Terrine Ham, pork Bratwurst Ham, turkey Cooked salami Head cheese (brawn) Cooked sausage Liver cheese or liver loaf Cooked smoked sausage Luncheon meat Frankfurters, sausage Meat in aspic Fresh and lightly cooked sausage Meat specialities Kielbasa, sausage Pastrami, beef Knackwurst, sausage Pastrami, pork Landjaeger cervelat Pastrami, lamb Liver sausage, liverwurst Pork meat loaf Mettwurst, sausage Sulze Thuringer-Style sausage Uncooked smoked sausage Vienna sausage Weisswurst Wiener, sausage 19 EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

20 2.2. Methodologies Hazard identification Review on microbial risk assessments and models on listeriosis and L. monocytogenes in RTE foods The review activity has been performed following the systematic review approach in accordance with the guidelines provided by EFSA (2010). The review was prepared through the elaboration of a comprehensive review protocol (Appendix C), which covered the following aspects: (i) specific review questions to be addressed; (ii) eligibility criteria; (iii) resources; (iv) methodology of searching research studies; (v) methodology for selecting retrieved studies; (vi) methodology for collecting information and characterising the selected studies; (vii) methodology for reporting search process, results and processing the retrieved information. Following this protocol, the bias in selecting research studies is minimised and the reproducibility of the search strategy is assured. Some important issues of the review protocol (Appendix C) are summarized below. Review questions and eligibility criteria The studies search was conducted in such a way the eligibility criteria were set a priori. Since the aim of the present review was to collect and characterize existing microbial risk assessments on listeriosis and L. monocytogenes in RTE foods, few restrictions were implemented in a first instance. For practical reasons, and taking into account the characteristics of the review questions and the general aim of the literature review, it was decided to group all review questions (i.e., lumping ) in a common literature search, the strategy of which is described below. In this way, a more sensitive search can be expected (EFSA, 2010). Search in bibliographical databases and other sources For each information source a searching strategy was developed, tested and implemented. Two main types of information sources were considered: (A) Scientific bibliographical databases (Scopus, Web of Science and Medline) and (B) other sources mainly based on specific web portals or sites, including institutional websites. Besides the formal search through bibliographic databases, during the screening of full text and specially when collecting relevant information from selected studies, the reference list at the end of relevant studies as well as citations of key articles and reports were screened. The screening of references and citations enabled the clarification of some confusing duplicates (e.g. the same study published in different forms). Selection of studies The retrieved references from the three bibliographic databases were automatically imported into a single EndNote file, including citation information and abstract. Duplicates were removed using the EndNote tool followed by manual searching. After removal of duplicates, a first-level screening of the title and/or abstract of references was performed based on the inclusion criteria as described in the review protocol (specifically, Eligibility criteria). This classification was recorded with a customised field within the EndNote file, which allowed a rapid selection of potentially interesting references. In case the screening of the title and abstract of the study did not provide a clear insight of the definitive eligibility status, the study was selected for the full text revision EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

21 For excluded references, the reason for exclusion was recorded (e.g., not dealing with L. monocytogenes, not being a full risk assessment or both). Moreover, for a number of excluded references, key words were used to identify and classify the contents or data that could be useful for further activities of the project (i.e. L. monocytogenes prevalence/concentration, storage conditions, dose-response model, exposure assessment model, predictive model, etc.). After the screening of the included studies retrieved from bibliographic databases, the relevant studies retrieved from other sources were added manually to the EndNote file, after checking for duplicates. The studies were reviewed by two independent reviewers and in case of disagreements, a discussion involving at least a third researcher was carried out until reaching a consensus. The full-text of pre-selected references was downloaded for refining the selection of studies and their further review. Examining full text for the eligibility of studies, characterization of selected studies, information and data collection A second-level screening was performed by examining the full text of the references remaining after the first-level screening. Some studies were further excluded in this process. For the finally selected studies analysed, the information collected was recorded into an excel file. The type of information (fields) together with the data dictionary used to collect information and data from selected studies are described in detail in the review protocol (Appendix C). Bibliometric information based on the pre-established fields was generated to provide a global overview of the characteristics of the risk assessment models available for L. monocytogenes/ listeriosis in RTE food (Appendix D). Basically, the information was grouped into: the scope of the study; the approach and technical aspects of the risk assessment; hazard characterization/dose-response features; exposure assessment features; risk characterisation features. Moreover, relevant details retrieved from each selected and reviewed study was collected in an excel file with different fields, covering aspects such as: scope, approach and technical aspects, hazard characterization/dose-response information, exposure assessment and risk characterization Hazard characterization A hazard characterization or dose-response (DR) mathematical model quantifies the risk of becoming ill given a certain dose of a pathogenic agent in a food. This likelihood is dependent on the integration of host, pathogen, and food matrix issues, interactions often referred to as the infectious disease triangle (Buchanan et al., 2000). An accurate dose-response model of a foodborne pathogen should reflect all these interactions. However, this is a challenge for researchers and modellers because of: (i) the high complexity of these - and in the case of some foodborne pathogens such as L. monocytogenes and (ii) the impossibility of undertaking voluntary human feeding. Nevertheless, several attempts have been made to characterize L. monocytogenes (Appendix E, Table E.2), based on different assumptions, for which attention should be paid EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

22 Approach for the selection of a hazard characterization (dose-response) mathematical model In order to use the most appropriate dose-response (DR) model for the purpose of the present risk assessment, the following procedure was established: i) a literature review on L. monocytogenes DR models was performed based on both the studies dealing with full L. monocytogenes risk assessments (Objective 1) and other studies not addressing full risk assessments, but still dealing with L. monocytogenes DR models; ii) a selection of a specific mathematical model (form of the model) based on the properties and performance of the model as reported in the different studies reviewed; iii) application of the so-called Numerical Unit Spread Assessment Pedigree (NUSAP) system (Boone et al., 2009) to score the quality of the different dose-response models parameters, resulting in a ranking of the models; and iv) a final discussion and selection of the most relevant DR models was provided Application of Numeral Unit Spread Assessment Pedigree (NUSAP) system Introduction The NUSAP system (Boone et al., 2009) is able to address aspects of data quality resulting from uncertainties that are hard to quantify such as methodological and epistemological uncertainties, and that are not systematically taken into account in scientific studies. The NUSAP system was adapted to the particular case of dose-response models. It allowed a final semi-quantitative evaluation of the quality of the models (r parameter of the exponential model), and thus, a ranking of the models. According to Boone et al. (2009), for a pedigree evaluation it is crucial the use of a pedigree matrix, which is expressed by scores on a discrete numeric scale from weak (lowest score) to strong (highest score) for a set of criteria that determine the quality of data. The NUSAP matrix was implemented based on the four pedigree criteria proposed by van de Sluijs et al. (2005) and Boone et al. (2009): proxy, empirical basis, methodological rigour, and validation. Proxy criterion: evaluates the closeness of resemblance between the input parameter available from the data source and the actual variable that would be required in the model (Boone et al., 2009). Van der Sluijs et al. (2005) defined proxy criterion as a tool to evaluate how good or close the quantity that we measure is to the actual quantity about which we seek information. For the purpose of this project, the UCO-IRTA consortium considered proxy criterion in terms of time and geographical closeness. Empirical criterion: evaluates the degree to which direct observations were used to estimate the input parameter. A higher pedigree score for empirical basis was attributed to input parameters obtained from field data compared with indirect, modeled data or data obtained by expert judgment (Boone et al., 2009) (Van der Sluijs et al., 2005). The methodological rigour refers to the norms used in the collection and checking of data and the degree of acceptance of these norms by the peer community of the relevant discipline. Lastly, the validation criterion evaluates the degree to which one was able to crosscheck the data against independent sources. Objective score (OS), Expert elicitation (EE) weights, expert s self-assessment (SA) and EE consistency A systematic scoring of the pedigree criteria applicable to DR studies was carried out by the UCO-IRTA consortium ( Objective Score ; OS), according to the guide provided in Table F.1 of Appendix F. The scoring was based on the different levels of achievement or quality, which 22 EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

23 could be defined as the closeness to the ideal L. monocytogenes DR model in the EU population; the more closeness to the ideal model, the higher score of every pedigree criterion. In parallel, an expert elicitation ( Expert Elicitation ; EE) was performed to give different weights to the pedigree criteria as well as to their own assessment as an expert on doseresponse modelling ( Self-Assessment ; SA). The experts contacted were selected based on their internationally recognized experience on dose-response modelling, and in general, on microbial risk assessment. The weights of EE were applicable across all DR models. To this aim, a guide document was prepared for experts, containing a brief description of the framework of the expert knowledge elicitation process, the type of the quantitative information requested and a form to be filled up. These instructions were provided to the seven experts contacted (Appendix F, Section F.1). In summary, the scoring of every dose-response model resulted from two different sources: (i) objective information extracted by the DR studies assessed, which were scored according to the guide provided in Table F.1 (OSs); and (ii) expert elicitation to weigh the different pedigree criteria (EE) and experts Self-Assessment (SA) on their expertise. In Appendix F, Table F.2 shows the OSs given to the Exponential DR models of L. monocytogenes published. Table F.3 shows the EEs given by experts (7 experts) to the different pedigree criteria and SAs. The data collected was computed as follows. Every i study was evaluated according to j Pedigree Criteria by the UCO-IRTA consortium. Additionally, k=7 experts provided their assessment on the j pedigree criteria, as well as their self-assessment as an expert on DR models. The final score for every DR study (S i ) was calculated as the sum of the OS weighed according to EE and SA, as can be seen in Eq. 1. N N k j 1 i k kj ij k 1 N j j 1 S SA EE OS (Eq. 1) where SA k is the expert s self-assessment (per unit basis) of the k expert; N j is the number of pedigree criteria considered in the calculation; EE kj is the weight (per unit basis) provided by the k expert to the j pedigree criterion; and OS ij stands for either the OS given to a pedigree criterion, or the average of the items scores within a pedigree criterion. Through the S i scores, a final semi-quantitative evaluation, i.e. ranking, of the models (parameters) was provided. The consistency (C j ) in the weights given for each pedigree criterion by EE, was evaluated in the following manner: SD j C j 1 SD j max (Eq. 2) where C j was the consistency (per unit basis) of the j pedigree criterion; SD j is the standard deviation of the weights given for the j pedigree criterion; and SD j,max is the maximum standard deviation of the weights given for the j pedigree criteria (theoretically possible). SD j,max is achieved only when k/2 experts give the maximum weight W, i.e. 1, and k/2 give the minimum weight w, i.e. 0. When dealing with an odd number of experts, either (k+1)/2 give the maximum weight W and (k-1)/2 give the minimum w, or viceversa. In this way, the calculation of the SD j,max was as follows (Boone et al., 2009): 23 EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

24 SD j max W w 2 k 1 k 1 W w k 2 in case k is even number (Eq. 3) in case k is odd number When all experts provide the same weight, the SD j is 0, and C j equals 1. When the scoring experts maximally disagrees, the SD j equals the theoretical maximum SD (SD j,max ) 1, so C j is Exposure Assessment The exposure assessment model is intended to represent the potential growth of L. monocytogenes in the product from retail to consumption in three different types of RTE foods (i.e. heat-treated meat products, soft/semi-soft cheeses, smoked and gravad fish) to estimate the concentration at the moment of consumption, i.e. the final ingested dose of the pathogen. The scheme shown in Figure 1 represents the different food steps covered by the exposure assessment model. Figure 1: Scope of the exposure assessment model The exposure assessment model is based on the interaction of four quantitative components: Prevalence/concentration distributions of L. monocytogenes in the RTE foods at retail; Stochastic model for the growth of L. monocytogenes in the RTE foods as a function of e.g. temperature, lactate concentration and time; Temperature-time profiles of the RTE foods during food transport and home storage; Food serving size and number of serving per year in the EU. The variable prevalence and concentration are modelled, separately. Prevalence is assumed not to change after retail and to remain the same until consumption. The impact of cross contamination at home is not considered in the present model. Therefore, prevalence at retail will be used to describe prevalence at the end of consumption. Concentration was modelled using initial distributions of Listeria contamination in the selected food sub-categories together with growth models. The unit being considered in the model is serving size. Figure 2 represents how the different quantitative elements interact in the exposure assessment model EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

25 Temperature ( C) Quantitative risk characterization on Listeria monocytogenes in ready-to-eat foods Food subcategorization for risk modelling According to the Tender specifications the RTE foods should be subcategorized to better define the level of precision in outcomes within the exposure assessment framework. The food categories should at least be focussed on the three RTE food categories (packaged (not frozen) smoked or gravad fish, packaged heat-treated meat products and soft or semi-soft cheeses, excluding fresh cheeses) sampled during the BLS Time (h) Figure 2. Scheme representing the interaction between the different quantitative components for the exposure assessment Splitting categories will increase our understanding on how the different control measures impact risk over the different foods. For example, the RTE food category packaged (not frozen) smoked or gravad fish could be split into the subtypes of the RTE fish that was sampled: cold smoked fish, hot smoked fish, unknown smoked fish and gravad fish. The food category packaged heat-treated meat products could, for example, be split into the type of the meat product: sausage, pâté, or cold, cooked meat product. The food category soft or semi-soft cheeses, excluding fresh cheeses could, for example, be split into the subtypes of the RTE food 25 EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

26 that was sampled (smearripened, mould-ripened, brine-matured, otherwise ripened) and/or the type of milk treatment (pasteurised milk, raw milk, and thermised milk). Although major level of breakdown in food categories is preferred since a higher resolution can be achieved in risk estimations, an excessive number of food sub-categories can turn risk assessment modelling into a very a laborious task given the high number of inputs to be considered and the lack of data often associated with many of them (composition, consumption frequency, etc.). Therefore, the process of sub-categorization should be carried out considering those aspects most significant in the risk estimation. The approach taken is based on splitting an initially high number of foods sub-categories and subsequently, as a function of data availability, to proceed with merging groups if needed. Prevalence levels and growth potential of L. monocytogenes were considered to drive food subcategorization in the risk assessment process. In addition, a scenario analysis approach was deployed for those risk factors with insufficient information to allow for a full integration into the risk assessment model Prevalence-based sub-categorization The BLS report Part B (EFSA, 2014) based on the Part A report (EFSA, 2013) has identified statistically certain factors affecting prevalence of selected RTE food categories (e.g., type of packaging, type of product, post-heat treatment). However, this work also highlights that caution should be taken in the interpretation of the statistical analysis due to the sparseness problem derived from the large number of factors analysed, the high variability in the characteristic of the analysed samples, and the imbalance sampling design between factors and sample types Modelling prevalence of L. monocytogenes for selected RTE food categories Prevalence of L. monocytogenes in RTE food sub-categories was considered for further splitting into food sub-categories, based on their relevance to risk and according to the review analysis. In this respect, the BLS Part B (EFSA, 2014) identified certain factors that statistically affected the prevalence. The initial prevalence of L. monocytogenes in the seven RTE food subcategories was modelled using various statistical distributions considering different scenarios. The RTE food subcategories considered for prevalence were: RTE fish: (i) hot smoked fish, (ii) cold smoked fish and (iii) gravad fish; RTE meat: (i) pâté, (ii) cold, cooked meat and (iii) sausage; RTE cheese: (i) soft and semi-soft cheese. For modelling purposes, prevalence scenarios were considered for slicing /non-slicing RTE foods. Packaging conditions, i.e., Reduced-Oxygen Packaging (ROP) and normal were not considered to influence L. monocytogenes prevalence so that type of atmosphere was uniquely considered for microbial growth. Different data sources were considered according to the data availability and representativeness. Namely, prevalence data were processed from the following sources: The BLS on the prevalence of L. monocytogenes in certain RTE foods in the EU, ; The EU monitoring data of food-borne zoonotic infections ( ); Scientific studies retrieved from NP/EFSA/BIOCONTAM/2015/04, activity 1 report (Jofré et al., 2016) EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

27 Prevalence calculations: Prevalence concerned the total number of samples as well as the number of positive sample found in each study per selected food category. These data were further used to estimate global prevalence together with EU monitoring data (Section 2.1.2) and BLS data. Regarding concentration, this information (concentration ranges in CFU/g) was used to populate the initial concentration data of L. monocytogenes. In this sense, the studies were filtered according to the food categories as RTE cheese, cooked meat, sausage, pâté, cold and hot smoked fish, and cured-salted fish (gravad fish). The following criteria were applied for the data selection and the eligibility of studies: Studies selected were those where concentration was expressed in CFU/g. other concentration units such as MPN/g or CFU/cm 2 were excluded. Those studies where the total number of positives is reported were selected for the analysis. Those studies with undefined number of samples and/or food category were excluded from the analysis. For the inclusion of prevalence data, it was assumed that the microbiological analyses performed in the collected studies are without error. In other words, Listeria is supposed to be detected during the analyses if it is present in the analysed portion. The sensitivity (probability of detecting Listeria knowing that it is present in the analysed portion) is thus considered equal to 1. In the same way, specificity (probability that the test is negative knowing that Listeria is not present in the analysed portion) is considered as equal to 1. Considering each data source for prevalence, different estimations were provided. A. Prevalence scenarios were considered basing on the effect of slicing conditions of RTE foods. To do so, BLS data were exclusively used as information concerning slicing conditions was not accurately provided for the monitoring data and data from the activity 1 report. Estimations were calculated for 26 sub-categories: Cold smoked fish: sliced and non-sliced; Hot smoked fish: sliced and non-sliced; Gravad fish: sliced and non-sliced; Cold, cooked meat: sliced and non-sliced; Sausage: sliced and non-sliced; Soft and semi-soft cheese: sliced and non-sliced. B. Besides, global prevalence estimations were calculated using merging BLS data, monitoring data ( ) and data from the activity 1 report (Jofré et al., 2016). For each RTE food sub-category the data sources used were: Cold smoked fish: BLS and monitoring; Hot smoked fish: BLS and monitoring; Gravad fish: BLS, monitoring and activity 1; Cold, cooked meat: BLS and monitoring; Pâté: BLS and monitoring; Soft and semi-soft cheese: BLS, monitoring and activity EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

28 Approaches used for modelling prevalence of L. monocytogenes in the selected RTE food subcategories. For the definition and characterization of prevalence uncertainty, Beta distributions were firstly chosen as candidates given their ease of interpretation and suitability to be used in different risk assessment studies to model microbial prevalence. For the statistical analysis of L. monocytogenes prevalence, it was assumed that prior knowledge on prevalence population is unknown. Beta distributions were constructed considering two parameters (α = s+1, β = n s+1); being N the total number of samples belonging to this sub-category and s the positive samples obtained. This distribution is widely used as the description of the uncertainty or a random variation of prevalence. The output values of prevalence distributions would describe the prevalence of L. monocytogenes in a food sub-category in the EU Concentration of L. monocytogenes for selected RTE food categories in retail For the estimation of distributions of initial concentration of L. monocytogenes, EU monitoring data from together studies retrieved from the activity 1 report (Jofré et al., 2016) were used as data sources for RTE cheese and meat products. For RTE fish products this information was further complemented with the concentration data reported in the BLS, as shown in Table 4. Table 4: Data sources used to describe initial concentration of L. monocytogenes in the selected RTE food categories Food category Sub-category Data source RTE fish products Cold-smoked fish BLS/monitoring/Activity 1 Hot-smoked fish BLS/monitoring/Activity 1 Gravad fish BLS/monitoring/Activity 1 RTE meat products Cooked meat Monitoring/Activity 1 Sausage Activity 1 Pâté Monitoring/Activity 1 RTE cheese products Soft and semi-soft cheese Monitoring/ Activity 1 BLS: EU-wide baseline survey. Concentration values were split into different ranges (CFU/g) according to the sensitivity of the analytical techniques. It was assumed that the ISO method for detection and enumeration of L. monocytogenes (ISO and ) was used so that the limit of detection was primarily determined by the amount of food analysed (aliquot). Food aliquots varied from 0.01 g to 200 g, but the most common quantity was 10 or 25 g. The limit of detection (LOD) for agar-based methods was calculated through the probability of detection of one positive colony forming unit on selective agar per aliquot (i.e. for 25 g aliquot, the LOD = 0.04 CFU/g) Approaches used for modelling initial concentration of L. monocytogenes in the selected RTE food sub-categories Initial concentration was assumed at time of sampling (retail) and growth was predicted during distribution and storage. The lognormal distribution was the most suitable distribution to represent mean concentration distribution, and several works support its use (Lorimer and Kiermeier, 2007; Pouillot and Delignette-Muller, 2010; Commeau et al., 2012). The maximum likelihood estimation (MLE) approach was initially combined with statistical censoring to fit a single log normal distribution to the full dataset. Therefore, the distributions were fitted with MLE by using the R package (cran.r-project.org) fitdistrplus for censored data 28 EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

29 (Pouillot and Delignette-Muller, 2010) and then classified according to the goodness of fit using Standard Error, Akaike Information Index (AIC) and the Bayesian Information Criterion (BIC). Concentration distribution in the present model describes variation of mean concentrations of L. monocytogenes between samples, hence lognormal distribution is used together with a Poisson distribution in a mixture distribution Lognormal x Poisson: First, the lot is simulated with the lognormal distribution returning the mean concentration for the simulated lot. Then, this value is used to estimate the actual concentration in the serving using the Poisson distribution, assuming that cell distributions is homogenous/random in lot Modelling serving size The serving size is defined as the portion of food (i.e., targeted RTE products) consumed in a single eating event and expressed in grams. The serving size might be different depending on the consumer group and the seven food sub-categories described above. Therefore, these variables should be considered in estimating serving size unless that data analysis evidences no effect on serving size. The serving size was estimated for each population group (i.e., elderly, pregnant, adult). Serving size calculations were made based on the data extracted from EFSA s food consumption database (EFSA, 2015). FoodEx 1 is the data standardization system applied to the currently published database, but lacks some details in smoked and gravad fish and semi-soft and soft cheese. Therefore, for these food categories, FoodEx 2 was used. The database was filtered for those products included in the considered sub-categories. It should be noted that in some cases, the database reports more than one consumption of the same food by an individual in a day, which was considered as different independent servings. The serving size was then estimated as an average from the total amount consumed in a day (data reported by the database). For example, if the database reports that one individual consumes 100 g sausage two times in a day, the serving size will result from 100 g/2=50 g (i.e., 50 g in each eating event). In the case of heat-treated meat products, FoodEx1 allowed for distinction between the three food subcategories. In turn, for gravad and smoked fish and semi-soft and soft cheese, descriptions and terms given in surveys of FoodEx2 were insufficient to distinguish between specific subcategories and therefore, the serving size was estimated for the whole food category. For the case of fish, only smoked fish data were available; therefore, given the limitation of data, and assuming that the similarities between both types of minimally preserved fish would produce similar food consumption patterns, the serving size for gravad was assumed to be similar to the one for smoked fish. Serving size data were tabulated and submitted to statistical analysis to find suitable probability distributions to describe this variable Modelling number of servings Number of serving in the exposure assessment model refers to the total number of food portions of a specific food category or sub-category consumed in one year by a specific riskbased sub-population (i.e. elderly, pregnant, adult) in the 28 EU MSs. To estimate the number of servings, EFSA s food consumption database was used and the number of servings was collected for the available food sub-categories. As explained above, in some cases it has been reported that individuals might consume the same food several times a day. Each consumption event was considered an independent serving. To estimate the total number of servings for each food subcategory, data from the database had to be extrapolated to the total EU population, considering the group of 28 EU countries. To this end, different data treatments were applied EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

30 Figure 3: Visual representation of extrapolation method applied to estimate the number of servings in the EU from data from 24-h food recalls, 48-h food recalls and food records First, metadata from the surveys used in each country were collected and tabulated. In parallel, demographical data from 28 EU MSs were taken from EUROSTAT database (European- Commission, 2016), and tabulated per country and age group. The age group was intended to reflect the three risk-based subpopulations considered in the risk model, that is, elderly population ( 65 years), pregnant population and healthy population (10-65 years, adolescent and adults). For pregnant, estimates were based on the number of birth. Assuming that the birth rate is constant during and between consecutive years and considering a gestation period of 9 months, the number of pregnant women can be assumed equal to the number of births. In the case of pregnant giving birth early in the year (i.e., 6 first months), they would be taken into account for the whole year since the number of servings in the remaining 6 months would be equally explained by the number of women getting pregnant in the last 6 months of the year giving birth the following year. The demographical data (population size) were used to extrapolate results from the surveys included in the consumption database and referred to small study group to the total population of every single MS. The number of respondents for each risk-based subpopulation in each survey and country was extrapolated to the total subpopulation in the country by multiplying the consumption (eating event) frequency per day and respondent (Section 2.1.4) for each food category or subcategory of each risk-based population from the surveys of the country by the total number of people belonging to each risk-based population in the country (Figure 3). The estimates were subsequently transformed into annual consumption by multiplying them by the number of days in a year (i.e., 365). Then, annual estimates from 28 EU MSs were summed to obtain the total number of servings for each subpopulation and food category or subcategory. That was the input for the exposure assessment model. In the case of pregnant, given the limitation in the number of consumption data (i.e. only one country reporting data for this subpopulation), data from adults were used instead and extrapolated to the pregnant population (number of births) following the same procedure as described above. When survey data were limited to specific subpopulations, extrapolations from surveys to the total country population was performed based on the total number of respondents in the study regardless the type of subpopulation (e.g., heat-treated meat). These extrapolations were mainly applied to adjoining age groups, where consumption patterns are not expected to be very different, such as between adolescent and adult or elderly and very elderly EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

31 Results for gravad and smoked fish and semi-soft and soft cheese were not sufficiently reliable since from FoodEx2 only a few surveys were available, limiting the number of data with respect to country and type of sub-population. For the case of smoked and gravad fish, the consumption apparent was estimated using the production, export and import data in EU. The total amount consumed in each case was then divided by the mean serving size of the category to derive a total number of serving per country and subpopulation. For these food categories, estimates were less accurate due to the performed extrapolation from available surveys to the rest of the 28 EU Time to consumption Time-to-consumption is defined as the elapsed time between food purchase and consumption (Figure 4). There is a huge variability in time-to-consumption due to the fact that products may have different shelf-lives, and due to the variable consumer behaviour. Information about these aspects is not available, and covering the whole variability range in all considered products is also a challenging issue. Therefore, modeling time-to-consumption was addressed based on initial assumptions and existing data. UBD: Use-by-date. Figure 4: Conceptual representation of the time variables applied in the exposure assessment model In literature, exponential distributions are often used to represent the time between successive occurrences of an event, given a constant probability of occurrence of that event per unit time (Vose, 2000). In risk assessment, the exponential distribution (Figure 5) has been frequently used to describe the distribution of the time that the products are kept in the refrigerators before consumption, i.e. time-to-consumption (Nauta et al., 2003). In this distribution, the parameter β corresponded to the mean value of data, which can be calculated as the inverse of the rate parameter (λ). As shown in Figure 5, by modifying the parameter β, different levels of skewness and kurtosis can be obtained in the distribution. As β increases, the distribution shape becomes flatter, which means that the probability of occurrence of longer times tend to be higher. In food consumption, distributions are expected to be skewed and peaked, meaning that consumption is mostly given in shorter times, despite there can be very long times to consumption but with much lower probability. Hence, in the present model, time-to-consumption was defined by an exponential function. To populate the distribution, the best data source resembling the model scope was the BLS, in which the purchase date (PD) and use-by-date (UBD) for RTE products are reported. Using both types of data, the remaining shelf-life for each analyzed sample in the BLS was calculated by subtracting UBD to PD EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

32 1 0.8 f(x) =exp(-x/β)/β β=1 β=2 β= Figure 5: Representation of the Exponential model (density function) Time-Temperature profiles Temperature-time profiles obtained from the FRISBEE project database 11 encompassing different RTE products such as deli-meat, cheese and vegetables were used as input parameters to estimate L. monocytogenes growth from retail to consumption. No profiles were found for fish products so that it was assumed that temperatures for smoked and gravad fish product could be similar to those registered in deli-meat (i.e. RTE meat products) since both type of products are usually located in the same cold displays at retail or shelves in domestic storage. The database includes metadata providing information about the cold chain step(s) (retail display, distribution, etc.) monitored in each profile, type of product, packaging method, country, etc. (Figure 6). This information was used to select those profiles covering from retail to consumption for the range of products targeted by the risk assessment. These profiles include records of temperature every 1-10 minutes for the considered the target food chain step (e.g., retail to consumption). However, to adapt them to the scope of the model and to minimize consumption of simulating resources, datasets were reduced to include temperature records every 5 hours and only from retail to consumption. A summary of the metadata is presented in Appendix G. Figure 6: Snapshot of the excel containing the time-temperature profile metadata information given by Frisbee database 11 Adapted profiles were introduced in the model and a noise variable was applied to temperature values in each profile to consider possible variations in the profile. The noise allows introducing variability in profiles and increase number of different profiles in simulation while they maintain a realistic base as they have been derived from observed profiles. This variability is expected to occur in real world, e.g., a same distribution route carried out daily, or the EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

33 Temperature ( C) Temperature ( C) Quantitative risk characterization on Listeria monocytogenes in ready-to-eat foods differences of temperature between different locations in regrigerated shelves at retail. This variable was modelled as a uniform distribution varying between -1 and 1 C. This value is added or subtracted to the simulated temperature to produce slightly different profiles. An example of this random process can be shown in Figure Time (h) Time (h) The main graph includes a set of ten different time-temperature profiles of four delineates. The graph at the top right corner represents a single profile (line in black) taken from the main graph which a variability (noise) component has been added to, generating a new set of profiles (blurry lines) of a similar pattern to original one. Figure 7: Examples of time-temperature profiles of four delimeats collected from the Frisbee database 11 The model setting can be modified to increase noise, by making these values greater thereby testing different risk scenarios (abuse temperature, or better temperature controls). As the shelf-life defined in the model does not match with a specific time-temperature profile, given they both are obtained from different sources and products, an algorithm was designed to truncate the time-temperature profile, by taking from the profile, time values according to the shelf-life simulated. Since combinations of high (mean) temperatures and long storage times could lead to deterioration sensory of the products, making them unsuitable for consumption, they are not expected to significantly contribute to listeriosis risk. To avoid these unrealistic combinations in risk estimates, a deterioration threshold was set based on the concentration of lactic acid bacteria, which were used as indicator of deterioration. This microbial group is usually responsible for the undesirable sensory changes in (packaged) foods. The threshold was set as a range between 8 and 9 log CFU/g for heat-treated meat and smoked and gravad fish. For cheese products, no threshold was set since in many cases, deterioration, in this product, is less frequent and other microbial groups not included in this risk assessment are involved. A specific profile was selected randomly per iteration during simulation. This fact means that each iteration reflects the whole growth from retail to consumption for a specific product serving EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

34 Modelling specific exponential growth rate (EGR) in the different food subcategories The EGR values at different temperatures from the growth DB were standardized at 5ºC by applying equation 4 based on the root-square equation for bacterial growth: EGR 5º C EGR DB 5º C T TDB T min min 2 (Eq. 4) where EGR 5ºC is the exponential growth rate at 5ºC; EGR DB is the exponential growth rate recorded in the database at a specific temperature (T DB ) and T min is the notional minimum temperature for L. monocytogenes growth which is taken from literature (-1.18 C) (FDA/FSIS, 2003). The EGR 5ºC values corresponding to products formulated with lactate and acetate were excluded for this analysis since they were considered separately as a specific growth variable (see the section Effect of Lactate formulation on Listeria EGR). Once EGR values were standardized at 5 C, they were analysed statistically considering growth factors and variables recorded in the growth DB of each food category. Factors significantly affecting EGR 5ºC were used as criteria to split each RTE food product category into subcategories. Then, growth data were pooled and different probability distributions were fitted by Excel addin (Palisade, NY). Estimating Listeria EGR at specific temperatures In order to estimate EGR T at specific temperatures, first EGR 5ºC values were simulated from the above-mentioned distributions for specific subcategories and scenarios (ROP and air). Then, the simulated EGR 5ºC values were combined with the simulated temperature (time-temperature profiles) to input Equation 5 (Ratkowsky et al., 1982) and provide an estimate of EGR T. This value was later used to simulate Listeria growth. EGR T EGR 5C T T min 5º C T min 2 (Eq. 5) Effect of Lactate formulation on Listeria EGR EGR 5ºC values from products with lactate/acetate formulation were excluded from probability distributions. Thus, the baseline growth model does not include the lactate effect on L. monocytogenes growth and hence listeriosis risk. Given the interest of investigating the effect of lactate formulations on Listeria growth and the final risk of listeriosis, a new variable was built in the model. This variable was derived from the kinetic data contained in the EGR database. A regression analysis of data revealed that there was a linear relationship between lactate (ppm) and EGR 5ºC mean. This linear relationship can be mathematically formulated as (Eq. 6): (Eq. 6) where EGR lact is the mean EGR at any specific temperature (T) and concentration (ppm) of lactate (Lact); Lact max is the maximum concentration of lactate; and a is a regression parameter (slope) EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

35 This relationship can be found for the two sub-categories of heat-treated meat (a=0.01; Lactmax=24,000 ppm) and for gravad and smoked fish (a=0.02; Lactmax=18,500 ppm). For cheese, no data were available concerning lactate formulation or other significant preservative regarding L. monocytogenes. Usual concentrations for lactate (added lactate) and residual lactic acid were obtained from a specific study carried out by researchers from IRTA, who analytically determined the amount of lactate of RTE heat-treated meat products (Bover-Cid, personal communication). Concentration values were used to construct specific probability distributions by means software in order to describe variability associated with lactate concentration. For smoked and gravad fish, the probability distribution proposed by Mejlholm et al. (2015) describing added lactate was included in the risk model (Gamma(38.2; 229) ppm). Given that not all products are usually formulated with lactate, specific scenarios were included in the model to reflect the percentage of use of lactate in the different target food categories. According to a thorough analysis of the Mintel database (see Appendix H), around 8%, 2% and <0.5% of RTE meat (cooked meat, sausage and pâté), marinated/smoked fish and semi-soft and soft cheese products, respectively, are formulated with lactate. As in soft and semi-soft cheese, the levels reported were low and considering that the lactic acid produced endogenously is usuallly higher that the added one, it was not considered relevant to risk and therefore added lactacte effect was not included in the case of soft and semi-soft cheese. For the other food categories, equation 6 was applied, during the simulation, to the proportion of products with formulated lactate considering the added content Mathematical model for Listeria growth simulation Growth was simulated based on the growth model by Baranyi and Roberts (1994) which is defined in its differential equation form as (Eq. 7): (Eq. 7) where N(t) is the concentration of microorganisms at time t; EGR T is the specific maximum growth rate at a specific temperature (obtained as explained in previous sections); stands for the logistic function describing lag time (stationary phase); and corresponds to the deceleration logistic function describing transition from exponential growth to stationary phase. The lag time was accounted in the present model through the function, where the existence of a limiting substrate (q) is considered in order to model transition from stationary to exponential phase. Thus: (Eq. 8) where stands for the concentration of a limiting substrate at time t with and The function represents for the physiological state of cells at time 0, where defines the initial concentration of a hypothetical substrate needed for starting growth. As described by Eq. 8, the duration of lag time will depend on the initial state of cells, i.e. the value for. It has been demonstrated that can be defined based on the product, so-called h 0. To solve the differential equations for simulating growth under dynamic conditions, a value should be provided for (i.e. Initial Value Problem for solving Ordinary Differential Equations). To estimate the initial value,, h 0 values were estimated. The h 0 values derived from the initial temperature in the profile are used based on predictions given by the EGR and lag secondary models. However, estimated based on models generated in laboratory is usually different from that given in real world that relies on the history of cells, which is unknown. Therefore, 35 EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

36 was calculated based on the use of probability distributions of h 0 describing variability in the physiological state reported by studies. Some previous work has been performed by consortium on the application of variability to the simulation of physiological state in probabilistic risk assessments (De Cesare et al., 2013). In the section Modelling lag time, a detailed description is provided on the methodology applied to derive h 0 probability distribution as well as the data sources used for each food category. Modelling lag time The lag time, i.e. the time elapsed from food colonization (readjust to new food environment) to the commence of growth, is included as parameter in the Listeria growth model. The risk model allows including or excluding the lag time from the risk model simulation, enabling to generate specific scenarios regarding this parameter. As described later, the baseline risk model did not include lag time since it was assumed that this was already over at retail step. The lag time, when included in the simulation, was modelled based on different assumptions and using available data in literature for the different target RTE food categories. A normal distribution N(2.8, 4.6) was applied to describe h 0 of L. monocytogenes in fish products (Couvert et al., 2010), being truncated at 0 and 8 for the minimum and maximum values, respectively. Couvert et al. (2010), when validating a stochastic model to predict L. monocytogenes growth in refrigerated foods, tested a normal distribution with arbitrary parameters and a fixed value for h 0 so that their stochastic model predicted comparable growth variability to the observed values. Although these authors reported that the best agreement was obtained by setting h 0 at a fixed value (4.5), it is widely acceptable that the physiological state is highly uncertain, and thus, very difficult to reproduce. For this reason, and for risk assessment purposes, we deemed appropriate to consider an uncertain normal distribution for h 0, with bounds at 0 and 8 as minimum and maximum values (Guillier and Augustin, 2006; Dupont and Augustin, 2009), as h 0 is generally encountered within this range in the case of stressed L. monocytogenes cells, usual state of cells naturally contaminating foods. The physiological state (h 0 ) of L. monocytogenes for the case of cooked meat products was described through normal distributions with parameters N(1.40, 1.51) for pâté, and N(0.78, 0.99) for cooked meat and sausages, as data was available from literature. Augustin et al. (2011), in their work aimed at assessing the variability of L. monocytogenes growth in foods, reported the growth parameters for different meat categories (x 0, x max, lag and μ max ). As these growth parameters corresponded to different replicates, batches, physiological states of the pathogen and laboratories, the standard deviation of the products of lag by μ max inherently described h 0 variability. As widely accepted, the lag x μ max product gives a constant for a given physiological state (Swinnen et al., 2004). Appendix I shows the growth parameters reported by Augustin et al. (2011) for different meat categories; those of Vacuum-packed pork pie were assumed for pâté, and those obtained from experiments in Cooked ham packed under modified atmosphere and Cooked chicken were assumed for cooked meat and sausages. h 0 was calculated for the food categories of interest as lag x μ max, and its mean and standard deviation was calculated. These were the parameters of the normal distribution for h 0, whose values are stated above. As in the case of fish products, bounds at 0 and 8 as minimum and maximum values of h 0 were set (Guillier and Augustin, 2006; Dupont and Augustin, 2009). In the case of cheese products, a lack of ready usable information was observed for h 0 of L. monocytogenes in either challenged or naturally contaminated cheese. Subsequently, the database of growth data of L. monocytogenes in cheese prepared for the present risk assessment was used, and the lag x μ max product, i.e. h 0, was calculated whatever the growth conditions or type of cheese. The most relevant information concerning L. monocytogenes growth employed can be seen in Table I.2. The h 0 mean and standard deviation was calculated as 3.50 and 5.74, respectively. This information was used to build a normal distribution with bounds at 0 and 8, as in the case of fish and meat products EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

37 Simulating growth under dynamic temperature conditions In order to simulate growth under changing temperature conditions, the differential form of Baranyi s model was applied (Eqs. 6-7). To solve the differential form under dynamic conditions, 4 th order Kutta Runge method was implemented in Visual Basic for Applications (VBA) (Microsoft, Redmond) and applied and included in the VBA procedure risksim (see VBA code in risk model spreadsheets). The algorithm was used for each time-temperature profile simulated in the model. Profiles were included in excel rows (time, temperature) and Kutta- Runge method was applied to them in a sequential manner in order to keep a better traceability of data. Thus, bacterial increase over time can be observed in the model Excel spreadsheet. The time-temperature profiles retrieved from Fresbee project were imported to sheet data in the risk model and used as input for estimating Listeria growth under dynamic conditions. Nonetheless, the number of growth data was limited in Excel rows so as to reduce consumption of computer resources during simulation. Alternatively, a simplified version of the Listeria growth model was included in the risk model to predict growth under statistic temperature conditions, where the input temperature is not a profile but a single/static value (calculated as the mean of the profile). The equation used to predict growth under static temperature conditions corresponded to the explicit (integrated) form of the differential function of the Baranyi s model which is presented in Appendix J. Modelling Maximum Population Density Maximum population density (MPD) is a kinetic parameter that stands for the maximum concentration (log CFU/g) that a microbial population can reach during growth under specific physiological and environmental condition. The stationary phase of bacterial growth curve is defined by MPD which is usually obtained by fitting empirical models to observations. MPD is mainly affected by environmental factors (e.g. temperature, ph, etc.) and accompanying microbiota that compete for nutrients. Studies and data analyzed in the present work show that MPD is strongly affected by temperature when subjected to experiments in naturally contaminated products (Koseki and Isobe, 2005; Perez-Rodriguez et al., 2007), however, this effect is not directly observed in pure cultures where there is not competitive microbiota present (Le Marc et al., 2009). This fact evidences that probably temperature is not a factor directly affecting MPD but rather, the competitor microbial population, that increases growth potential as temperature rises and in consequence competitiveness. Therefore, the first approach for modelling the L. monocytogenes MPD assumes that it is specially affected by competitor microbiota, considering the Jameson effect, where the minor population decelerates when the major population reaches a maximum. In our hypothesis, MPD of L. monocytogenes as minor population is obtained from the deceleration produced by the lactic acid bacteria growth, as majority population. To consider the inhibitory effect of lactic acid bacteria on L. monocytogenes in the different food categories, lactic acid bacteria growth was modelled following the same mathematical framework as for L. monocytogenes, by applying suitable secondary models for EGR (i.e., cardinal type model) for each food category and combining with the primary model by Baranyi and Roberts (1994). In the section Modelling the effect of Lactic acid bacteria on Listeria growth, further detail is provided on how the effect of lactic acid bacteria on Listeria MPD was modelled. The second approach reduces mathematical complexity by considering a pseudo-stochastic model instead of a deterministic one (i.e., a mathematical relationship between response variable and predictor variables can be set). In this modelling approach, MPD population is defined as normal distributions defined with mean (µ MPD ) and standard deviation (σ MPD ), based on existing data taken from scientific literature and included in the growth DB. In the case of fish, µ MPD and σ MPD were taken from a study by (Delignette-Muller et al., 2006) that specifically addressed this aspect, while for heat-treated meat and cheese, no specific studies dealing with this issue were found, and distribution was defined with µ MPD from the growth DB for each 37 EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

38 subcategory, whereas σ MPD was assumed to be similar to that applied to fish. These normal probability distributions are intended to represent for the variation of MPD related to food formulation, food micro-structure, accompanying microbiota diversity, etc. in each food category. This second modelling approach allows for catching these sources of variation by using probability distributions built on experiments carried out in real foods. Since MPD is indirectly related to temperature as discussed above, this relationship was also considered by correlating distributions with temperature in the simulation. Note that the main physical variable driving L. monocytogenes growth is temperature (also in this risk model) and therefore, the effect of this variable should be also considered for MPD so that values in MPD are in concordance with the values estimated for growth rate and lag time. The correlation was built on a deterministic approach, using an equation that relates the mean (µ) of the normal distribution describing MPD and the mean temperature ( from the simulated t-t profile (sheet data ). The function introduced in the risk model is as follows: Normal (µ MPD (, σ MPD ) (Eq. 9) The equation for each food subcategory was derived from the analysis of growth data taken from the growth DBs of the different food categories and collected scientific studies (see Appendix K), where in several cases, a trend among both variables (T vs. MPD) was observed though accompanied with a large variability due the great heterogeneity of products and experimental conditions at which data were obtained (see Appendix K). For heat-treated meat and cheese, equation was derived from an overall study of data from the growth DB. In turn, for smoked and gravad, a single study was used (Hwang and Sheen, 2009). The type of equations that better represented for data, considering the parsimony principle, corresponded with a first-order polynomial able to describe both a straight line and more curvilinear behaviour (Appendix K, Table K.2). For that, final concentration and temperature were collected from the growth DB and submitted to a regression analysis by using suitable algorithms in R software version (R development Core team, 2008). Moreover, Excel software (Microsoft, Redmond) was used to represent trend lines between temperature and maximum density population. A correlation between the L. monocytogenes inoculum size and MPD has been reported for various foods (Guyer and Jemmi, 1991; Peterson et al., 1993; Pelroy et al., 1994; Carlin et al., 1995). Initial concentration level can influence MPD due to, among phenomena (food texture, inhibitory compounds, etc.), the competition between microbial populations, where the microbial population with higher concentration usually reaches a higher MPD than the minor population (e.g., Jameson effect). Inoculum sizes greatly varied between the studies used to derive the MPD distributions, even though high inocula (i.e. 2-4 log CFU/g) were more often utilized due to these levels enable researchers to better detect growth over time. By contrary, in real world, L. monocytogenes is usually found at very low levels (< 1 log CFU/g) as highlighted in previous sections in this report. To avoid a bias in the MPD estimates, a positive correlation parameter was applied, in the model, between the MPD distributions and initial level distribution, making that sampling low MPD values is more probable when low initial concentrations of L. monocytogenes are simulated. The level of correlation was assessed based on the analysis of growth data for the different food matrices, by plotting MPD values against initial concentration values and calculating the Pearson correlation coefficient and Spearman s rank correlation coefficient (see Appendix K, Table K.2). In general, positive values were obtained for both correlation coefficients with higher values in the case of Pearson. Pearson correlation coefficient is more sensitive to outliers and non-normally distributed data, resulting in higher values in these cases. The Spearman s rank correlation coefficient was chosen instead of Pearson correlation since this is a non-parametric test that does not assume normality in data and allows a better analysis of data showing non-linear relationship. For heat-treated meat and cheese, the analysis was performed on growth DB whereas for smoked and gravad fish, data from a study specifically dealing with this issue was preferred (Besse et al., 2006). The values of the Spearman s rank correlation coefficient ranged between 0.12 and 0.26 for heat EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

39 treated meat products whereas for cheese and smoked and gravad fish corresponded to 0.69 and 0.60, respectively. These values were used software to set correlation between distributions, using the function Define Correlation Matrix. Although, the software does not provide detailed technical information on the algorithm used for setting correlation between distributions, it is expected that the correlation matrix is estimated based on the Cholesky decomposition Modelling the effect of Lactic acid bacteria on Listeria growth Autochthonous microbiota in the different products can exert a significant effect on L. monocytogenes growth (Mataragas et al., 2003; Ostergaard et al., 2014). This effect is partly considered in the EGR distributions generated for the different subcategories, since kinetic data were mostly obtained from naturally contaminated products. However, another aspect related to inhibitory effect is that it is not reflected in EGR distributions such as the reduction of MPD. This parameter has been signaled as one of the main risk factor in listeriosis since infective doses for this pathogen are set at relatively high levels as compared to other enteric pathogens such as Salmonella (McLauchlin et al., 2004; Perez-Rodriguez et al., 2007). Therefore, considering the effect on MPD is crucial in order to derive a more accurate estimate of the number of listeriosis cases. Two approaches are mostly considered to explain and model the interaction between microbial populations, i.e. the Jameson effect and the Lotka Volterra model. The former states that the minority microbial population slows down when the total population levels reaches its maximum (Cornu et al., 2011). Furthermore, interaction is only limited to the reduction in the MPD, and lag time and growth rate are not affected. The latter approach states that the microbial populations compete for a common substrate, which is described by two parameters (Vereecken et al., 2000). Lactic-Acid Bacteria (LAB) are one the main microbial groups in RTE products. In many cases, LAB can be responsible for food deterioration in heat-treated meat and smoked fish (Nassos et al., 1983; Korkeala and Bjorkroth, 1997; Gimenez and Dalgaard, 2004), but in other cases they are used as starters like, for example, in cheese and fermented meat product (Tamime and Robinson, 2007; Talon and Leroy, 2014). Moreover, some LAB strains are able to produce proteins with inhibitory capacity (i.e. bacteriocin) against certain bacterial species, like L. monocytogenes, with several characterized bacteriocins against this pathogen (Calo-Mata et al., 2008). According to several studies, LAB can reduce Listeria growth through different mechanisms such as competition for nutrients, production of organic acids, bacteriocins, etc. The present model includes the growth of LAB population in the targeted food products in order to consider the inhibitory effect on the Listeria MPD. Taking into account that the inhibitory capacity of LAB is already considered in EGR distributions, the Jameson effect is the approach that better adapts to the purpose of the modelling as it can be only focused on the MPD. The MPD reduction is a consequence of growth deceleration produced when N (t) approached to MPD. To include the effect of LAB growth on the deceleration of Listeria growth,, as proposed by Mejholm and Dalgaard (2007), was applied to the Baranyi s model (Eq. 10): (Eq. 10) where and ) describe Listeria and LAB concentration at time t, and and correspond to MPD for L. monocytogenes and LAB, respectively. Also, a specific N max might be used when the target population is more sensitive and it stops before reaching the stationary phase as observed by Le Marc et al. (2009). This approach, together with other alternatives of Jameson effect (Cornu et al., 2011), may be later applied as specific scenarios to assess how the modelling of interaction between Listeria and LAB can affect the final listeriosis risk EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

40 Therefore, to simulate inhibition of Listeria growth by LAB, Eq. 7, 8 and 10 had to be jointly applied. To define LAB kinetic variables (, EGR, h 0 ) contained in these equations, predictions of specific secondary models validated in the target products were used (Devlieghere et al., 2000; Mejlholm and Dalgaard, 2007; Mejlholm et al., 2010; Mejlholm and Dalgaard, 2013; Ostergaard et al., 2014). The environmental variables (preservatives and physico-chemical parameters) were defined based on outcomes of product analyses, using probability distributions to reflect the inherent variability to each variable. When information was not available, expert opinion was applied to derive the most representative value of the environmental variable. The values of EGR T, and lag time used in the above equations were defined according to specific mathematical approaches explained in the sections Modelling Maximum Population Density, Modelling specific exponential grow rate (EGR) in the different food subcategories and Modelling lag time, respectively. The initial concentration was established based on a literature review for the levels usually found in the target food categories and described by a triangular probability distribution using the values maximum, mean and minimum (Table 5). Appendix L includes the literature sources to define initial concentrations of LAB. Table 5. Main statistics for the initial concentration (log CFU/g) of lactic acid bacteria found in the target ready-to-eat (RTE) food categories RTE food category Minimum Maximum Mean Heat-treated meat Smoked and gravad fish Soft and semi-soft cheese Considering percentage of food servings supporting Listeria growth Food categories targeted in the risk assessment are generally considered as food matrices that can pose a risk of L. monocytogenes, however it should be noted that specific food conditions can surpress Listeria growth. Neglecting this fact could be lead to very conservative listeriosis risk estimates (by overestimating growth), since it should be expected that not all servings support Listeria growth. To consider this aspect in the risk modelling process, a percentage of servings supporting growth for the targeted food categories was set in the simulation based on a data review. Growth data for cooked meat and smoked fish shown in Table 6 were taken from Uyttendaele et al. (2009) in which several L. monocytogenes challenge tests were performed in different food categories. Table 6: Number and percentage (%) of RTE food categories which supported growth of L. monocytogenes All food sub-categories Number of samples Number of Total % supporting growth challenge tests Heat-treated meat products Fish (smoked + marinated) Soft and semi-soft Cheese Challenge testing aimed to assess the growth of this pathogen in ca. 100 g of a representative food sample by artificially inoculating the specific foodstuff with CFU/g of a mixed inoculum of three strains of L. monocytogenes. In total, 140 challenge tests were performed for heat-treated meat, 38 for smoked fish, and four for soft and semi-soft cheese (personal communication). This figures corresponded to those products matching the definition for those food categories targed in the risk assessment. In the case of smoked fish, marinated products were included given that in many cases, this typology can resemble characteristics in gravad 40 EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

41 fish. This study was deemed to be presentative given the different types of products tested; nevertheless, a relevant component of uncertainty is still expected. This percentage can be modified by the user in order to contemplate other possible risk scenarios in the simulation Selection of the growth modelling approach in the risk model The risk model allows considering different Listeria modelling approaches as already mentioned in previous sections. In summary, three types of growth model could be applied to simulate L. monocytogenes growth, resulting into three possible settings in the risk model. The modeling options can be selected in the cell Gr_model switcher by assigning 0, 1, or 2 which correspond to: Option 0. The Integrated form the Baranyi s growth model is used to reflect the growth of Listeria using a static value of temperature, obtained as the mean of the time-temperature profiles. This approach does not consider interaction of lactic acid bacteria nor the effect of changing temperatures. The MPD density is modelled following the second approach described in the section Modelling Maximum Population Density. Option 1. The differential form the Baranyi s growth model is used to reflect the growth of Listeria under dynamic temperature conditions, simulating specific timetemperature profiles. This approach does not consider interaction of lactic acid bacteria. The MPD density is modelled following the second approach described in the section Modelling Maximum Population Density. Option 2. The differential form the Baranyi s growth model (system of 4 differential equations) is used to reflect the effect of lactic acid bacteria growth on the Listeria growth (i.e., MPD) under dynamic temperature conditions. The MPD density is modelled following the first approach described in the section Modelling Maximum Population Density. In this approach, the MPD distribution was truncated to the range 7-9 log CFU/g to be used as the theoretical Listeria MPD in the differential equations. The actual Listeria MPD simulated in the risk model would result of applying the interaction model of lactic acid bacteria. The models were implemented in VBA code and run by calling during simulation the process risksim i () with i=0, 1 or Risk characterization Estimation method for the number of listeriosis cases The number of listeriosis cases was separately estimated for food category and risk-based subpopulation hence the total number of cases in the EU was obtained as the sum of the cases derived from each type of food and subpopulation. Prevalence in this risk assessment model was considered conceptually as a single value representing the overall prevalence of EU 28 as a whole. The prevalence distributions (Beta) were used as means to represent the uncertainty on the overall prevalence for each food subcategory and scenario. In some categories and scenarios, prevalence can be a very low value which implies that to simulate a representative number of positive servings, a huge number of iterations is needed (>100,000 iterations), which is usually unaffordable in multiparameters and time-based probabilistic models as herein presented. Therefore, to allow for saving calculation computer resources while obtaining representative results, prevalence and concentration were simulated separately. Thus, each model iteration was assumed to represent a fraction of contaminated servings which was calculated as follows: 41 EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

42 #cont_servings ij = #total _serving j n prev i (Eq. 11) where n corresponded to the number of iterations to be simulated (i.e. simulation settings), prev i stands for the prevalence simulated in the iteration i; #total_serving j is the number of servings consumed by a specific subpopulation j per year in the EU and #cont_serving i,j is the fraction of contaminated servings corresponded to the iteration i and population j. Additionally, a probability value (s) was incorporated into the equation to determine the probability of occurrence of pathogenic serotypes. EFSA and ECDC (2015a) reported that seven EU MS and Norway provided information from conventional serotyping of L. monocytogenes in confirmed listeriosis cases (accounting for 23.3% of all confirmed cases). The most common serotypes in 2013 were 1/2a (57.5%) and 4b (34.3%), followed by 1/2b (6.4%), 1/2c (1.4 %), 3a and 3b (both 0.2%). The percentages of isolation of the three most relevant serotypes (1/2a, 1/2b and 4b) representing for more than 90% cases was determined by Jofré et al. (2016). The mean values corresponded to 83.26, and 32.13% for RTE cooked meat, fish and dairy products, respectively and could be used to estimate s in each category. Nevertheless, the baseline simulations set s=1 as a conservative approach to estimate the number of listeriosis cases. #cont_servings ij = #total _serving j n prev i s (Eq. 12) s stands for a probability of occurrence of the most relevant serotypes associated to human listeriosis for each RTE food category. The method utilized to estimate the number of listeriosis cases first consisted of applying the DR model (in each iteration) considering the doses of L. monocytogenes simulated in each i (d i ). The simulated dose (d i ) was introduced in the DR model as shown in Eq. 13 together with the corresponding r-value, based on the type of DR and subpopulation. The result from each DR model instance corresponded to the probability of getting ill associated with each fraction of contaminated servings i and subpopulation j (Pill ij ). The following equation summarizes the method applied: Pill i,j = d i r j (Eq. 13) where Pill ij represents for the probability of getting ill for iteration (i=1, 2 n) and subpopulation (j=1, 2, 3); d i the dose simulated obtained by multiplying the concentration at the moment of consumption with the serving size simulated in each iteration (i); r j is the r-value for each subpopulation (j). Then, #cont_serving i,j corresponding to the fraction of contaminated servings for the iteration i and population j was estimated using Eq. 11 with the simulated prevalence values ( from the beta distributions, assuming s=1. Note that prevalence values are representing uncertainty on this value, and therefore, this step can be considered as a second simulation level in the model intended to characterize the uncertainty component (i.e., two-dimention model). Finally, the total number of listeriosis cases was derived by summing up the integration of Pill ij values simulated from Eq. 13 and the number of contaminated servings from Eq. 12: n,p Number of cases = Pill i,j #cont_serving i,j i=1,j =1 (Eq. 14) Probabilistic Risk Assessment models for estimating the risk by L. monocytogenes in RTE food products in the EU 42 EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

43 A spreadsheet-based model was built for each RTE food category with Excel Microsoft and Excel software (Palisade, NY). Model inputs were included by means of probability distributions and point-estimate values following the modelling approaches and applying the predictive models and variables described in previous sections. Models were simulated by using Latin-Hypercube methods, and main outcome were recorded for further analysis. The final output corresponded to the number of listeriosis cases in the EU28. The main risk factors were also analysed in regard to their impact on the listeriosis cases. Specific model settings and modelling approaches as well as exposure scenarios were tested. The model files, settings and spreadsheet descriptions are included in Appendix M. Baseline model and scenarios analysis The risk assessment outputs (i.e., number of listeriosis cases/year per subpopulation group) were evaluated by following a scenario analysis, where various risk variables/factors were considered. The scenario analysis was subdivided in two bench-testing modules based on the modelling approach and model parameters. This allowed the optimal selection for the Baseline scenario. Different modelling approaches were initially tested. a) Growth model approach: Model 0 (static conditions for Listeria) Model 1 (dynamic conditions for Listeria) Model 2 (dynamic conditions for Listeria and LAB) Growth models were tested and selection of the most suitable one was based on the accuracy of the estimation and the computing resources needed to perform the simulation. b) Dose-response (DR) model: FAO/WHO (deterministic) Pouillot model using distributions for r parameter The comparison of the three growth models using the Model 0 and the FAO/WHO deterministic DR relationship yielded to an overestimation in the number of listeriosis cases: 1,523 cases for healthy population and 16,036 cases for the susceptible population. This is an expected result since growth at static temperature conditions may produce higher dose levels and probability of ingesting heavily contaminated servings. Besides, it should be noted that the FAO/WHO model does not consider elderly and pregnant as separate subpopulation groups. Using Model 1 (dynamic conditions for Listeria without LAB) and the FAO/WHO DR model, the number of estimated cases were reduced to 605 and 1,437 for healthy and susceptible populations respectively. Growth model 2 (dynamic conditions for Listeria and LAB) in combination with the FAO/WHO dose response model resulted in a slight lower estimated number of listeriosis cases, namely 361 and 862 for healthy and susceptible populations, respectively. These results are expected since Model 2 includes interaction with LAB, thus, growth of L. monocytogenes is being slower. Further, the use of the Pouillot DR model allows the selection of probabilistic values of r parameter together with the distinction of three subpopulation groups (elderly, healthy and pregnant). From the preliminary tested approaches it was then concluded that the Model 2 in combination with the Pouillot DR model was selected for the Baseline scenario. Moreover, the definition of the Baseline scenario was: Growth model at dynamic conditions for Listeria considering LAB interaction; Pouillot dose response model; 43 EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

44 Lag phase not included (h 0 =0); Default prevalence (prevalence data from BLS considering atmosphere packaging conditions and slicing conditions); Noise of the uncertainty exposure (variation factor) set as 1; The probability of occurrence of the most relevant serotypes associated to human listeriosis for each RTE food category (i.e., s-value) was fixed to 1. This Baseline scenario was then compared to alternative scenarios modifying selection of factors in the exposure assessment model. Starting with the original Baseline model, the effect of different variables affecting prediction of the number of listeriosis cases were assessed through a sensitivity analysis. The baseline model was the same used to previously calculate the number of listeriosis cases in EU per year, however, for the sensitivity analysis the risk model was run with 5,000 iterations and estimations were provided per 10 6 servings. This was done in order to obtain a relative measure of the number of cases per scenario evaluated and to facilitate comparison among scenarios and food categories. Within each scenario, modelling parameters were modified simulating worst- and best-case examples in comparison to the baseline scenario. For each food category the evaluated scenarios were: 1. Effect of maximum value in the initial concentration distribution. This scenario was aimed at assessing the impact of maximum concentration values within the initial concentration distribution of Listeria and how this effect can influence on the number of cases. For RTE cheese the effect was assessed by increasing and decreasing the maximum concentration in 2 log 10 CFU/g. 2. Shelf-life was also evaluated by changing the time to consumption parameter. For this scenario, worst- and best-case examples were simulated increasing and decreasing a 25% of time respectively. 3. The impact of storage temperature was evaluated using an increase and decrease of 3-4ºC on the final number of listeriosis cases per 10 6 servings. 4. Finally, the inclusion of lag time was also compared to the baseline scenario (without lag) to see the effect of this parameter on Listeria growth. For each simulation, 97.5 th, 50 th and 2.5 th percentiles were obtained for the three subpopulation groups, as well as for the total number of cases. Besides, two additional model outputs were collected, being the final concentration of Listeria (Nf_List, as shown in the risk model spreadsheet) and the %gr_list which is a relative measure indicating the growth potential of Listeria expressed as a percentage of MPD. This is calculated by dividing the final concentration by the MPD. Identification and description of the sources of uncertainty Within a risk assessment an analysis of uncertainty sources is highly relevant component of risk characterization. The outcome of such analysis provides the estimate range of values for the different model outputs (i.e. number of listeriosis cases in the EU population). Uncertainties could be originated from the data sources or from the structure of any models used to define the relationship between exposure and adverse health effects. In the EFSA context, the term uncertainty is intended to cover all types of limitations in knowledge, at the time it is collected in the risk assessment process (EFSA, 2009). Therefore, it is recognised that in a risk assessment it is important to characterise, document and explain all types of uncertainty EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

45 The public health risk associated to L. monocytogenes prevalence and concentration in the RTE food categories selected relies on some important set of assumptions: Expected prevalence and concentration of L. monocytogenes at retail. This was related to the shape and the parameters of the distributions used to account for both variability and uncertainty. The assessment of growth of L. monocytogenes from retail up to consumption. In this case, uncertainty is related to the choice of environmental factors considered in the risk model and how they affect the microbiological process. Consumption patterns among EU population (i.e., serving size, frequency of consumption). The first and last point are both related to differences among EU countries that imply the need to account for the variability in the risk assessment process. This is related to expected prevalence in northern and southern EU countries, variability regarding concentration between lots, or variability in consumption patterns. In most cases, due to the lack of data, or lack of reliable information statistical distributions had to consider expert opinions or best-guesses (use of specialized databases as MINTEL, FoodEX2, or the data reported in the Zoonosis Report or in the BLS). The second source, is more related to the magnitude and direction of the effect of public health risk associated with the presence of L. monocytogenes in the studied RTE foods. In Table 7, a description of the sources of uncertainties is listed. Further, Appendix N details the assumptions and the modelling approach of the present risk assessment EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

46 Quantitative characterization on Listeria monocytogenes in ready-to-eat foods Table 7: Description of the assumptions for the main variables in the risk model Variable Description Assumptions considered in the risk assessment model Prevalence Number of L. monocytogenes positive samples in the selected RTE food sub-categories at retail Microbiological analyses performed in the BLS are without error. Prevalence does not change after retail. Slicing is the only factor considered to influence on prevalence. Initial concentration Serving size Number of servings Time to consumption Timetemperature profiles Lactic-acid bacteria and L. monocytogenes growth models L. monocytogenes initial concentration in the selected RTE food sub-categories at retail Grams of consumed product by each risk-based subpopulation. Total amount of servings consumed by the EU population corresponding to the selected RTE food categories. Time elapsed from the purchase of products to consumption. Storage conditions of RTE products describing cold distribution chains. Microbial growth of L. monocytogenes and lactic acid bacteria during storage from retail to consumption phases in the selected RTE food categories. Initial concentration was referred to variation in mean concentration between lots (log normally distributed). Individual concentration data from EU monitoring was assumed to represent different batches. Monitoring data in EU countries (in ) and BLS data for fish are representative for L. monocytogenes levels. Serving size is similar between the different sub-populations. Servings from consumption database are not heat-treated. The same RTE food sub-categories as the serving size apply. Extrapolation between countries and subpopulations are valid. Remaining shelf-life was calculated by subtracting the use-by-date and the date of purchase reported in the BLS. No distinction between the remaining shelf-life for cheese category as well as for smoked and gravid fish. Data retrieved from the FRISBEE database are representative of the cold distribution chains of the selected RTE food categories in the EU. Profiles for heat-treated meat are representative for the RTE fish category. No distinction was made between retail and home stages, so profiles were applied as a whole in the simulation process. Lactic-acid bacteria are the main microbiota present in the selected RTE food categories affecting L. monocytogenes growth. Growth is mainly influenced by ph, a w, CO 2, temperature, and lactate content. Influencing environmental factors through expert opinion and information retrieved from databases (MINTEL) are valid. Product formulation data were assumed to be the same as those reported in the BLS Specific primary (Baranyi) and secondary (cardinal) models are valid to evaluate microbial growth. Percentage of products with lactate was assumed to be 8% according (MINTEL database). Lag time was considered for both lactic acid bacteria and L. monocytogenes growth EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

47 3. Assessment/Results 3.1. Hazard identification Search for studies The implementation of the review protocol (Appendix C) identified a total of 1,570 records from the bibliographic databases used (Scopus, Web of Science and PubMed), from which a 645 duplicates were detected. As shown in Figure 8, the first-level screening (screening of title and abstract) identified 115 potentially suitable references for further analysis. Within the 874 references excluded, nearly 5.8% did not deal with L. monocytogenes, 50.6% did not correspond to formal full risk assessments (i.e., including hazard characterization, exposure assessment, risk characterization), and 43.6% were neither full risk assessments nor dealt with L. monocytogenes. However, a considerable amount of excluded records (about one third) were identified as potentially interesting for other activities within the entire project and kept for further use. These excluded references provide valuable information about e.g., levels of contamination (prevalence and/or concentration of L. monocytogenes in RTE food), consumption data, exposure assessments, predictive models and data on L. monocytogenes behaviour in food, dose-response models as well as risk assessment methodological aspects. Besides the scientific articles retrieved from bibliographical databases, within the preselected records, seven reports from other sources were included for further evaluation. The search, selection and registration to the EndNote file of these studies were done manually. Each potentially interesting study was checked and compared with related scientific articles (e.g., dissertations consisting of a compilation of scientific articles). In the revision process of the full text articles, 70 records were further excluded. The reason for this further exclusion was mainly due to the study not being a full risk assessment (e.g., doseresponse or exposure assessment). These references were identified as potentially useful for specific steps in the risk modelling process. Moreover, some records were considered duplicates, as they were the same risk assessment published in different types of records (e.g., report and scientific peer-review article) or interpretative summary of technical reports already included. After the selection process, 47 eligible records were identified (selected) and further analysed to collect relevant information. The records included 40 scientific articles and 7 reports, all published between 1996 and The distribution of retrieved references according to the publication year is shown in Figure EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

48 Figure 8: Flowchart summarizing the process and results of the literature search 48 EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

49 Number of studies Quantitative risk characterization on Listeria monocytogenes in ready-to-eat foods Figure 9: Distribution of the 47 eligible records by year of publication The results of the analysis of the selected references were quantitatively summarised, including the number of references identified and the corresponding percentage per evaluated field (see Tables D.1 and D.2). For several fields, the total number of inputs recorded corresponded to multiple-choice answers (e.g. sub-category of food, country/region, DR model type, steps of the food chain, etc.), therefore the sum of percentages shown in Tables D.1 and D.2 can be higher than 100%. In case of non-availability of information, unknown or not applicable, a zero number was recorded in the excel file. This information is included in the summary Tables of Appendix E as Unknown/not available/not applicable. In the following sections, the main results obtained in each of the evaluated field are described. Moreover, relevant details retrieved from each selected and reviewed study was summarised in several tables enclosed in Appendix E Scope of the risk assessment, approach and technical aspects The RA framework corresponded mostly to research studies aiming to perform a global RA and/or being a case study with a specific aim (i.e. studies dealing with a specific type food product or within a specific geographical area, to explore methodological approaches or assess specific scenarios). The number of institutional RA was limited (7 out of 47). Most of the references retrieved corresponded to research risk assessments on specific or localized food matrices. Among the food categories presented, 25 studies (53%) included meat products, and specifically, 38% on cooked meat (deli meat). Fish, dairy and produce were included in 14, 17 and 11 risk assessment studies, respectively. However, it should be noted that for RTE food categories, sub-categorization yielded a variety of definitions for specific products, such as RTE cheese (i.e., fresh cheese, soft cheese, cheese in general etc.). Risk assessment studies including produce mainly considered leafy greens (8 out of 9 studies). Regarding the implemented approach of the risk assessment studies (Appendix D; Table D.2) the vast majority of them were quantitative (42 out of 47) and all but one of these stochastic (i.e. they included an estimation of the variability on the final outcome). It should be noted that stochastic approaches were considered if at least one of the inputs was presented as statistical distribution. Data most frequently described stochastically corresponded to prevalence/concentration of L. monocytogenes, storage time/ temperature of the food product and the consumption serving size EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

50 Point-estimate approaches were mainly applied for qualitative studies aiming to perform a risk ranking or a categorization of food/hazards combinations. Data regarding variability and uncertainty in selected RA studies were treated separately in 2 nd order model. Noteworthy, these studies included separation of variability and uncertainty for some variables or parameters (e.g., r parameter of DR models; initial concentration of L. monocytogenes in the product, prevalence, storage conditions, kinetic model parameters). The peer-review level of the selected studies was in accordance with the type of document and the RA framework. The eight institutional risk assessment studies were submitted to a public consultation, whereas the 40 scientific articles were published under the usual peer-review procedure applied by the scientific journals. In Appendix D, Tables D.1 and D.2 show detailed information about general characteristics of the reviewed L. monocytogenes risk assessments. It should be noted that some studies evaluated the risk associated with various food matrices (i.e., meat, vegetables, fish products, etc.). However, the percentages described in Tables D.1 and D.2 refer to the total number of studies evaluated (47). Regarding the food chain steps covered, all retrieved studies considered at least the retail phase, and growth of L. monocytogenes was quantified in some way from retail to consumption in several risk assessment studies. Indeed, most risk assessments were case-studies aiming to quantify the final concentration of L. monocytogenes before consumption. A few of them (mainly institutional studies) comprehensibly described also the principles and fundamentals of the risk assessment methodology providing a detailed description about compared methodological procedures as well as data gathering and data processing to generate the model inputs. Finally, regarding description of risk assessment outcomes, it was shown that the risk was described per annum, risk per serving or per habitant. Some specific papers (e.g. Domenech et al., 2007) also intended to quantify the economic cost lost per annum, serving or habitant. Other risk measures were referred to DALY (disability-adjusted-life-year), or sensitivity analysis results Hazard characterization/dose-response The main results regarding the hazard characterization/dose-response (DR) considered by the 47 selected studies are shown in Appendix D (Table D.3) including information about the model type, model parameters, endpoint, source of data and target population (type and country). Regarding model type, most authors preferred to follow an Exponential model by estimating the r parameter (i.e. the estimated probability that one single cell could cause a response, being the endpoint of the response: infection, illness or death). Other model types such as Weibull- Gamma, logistic or linear models were also reported. Generally, data used were based on epidemiological information from scientific studies or national reports. Additionally, other studies adapted the DR models from the risk assessments developed by FDA/FSIS (2003) or FAO and WHO (2004b) with slight modification in the parameters, when different epidemiological data were used to fit the mathematical model. Illness was considered the endpoint in more than 80% of the studies retrieved. Regarding the target population to which hazard characterisation was applied, some inconsistencies were found for description of population subgroups or the categories considered. Some studies divided the population into perinatal, elderly and intermediate-age population (FDA/FSIS, 2003). In that study, perinatal was defined as foetuses and neonates from 16 weeks after fertilization to 30 days postpartum. The neonatal cases are assumed to be pregnancyassociated cases where exposure occurs in utero as a result of foodborne L. monocytogenes infections of the mothers during pregnancy. Manifestations of listeriosis for this subpopulation group include spontaneous abortions, stillbirths, and neonatal infections. Elderly subpopulation included individuals who are 60 years old or older, whereas the elderly threshold was set at 50 EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

51 65 years in the assessment developed in other studies (e.g., Oh et al., 2009; Vasquez et al. 2014). The subgroup called intermediate-age in the FDA/FSIS (2003) comprised the remaining population (different from perinatal and elderly). Therefore, both healthy individuals and certain susceptible subpopulation groups with increased susceptibility to listeriosis (e.g. AIDS patients, people under immunosupressing therapy) were considered within the intermediate-age subgroup because there was insufficient data to separate them into different subgroups. However, for summarizing variables description, in the present review, intermediate-age population considered in the FDA/FSIS (2003) study was counted as general population (generally as non-susceptible population) as described by most of studies. The study of FAO and WHO (2004a) divided the population into susceptible groups, including elderly (persons over the age of 60 65), infants, pregnant women and immunocompromised patients) and low-risk population" groups (mainly healthy children and immunocompetent adults). Within the susceptible group, a classification scheme was proposed for differentiating the manifestations of syndromes associated with L. monocytogenes that took into consideration host status, route of transmission, severity and incubation period. Finally, the Joint risk assessment developed by the FDA and Health Canada (2012) considered elderly population (over 60 years old), pregnant women and immunocompromised individuals as susceptible, following the scheme presented by FAO and WHO (2004c). The immunocompromised population includes individuals having more susceptibility to invasive listeriosis and often require for a special medical condition. The variable geographical area in Table D.3 referred to the country/ies where epidemiological data were retrieved and thus the country of the target population. The country where most of the studies were conducted corresponded to United States, with 66% of the studies. Table D.3 includes more relevant information regarding dose-response assessments. Overall, the majority of published works provided information about model parameters mainly for Exponential and Weibull-Gamma models. However, these values should be considered with caution because in many cases they referred to adaptations of previous published models, or to specific target populations. Likewise, the r parameter of Exponential models was different for the specific subpopulation groups considered, and additionally, each study considered different classification of these subgroups, as mentioned above. This parameter is defined either as a point-estimate value, as a statistical distribution (i.e., Pert) or by a description of the most representative statistical estimates (i.e., 5 th and 95 th percentiles, mean, median, standard deviation). As it can be seen in Table D.3, different endpoints were stated in the selected studies. Nevertheless, most of the studies considered illness attribution, while a few works extended the scope of the DR model to death. Care should be taken in establishing separate definitions for illness (implying an adverse effect on the consumer as a consequence of pathogen ingestion) and infection (which does not necessarily impliy an adverse effect, but the ingestion of a contaminated food and colonization of the intestinal tract of the host by the pathogen. Infection can affect to both symptomatic patients and asymptomatic carriers). Overall, the published studies not always clarify the exact meaning of the endpoint considered (illness or infection), though the probability of illness is the mostly reported. In case of using data from animal trials, the term infection may also be used, as the amount of L. monocytogenes in internal organs (e.g. spleen) of the challenged animals are analysed (e.g. Notermans et al., 1998). In studies dealing with (human) epidemiological data, illness is the term used, because it corresponds to a reported symptomatic listeriosis case. Asymptomatic infections are not recorded in epidemiological statistics EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

52 Exposure assessment Information about the exposure assessment is shown in Appendix D (Table D.4). This comprised mainly the covered food chain steps, the inputs included (i.e., factors), inputs data type, information about predictive models used, consumption data, target population (from which consumption data was retrieved) and variability/uncertainty management. As mentioned above, all risk assessment studies covered the retail phase, and 74% of the retrieved studies were expanded to the consumer storage phase (i.e., storage, handling and eating at home, although three records evaluated the risk in catering establishments, as salad bars or school cafeterias). Other steps set earlier in the food chain were less frequently included (less than 50% of studies). Factors considered as representative to describe L. monocytogenes growth were mainly time and temperature (retail/distribution/consumer). This makes sense since L. monocytogenes is a psychrotrophic microorganism able to grow at refrigeration conditions, thus time/temperature values especially in RTE foods, are highly important to provide an accurate estimate of the final concentration before consumption as stated in FDA/FSIS (2003). High variability was found in the studies retrieved regarding the physico-chemical factors of the food being considered included in the risk assessment. This depended mainly on the food characteristics and risk assessment outcome. Among the main factors included, ph, a w or packaging conditions were considered the most frequently reported. However, some authors (around 25% of the studies) considered as factors kinetic parameters of predictive models such as lag phase, exponential growth rate (EGR), maximum population density (MPD) or competing microbiota (e.g., lactic acid bacteria). Input data type can reflect the modeling approach that was followed. Point-estimates only were provided in 6% of the retrieved studies, while in most studies some of the inputs were described as statistical distributions. This implied the use of stochastic approaches that could better represent the variability of L. monocytogenes behaviour of heterogeneous food matrices. Dealing with the model structure, most of the studies did not provide sufficient information about the primary predictive model (64%). However, for secondary predictive models, Ratkowsky-type was chosen in 45% of the studies given its simplicity and flexibility in the addition of terms describing the effect of environmental factors. Despite this, around 40% of the studies did not describe with sufficient clarity the secondary modeling structure. For the target population area, it was observed the same tendency as for DR models 40% of the studies corresponded to United States, although a representative amount of papers was retrieved from European countries, Asia and South America. Table D.4 shows data addressing exposure assessment modules of the selected studies, including information about predictive models, pathogen levels (in terms of prevalence and concentration) as well as time/temperature conditions. Regarding prevalence and concentration data, there is a substantial distinction between case-study papers, where pathogen levels are reported as point-estimate values, and papers where multiple food matrices are examined. In the latter ones, a great variability in data provided was obtained. Despite some exposure assessment considered high prevalence and/or concentration values (either as point estimate or as part of a probability distribution), in the majority of the studies, the data used as input for L. monocytogenes levels were below 10% prevalence, while concentration levels varied depending on the study and food category. In some works, values were reported in Most Probable Number/gram (MPN/g). On the other hand, time/temperature data were described as statistical distributions. Among them, Normal, Pert, Logistic, Exponential and Gamma distributions were most commonly used. Regarding consumption data, national surveys are the source most frequently reported (74%). Among them, 23% are based on data provided in the FDA/FSIS (2003) L. monocytogenes risk 52 EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

53 assessment, which used data from two large-scale, nationwide food consumption surveys to provide estimates of exposure to the pathogen, i.e., CSFII and NHANES III (USDA and ARS, 1998; USDHHS and NCHS, 1998). Two of these studies submitted data from FDA/FSIS (2003) to expert elicitation, while other published risk assessments used survey data from other countries (providing or not the corresponding reference). Finally, 13% of them based their estimations on data from previously published articles. Exposure assessment inputs were mainly represented by distributions, and either 1 st as 2 nd order estimates were provided regardless the type of studied food matrix. In Table D.4, information related to consumption data is described (serving size, frequency intake and country/source). Serving size was usually lower than 100 g, although this information depends on the type of food evaluated. Serving size data are described by pointestimate values as well as probability distributions. It can also be deduced that frequency intake (servings/capita/year) was described according to the population group to whom the intended food was destined (Latorre et al., 2011; Chen et al., 2013) Risk characterisation Risk assessment outputs and data processing varied according to the study evaluated. Information about risk characterization step is shown in Appendix D (Table D.5). Listeria risk was mainly expressed per annum and/or per serving (57 and 45% of the studies, respectively). However, 19% of the studies adopted additionally other risk measure as described earlier (i.e. DALY, economical cost lost per year, etc.). For simulation methods and software used, Monte- Carlo simulation (Palisade, Newfield, NY, USA) was the most frequently reported. Sensitivity analysis was provided in approximately 50% of the studies, while additional information was found on the application of the results obtained (evaluation of interventions/strategies, establishment of risk-based metrics, etc.) Hazard characterization Introduction Listeriosis is a rare illness but with a high mortality rate in vulnerable populations. In 2015, EFSA and ECDC (2016) reported 2,206 confirmed human cases of listeriosis in the EU (EU notification rate of 0.46 cases per 100,000 population/year) (EFSA and ECDC, 2015). This rate was very similar to that found in 2013 and The highest MS-specific notification rates were reported by Spain, Malta, Estonia and Finland (0.99, 0.93, 0.90, 0.84 and 0.84 cases per 100,000 population/year, respectively). The vast majority of cases (>98%) were reported to be domestically acquired. From the known hospitalized cases, 97.4% of cases were hospitalised, with a fatality rate of 17.7% among those cases with known outcome. France reported the highest number of fatal cases (75) followed by Germany (45). Listeria infections were mostly reported in the age group over 64 years (fatality rate of 20.3% in this age group). Nineteen MSs reported 270 deaths due to listeriosis in 2015, the highest number of annual deaths recorded since This information points out the need for assessing the risk posed by L. monocytogenes through the consumption of different categories of foods. One of the key elements in such risk assessment is the estimate of the risk (and severity) of acquiring listeriosis given a dose of Listeria cells in a food serving, i.e. hazard characterization EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

54 General review on dose-response models of L. monocytogenes Available data on DR relationships were reviewed and summarized. Dose-response models available up to now could be divided in two categories: empirical and mechanistic. The empirical models results from fitting cumulative distribution functions to a set of data obtained from experimental challenge studies (FAO and WHO, 2004a). Empirical models have been extensively applied to quantitatively characterize DR relationship of foodborne pathogen, including L. monocytogenes. However, empirical models are limited in case of extrapolation. Mechanistic models could be potentially more flexible since they are developed on a set of biologically plausible, mechanistic assumptions. Thus, they should account for the host-pathogen-food interaction (Hoelzer et al., 2013). Empirical models can be considered to model the distribution of tolerance in a population, assuming that each individual in an exposed population has some individual tolerance to the adverse agent (FAO and WHO, 2004a). If the population is exposed to a certain dose, all individuals with tolerance levels below the exposure dose will exhibit the adverse effect, whereas individuals with higher tolerance levels will not exhibit the adverse effect. Population tolerance is then described by a distribution function such as log normal, log-logistic or Weibull. The model can accommodate sublinear behaviour in the low-dose region, and depending on the parameter values, in can be reduced to the Beta-Poisson or log-logistic models. The Weibullgamma model is a flexible empirical model commonly used for L. monocytogenes DR analysis (Buchanan et al., 1997; Bemrah et al., 1998; Lindqvist and Westoo, 2000; Di Luch, 2003; FDA/FSIS, 2003; Carrasco et al., 2010; Busschaert et al., 2011). Mechanistic models used so far are based on the fundamental assumption of independence of action, which means that the probability that a given microorganism causes the adverse effect is independent of the number of other ingested microorganisms (FAO and WHO, 2004a). Mechanistic models with threshold have been described, thought they are not usually employed as experimental data are inconsistent with them (Haas et al., 1999). No-threshold mechanistic models such as the Exponential and the beta-poisson model, are the most frequently used (NRC, 2003; FAO and WHO, 2004a). Both models assume that the microorganism is Poisson distributed in the food and that there is not any dose threshold below which there is zero probability of causing an adverse effect. However, while the former assumes that the probability of causing infection (denoted by the parameter r) to a given host is the same for each ingested cell and independent of the size of the inoculum (FAO and WHO, 2004a). The latter, on the contrary, assumes that the probability of infection is variable and beta distributed with the population of hosts, therefore introduces heterogeneity in the pathogen-host interaction by the parameters α and β (FAO and WHO, 2004a). Exponential DR models have been extensively used for the characterization of L. monocytogenes DR relationship (Farber et al., 1996; Notermans et al., 1998; Lindqvist and Westoo, 2000; Chen et al., 2003a; FDA/FSIS, 2003; FAO and WHO, 2004b; Franz et al., 2010; Mataragas et al., 2010; Tromp et al., 2010; Busschaert et al., 2011; Sant'Ana et al., 2014; Vásquez et al., 2014). As reported in the Hazard Identification (Section 3.1), the mathematical model more repeatedly used for L. monocytogenes hazard characterization purposes within the revised risk assessment studies is the Exponential model (77%) followed by the Weibull-gamma model (13%). Special attention deserve the exponential DR approaches adopted by FDA/FSIS (2003) and FAO and WHO (2004b) in their works on risk assessment of L. monocytogenes in RTE foods, because of the scientific and regulation/guidance international recognition of these institutions. The work developed by these institutions consist of the first institutional risk assessments and the comprehensive nature of the work make them to be a reference for the majority of risk assessments developed afterwards. In terms of dose-response, shows that the model of FDA/FSIS (2003) has been employed in 21% of Risk Assessments of L. monocytogenes, while the model of FAO and WHO (2004b) has been applied in 32% of works EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

55 Recently, the background and relevant data for L. monocytogenes dose-response assessments were described together with the new insights in the light of recent advances in the understanding of L. monocytogenes pathophysiology or strain diversity (Hoelzer et al., 2013). In this work, the outcome of a Dose-Response Workshop co-organized by the Interagency Risk Assessment Consortium (IRAC) and the Joint Institute for Food Safety and Applied Nutrition (JIFSAN) in 2011, is summarized, and a number of strategies for modeling L. monocytogenes dose-response are discussed. Based on the recommendations raised from the mentioned Dose- Response Workshop, a summary of data needs is presented in Table 8. Information detailed in this table would allow a better refinement of DR model/s. Table 8: Summary of data needs, based on the Dose-Response Workshop outcomes (adapted from Hoelzer et al. (2013)). Pathogen Host Food matrix Short- & medium-term recommendations New data available to date: Virulence of strains with/without PMSC in inla. Outbreak data, species differences in pathophysiology, guinea pigs and rhesus monkey dose-response data. Prevalence and concentration of strains with/without PMSC in inla. Future data needs: Determinants of virulence variability among strains: fixed genetic determinant, transient determinants (e.g., stress response). Differential gene expression as a function of the environment (e.g. food). Long-term recommendations Dose-response data for strains with varying virulence. Association of strains with clinical manifestations. (e.g., meningitis, diarrhoea). Growth and concentration of L. monocytogenes in the intestinal lumen. Impact of L. monocytogenes gene expression at each step of infection. PMSC: Premature Stop Codons; inla: Internaline A. Improved outbreak data regarding: attack rate, patient cohort (e.g., co-morbidities, genetic predisposition, etc.), exposed asymptomatic individuals, exposure (e.g., impact of multiple doses ). New animal models: better reflection of human pathophysiology, most relevant disease endpoints, most relevant animal life stage. Improved understanding of determinants of susceptibility such as immune status, genetic determinants, stress Alternative models Improved understanding of L. monocytogenes pathophysiology, like incubation period or the role of microbiota in attachment and entry into enterocytes. Improved understanding of host susceptibility like definition of immunecompromised hosts or the role of humoral and cellular immunity in controlling disease Foods implicated as vehicles in outbreaks; typical handling, ability to support L. monocytogenes growth, sampling plan & detection/enumeration method. Prevalence and concentration of the different L. monocytogenes strains in foods and clinical samples. Food-matrix effects. Extrapolation across food matrices. Impact of food matrix on L. monocytogenes gene expression. Characteristics of foods frequently associated with outbreaks/sporadic illnesses including composition, ph, and ability to support bacterial growth during home storage. Concentration of L. monoytogenes in outbreak foods. Based on the scientific evidence on DR models of L. monocytogenes, the exponential form of the DR model relationship was the model of choice. This model has the following desirable features: it is mechanistic, it does not present threshold, it is a one-parameter model, and it has 55 EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

56 been widely used by different organizations including FAO/WHO and FDA/FSIS, what makes this type of model highly pertinent and relevant. All dose-response exponential models reviewed were submitted to evaluation throsugh the so-called Application of Numerical Unit Spread Assessment Pedigree (NUSAP) system. Beside this, some three non-exponential dose-response models were also scored based on their recognized relevance (Smith et al., 2008; Williams et al., 2009; Van Stelten et al., 2011), where the response of pregnant guinea pigs and rhesus monkeys to L. monocytogenes artificial infection was studied NUSAP assessment Pedigree Criteria scoring and Expert Elicitation on Pedigree Criteria weights A total of 41 studies were scored (Appendix F, Table F.2) according to the guide on Pedigree Criteria scoring (Table F.1), and taking into account the weights given to each Pedigree Criterion by Expert Elicitation (Table F.3). The consistency in the weights was calculated as explained in Methodologies section. Table 9 shows Consistency values and weights average and range. Table 9: Consistency, average and range in the weights assigned to the pedigree criteria by expert elicitation as based on the scoring by seven experts Pedigree criteria Proxy - Proxy - Empirical Methodological Validation Time Space basis rigour Consistency (a) Consistency (expertise >50%) (a) Weight average (a) Weight range (w-w) (a) 0.06 ( ) 0.06 ( ) 0.37 ( ) 0.26 ( ) 0.24 ( ) w: minimum weight value; W: maximum weight value. (a): Data expressed per unit basis. Table 9 reveals that the pedigree criteria do not have the same impact in assessing the quality of dose-response models. Proxy-Time and Proxy-Space are by far the pedigree criteria having less impact on the quality of the dose-response models, with weight averages of 0.06 (range ). On the contrary, Empirical basis is the criterion with more impact, with a weight average of 0.37 (range ). With reference to the consistency of the weights given by experts, it can be seen that Proxy and Validation were the pedigree criteria exhibiting major consistency (close to 1), followed by Methodological rigour and Empirical basis. Leaving apart the logical natural variability in experts assessments, a possible reason for the lower consistency would be the existence of various items of different nature within the pedigree criteria Methodological rigour and Empirical basis, which may receive different unconscious subweights, producing finally a pedigree criterion s score notably difference between experts (see the range of weights in Table 9). However, not all experts assign themselves a high level of expertise; taking into account only those experts with an expertise higher than 50% (three experts), the consistency in Methodological rigour and Empirical basis did not improve Dose-response quality assessment (NUSAP-based ranking) A ranking of 41 dose-response studies based on the NUSAP scoring system is provided in Figure 10. From these, one of the studies (Notermans et al., 1998), was discarded from the ranking. Although with high score (2.31), this study provided a DR model based on mice experiments without further refinement for adjustment for the human case. It has been widely recognized that mice are physiological and immunologically very different from humans, so the uncertainty of the model is very high. The high score of the work of Notermans et al. (1998) was due to 56 EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

57 issues like the high number of subpopulation considered (immunosuppressed protected and non-protected, normal protected and non-protected) or the number of end-points (illness and death), whose score coincided with that of the model of FDA/FSIS (2003) and the models based on it. The scores ranged from 1.77 to 3.20, and many models were scored equally because they are based on the same model. Also, it can be noticed that the models scores, in general, are similar. Taking into account that the NUSAP system must rely on the design of the score scale (Table F.1), which ranged in this case from 1 to 4 in an arithmetic sequence; if, and that, for example, a geometric sequence was applied (e.g. 1, 2, 4 and 8), major differences could have been obtained. However, the UCO-IRTA consortium deemed appropriate to follow the procedure established by Boone et al. (2009). The study of Pouillot et al. (2015) received the highest score (score of 3.20), followed by a number of studies with roughly the same score: 12 studies scoring 2.31 and 11 studies scoring 2.28 The first group is comprised of the model of FDA/FSIS (2003) and some other models based on it; the second group comprises the model of FAO/WHO (2004b) and some other models based on it. The three non-exponential DR models included in the ranking (Smith et al., 2008; Williams et al., 2009; Van Stelten et al., 2011) received a score of 2.06 (the former two) and 2.19 (the latter) Discussion and final selection of the dose-response models of L. monocytogenes Based on the NUSAP system, the DR models of Pouillot et al. (2015), FDA/FSIS (2003), FAO and WHO (2004b), and the studies which used the models of the latter two, received the highest scores. There is considerable number of studies which have employed the models of FDA/FSIS (2003) and FAO and WHO (2004b), probably due to the well-recognized importance of these organizations and their comprehensive and deep research on L. monocytogenes DR modelling. Table F.2 can be consulted to check the scores given to the models of Pouillot et al. (2015), FDA/FSIS (2003) and FAO and WHO (2004b). Below are comments on their basis and main characteristics are provided. FDA/FSIS (2003) performed a risk assessment of listeriosis acquired from consumption of selected categories of RTE foods. The study addressed three subpopulations group: perinatal (foetuses and neonates infected in utero by contaminated foods consumed by their mothers), elderly (60 years old and older), and intermediate-age (including both the healthy population and some susceptible individuals such as AIDS patients or individuals with an impaired immune system, cancer or transplant patients). For the first time, variability coming from L. monocytogenes strains and host susceptibility was explicitly incorporated through animal (mice) experiments information; also, an uncertainty component accounting for the differences in susceptibilities between laboratory mice in a controlled environment and humans in an uncontrolled environment was incorporated by calculating an adjustment factor suitable for each subpopulation group through surveillance data (FoodNet). Without this adjustment, a strong overestimation of listeriosis could be obtained. Initially, various mathematical models were adjusted to the raw data in mice for lethality, being the Exponential function the model receiving the greatest weight as it was exhibited the best fitting to data. Subsequently, several multipliers were applied to account for all variability and uncertainty features aforementioned EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

58 Studies Quantitative risk characterization on Listeria monocytogenes in ready-to-eat foods Pouillot et al., 2015 Notermans et al., 1998 Stasiewicz et al., 2014 Pradhan et al., 2011 Pradhan et al., 2010 Hicks Quesenberry et al., 2010 Endrikat et al., 2010 Pradhan et al., 2009 Pérez-Rodríguez et al., 2007 Domenech et al., 2007 Yang et al., 2006 Gallagher et al., 2003 FDA/FSIS, 2003 Gkogka et al., 2013 Gallagher et al., 2013 FDA, 2013 Ding et al., 2013 Chen et al., 2013 FDA and HC, 2013 Latorre et al., 2011 Garrido et al., 2010 Ross et al., 2009 Pouillot et al., 2009 FAO/WHO, 2004 Williams et al., 2009 Giacometti et al., 2015 Giovannini et al., 2007 Saana et al., 2004 van Stelten et al., 2011 Smith et al., 2011 Sant Ana et al., 2014 Mataragas et al., 2010 Chen et al., 2006 Franz et al., 2010 Tenenhaus-Ariza et al., 2014 Trompt et al., 2010 Linqdvist and Westoo, 2000 Horigan et al., 2014 Chen et al., 2003 Vásquez et al., 2014 Buchanan et al., ,31 2,31 2,31 2,31 2,31 2,31 2,31 2,31 2,31 2,31 2,31 2,31 2,28 2,28 2,28 2,28 2,28 2,28 2,28 2,28 2,28 2,28 2,28 2,19 2,14 2,14 2,14 2,06 2,06 2,05 2,05 2,03 2,03 2,02 1,96 1,89 1,85 1,83 1,77 1,77 3,20 0,00 0,50 1,00 1,50 2,00 2,50 3,00 3,50 NUSAP scores Figure 10: Ranking of studies including dose-response models of L. monocytogenes through the Numeral Unit Spread Assessment Pedigree (NUSAP) system 58 EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

59 The distribution for every subpopulation group accounts for the following sources of uncertainty/variability: (i) the variation in the virulence of different strains and uncertainty in the animal data used to characterize those strains; (ii) the uncertainty in the primary mouse model curve; and (iii) the uncertainty in the DR adjustment factor. For perinatal and the elderly, an additional source is included to explain the increased host susceptibility, i.e. the variability in animal susceptibility and the uncertainty in the animal data. Apart from lethality as endpoint, FDA/FSIS (2003) also considered severe illness in the risk assessment, assuming the number of serious illness is five times the number of deaths (information derived from FoodNet data estimates). FDA/FSIS (2003) stated that the relationship between infection in mice and the spectrum of clinical illness in humans is not understood, especially at lower doses; in this way, FDA/FSIS considered more appropriate to use mortality rather than infection as the endpoint to model human dose response. In the risk assessment performed by FAO and WHO (2004b), the r parameter of the DR Exponential model was derived following the procedure described by Buchanan et al. (1997), i.e. r was calculated from both the exposure of a population to L. monocytogenes via foods and the incidence of the disease. Data from exposure of a population to L. monocytogenes via foods were used from the FDA/FSIS (2003) risk assessment, whereas the annual incidence of listeriosis was that reported by the Centers of Disease Control and Prevention (CDC) and published by Mead et al. (1999). FAO and WHO (FAO and WHO, 2004b) considered two subpopulation groups: susceptible population, including elderly (persons over the age of 60-65), infants, pregnant women and immunocompromised patients, and healthy population. Some sources of uncertainty were considered for r calculation: (i) uncertain percentage of the population who are at increased risk (range from 15 to 20%); (ii) uncertain percentage of total cases in the annual disease statistics associated with the increased susceptibility population (range from 80 to 98%); and (iii) the total number of listeriosis cases in the United States of America (2,518 cases ± 25%). Consequently, the r values were estimated uncertainties determined by Monte Carlo simulation. In parallel, two different types of calculation were carried out: (i) assuming that only one maximum dose was possible (either 7.5 or 8.5 or 9.5 or 10.5 log 10 CFU) and (ii) as it remains unknown the maximum dose, an uncertainty distribution was applied to consider four possible doses with theoretical equal probability to happen, i.e. a discrete uniform distribution for the following doses: 7.5, 8.5, 9.5 and 10.5 log 10 CFU. This issue was considered given the fact that the maximum levels of L. monocytogenes encountered in individual servings of the different foods have a large impact on the calculated mean ingested doses (FDA/FSIS, 2003; FAO and WHO, 2004b). r values were calculated for three different scenarios: a) It was assumed that the incidence of listeriosis is due to those servings of foods with the highest levels of contamination; b) Same approach as in scenario a), but calculating point-estimates for the three sources of uncertainty described above, i.e., 17.5% of high-risk population, 83% of cases associated with high-risk population and 2,518 cases of listeriosis per year; c) It was assumed that all doses contribute to the overall incidence of listeriosis, and as in scenario b), point-estimates for the three sources of uncertainty were considered, i.e., 17.5% of high-risk population, 83% of cases associated with high-risk population and 2,518 cases of listeriosis per year. In the FAO and WHO approach, some issues that obviously exert an effect on the incidence of listeriosis, such as the virulence of L. monocytogenes strains or the host susceptibility are inherently considered in the r final estimate, as no distinction was considered in these issues. In the same manner, the biological end-point used for the DR relationship is listeriosis, referring this term to severe infection or invasive listeriosis, i.e. individuals suffering from lifethreatening, systemic infections such as perinatal listeriosis, meningitis or septicaemia. FAO/WHO (2004b) encountered that the probability of a single ingested bacterial cell causing 59 EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

60 disease for the healthy population was about 1-2 logs lower than for the susceptible population. Some authors (Ross et al., 2009; FDA and Health Canada, 2012) agree on the fact that the model of FDA/FSIS (2003) varies with every iteration of the risk assessment and is neither readily reproduced nor readily defined. On the other hand, the FAO/WHO (2004b) approach leads to estimates consistent with other estimates of the r value when differences in assumptions are accounted for (e.g., number of RTE foods contributing to the exposure, extent of growth between retail and consumption etc.) and, due to its broader database, is probably the most-preferred of the L. monocytogenes dose response models currently available. Apart from these issues, some recent works based on animal studies (Smith et al., 2008; Williams et al., 2009; Van Stelten et al., 2011), when comparing in terms of LD 50 the predictions of FAO/WHO and FDA/FSIS models with their own models fitted to animal data (mainly loglogistic), they found that FAO and WHO model predictions were very similar to the results they obtained, while FDA/FSIS outcome differed substantially by various orders of magnitude (LD 50 much higher predicted by FDA/FSIS (2003). Consequently, given the practically equal assessment of both models through NUSAP system, and due to the issues stated above, the model of FDA/FSIS (2003) was discarded for the risk assessment object of this project. Pouillot et al. (2015) based their log-normal Poisson model for the r parameter on the conceptual meaning of Exponential model, which rely on the assumption of a Poisson distribution of Listeria cells in a dose, so r would represent the probability that one pathogen will survive the host-pathogen interaction to initiate infection and cause illness, within a given exposure of a consumer (Schmidt et al., 2013). This would lead to think that it is not correct to assume that r remains the same from exposure to exposure, as strains of L. monocytogenes may change, and so the virulence. Consequently, Pouillot et al. (2015) proposed that r may vary and may be represented by a random variable with distribution f(r). Besides, susceptibility of the host to L. monocytogenes was also claimed to be different within the subgroup of highrisk population based on previous studies (Mook et al., 2011; Goulet et al., 2012; Pouillot et al., 2012; Silk et al., 2012). Then, r parameter would be a function of the product of two independent probabilities: the probability p i that the pathogen successfully initiate infection in the host (events controlled by host factors), and the probability p s representing the virulence and pathogenity of L. monocytogenes, being strain-dependent (events controlled by bacterial factors), in such a way that r = p i x p s ; Pouillot et al. (2015) assumed these issues to follow a lognormal distribution accounting for variability. Variability description of both strain virulence and host susceptibility were assumed to be the same as those found in animal experiments and reported by FDA/FSIS (2003). This information was mathematically combined with exposure assessment information to derive the r parameter for 11 subpopulations groups (one group of healthy populations and 10 subgroups at increased risk). Exposure assessment information was taken from the study of Chen et al. (2003) (prevalence and levels of L. monocytogenes in RTE foods) and from FDA/FSIS (2003) (number of servings per year). The number of listeriosis cases in each subgroup was estimated according to the relative risks of listeriosis for each subgroup (rearranged) reported by Goulet et al. (2012). Pouillot et al. (2015) stated that DR models published so far (such as the FAO/WHO model) do not properly describe rare but highly relevant events such as the ingestion of a highly virulent L. monocytogenes strain by a highly susceptible individual. The lognormal-poisson model of Pouillot et al. (2015) extends L. monocytogenes DR modelling to explicitly consider variability in strain virulence and in susceptibility across population subgroups. As an example, Pouillot et al. (2015) provided an estimate of risk for one subpopulation group, i.e. transplant recipients, in an attempt to predict the results of a well-documented outbreak occurred in Finland in (Lyytikäinen et al., 2000), showing that their model successfully encompassed the outbreak outcome in a range of highly virulent strains of L. monocytogenes. Considering the scope of the present project and in the light of the NUSAP ranking and the discussion provided above, the UCO-IRTA consortium considers appropriate to apply separately two DR models for L. monocytogenes: (i) the FAO and WHO model; and (ii) the model of 60 EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

61 Pouillot et al. (2015), in the susceptible and normal population in the EU, and with the listeriosis as defined by FAO and WHO (2004b) as the end-point Exposure assessment Prevalence-based subcategorization Information provided from the analysis of factors related to prevalence in the BLS part B report (EFSA, 2014) was collected and analysed for RTE food subcategorization. Subcategorization for prevalence was considered for RTE fish and RTE meat products while for soft and semi-soft cheeses no distinction between subcategories could be made. After analysing the results, it was concluded that slicing was the most relevant factor impacting prevalence, hence it was considered in the modelling process. However, as no information is available on the real proportion of sliced products on the market, this was considered as scenario and not specifically a subcategory in the modelling process. Scenarios are included in the risk model by assigning statistical weights based the user guess Growth-based subcategorization The high number of factors and co-variables used in the analysis made the statistical design unbalanced. This means that the number of observations in the combinations of factor levels were different, with some combinations not containing observations. Those factors with combinations with missing data were discarded from the multi-factor analysis and were analysed separately. In order to obtain a more reliable outcome from the ANOVA analysis under this unbalanced designed, unweighted means were computed in the ANOVA instead of the typically applied weighting method. A more detailed explanation is given in Appendix O for the three food categories. In the case of heat-treated meat products, the statistical analysis (Appendix O) differed two subcategories based on type of product which corresponded to, on the one hand, cooked meat and sausage and, on the other hand, pâté. In relation to type of atmosphere, statistical analysis was not conclusive; however, literature review evidenced that MAP atmosphere usually results in lower Listeria growth in comparison with vacuum conditions (Tsigarida et al., 2000; Kaban et al., 2010; Kudra et al., 2012). This aspect is only clear when studies are treated individually, i.e. when both conditions are studied in the same type of product, but when growth rates from MAP and vacuum are compared from different studies the effect cannot be statistically validated as observed in the study of EGR 5C values performed in the present work. The main cause can lie in the huge differences among products (formulation, background flora, etc.) and experimental set-ups (listeria strains, packaging film, etc.) from different studies, which greatly influence Listeria growth. Nonetheless, this huge variability is not only restricted to scientific studies but also it is expected in real world given the huge diversity in RTE foods. The statistical analysis in the case of smoked and gravad fish was not able to yield a reliable estimate due to the limited number of data for some of the studied factors (Appendix O). Nonetheless, results showed no significant differences for the studied factors (preservatives, type of product and type of atmosphere). Due to the importance of considering the effect of type of atmosphere in the risk modelling process and given this factor was also considered in heat-treated meat as scenario, in the case of fish products, EGR 5ºC values were also split to consider type of atmosphere, separately. Results from the statistical analysis (Appendix O, Table O.10) evidenced that there were not significant differences between gravad and smoked fish. These results are not conclusive due to the insufficient data for gravad used in the analysis (n<10). In the literature, no reliable information on the listeria growth in gravad fish was found. Therefore, owing to the low number of data for gravad and assuming, on the basis of the ANOVA outcome, that the ample variety of smoked fish products could also include those physico-chemical characteristics inherent gravad fish affecting Listeria growth (e.g. ph >5; NaCl < 8%), EGR 5ºC for both RTE fish subcategories were considered jointly in the model EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

62 In the case of soft and semi-soft cheese, the statistical analysis assessed the effect of the milk heat treatment (raw and pasteurized milk) on Listeria growth, though, again data was limited, specially for raw milk. The output from the statistical analysis did not evidence significant differences for this factor, hence in the case of cheese, no further growth-based food subcategorization was applied on the EGR 5ºC values (Appendix O). Although both types could be modelled independently regardless of the statistical outcome, the number of data for raw milk was so scarce (three studies) that modelling would not be representative enough, and exclusively limited to specific growth values. The latter could lead to biased estimated when compared to pasteurized where more studies were used to obtain EGR 5ºC values. Therefore, data were merged into one single category which could describe all possible growth patterns derived from both raw and pasteurized milk. Nonetheless, the effect of raw and pasteurized milk in soft and semi-soft cheese could be assessed alternatively, by introducing different starting values for the concentration of lactic acid bacteria in the growth model. In raw milk cheese, a higher concentration of lactic acid is expected which could affect Listeria growth during storage. To properly include type of atmosphere in risk assessment, the proportion of each type of atmosphere on the market should be known; however, this information is not reported in consumption or market surveys. As alternative to assess the possible effect of the type of atmosphere on listeriosis risk, different scenarios were built based on both EGR 5C values for each type of atmosphere included in the database developed for the present work and the samples proportions obtained in BLS, which were assumed to be representative for the proportions of the same food categories on the EU market.to facilitate the scenario structures MAP and vacuum conditions were considered in the same scenario, referred as Reduced Oxygen Packaging (ROP). During simulation, values can be specifically extracted from the previous distributions considered in this scenario analysis as joint probability distributions using the function discrete distribution to sample to each type of atmosphere at different market percentages (see Risk modelling process structure) Risk modelling process structure Based on the outcomes from the statistical analyses, and considering a convenient and practical structure for the risk model, a subcategory and scenario based scheme describing different pathways was designed, which is presented in Table 10, and Figure 11, Figure 12 and Figure 13. The different types of product were simulated considering the combination of the different scenarios (package atmosphere, slicing, smoking process) and subcategories for each food subcategory. The subcategorization and scenario approaches had implications in the modelling process, since in each modelling step (prevalence, concentration, growth, etc.), the model was compartmented according to the categorization and scenarios proposed. In other words, each subcategory and scenario was mathematically represented by a specific model. Nonetheless, compartmentation was not always possible due to the limitations in data. Also, in some cases, the statistical analysis revealed that different subcategories could be merged as one as no differences were observed. For example, for the growth model, it was concluded that growth for cooked meat and sausage was statistically similar, and therefore, the growth for these food subcategories were described using the same model. To integrate the different possible combinations (i.e., types of products), in the simulation, within each food category, a pathway-based model structure was designed, where each pathway had a specific weight according to its relevance on the market, and therefore, also on the consumption and risk. Given no data were available about the proportions of these specific products on the EU market, the proportion obtained in the BLS was used as initial guess. Although the BLS did not include a representative sampling for each specific food type, it was deemed as a very realistic and reasonable approach to what is observed on the EU market since sampling was performed randomly for the selected food categories. Therefore, in the model, 62 EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

63 each scenario and subcategory was weighted according to the proportions found in the BLS (Table 11). Mathematically speaking, these weights are determining the probability of occurring of each scenario or subcategory is simulated. These probabilities are included in a probability tree by applying a discrete distribution, where all combinations are represented with their corresponding probability of occurrence. Based on this, the corresponding models are applied and combined in the modelling process. The result of this approach is that, during the simulation, the combinations with high proportions (high statistical weights) would be more frequently sampled than those with low proportions in the BLS, providing a faithful representation of what observed in the BLS. Additionally, the model allows that the user can introduce other different weights to those based on the BLS, assigning probabilities to each of the elements that are made up of the different scenarios and subcategories (i.e. slicing process, package atmosphere, smoking process). By combining the weights (as independent events), the probability of the designed scenarios is estimated and can be used for the simulation. Figure 11: Schemes of the risk modelling process for subcategories and scenarios of the heattreated meat 63 EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

64 Figure 12: Scheme of the risk modelling process for subcategories and scenarios of smoked and gravad fish Figure 13: Scheme of the risk modelling process for subcategories and scenarios of soft and semi-soft cheese 64 EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

65 Table 10: Food sub-categories and scenarios considered in the L. monocytogenes risk modelling process Food Category Food subcategory Scenarios Prevalence Growth Prevalence Growth Heat-treated meat (a) Cooked meat (b) Sausage (c) Pâté (a, b) Cooked meat & sausage (c) Pâté RTE Fish products (a) Cold smoked (b) Hold smoked (c) Gravad Soft and semi-soft No subcategories cheese ROP: Reduced Oxygen Packaging. (a, b, c) Gravad and smoked No subcategories (1.a) Sliced cooked meat (1.b) Sliced sausage (1.c) Sliced pâté (2.a) Non-sliced cooked meat (2.b) Non-sliced sausage (2.c) Non-sliced pâté (1.a) Sliced cold smoked (1.b) Sliced hot smoked (2.a) Non-sliced cold smoked (2.b) Non-sliced hot smoked (1.c) Sliced gravad (2.c) Non-sliced gravad (1.a, 1.b) ROP cooked meat & sausage (1c) ROP pâté (2a, 2b) Normal cooked meat & sausage (2c) Normal pâté (1.a, 1.b, 1.c) ROP smoked and gravad (2.a, 2.b, 2.c) Normal smoked and gravad Table 11: Proportion (%) of the different scenarios and subcategories found in the EU-wide baseline survey (BLS) and considered in the risk assessment Percentages/scenario ROP/sliced ROP/non-sliced Normal/sliced Normal/non-sliced Cold smoked fish 80% 16% 4% 0% Hot smoked fish 29% 43% 17% 10% Gravad fish 72% 6% 15% 7% Cooked meat 80% 7% 12% 1% Pâté 49% 25% 12% 13% Sausage 55% 23% 17% 5% Soft and semi-soft cheese 11% 13% 16% 61% TOTAL 46% 14% 14% 26% ROP: Reduced Oxygen Packaging EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

66 Prevalence distributions for L. monocytogenes in different RTE products in the EU Listeria monocytogenes prevalence distributions in the seven selected RTE food subcategories, for sliced and non-sliced products, are shown in Table 12. These distributions obtained are based on the Beta distribution. To assess the representativeness of using different distributions, a comparison exercise of the confidence intervals (CIs) obtained for the different distributions (Normal and Beta) for RTE fish products was firstly made with those reported in the BLS part A. Calculation was achieved through the comparison of CIs of Normal and Beta distributions using BLS data. This was established in order to validate the appropriateness of the use of Beta distributions for the selected RTE food categories. Table 12: Estimated parameters for the distributions of L. monocytogenes prevalence in the selected RTE food categories, considering slicing conditions as potential scenarios Food category RTE fish products RTE meat products RTE cheese products Subcategory Scenario Fitted Beta Mean distributions (a) [C.I. 95%] Cold-smoked fish Sliced RiskBeta(76+1; ) [ ] Cold-smoked fish Non sliced RiskBeta(18+1; ) [ ] Hot-smoked fish Sliced RiskBeta(20+1; ) [ ] Hot-smoked fish Non sliced RiskBeta(12+1; ) [ ] Gravad fish Sliced RiskBeta(30+1; ) [ ] Gravad fish Non sliced (b) RiskBeta(0+1;33-0+1) [ ] Cooked meat Sliced RiskBeta(43+1; ) [ ] Cooked meat Non sliced RiskBeta(3+1; ) [ ] Sausage Sliced RiskBeta(11+1; ) [ ] Sausage Non sliced RiskBeta(2+1; ) [ ] Pâté Sliced RiskBeta(7+1; ) [ ] Pâté Non sliced RiskBeta(2+1;70-2+1) [ ] Soft and semi-soft Sliced RiskBeta(5+1; ) [ ] cheese Soft and semi-soft Non sliced RiskBeta(8+1; ) [ ] cheese (a): Beta distributions were defined as (σ =s+ 1; β=n-s+1), being s +1; N-s+1) being s the number of positives and N the total number of samples per RTE food subcategory. (b): No positive samples were reported. A prior Beta (1,1) was considered for describing uncertainty in prevalence estimates. In Table 13, predictions given by the fitted Normal distributions yielded narrower CIs for smoked and gravad fish in comparison with Beta distributions and values reported in the BLS. Regarding this study, 95 th percentiles were wider than those predicted by the Beta distributions for smoked fish while Beta distributions predicted the highest 95 th percentile value for gravad fish. Accounting for this, the use of Beta distributions was considered appropriate since they are being used in other published studies (Miconnet et al., 2005) for describing microbial prevalence. Besides, they allow some flexibility to be skewed to left and/or right sides and they can be easily populated when new data become available. Therefore, discussion about prevalence results will be based on the Beta distributions outputs EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

67 Table 13: Estimated descriptive statistics and percentiles of Beta and Normal distributions for prevalence of L. monocytogenes in RTE fish products and comparison with those reported in the EU-wide baseline survey Percentiles of Beta distributions (a) Minimum 5 th percentile Mean 95 th percentile Maximum Hot smoked fish Cold smoked fish Gravad fish Percentiles of Normal distributions Minimum 5 th percentile Mean 95 th percentile Maximum Hot smoked fish Cold smoked fish Gravad fish Published percentiles in EU-wide baseline survey (b) Minimum 5 th percentile Mean 95 th percentile Maximum Hot smoked fish Cold smoked fish Gravad fish (a): Beta distributions were defined as σ =s+ 1 and β=n-s+1, s being the number of positives and N the total number of samples per RTE food subcategory. Note: Values indicate the range of confidence intervals using the different Generalized Estimating Equations fitting methods Prevalence levels of L. monocytogenes in RTE foods considering slicing conditions The results obtained (prevalence estimates) showed that Beta distributions were highly influenced by the number of data used for each RTE food subcategory. To proceed with the analysis of prevalence, BLS data were disaggregated according to the food subcategory and scenarios (slicing conditions). This produced scarcity of data for some combinations, (i.e. N<30) which led to a higher uncertainty in prevalence. In that case, maximum values increased. Therefore, the use of Beta distribution must be oriented to model uncertainty in prevalence. According to the mean values, prevalence levels were higher for RTE fish products, when compared to the rest of food categories. Differences can be found depending on the RTE food subcategory considered. Estimated mean prevalence values of RTE cold smoked fish showed similar results, ranging from to However, maximum values were higher for nonsliced ROP products. This fact is attributed to the right skeweness of Beta distributions when few data are available. In this case, uncertainty was much higher for non-sliced fish (N=102) than for sliced fish (N=511). For hot-smoked fish sliced products presented higher prevalence than non-sliced ones (mean prevalence =0.087 and respectively). This was also the case for gravad fish. Differences found in descriptive statistics are associated to the imbalanced number of samples per RTE food subcategory. Regarding RTE meat products, cold, cooked meat presented mean prevalence values around These values were higher for pâté (ROP) ranging from to Maximum values for non-sliced products were lower than those for sliced meat. However, this deviation is produced by the sensitivity of Beta distibutions to the number of data used to build the model (Figure 12). RTE sausage presented large differences in prevalence (mean values ranged from to 0.022), which could be related to the relative scarcity of data especially for non-sliced products. Much lower values were found for RTE cheese. Sliced cheese presented a mean prevalence of while this value was reduced to for non-sliced cheese. The results for the global prevalence estimates are represented in Table 14. Again, mean prevalence of RTE fish products presented the highest values (P>0.084) while L. monocytogenes was present in a lesser extent in cooked meat and pâté. Global prevalence estimates were obtained from the compilation of BLS data, monitoring data and from selected scientific studies retrieved in ACT1. The distributions obtained can be further used as potential baseline scenarios in the risk model EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is published complying with the transparency principle to which the Authority is subject. It may not be considered as an output adopted by the Authority. The European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

68 Table 14: Estimated parameters for the global prevalence distributions of L. monocytogenes prevalence in the selected RTE food subcategories resulting from merging data from EU-wide baseline survey, monitoring and activity 1 Food category Subcategory Beta distributions EU-wide baseline survey Monitoring data Activity 1 N s N s N s RTE fish products Cold-smoked fish RiskBeta(219+1; 2, ) , (a) - (a) Hot-smoked fish RiskBeta(406+1; 3, ) , (a) - (a) Gravad fish RiskBeta(185+1; 1, ) , RTE meat products Cooked meat RiskBeta(46+1;2, ) 2, (a) - (a) Sausage RiskBeta(13+1; ) (b) - (b) - (a) - (a) Pâté RiskBeta(12+1; ) (a) - (a) RTE cheese products RiskBeta(629+1; 23, ) 3, , , The total number of samples (N) and number of positives (s) for each RTE food subcategory is presented. (a) Data from activity 1 were not used for these RTE food subcategories. (b): Sausage was not available in the monitoring database as a single RTE food subcategory EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

69 Concentration distribution for L. monocytogenes in different RTE products in the EU The initial concentration of L. monocytogenes in the seven selected RTE food subcategories is shown in Table 15. It was shown that the median value for concentration was higher for gravad smoked fish (0.524 log CFU/g) than for cold smoked fish (0.394 log CFU/g) and hot smoked fish ( log CFU/g) (Table 15). Grafical representation of the concentration fitting is shown in Appendix P. The lognormal distributions as proposed herein can also be applied for non-prevalent (negative) samples. However, prevalence was separately modelled so that lognormal distribution was truncated to values only allowing for positive samples. The theoretical minimum concentration derived from the sample size typically used in the detection method for Listeria, that is, 1 cell/25g (-1.39 log CFU/g) was applied as limit in the distribution to exclude those values of initial concentration in the simulation with lower values. Regarding RTE meat products, separate distributions were built for cooked meat, sausage and pâté. As referred in Section 2.1.2, sausage could not be separately retrieved from the monitoring database, therefore sausage data were included in the same distribution as cooked meat. Moreover, raw data used to describe the initial distribution for RTE sausage concentration were taken from results of the activity 1 report. The highest median value was obtained for sausage (1.598 log CFU/g) while cooked meat presented the lowest growth for L. monocytogenes (median log CFU/g). Variability in microbial concentration was especially important for sausage. This can be attributed to the lack of information regarding this food subcategory as only data coming from activity 1 were used. Regarding RTE cheese, initial subdivisions of data into raw milk and pasteurized milk cheeses, and between soft and semi-soft cheeses was initially carried out. However, the estimated log normal parameters yielded extremely high values of standard deviation, thus leading to overdispersion of data. Therefore, final data were pooled into one cheese category. In this sense, it is assumed that there are not differences in the contamination levels of L. monocytogenes between raw, pasteurized, soft and semi-soft cheeses. Parameters so obtained presented a low median value (0.389 log CFU/g) and a 95 th percentile close to 5 log CFU/g. It should also be noted that concentration values obtained are referred to the mean concentration in a lot. Differences between lots (inter-lot variability) were not considered, as no information was available to provide accurate estimates. Table 15: Estimated parameters for the log normal distributions (log CFU/g) used to describe initial concentration of L. monocytogenes in the selected RTE foods subcategories at retail Food subcategory Mean SD 50 th 5 th 95 th LogL AIC BIC Perc. Perc. Perc. Cold smoked fish x x x10 3 Hot smoked fish x x x10 3 Gravad fish x x x10 2 Cooked meat x x x10 3 Sausage x x x10 1 Pâté x x x10 3 Soft and semi-soft cheese x x x10 2 SD: standard deviation; Perc: percentile; LoL: Log-likelihood; AIC: Akaike Information Criterion; BIC: Bayesian Information Criterion EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is without prejudice to the rights of the author(s).

70 Consumption patterns Serving size Statistical parameters and probability distributions parameters for serving size of the target food subcategories and different population groups in the EU are presented in Table 16. The data for pregnant women are based on a single available study performed in Latvia. The use of data specific to single country could lead to strongly biased estimates, hence, data from this country were not used and pregnant serving size was assumed to be similar to adult population. These distributions were intended to determine the pathogen dose ingested per eating event: i.e., concentration (CFU/g) serving size (g) Number of servings The total number of servings in EU 28 is presented Table 17. Due to the limited data for smoked fish in the EFSA food consumption database, the number of servings estimated was unexpectedly higher in elderly population than in adult population (data not shown). To overcome this issue, estimates of the number of serving were adjusted by using apparent consumption (Fishstat plus) where the balance between food production, export and import was estimated and assumed to correspond with the population consumption. Apparent consumption data did include gravad fish since this was included as part of several fish categories that were not possible to disaggregate to consider gravad fish in isolation. In risk calculations, gravad consumption was estimated as a percentage of the smoked fish consumption (estimated from apparent consumption), based on the ratio between sampled smoked and gravad fish obtained in the BLS where the gravad fish sampling corresponded to 22.4% smoked fish samples Time to consumption In the present model, time-to-consumption was defined by an exponential function. This function was defined using pooled remaining shelf-life values according to the product subcategories in the present study. Statistics were estimated which were further used to construct exponential distributions describing variability in the remaining shelf-life in each food subcategory and scenario. The exponential distributions for time-to-consumption were defined by using a location parameter and the 99 th percentile of the remaining shelf-lives. The location parameter, i.e., the minimum value of the shelf-life, was assumed to follow a triangular distribution described by the parameters min=0.04, max= 0.5 and mean= 0.16 expresssed in days. The 99 th percentile was defined based on BLS data using the probability distributions (i.e., exponential) of Table 18, Table 19 and Table 20, reflecting the maximum remaining time, defined from the BLS EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is without prejudice to the rights of the author(s).

71 Table 16: Main statistics for serving size (g) and fitted probability distributions of different RTE food subcategories and categories Food Statistics Healthy Elderly Pregnant (b) Subcategory Smoked fish (a) n mean median min max Distribution RiskPearson5( ;1.2895;RiskShift( ) RiskPearson5(1.5718;3.3287;RiskShift( ) I.D. Cheese n mean median min max Distribution RiskExpon(28.279;RiskShift(0.248)) RiskExpon(29.245;RiskShift( )) RiskExpon(22.65;RiskShift(11.734)) Cooked meat n mean median min max Distribution RiskLognorm(40.371;43.288;RiskShift( )) RiskLognorm(37.745;38.568;RiskShift(-2.178)) RiskExpon(54.093;RiskShift(14.442)) Sausage n mean median min max Distribution RiskExpon(56.696;RiskShift( )) RiskExpon(45.749;RiskShift( )) RiskExpon(74.167;RiskShift(9.6364)) Pâté n N/A mean N/A median N/A min 0 0 N/A max N/A Distribution RiskInvgauss(30.812;28.642;RiskShift( )) RiskPearson5(1.9347;26.151;RiskShift( )) I.D. N/A: Not available data; I.D.: Insufficient data. (a): The serving size of smoked fish was also used for gravad fish. (b): For those food subcategories where no probability distributions nor data were available, probability models of healty population (i.e. adults) were used to approach consumption patters of the pregnant population EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

72 Table 17: Estimated number of servings in the EU 28 per year based on consumption data from EFSA consumption database (EFSA, 2015), EU production data from the database Fishstat and demographic data from Eurostat Food subcategory <65 65 Pregnant Susceptible ( 65+pregnant) (a) Cooked meat Sausage Pâté Smoked fish (b) Gravad fish (c) Soft/semi-soft cheese (a): Combination used for the dose-response model by FAO and WHO (2004b) where only healthy and susceptible populations are considered. (b): Values were based the database Fishstat available at (c): Values were calculated as a percentage (42%) of the number of servings for smoked fish using the proportions between both types of product found in BLS. Table 18: Descriptive statistics from the remaining shelf-life (days) obtained from the EU-wide baseline survey (BLS) for heat-treated meat products and used to construct Exponential distributions describing the remaining shelf-life Food category Packagin Slicing Min Max Mean Distribution g Cooked meat ROP Non sliced RiskExpon(33.66;risktruncate(1;209)) (a) Sausage ROP Non sliced RiskExpon(25.21;risktruncate(1;126)) Pâté ROP Non sliced RiskExpon(28.66;risktruncate(3;99)) Cooked meat Normal Non sliced RiskExpon(40.29;risktruncate(2;160)) Sausage Normal Non sliced RiskExpon(25.38;risktruncate(1;106)) Pâté Normal Non sliced RiskExpon(24.83;risktruncate(3;149)) Cooked meat ROP Sliced RiskExpon(18.54;risktruncate(0;427)) Sausage ROP Sliced RiskExpon(16.49;risktruncate(0;143)) Pâté ROP Sliced RiskExpon(18.24;risktruncate(1;73)) Cooked meat Normal Sliced RiskExpon(17.20;risktruncate(1;99)) Sausage Normal Sliced RiskExpon(12.21;risktruncate(0;73)) Pâté Normal Sliced RiskExpon(14.30;risktruncate(5;32)) ROP = Reduced Oxygen Packaging. (a): The distributions were truncated to the minimum and maximum values found in the EU-wide baseline survey by using function risktruncate (min; max) Table 19: Descriptive statistics from the remaining shelf-life (days) obtained from the EU-wide baseline survey (BLS) for fish and used to construct Exponential distributions describing the remaining shelf-life Food category Packaging Min Max Mean Distribution Smoked fish (a) ROP RiskExpon(1;519;20.6; risktruncate(1;519)) (b) Smoked fish Normal RiskExpon(3;42;9.33;risktruncate(3;42)) Gravad fish ROP RiskExpon(3;393;21.97;risktruncate(1;393)) Gravad fish Normal RiskExpon(3;370;86.96;risktruncate(1;370)) ROP = Reduced Oxygen Packaging. (a): The same remaining shelf-life was for both hot and cold smoked fish. (b): The distributions were truncated to the minimum and maximum values found in the EU-wide baseline survey by using function risktruncate (min; max) EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is without prejudice to the rights of the author(s).

73 Table 20: Descriptive statistics from the remaining shelf-life (days) obtained from the EU-wide baseline survey (BLS) for soft and semi-soft cheese and used to construct Exponential distributions describing the remaining shelf-life Food category Packaging Min Max Mean Distribution Soft and semi-soft ROP RiskExpon(1;350;44.73;risktruncate(1;350)) (a) cheese Soft and semi-soft cheese Normal RiskExpon(0;411;29.52;risktruncate(;411)) ROP = Reduced Oxygen Packaging. (a): The distributions were truncated to the minimum and maximum values found in the EU-wide baseline survey by using function risktruncate (min; max) Growth modelling As mentioned in Section 3.3.2, the type of atmosphere and food subcategory were considered as the main factors affecting growth. For the type of atmosphere two scenarios were considered: Reduced Oxygen Packaging (ROP; including vacuum and gas formulations with passive or active atmospheres); Normal atmosphere. The selected EGR 5ºC values from each subcategory were pooled and different probability distributions were fitted by Excel addin (Palisade, NY) (Appendix Q). For example, in the case heattreated meat products, four distributions were built: Cooked meat & sausage in Air (i.e. normal); Cooked meat & sausage in ROP; Pâté in Air (i.e. normal); Pâté in ROP. For gravad and smoked fish, normal and ROP probability distributions were built, but without further subcategorization. In the case of semi-soft and soft cheese, no further subcategorization could be developed due to data limitation. Due to the lack of information, each scenario (ROP or normal) would be simulated based on different hypothetical market percentages. During simulation, values could be specifically extracted from the distributions considered in this scenario analysis as joint probability distributions using the function discrete distribution to sample from each type of atmosphere at different market percentages EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is without prejudice to the rights of the author(s).

74 3.4. Risk characterization Description of the dose-response model and r parameter The two dose-response models selected in Section 2.2.2, i.e. FAO and WHO (2004b) and Pouillot et al. (2015) were included in the risk model as different modelling approaches. The user can select one of them prior to simulation, by assigning a numeric value 1 (FAO and WHO, 2004b) or 2 (Pouillot et al., 2015) to the cell selector_d_r in the risk model spreadsheet. The mathematical structure of both models corresponded with the Exponential model (Eq.15), in which the r parameter is introduced in accordance with the DR model selected. P (ill; d, r) = 1-exp(-rd) (Eq. 15) where ill stands for illness, d refers to dose, and r is the probability of developing listeriosis from the ingestion of one bacteria cell in a given specific serving. In this work, the term listeriosis refers to severe infection or invasive listeriosis, i.e. individuals suffering from life-threatening, systemic infections such as perinatal listeriosis, meningitis or septicaemia, as considered by FAO and WHO (2004). The r parameter was estimated taking into account information from exposure assessment and incidence of listeriosis in a specific country. However, the study of FAO and WHO derived pointestimates values of r, while the model of Pouillot et al. (2015) built log-normal probability distributions for the r parameter. In the first case, r can be interpreted as an average probability that one cell of L. monocytogenes (all cells of L. monocytogenes will equal virulence or average virulence) will survive and initiate the infection and illness in a specific consumer. In the second approach (Pouillot et al., 2015) the log-normal probability distribution was used to describe the pathogen virulence and the host-susceptibility variability across servings. Table 21 and Table 22 provide the estimates for the r parameter of the DR models selected. Table 21: Estimates of the r parameter for different subpopulations of the model of FAO/WHO (adapted from FAO and WHO (2004c)) Subpopulation r Non-susceptible (a) 5.34x10-14 Susceptible (b) 5.85x10-12 Transplant (c) 1.41x10-10 Cancer Blood (c) 7.37x10-11 AIDS (c) 4.65x10-11 Dialysis (c) 2.55x10-11 Cancer Pulmonary (c) 1.23x10-11 Cancer - Gastrointestinal and liver (c) 1.13x10-11 Non-cancer liver disease (c) 7.65x10-12 Cancer - Bladder and prostate (c) 5.99x10-12 Cancer Gynaecological (c) 3.53x10-12 Diabetes, insulin dependent (c) 1.60x10-12 Diabetes, non-insulin dependent (c) 1.34x10-12 Alcoholism (c) 9.60x10-13 Over 65 years old (c) 4.01x10-13 Perinatal (d) 4.51x10-11 Elderly (60 years and older) (d) 8.39x10-12 Intermediate-age population (d) 5.34x10-14 Mean of the r-values assuming a maximum level of 7.5 log 10 CFU/serving. Data estimated using relative susceptibility information from France in Data estimated using relative susceptibility information from USA in (a): Mean of the r-values assuming a maximum level of 8.5 log 10 CFU/serving EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is without prejudice to the rights of the author(s).

75 Table 22: Estimates of the parameters of the log-normal distributions of r for different subpopulations of the model of Pouillot et al. (2015) as adapted from Pouillot et al. (2015) Subpopulation Parameters log-normal distribution of r µ (a) α (b) < 65 years old, no known underlying condition (i.e. "healthy adults") > 65 years old, no known underlying conditions Pregnancy Nonhematological cancer Hematological cancer Renal or liver failure (dialysis, cirrhosis) Solid organ transplant Inflammatory diseases HIV/AIDS Diabetes (type I or type II) Heart diseases (a): Mean. (b): Standard deviation. In the present risk assessment model, three subpopulations were considered to estimate the number of listerioris in the EU that corresponded to healthy population which was assumed to be constituted by the age range 10 to 64 years old, the elderly or adults > 65 years old and pregnant women. Hence, only the corresponding r-values were selected from the above tables and applied in the DR models. For the model of FAO and WHO (2004c), the selected r-values were 5.34 x for healthy adults and 5.85 x for the elderly and pregnant women. The parameters selected of the lognormal distribution of the model of Pouillot et al. (2015) were (-14.11, 1.62) for healthy adults, ( , 1.62) for the elderly and (-11.7, 1.62) for pregnant women. Due to the heavy-tail properties of the lognormal distribution, very high r-values can result from the simulation process (e.g. 1.54x10-7 for healthy population). The combination of these high r-values together with the L. monocytogenes contamination patterns shown by the RTE food categories of this risk assessment (e.g. a prevalence of 15% ca. in cold smoked fish) could lead to overestimate listeriosis cases when applied to the whole EU population. Therefore, to avoid these unrealistic risk combinations and for the sake of comparison/ equivalence with a well-tested model like that of FAO/WHO, lognormal distributions were truncated to the percentile 95 th as a reasonable confidence level. As result, the maximum r-value to be simulated was limited to 3.59 x 10-12, 6.83 x 10-11, 9.22 x for healthy, elderly and pregnant populations, respectively Assessment of the effect of different subcategories and scenarios of heat treatment products on the listeriosis risk in the EU The simulation output for the specific combinations of scenarios (slicing, type of package atmosphere) and food subcategories, in the baseline model, was assessed regarding their contribution to the listeriosis risk using a relative measure of risk expressed as the number of listeriosis cases per 10 6 servings. Number of cases was represented as the median value together with 5 th and 95 th percentiles for each food subcategory and population group. The results obtained corresponded to the model settings above described for the Baseline scenario (see Section 2.2.4) with 10,000 iterations. By using this relative risk measure (number of cases/10 6 servings), the effect of the different number of servings between subcategories was excluded from the final risk estimate, enabling a better and unbiased assessment/comparison of the different scenarios (packaging type, slicing process, food type). These results are a scientific basis to identify and rank the most relevant risk factors associated to listeriosis and on this basis, to propose suitable recommendations and effective control measures to reduce the listeriosis risk EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is without prejudice to the rights of the author(s).

76 Heat-treated meat products For heat-treated meat products, results evidenced that the type of product exerted a noticeable effect on the listeriosis risk per 10 6 servings (Table 23). According to the simulation outcome, considering the percentiles for each scenario of packaging and slicing, pâté presented the highest listeriosis risk (2.14x cases/10 6 servings) followed by cooked meat (2.72x cases/10 6 servings) and sausage (1.96x x10-1 cases/10 6 servings). In pâté, the population group exposed to the largest risk per 10 6 serving was pregnant, followed by elderly, and finally healthy population. The package atmosphere and slicing appeared to affect the listeriosis risk per 10 6 servings in all population groups, and though it was more evident in the pregnant population, the magnitude of the effect varied between population groups. Products under ROP led to the lowest risk for all population groups while the highest risk was derived from the consumption by pregnant population of sliced products packaged in normal atmosphere (median=1.32 cases/10 6 servings). Cooked meat presented the lowest risk for healthy population (50 th percentiles < 6.29x10-4 cases/10 6 serving). Pregnant was the population group that showed the highest risk in cooked meat, with levels higher than those found in sausage and lower than those observed in pâté. In general, differences between conditions (i.e. slicing and packaging) were less evident than other subcategories, although the highest numbers of cases/10 6 serving were obtained in ROP and slicing conditions (e.g., median: 4.23x10-1 cases/10 6 serving for pregnant). The highest risk in sausage products was associated with slicing conditions, which was maximum (at the 97.5 th ) when the product was packaged under ROP atmosphere (e.g. median=4.04x10-1 ; 95CI=2.02x x10-1 cases/10 6 servings for pregnant population). Again, the pregnant population was at highest risk levels for all scenarios simulated. The uncertainty range in the estimates is an aspect relevant that should be considered when types of product and populations are compared. In some cases, the 95CI ranges could reach more than one order of magnitude (e.g. in sausage with normal and non-slicing conditions for healthy), signaling that those situations are specially dominated by an uncertainty component and therefore the estimates should be carefully interpreted. In other instances, such as sausage for pregnant, the intervals for the highest risk combination (i.e. normal atmosphere and sliced) were narrower, demonstrating at a higher confidence level, the relevance of this combination to the listeriosis risk. Nevertheless, results for heat-treated meat evidenced that the pregnant population was the group exposed to the highest listeriosis risk, which can be, in some cases (e.g., cooked meat and sausage), around 1,000 times higher than that obtained in healthy population. As regard the type of product, i.e., type of package atmosphere and slicing/non-slicing, not a definitive nor general conclusion might be drawn. The risk (per 10 6 servings) mostly depended on the subcategory, but not on the type of process. Overall, it appears that slicing and normal atmosphere are more often related to higher listeriosis, although, as mentioned above, this was not a general rule and, for example, for sausage, the riskiest combination corresponded to sliced and ROP packaged product. It is likely that the combined effect of prevalence and the shelf-life associated with each product could play a relevant role in these differences Smoked and gravad fish The assessment of the risk of acquiring listeriosis by consumption of smoked and gravad fish was performed taken into account different scenarios as in the case of the other food categories. As can be seen in Table 23, per 10 6 servings, the median risk is, in general, higher for cold smoked fish and gravad fish, followed by hot smoked fish. In any case, and for any subcategory or scenario, the number of predicted listeriosis cases corresponded to < 2 cases/10 6 servings. Regarding cold smoked fish, differences were denoted regarding population groups. Cold smoked fish packaged under ROP in pregnant population presented the highest risk (expressed as cases/10 6 servings) since median values were 1.27x10-1 and 1.53x10-1 for sliced and non-sliced products. A 76 EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is without prejudice to the rights of the author(s).

77 possible explanation for the higher risk associated to ROP conditions can be derived from the growth rate used in the growth model which was slightly higher in the case of ROP conditions. For healthy population, risk was approximately times reduced regardless atmosphere composition and slicing conditions. For hot smoked fish, the model predicted a much lower number of cases ( times lower) in comparison to cold smoked fish due to the lower prevalence values, and the expected lower growth of L. monocytogenes during storage. The population group exposed to the largest risk was again pregnant, followed by elderly, and finally healthy population. Sliced hot smoked fish under ROP corresponded to the higher number of listeriosis cases. The riskiest scenario was that corresponding to pregnant population consuming sliced gravad fish since risk was around 2 cases/10 6 servings. As for the heat-treated meat risk outcomes, some scenarios were dominated by an uncertainty component and therefore the estimates should be carefully interpreted. For the elderly and healthy subpopulation, the risk approximately decreased, in general for the whole RTE fish category, 10 and 100 times. However, for some specific scenarios such as gravad fish sliced and packaged with normal atmosphere, this decrease was highly marked, i.e. 1,000 times less Soft and semi-soft cheese Risk outcomes per 10 6 servings for soft and semi-soft cheeses indicate notorious differences according to the slicing procedure. Specifically, slicing increased the risk by 2 times depending on the population group (e.g. the median increased from 6.27x10-5 to 1.15x10-4 for elderly). The pregnant population group presented aproximately 10 times more listeriosis cases per million servings than for the elderly population and a hundred times higher than for the healthy population (Table 23). These results are comparable with those reported in other risk assessments (FDA, 2003; Sanaa et al. 2004), where soft and semi-soft ripened cheeses were classified as low risk foods while soft unripened cheeses were catalogued as moderate risk foods. In the risk model, growth did not distinguish between ROP and normal atmosphere for RTE cheeses. Predicted log increases were low for all evaluated scenarios. Moreover, for this RTE food category, it seems that the effect of slicing contributed mostly to the increase in the number of cases. This outcome could be attributed to the higher prevalence obtained for sliced cheese than for non-sliced. These results confirmed the significant effect of slicing procedures in prevalence of L. monocytogenes in RTE cheeses as already shown in the BLS and other published studies (Lahou and Uyttendaele, 2017) EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is without prejudice to the rights of the author(s).

78 Table 23: Number of listeriosis cases per million servings associated to the scenarios in RTE food subcategories Scenarios Population subgroups Healthy Elderly Pregnant Cold smoked fish ROP/sliced 5.74x10-4 (4.36x10-4, 7.11x10-4 ) 4.37x10-3 (3.32x10-3, 5.42x10-3 ) 1.27x10-1 (9.68x10-2, 1.58x10-1 ) ROP/non sliced 6.88x10-4 (4.13x10-4, 1.05x10-3 ) 5.24x10-3 (3.15x10-3, 8.04x10-3 ) 1.53x10-1 (9.16x10-2, 2.34x10-1 ) Normal/sliced 4.19x10-4 (3.19x10-4, 5.20x10-4 ) 6.46x10-3 (4.91x10-3, 8.01x10-3 ) 7.89x10-2 (6.00x10-2, 9.78x10-2 ) Normal/non sliced 5.03x10-4 (3.02x10-4, 7.71x10-4 ) 7.72x10-3 (4.63x10-3, 1.18x10-2 ) 9.46x10-2 (5.68x10-2, 1.45x10-1 ) Hot smoked fish ROP/sliced 3.26x10-6 (2.01x10-6, 5.02x10-6 ) 3.72x10-5 (2.29x10-5, 5.73x10-5 ) 2.96x10-4 (1.82x10-4, 4.55x10-4 ) ROP/non sliced 1.51x10-6 (7.53x10-7, 2.76x10-6 ) 1.72x10-5 (8.59x10-6, 3.15x10-5 ) 1.37x10-4 (6.83x10-5, 2.50x10-4 ) Normal/sliced 9.89x10-7 (4.94x10-7, 1.81x10-6 ) 1.36x10-5 (6.78x10-6, 2.48x10-5 ) 7.48x10-5 (3.74x10-5, 1.37x10-4 ) Normal/non sliced 2.14x10-6 (1.32x10-6, 3.30x10-6 ) 2.94x10-5 (1.81x10-5, 4.52x10-5 ) 1.62x10-4 (9.97x10-5, 2.49x10-4 ) Gravad fish ROP/sliced 5.27x10-3 (3.44x10-3, 7.33x10-3 ) 5.86x10-2 (3.82x10-2, 8.16x10-2 ) 1.13x10 0 (7.35x10-1, 1.57x10 0 ) ROP/non sliced 4.58x10-4 (2.16x10-3, 3.66x10-3 ) 5.10x10-3 (6.85x10-2, 4.08x10-2 ) 9.80x10-2 (5.08x10-1, 7.84x10-1 ) Normal/sliced 3.72x10-3 (2.42x10-3, 5.17x10-3 ) 3.73x10-2 (2.44x10-2, 5.20x10-2 ) 1.09x10 0 (7.09x10-1, 1.51x10 0 ) Normal/non sliced 3.23x10-4 (2.30x10-3, 2.59x10-3 ) 3.25x10-3 (6.90x10-2, 2.60x10-2 ) 9.46x10-2 (5.10x10-1, 7.57x10-1 ) Cooked meat ROP/sliced 6.19x10-4 (3.09x10-4, 9.28x10-4 ) 1.48x10-2 (7.41x10-3, 2.22x10-2 ) 4.23x10-1 (2.11x10-1, 6.34x10-1 ) ROP/non sliced 6.25x10-4 (3.79x10-4, 1.87x10-3 ) 1.49x10-2 (2.72x10-4, 4.47x10-2 ) 4.19x10-1 (1.59x10-2, 1.26x10 0 ) Normal/sliced 6.29x10-4 (3.14x10-4, 9.43x10-4 ) 1.48x10-2 (7.39x10-3, 2.22x10-2 ) 4.17x10-1 (2.09x10-1, 6.26x10-1 ) Normal/non sliced 6.16x10-4 (3.29x10-4, 1.85x10-3 ) 1.48x10-2 (2.80x10-4, 4.43x10-2 ) 4.17x10-1 (1.59x10-2, 1.25x10 0 ) Sausage ROP/sliced 1.42x10-3 (7.11x10-4, 2.84x10-3 ) 1.62x10-2 (8.12x10-3, 3.25x10-2 ) 4.04x10-1 (2.02x10-1, 8.17x10-1 ) ROP/non sliced 7.25x10-4 (1.96x10-5, 2.90x10-3 ) 8.26x10-3 (2.43x10-3, 3.30x10-2 ) 2.07x10-1 (8.51x10-3, 8.28x10-1 ) Normal/sliced 1.42x10-3 (7.08x10-4, 2.83x10-3 ) 1.61x10-2 (8.04x10-3, 3.22x10-2 ) 4.04x10-1 (2.02x10-1, 8.08x10-1 ) Normal/non sliced 7.14x10-4 (1.96x10-5, 2.86x10-3 ) 8.17x10-3 (2.43x10-3, 3.27x10-2 ) 2.04x10-1 (8.51x10-3, 8.15x10-1 ) Pâté ROP/sliced 1.67x10-4 (7.55x10-5, 4.42x10-4 ) 1.15x10-3 (5.19x10-4, 6.72x10-3 ) 3.03x10-2 (1.37x10-2, 1.33x10-1 ) ROP/non sliced 2.20x10-3 (2.14x10-5, 6.60x10-3 ) 6.27x10-3 (1.47x10-3, 1.64x10-2 ) 6.54x10-1 (3.88x10-2, 1.96x10 0 ) Normal/sliced 4.45x10-3 (1.78x10-3, 8.45x10-3 ) 6.76x10-2 (2.71x10-2, 1.29x10-1 ) 1.32x10 0 (5.29x10-1, 2.51x10 0 ) Normal/non sliced 2.19x10-3 (2.20x10-5, 6.58x10-3 ) 3.20x10-2 (1.47x10-3, 9.59x10-2 ) 6.25x10-1 (3.88x10-2, 1.95x10 0 ) Soft and semi-soft cheese Sliced 2.04x10-5 (4.39x10-6, 7.84x10-5 ) 1.15x10-4 (4.49x10-5, 9.78x10-4 ) 1.98x10-3 (7.69x10-4, 1.40x10-2 ) Non sliced 1.11x10-5 (5.27x10-6, 2.01x10-5 ) 6.27x10-5 (2.98x10-5, 1.14x10-4 ) 1.07x10-3 (5.10x10-4, 1.95x10-2 ) ROP: Reduced Oxygen Packaging. Numbers outside brackets represent 50 th percentile; numbers between brackets represent 2.5 and 97.5 th percentiles EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

79 Estimated number of annual cases of listeriosis associated with the consumption of RTE foods in the EU The overall risk estimates were obtained for each food subcategory expressed as listeriosis cases per year in the EU, derived from the number of servings in the EU MSs and their proportions on the market (Table 24). These risk estimates based on the consumption patterns specific to each food subcategory and expressed as cases/year, were intended to provide an overall estimate of the contribution of each RTE food subcategory into the listeriosis cases in the EU. These risk model outcomes are a reliable representation of the listerioris risk derived and can be subsequently applied to priorize risk mitigation strategies and food policies in the related EU food sectors and identify those high-risk population groups for which educational programmes and compaigns might be needed. The model estimated, 2,318 (95CI: 1,450-3,612) listeriosis cases per year in the EU considering the seven RTE food subcategories altogether. Most cases were estimated for the elderly population (1,103; 95CI: 685-1,733 cases/year ) which corresponded to 48% total listeriosis annual cases simulated, closely followed by the pregnant (961; 95CI: 603-1,487 cases/year), and finally the healthy population (254; 95CI: cases/year) which contributed to the simulation with 41% and 11% listeriosis annual cases, respectively. Considering the estimates of the number of cases per RTE food subcategory, pâté, sausage, soft and semisoft cheese, gravad and cold smoked fish caused the most cases in the elderly population (Table 24) whereas for the pregnant population cooked meat, followed by hot smoked fish, were the subcategories with the highest number of cases. Cooked meat and sausage consumption caused more cases than the other RTE food sub-categories, with 61% cases, followed by gravad and cold smoked fish, with 31 % cases. The explanation can lie in the differences in the number of servings since cooked meat was the most consumed subcategory. The higher number of cases for healthy population was attributed to cooked meat. In constrast, hot smoked fish and soft and semi-soft cheeses presented the lowest values of the number of listeriosis cases for all population groups. For heat-treated meat, cooked meat was the subcategory that yielded a higher number of total cases (median=863 cases/year) followed by sausage (median = 541 cases/year) and finally, pâté (median=158 cases/year). The same trend was observed when population groups were analyzed separately. The largest total number of cases were derived from pregnant population, where the major number of cases were attributed to cooked meat (median = 477 cases/year), though in sausage and pâté, the highest risk population group corresponded to elderly. Regarding smoked and gravad fish, the highest number of cases was predicted for gravad fish in the elderly subpopulation (median = 230 cases/year), while for healthy and pregnant populations, cold smoked fish gave the highest number of listeriosis cases (median = 54 and 104 cases/year respectively). Among the susceptible populations, the elderly was by far the most affected subpopulation, especially when consuming gravad fish. For hot smoked fish, the number of cases was relatively low as compared to the other subcategories with similar values between the healthy and elderly subpopulations (<1 cases and 1 cases/year, respectively). As opposed to the other subcategories, the highest incidence of listeriosis cases in hot smoked fish was observed in pregnant population. Soft and semi-soft cheeses were estimated to cause more cases in the elderly population (median= 11 95CI: 5-20) followed by healthy (median=5 cases/year) and pregnant population which showed the lowest risk in this food category (median= 3 cases/year). These results contrasted to those obtained per 10 6 servings where the pregnant group was the subpopulation with the highest risk followed by elderly population and healthy population This fact evidences the different type of information provided by each type of risk measure, i.e. cases/10 6 servings and annual cases. The former risk measure is more focused on invidual risk and the influence of risk factors. In turn, the latter is more related to public health aspects and overall risk. Moreover, these differences between the two types of analysis highlight the importance of the contribution of the consumption patterns of each food subcategory, and its uncertainty, in the estimation of the population risk EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is without prejudice to the rights of the author(s).

80 The overall model estimate of 2,318 (95CI: 1,450-3,612) listeriosis cases per year in the EU is close to the total confirmed cases of 2,206 reported in 2015 (EFSA and ECDC, 2016) showing the good performance of the model. Nevertheless, although values were similar, other types of food, not included in this study (e.g. vegetables), are expected to contribute to the total EU listeriosis cases, thus indicating the model could produce a relatively higher risk than that observed in reality. The overestimation produced by the model can be caused by multiple factors, mainly related to both the underlying assumptions applied in the baseline risk assessment model (i.e., no lag, growth modelling and DR model) and the limitation of data, such as the estimate of number of servings, the limited number of concentration data, the representativeness of the BLS data, considering the great food diversity across 28 EU MSs, etc EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is without prejudice to the rights of the author(s).

81 Table 24: Estimation of the number of listeriosis cases per year in the RTE food subcategories and population groups RTE food subcategory Population subgroups Healthy Elderly Pregnant TOTAL (d) Cold smoked fish 54 (42, 68) (a) 201 (154, 254) 104 (75, 138) 358 (271, 460) Hot smoked fish nc (b) (nc, 1) 1 (nc, 1) 6 (4, 8) 7 (4, 10) Gravad fish 48 (33, 70) 230 (160, 320) 92 (63, 129) 370 (257, 519) Cooked meat 71 (50, 98) 316 (218, 449) 477 (337, 659) 863 (604, 1207) Sausage 64 (31, 118) 252 (120, 469) 225 (107, 417) 541 (258, 1003) Pâté 12 (4, 27) 92 (28, 220) 54 (16, 130) 158 (48, 377) Soft and semi-soft cheese 5 (2, 10) 11 (5, 20) 3 (1, 6) 19 (8, 36) TOTAL (c) 254 (162, 392) 1,103 (685, 1,733) 961 (603, 1,487) 2,318 (1,450, 3,612) (a): Numbers outside brackets represent 50 th percentile. Numbers between brackets represent 2.5 and 97.5 th percentiles. (b): nc stands for no cases, which refers to values <0.5 cases/year (c): TOTAL is referring to the arithmetic sum of the number of cases. (d): The total for each food category was calculated from the sum of the exact values obtained for each population group, and then rounded up. Owing to that fact, total values can show slight differences when compared to the sum of the values displayed in the table EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

82 Uncertainty When assessing uncertainty, the Scientific Committee of EFSA endorsed this principle in its guidance on transparency in risk assessment (EFSA, 2009). This document explains that for risk assessment processes it is important to characterise, document and explain all types of uncertainty on the outcome. In this report, uncertainty was assessed against the yearly number of listeriosis cases in the EU for the three RTE food categories. In the methodology section uncertainty sources in the risk assessment model were identified and further described (Section 2.2.4). For the assessment of uncertainty, the effect of individual variables was qualitatively assessed through determining its direction on the increase or decrease in the final number of listeriosis cases per year in the EU population. Apart from prevalence uncertainty was not separated from variability for the rest of model inputs (due to the scarcity and spare information for some variables). Thus, the combined effect of all the uncertainties is difficult to be measured and quantified. However, it should be remarked that the uncertainties associated to the assumptions made and to the data used in this assessment could affect the absolute estimates of the risk in a positive (+) or negative (-) direction (i.e. increasing or reducing estimated values). In Table 25, the potential sources of uncertainty from model variables is shown together with their potential impact (+/-) on the yearly number of listeriosis cases. For each variable, the assessment was done considering uncertainty originating from data, from model parameters and from the model assumptions considered (already explained in Table 7) EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is without prejudice to the rights of the author(s).

83 Table 25. Potential sources of uncertainty arisen from the risk modelling process and the direction of their impact (+ ) on the final number of listeriosis cases per year in the selected RTE food categories Variable Prevalence Initial concentration Serving size Number of servings Assessment of the uncertainty Data Model parameters Model assumptions L. monocytogenes serotypes, processing conditions and differences in product formulations among EU countries are not considered. Positive samples were considered with a detection limit of 0.04 CFU/g. The level of the uncertainty is mainly influenced by the availability of data for some subcategories (i.e. hot-smoked fish). (+/-) Data were only gathered from individual samples (not batch samples). Most of collected data were censored (left, interval and right). For some cases, databases used did not properly describe the exact food types according to the purpose of Tender specifications. (+/-) For some cases, more than one consumption of the same food by an individual in a day was considered as different independent servings. The serving size was then estimated as an average from the total amount consumed in a day (data reported by the database). (+/-) The adult consumption data were used to extrapolate consumption of the pregnant population (number of births). (+/-) The apparent consumption data used for smoked and gravad fish can lead to less accurate estimates in the number of servings and overestimate household consumption. (+/-) Prevalence is described by Beta distributions considering a prior uniform distribution (1,1). The width of the CI could have an impact on the final outcome. (+/-) Concentration was described through log normal distributions with mean and standard deviation to define lot variability. (+/-) Serving size was assumed to follow an Exponential distribution, described by β that corresponded to the mean value of data. Distributions were truncated to those values observed in the EFSA consumption database. Modifications in these parameters could produce a impact on the final outcome. (+/-) No sources of variation or uncertainty was included into number of serving and were defined as point-estimate values. The model assumes a global and single prevalence value for the EU. (+/-) Initial concentration data were merged for cooked meat and sausage as well as cold and hot smoked fish. Maximum concentration values were those reported in the monitoring database for the correspondent food subcategory. This would produce an underestimation of the final outcome since maximum distribution values were truncated. (-) A single serving size distribution was built for each single meat subcategory: cooked meat, sausage and pâté. The same serving size distribution was assumed for smoked and gravad fish, as well as for soft and semi-soft cheese. (+/-) The food proportions found in BLS are assumed to be representative for the EU market. Therefore, number of serving for specific scenarios and subcategories are calculated based on those proportions. (+/-) 83 EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

84 Time to consumption No data were available about the time elapsed from purchase to consumption. Time to consumption was extrapolated from the remainng shelf-life reported by the BLS. (+/-) Time to consumption was described by a Exponential distribution with the parameter 99 th that corresponded to the remaining shelflife. This implies that 1% servings can reach longer times than those observed in the BLS. (+) Packaging type considered as influencing factor, though vacuum and MAP data were merged (ROP). (+/-) Time - temperature profiles Listeria growth models Dose-response model (Pouillot et al. 2015) Data corresponded to domestic refrigerators (top, middle and bottom) mostly referred to RTE meat in central Europe. Most of the selected profiles included low refrigeration temperatures being representative of central and northern Europe rather than southern Europe. This could have a negative impact on the increase of the outcome. (-) EGRs were obtained from laboratory studies, where growth conditions trend to be more optimum than those given in real world. (+) The DR model was built on epidemiological data from both USA and France. The susceptibility to L. monocytogenes (i.e. infective doses) of Europeans and Americans can be different. (+/-) Profiles were set at a step size 5h to estimate microbial growth during storage. This could reduce accuracy in Listeria growth prediction, leading to both overestimates and other underestimates. (+/-) EGR distributions (i.e. occurrance frequencies) are influenced by the types of product, preservatives, storage conditions that are of major concern for researchers, which can be different to those given in the real products. (+/-) As pointed out by Pouillot et al. (2015), the assumptions made in the estimation of r- parameters leads to higher risk predictions at low dose L. monocytogenes. (+) The simulated time-temperature series were obtained from the bootstrapping of the collected profiles (+/-) No lag time was assumed in the baseline risk model. (+) Products within a specific subcategory are accounted by the same EGR distribution. In some cases, two subcategories have been represented by the same EGR distribution (e.g. cooked meat and sausage). (+/-) Only three subpopulation groups are considered. Immunocompromised due to different chronic illness are not included. (-) BLS: EU-wide baseline survey, CFU: Colony Forming Unit, CI: Confidence Interval, DR: Dose-Response, EGR: Exponential Growth Rate, EU: European Union, ROP: Reduced-Oxygen Packaging, RTE: ready-to-eat EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the author(s).

85 Evaluation of risk model stability The model convergence and stability, in relation to the estimates of number of cases, was further assessed through running different simulations. Specifically, the effect of the fixed seed used and number of iterations was studied as follows for each subcategory: - Seeds were tested to be fixed at 1, 5 and at different random seeds for each RTE subcategory; - Simulations using each seed were run considering 1,000; 5,000; and 50,000 iterations. Overall twelve simulations were run per food subcategory and the effect on the 50 th percentile of the number of listeriosis cases were analysed (considering the whole number of servings). InTable 26: Coefficients of variation (%) of the number of listeriosis cases in the RTE food subcategories and population groups for the different seeds at 50,000 iterations.table 26, the coefficients of variation (CV, %) of the number of listeriosis cases in the RTE food subcategories and population groups for the different seeds (1, 5, and different random seeds) is presented. CV values were calculated using those simulations with 50,000 iterations and taking the 50 th percentile values for the number of listeriosis cases per subpopulation and subcategory. Regarding the different RTE food subcategories, it is shown that the effect of the seed used to run the models did no exert an remarkable effect on the number of cases, even though for gravad and smoked fish models values ranged from 19.67% to 77.28% for the elderly population group. Hotsmoked fish presented the highest values of CV, however, this was attributed to the few number of listeriosis cases for this RTE subcategory, what led to conclude that relative differences between the different estimations were larger than the obtained for those subcategories having higher number of cases (Appendix R). For heat-treated meats, the biggest effect was observed for pâté the healthy subpopulation being that with the highest variation (25. 59%). Finally, for RTE cheese, significant effect of seeds was observed for susceptible subpopulations (both elderly and pregnant), with values above 30.68%. By comparing the different subpopulations, a clear pattern could not be observed. It can be said that more variability was observed for susceptible groups, though the effect of using different seeds is not affecting CV values in a systematic way for the three subopulations. Table 26: Coefficients of variation (%) of the number of listeriosis cases in the RTE food subcategories and population groups for the different seeds at 50,000 iterations. Healthy Elderly Pregnant TOTAL Cold smoked fish Hot smoked fish Gravad fish Cooked meat Pâté Sausage Soft and semi-soft cheese According to previous literature, model convergence is not straightforward task when estimating the number of cases based on a numerical integration, especially in those models where several skewed distributions are used. For instance, Pérez-Rodríguez and Zwietering (2012) obtained relatively high values of CV using a simplistic model based on a multiplicative effect of prevalence, concentration and r-values. An earlier study of Pérez-Rodríguez et al. (2007) analysed the model uncertainty using different RNG seeds. They reported values for CV for the mean number of cases per year around 85 EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is without prejudice to the rights of the author(s).

86 31%. The ample variation found could have been attributed to the important influence of RNG seed value on sampling in the extreme right side of certain distributions, in which the highest values and the least probable values (e.g., of concentration or temperature) are situated. For median, 5 th and 95 th confidence percentiles (90% CI), the CV was 64, 56, and 2%, respectively. The variation in the mean number of cases per year seemed to be reasonable and in accordance with the real variation in number of cases per year according to these authors. A similar analysis was conducted for the present risk assessment by estimating and comparing CV obtained from incidence data for heat-treated meat (i.e. the food category better matching the reporting system terminology) reported by different Foodborne Disease Outbreak surveillance systems in Europe and USA. Thus, the annual case variation in Europe in the period (EFSA and ECDC, 2012, 2013, 2014, 2015b, a), expressed as CV, resulted in CV=167% while for USA data for the same category in the period based on data collected by the CDD Foodborne Disease Outbreak Surveillance System (Cartwright et al., 2013), the CV estimated was equal to 122%. The comparison of these values with those presented in Table 26 shows that model output variations due to RGN seed were below or equal to those calculated from actual outbreak cases. The fact that risk is calculated for very specific food subcategories can make the model more sensitive to slight changes in the model structure, variables and starting point (RGN seed) as indirectly observed in real world for these subcategories. Therefore, based on these results, and considering the model complexity and real incidence in these food subcategories, the model uncertainty can be appraised acceptable to respond risk management questions and to compare food (sub) categories in terms of risk (i.e. listeriosis cases) Scenario analysis A scenario analysis was performed to determine the influence of the most relevant model inputs on the final risk estimate (i.e. listeriosis cases). The analysis was focused on those variables that were deemed to be important sources of uncertainty in the model. Thus, Maximum time to consumption, Maximum limit below the initial concentration distribution (i.e. setting different maximum values (log CFU/g) for microbial concentrations in the initial concentration distribution), Temperature and the presence or absence of lag time were included in the analysis. To perform the scenario analysis, the selected variables were modified to values representing worst and best case scenarios. Results were expressed as variation percentages (%) in the number of cases with respect to the outcome from the baseline model (Section 2.2.4) expressed per 10 6 servings in both cases. Models were simulated for 10 6 servings to enable comparison between food categories with different consumption patterns (i.e., number of servings). In general, results highlight that the factors tested are of great relevance to listeriosis risk. The variable most impacting listeriosis risk corresponded to temperature Scenario analysis for heat treated meat In the case of heat-treated meat (Figure 14), the effect of Temperature from retail to consumption was assessed, by increasing or reducing temperature in the simulated profiles (between 1-2 C). The increase of Temperature yielded a noticeable rising in the number of cases/10 6 servings (95CI: %). There was no symmetric effect, as decreasing temperature in the time-temperature profiles resulted in lower reduction of the number of cases (95CI: 4-(-)37%). Increasing or reducing the Maximum limit in the L. monocytogenes concentration in the RTE food by 2 log CFU resulted in a great rise (95%CI: %) or decrease in the number of listeriosis cases per 10 6 servings (95CI: (-)88-(-)89%), respectively. According to the simulated distributions, high Listeria concentrations were in much lower proportions than lower concentrations. This fact is corroborated by previous reported data where maximum concentrations are seldom detected in Listeria monitoring studies. The probability of high concentration levels in the products is not well known, and the use of a maximum concentration, as limit, in the probability distributions confers an important uncertainty component into the model that should be considered when the estimates of the illness burden are assessed. The maximum concentrations were estimated based on few data, and therefore, the extrapolation made from these specific studies to represent for contamination pattern in the EU had 86 EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is without prejudice to the rights of the author(s).

87 Porcentage of variation Quantitative risk characterization on Listeria monocytogenes in ready-to-eat foods an impact on the estimated high number of cases. Maximum concentration also showed very broad ranges of uncertainty, probably due to the relevance of this parameter in the occurrence of high doses that are related to a raised probability to yield listeriosis. 500% 400% 300% 200% 100% 0% -100% -200% Tested variables are represented in the horizontal axis and corresponded to the change in Maximum limit of initial concentration levels (Max concentration: ± 2 log 10 CFU/g); Time to consumption (± 25%); Temperature (± 1-2 C) and inclusion of lag time. Worst-case scenarios are those represented in red while best case scenarios are visualised in the blue bars. Symbols represent changes in 97.5 th and 2.5 th percentiles. Figure 14: Scenario analysis of the risk assessment model for heat-treated meat using as criterion the variation percentage (%) in the number of listeriosis cases per 10 6 servings with respect to the baseline model For Timetoconsumption, which was tested by modifying the maximum time-to-consumption by ± 25%), the effect was not as evident as for the Temperature and the Maximum limit. The worst-case scenario resulted in no increase of the number of cases/10 6 servings. On the contrary, reducing time decreased the number of cases/10 6 servings (95CI: 33-(-)38%). For heat-treated meat, the inclusion of lag time in the heat-treated meat influenced the number of cases/10 6 servings, reducing up to 57% the values obtained in the baseline model. The existence or no existence of lag time can depend on each specific case, and specially, the specific conditions which products and microorganims have been submitted to. Nonetheless, the assumption of no lag made for the baseline model can be deemed plausible considering that the time elapsed from industry to retail (first step in the risk model) can be sufficient so that Listeria reaches the end of lag phase and start to grow Scenario analysis for RTE fish The results of scenario analysis of RTE fish products are presented in Figure 15. The variable that resulted in the highest variation percentage corresponded to Maximum limit in the initial concentration distribution, with an increase of almost 400% (median) the number of cases/10 6 servings when the worst-case scenario (increasing 2 log the maximum concentration limit) was simulated. The worstcase scenarios of the Time to consumption and Temperature produced a slighter increase of the number of cases/10 6 servings, but Time to consumption had a more marked effect than Temperature as can be seen in Figure 15 (214% and 64%, respectively). Conversely, the best-case scenarios of the three variables produced a much more similar reduction in the number of listeriosis cases per EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is without prejudice to the rights of the author(s).

88 Percentage of variation Quantitative risk characterization on Listeria monocytogenes in ready-to-eat foods servings, though at a lower magnitude (between -99% and -75.5%). The inclusion or not of lag time produced no effect in the number of listeriosis cases per 10 6 servings as can evidenced in Figure % 600% 500% 400% 300% 200% 100% 0% -100% -200% Tested variables are represented in the horizontal axis and corresponded to the change in the Maximum limit of initial concentration levels (Max concentration: ± 2 log 10 CFU/g); Time to consumption (± 25%); Temperature (± 1-2 C) and inclusion of lag time. Worst case scenarios are those represented in red while best case scenarios are visualised in the blue bars. Symbols represent changes in 97.5 th and 2.5 th percentiles. Figure 15: Scenario analysis of the risk assessment model for RTE fish products using as criterion the variation percentage (%) in the number of listeriosis cases per 10 6 servings with respect to the baseline model Scenario analysis for soft and semi-soft cheese The results obtained for the RTE cheese food category are shown in Figure 16. The effect of each scenario was calculated as a percentage of variation in the number of listeriosis cases per million servings in comparison to the baseline scenario (which would be set at 0% in Figure 16). It can be seen that the two scenarios mostly affecting the number of listeriosis cases per 10 6 servings were Temperature and Maximum limit of the initial concentration of L. monocytogenes. The impact of increasing storage Temperature by 3-4 C produced an increase of 530 % cases/10 6 servings with respect to the baseline model. However, the effect was clearly shown in the variation in 2.5 th percentiles, where the increase was of more than 4,000% in the number of cases. Likewise, the 97.5 th percentile was five times higher in the worst-case example of the temperature scenario than that obtained from the baseline scenario. This effect could be attributed to the rise in the %gr_list from 2x10-5 % to 0.024% in the baseline and worst-case example of the temperature scenario respectively. On the contrary, decreasing storage temperature did not produce a substantial variation in the number of listeriosis cases per 10 6 (approx. 4%) since temperature conditions in the baseline scenario did not allow for growth of Listeria in RTE cheese. Therefore, growth is neither observed at lower temperatures. The second scenario affecting the estimations of listeriosis cases per 10 6 servings in RTE cheese was truncation of maximum concentration values in the initial concentration distribution. As shown in Figure 16, increasing maximum values from 5 to 7 log 10 CFU/g produced an increase of more than 88 EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is without prejudice to the rights of the author(s).

89 2,500% in the number of cases. This large effect was produced as a consequence of the variation in 97.5 th percentiles since as previously explained for the former scenario, variation in percentiles differed. Relative variation was much higher for 2.5 th percentiles (3,000%). In the case of the median value, the number of cases per 10 6 servings increased 6 times, in the worst case example of maximum concentration scenario, on the value from the baseline model By increasing maximum concentration values in the initial concentration distribution of Listeria MPD concentration are implicitly higher, thus producing a significant impact on the number of cases. This can be seen in the increased values of 97.5 th percentiles of initial concentration distributions (from 4.42 to 5.70 log 10 CFU/g) and also in %gr_list being the 97.5 th percentile in the worst-case example of the maximum concentration scenario equal to 5.59%. Decreasing the maximum concentration from 5 to 3 log 10 CFU/g produced a decrease up to 98% in the number of listeriosis cases per 10 6 servings. Likewise, this variation was not so significant as growth of Listeria was very low in RTE cheese. However, for the best-case example of maximum concentration scenario, variation in the 97 th percentiles of final concentration of Listeria showed a reduction from 4.42 (baseline scenario) to 2.38 log 10 CFU/g. Tested variables are represented in the horizontal axis and corresponded to the change in the Maximum limit of initial concentration levels (Max concentration: ± 2 log 10 CFU/g); Time to consumption (± 25%); Temperature (± 3-4 C) and inclusion of lag time. Worst-case scenarios are those represented in red while best case scenarios are visualised in the blue bars. Symbols represent changes in 97.5th and 2.5th percentiles. Figure 16: Scenario analysis of the risk assessment model for RTE soft and semi-soft cheese using as criterion the variation percentage (%) in the number of listeriosis cases per 10 6 servings with respect to the baseline model Increasing the Time to consumption in 25% produced an increase of 223% in the number of cases per 10 6 servings. However, variation in 2.5 th percentiles was notoriously higher since the number of cases per 10 6 servings increased up to 545%. Although variation in the Time to consumption variable could have an impact in the number of cases, its effect was lower than the two scenarios above explained. Decreasing Time to consumption in 25% produced a decrease of 33% in the number of listeriosis cases per 10 6 servings. Finally, the effect of lag time was limited as it only produced a reduction of 30% in the number of cases per 10 6 servings. Besides, lag time did not produce a change in growth potential of Listeria in RTE cheese since environmental conditions and product formulation led to very low exponential growth rates EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is without prejudice to the rights of the author(s).

90 4. Conclusions The risk assessment model developed in the present work provided estimates of the number of cases of listeriosis in the EU for elderly, pregnant and healthy population groups by consumption of three RTE food categories: heat-treated meat (cooked meat, pâté and sausage), RTE fish (hot smoked fish, cold smoked fish and gravid fish) and soft and semi-soft cheese. The yearly total number of human listeriosis cases estimated by the model corresponded to 2,318 (95CI: 1,450-3,612). The elderly population obtained the highest number (1,103, 95CI: 685-1,733 cases/year) followed by the pregnant population (961; 95CI: 603-1,487 cases/year), and finally healthy population (254; 95CI: cases/year). These estimates were similar to the values reported by the surveillance system, confirming, in addition, the observed higher incidence in elderly population and its relation with the demographic ageing within the EU. These facts demonstrate that the model was able to produce reliable estimates for listeriosis incidence in EU. Furthermore, model structure and estimates may be useful tools to provide insight into the most risky food categories, vulnerable populations and processing conditions considered in the model. Concerning food categories, overall risk estimates (cases/year) demonstrated that heat-treated meat was the RTE product entailing the highest overall risk of listeriosis specifically for the subcategory cooked meat, with more than 863 cases/year. Pâté showed the lowest risk in the heat-treated meat category. The following categories presenting high risk corresponded to cold smoked fish and gravad, followed by soft and semi-soft cheese, and finally hot smoked fish, which was the subcategory resulting in the lowest estimated risk as expressed per number of cases (seven cases/year). Aspects related to the consumption patterns, shelf-life and processing were key in the differences found between these subcategories. When considering each food subcategory separately, the listeriosis cases distribution between population groups differed from those estimated from food categories. Thus, the highest risk population group corresponded to elderly, followed, in most subcategories, by pregnant and healthy, with the exceptions of cooked meat and hot smoked fish in which pregnant presented higher risk than elderly. The risk estimates simulated as number of cases per 10 6 servings were used to assess the impact of the processing conditions on the (individual) listeriosis risk regardless the effect of the consumption patterns (i.e., number of servings). The highest number of cases per 10 6 servings was found for normal packaged and sliced Pâté in pregnant population. In turn, the lowest risk values were observed for non-sliced hot smoked fish and soft and semi-soft cheese. Simulated data seem to indicate that risk depended on the combined effect of growth capacity (i.e. growth rate), prevalence levels and growth conditions associated to each combination. In spite of these results, the model output was not able to shed light on the effect of the processing conditions on the number of cases (expressed as cases/10 6 servings) possibly due to the high uncertainty of the overall model. The analysis of selected worst and best-case scenarios evidenced that the influence of the input tested (initial maximum concentration, temperature, time-to-consumption and lag time) varied among food categories. In the case of soft and semi-soft cheese and heat-treated meat products, risk was strongly influenced by temperature, although the great associated uncertainty in both food categories suggest that conclusions concerning factors with influence on risk should be drawn with caution. In contrast, for gravad and smoked fish, risk was strongly incluenced by the maximum concentration, showing that in this food category, growth is not the most important factor in listeriosis risk. Indeed, results in the risk model seemed to hint that the growth component was less determinant than expected, which can be due to the used time-temperature profiles, the level of interaction with lactic acid bacteria, food spoilage and percentage of servings supporting growth. Uncertainty on these elements and predictive models should be considered if results are used to derive specific interventions or mitigation strategies. Furthermore, it was shown that the risk models with fewer cases (e.g. hot smoked fish) were particularly sentitive to the RNG seed, hence special precaution should taken to select the seed value. In this sense, it is crucial that the same seed is used to enable an suitable comparison between simulations. All in all, the present risk assessment model is comprised of more than 100 input variables and was designed following a modular structure, allowing for testing 90 EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is without prejudice to the rights of the author(s).

91 and further analysis as more knowledge or data become available or when new modelling approaches are to be tested. 5. Recommendations According to the risk assessment model and scenario analysis, maximum concentration and temperature have been identified as important risk factors in the incidence of listeriosis in the EU. The model output also showed that decreasing storage time by 25% and temperature 1 2 or 3 4 C can be effective in reducing listeria growth and finally risk for the consumer. Nevertheless, these factors should be controlled along the food chain by applying an effective suppliers control and temperature-control technologies. The higher listeriosis incidence detected in specific population groups (elderly population and pregnant women) evidences that educative programs and specific recommendations should be put in place oriented to mitigate the risk in these more susceptible populations. The sources of uncertainty detected in the present risk assessment also show that there are still important gaps that should be further studied and filled in to increase the accuracy in the risk estimates and applicability of the model output such as the maximum concentrations of L. monocytogenes at retail, time-temperature profiles and consumption patterns. In the latter case, although the EFSA consumption database is a very comprehensive source of food consumption data in the EU, there are still some aspects that should be better adapted for their application to microbial risk assessment in order to enable more accurate risk estimates at a population level such as a better representation of the consumption patterns of at-risk populations (i.e., pregnant population). References ABS (Australian Bureu of Statistics), National Nutrition Survey. Food Eaten Australia ; pp., ACM (Alliance Consulting and Management), Safe Retail Storage of Meat. Project MSQS.011 (Final Report). Meat and Livestock Australia, Sydney, Australia. 1998; pp., Afchain A, Derens E, Guilpart J and Cornu M, Statistical modelling of cold-smoked salmon temperature profiles for risk assessment of Listeria monocytogenes. Acta Horticulturae, 674, Albert I, Grenier E, Denis J-B and Rousseau J, Quantitative Risk Assessment from Farm to Fork and Beyond: A Global Bayesian Approach Concerning Food-Borne Diseases. Risk Analysis, 28, AMI (American Meat Institute), Consumer handling of RTE meats. 2001; pp., Audits Intl/FDA US food temperature evaluation design and summary pages. Available at (Accessed 1/4/2015). 1999; pp., Augustin JC, Bergis H, Midelet-Bourdin G, Cornu M, Couvert O, Denis C, Huchet V, Lemonnier S, Pinon A, Vialette M, Zuliani V and Stahl V, Design of challenge testing experiments to assess the variability of Listeria monocytogenes growth in foods. Food Microbiology, 28, Back JP, Langford SA and Kroll RG, Growth of Listeria monocytogenes in camembert and other soft cheeses at refrigertion temperatures. Journal of Dairy Research, 60, Baka M, Noriega E, Tsakali E and Van Impe JFM, Influence of composition and processing of Frankfurter sausages on the growth dynamics of Listeria monocytogenes under vacuum. Food Research International, 70, Baranyi J and Roberts TA, A Dynamic Approach to Predicting Bacterial-Growth in Food. International Journal of Food Microbiology, 23, Barmpalia IM, Koutsoumanis KP, Geornaras I, Belk KE, Scanga JA, Kendall PA, Smith GC and Sofos JN, Effect of antimicrobials as ingredients of pork bologna for Listeria monocytogenes control during storage at 4 or 10 degrees C. Food Microbiology, 22, EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is without prejudice to the rights of the author(s).

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102 Van der Sluijs JP, Risbey J and Ravetz J, Uncertainty assessment of VOC emissions from paint in the Netherlands using the NUSAP system. Environmental Monitoring and Assessment, 105, Van Stelten A, Simpson JM, Chen Y, Scott VN, Whiting RC, Ross WH and Nightingale KK, Significant Shift in Median Guinea Pig Infectious Dose Shown by an Outbreak-Associated Listeria monocytogenes Epidemic Clone Strain and a Strain Carrying a Premature Stop Codon Mutation in inla. Applied and Environmental Microbiology, 77, Vásquez GA, Busschaert P, Haberbeck LU, Uyttendaele M and Geeraerd AH, An educationally inspired illustration of two-dimensional Quantitative Microbiological Risk Assessment (QMRA) and sensitivity analysis. International Journal of Food Microbiology, 190, Vereecken KM, Dens EJ and Van Impe JF, Predictive modeling of mixed microbial populations in food products: Evaluation of two-species models. Journal of Theoretical Biology, 205, Vose D, Risk analysis. A quantitative guide Second, John Wiley & Sons Ltd, Chichester, UK, pp. Whitley E, Muir D and Waites WM, The growth of Listeria monocytogenes in cheese packed under a modified atmosphere. Journal of Applied Microbiology, 88, WHO and FAO (FAO), Risk characterization of Salmonella spp. in eggs and broiler chickens and Listeria monocytogenes in ready-to-eat foods. Joint FAO/WHO Expert Consultation on Risk Assessment of Microbiological Hazards in Foods 2001; pp., Williams D, Castleman J, Lee C-C, Mote B and Smith MA, Risk of fetal mortality after exposure to Listeria monocytogenes based on dose-response data from pregnant duinea pigs and primates. Risk Analysis, 29, Williams D, Castleman J, Lee C-C, Mote B and Smith MA, Erratum to "Risk of fetal mortality after exposure to Listeria monocytogenes based on dose-response data from pregnant duinea pigs and primates" Risk Analysis, 29(11): Risk Analysis, 30, 710. Willocx F, Hendrickx M and Tobback P, Temperatures in the distribution chain. pp [oral presentation], in: Proceedings of the First European Sous vide cooking symposium. Leuven, Belgium, March. Proceedings of. Yang H, Mokhtari A, Jaykus LA, Morales RA, Cates SC and Cowen P, Consumer phase risk assessment for Listeria monocytogenes in deli meats. Risk Analysis, 26, Yoon KS, Burnette CN, Abou-Zeid KA and Whiting RC, Control of growth and survival of Listeria monocytogenes on smoked salmon by combined potassium lactate and sodium diacetate and freezing stress during refrigeration and frozen storage. Journal of Food Protection, 67, Zhang L, Moosekian SR, Todd ECD and Ryser ET, Growth of Listeria monocytogenes in Different Retail Delicatessen Meats during Simulated Home Storage. Journal of Food Protection, 75, EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as author(s). This task has been carried out exclusively by the author(s) in the context of a contract between the European Food Safety Authority and the author(s), awarded following a tender procedure. The present document is without prejudice to the rights of the author(s).

103 Abbreviations ACT1 Activity 1 AIC Akaike Information Index BIC Bayesian Information Crite BLS EU-wide baseline survey CAC Codex Alimentarius Commission CDC Centers of Disease Control and Prevention CFU Colony Forming Unit CI Confidence Interval DALY Disability-adjusted-life-year DB Data Base DCF Data Collection Framework DR Dose Response EE Expert Elicitation EFSA European Food Safety Authority EGR Exponential Growth Rate EU European Union FAO Food and Agriculture Organization of the United Nations FDA Food and Drug Administration FoodEx1 Preliminary detailed food classification developed by EFSA in 2008 FoodEx2 Multipurpose food classification and description system developed by EFSA. Revision 1 FSIS USDA Food Safety and Inspection Service IRAC Interagency Risk Assessment Consortium IRTA Institut de Recerca i Tecnologia Agroalimentàries JIFSAN Joint Institute for Food Safety and Applied Nutrition LAB Lactic Acid Bacteria MAP Modified Atmosphere Packaging MLE Maximum Likelihood Estimation MPD Maximum Population Density MPN Most Probable Number MS Member State NUSAP Numeral Unit Spread Assessment Pedigree OS Objective Scores PD Purchase Date RA Risk Assessment ROP Reduced Oxygen Packaging RTE Ready-to-eat SA Experts Self Assessment SR Systematic Review UBD Use-By-Date UCO University of Cordoba VBA Visual Basic for Applications WHO World Health Organization WP Working Package YOPI Young, Old, Pregnant and Immunocompromised EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

104 Appendix A Growth databases Scientific literature sources used to populate and complete the (combase-based) growth databases for the following RTE food categories: heat-treated meat products (Table A.1), fish products (Table A.2) and soft and semi-soft cheeses (Table A.3). Table A.1: Heat-treated meat products Reference Barmpalia et al. (2005) Baka et al. (2015) Blom et al. (1997) Buncic et al. (1991) Burnett et al. (2005) Cornu et al. (2011) Daminelli et al. (2014) Devlieghere et al. (2001) Geornaras et al. (2006) Hereu et al. (2014) Hudson and Mott (1993) Hwang and Sheen (2011) Hwang and Tamplin (2007) Hwang et al. (2012) Hwang et al. (2015) Lardeux et al. (2015) Linau et al. (2007) Mataragas et al. (2006) McKellar et al. (1994) Mejlholm et al. (2010) Pal et al. (2008a) Pal et al. (2008b) Samapundo et al. (2015) Zhang et al. (2012) Sub-category Cooked meat Sausage Cooked meat Sausage Cooked meat Cooked meat Cooked meat Cooked meat Sausage Cooked meat Pâté Cooked meat Cooked meat Cooked meat Cooked meat Cooked meat Cooked meat Cooked meat Sausage Cooked meat/sausage Sausage Cooked meat Pâté Cooked meat Table A.2: Fish products Reference Gimenez and Dalgaard (2004) Hwang and Sheen (2009) Lakshmanan and Dalgaard (2004) Mejlholm and Dalgaard (2007) Yoon et al. (2004) Hwang (2007) Tang et al. (2013) Kang et al. (2014) Sub-category Smoked Smoked Smoked Smoked/gravad Smoked Smoked Smoked Smoked Table A.3: Soft and semi-soft cheeses Reference Whitley et al. (2000) Tiwari et al. (2014) Rosshaug et al. (2012) Lobacz et al. (2013) Ferrier et al. (2013) Schvartzman et al. (2014) Jordan et al. (2010) Milk heat treatment Raw/pasteurized Pasteurized Pasteurized Unknown (possible pasteurized) Pasteurized Raw/Pasteurized Raw EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

105 Appendix B Representativeness of the formulation recorded in the growth databases Criteria applied to evaluate the representativeness of the formulation of the products from the growth database The following tables show the criteria applied to evaluate the formulation parameters from the cooked meat (Table B.1) and fish products (Table B.2) databases of the exponential growth rate (EGR). Criteria were established based on the expert consultation (see details below). The ComBase Browser records representing food products containing at least one parameter with levels considered unrealistic according to the table values (levels higher than the usual or not allowed in Europe) were considered not representative from the current commercial products and were excluded from the modelling process. Table B.1: Criteria to weight the representativeness of exponential growth rate (EGR) values regarding cooked meat products Parameter Usual levels Unrealistic Nitrite Up to 150 ppm >150 ppm Nisin Not allowed Any value EDTA Not allowed Any value Lactic acid or lactate 18,000-20,000 ppm >20,000 ppm Acetic acid or actetate ppm >2000 ppm Benzoic acid <50,000 50,000 ppm Sorbic acid <50,000 50,000 ppm CO 2 30% >30% Table B.2: Criteria to weight the representativeness of exponential growth rate (EGR) values regarding fish products Parameter Usual levels Unrealistic Phenol Up to 20 ppm >20 ppm Nisin/Diversin Not allowed Any value Lactic acid 18,000-20,000 ppm >20,000 ppm Acetic acid ppm >2000 ppm CO 2 30% >30% Records about the expert consultation Below, the summary of the information gathered from the consultation of several experts regarding the use of organic acid based antimicrobials and the type of packaging for RTE cooked meat, fish and dairy (cheese) products is described, highlighting in bold the most relevant information. EXPERT CONSULTATION ON: the use of organic acid based antimicrobials in RTE food DATE: February 2016 VIA: Phone interview EXPERT: Sales Manager from Corbion (Mrs. Fina Collell) SUMMARY: see below EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

106 MEAT PRODUCTS Corbion product portfolio for the meat industry includes liquid and powder products. Shelf life products contain sodium or potassium lactate (60% in aqueous solution) and are usually applied at a concentration of 3% (corresponding to a 1.8% of pure sodium/potassium lactate). The maximum concentration would be 3.5%. Some important companies apply Corbion sodium lactate (HiPure) at 3% and decrease the % of NaCl added to the product. Nowadays the usual concentration of sodium or potassium lactate applied by the meat industry is 1.8%. Food safety products contain combinations of lactate and diacetate. Diacetate is used to inhibit the pathogens. Opti.Form PD 4 Ultra is a formulation of L-potassium lactate and food grade sodium di-acetate. Sodium diacetate is usually applied at %, as higher levels (e.g. 0.2%) result in unacceptable off-flavors (vinegary taste) in the finished product. However, higher concentrations of diacetate (a maximum of 0.3%) and acetate (a maximum of 0.5%) is technologically and organoleptically feasible for particular products, mainly comminuted and fatty products such as mortadella or chopped. When lactate is combined with acetate/diacetate, then it is usually applied at 1.6% (pure lactate concentration). Other products in the portfolio include: (i) Cost effective products, which have a balanced formulation of organic acid salts (potassium acetate and potassium lactate) and are specifically developed to be used at a low addition level while maximizing shelf life within a product formulation. The high level of acetate in combination with lactate results in lower use levels, increasing cost-effectiveness. The rules regarding the amount of organic acids in the products are equivalent to the above mentioned; (ii) Clean label products, which are nonpurified fermented products that in Europe are declared as natural aroma or vinegar (called fermented sugar in the USA); and (iii) Powdered products, usually bought by clients that prepare their own mixtures. FISH PRODUCTS In fish products lactate and diacetate are used at levels similar to those applied to meat products. For example, in salmon or desalted cod, lactate/diacetate are nowadays applied through brine injection. DAIRY PRODUCTS In cheese the use of lactate and diacetate as food additives is unusual. No case known by the consulted expert. EXPERT CONSULTATION ON: the use of organic acid based antimicrobials in RTE meat products DATE: January 2016 VIA: Face to face interview EXPERT: Technical staff form the Food Innovation section of the Food Technology Programme at IRTA (Mr. Filiberto Sánchez) SUMMARY: see below IRTA: Institut de Recerca i Tecnologia Agroalimentàries. The maximum amount of lactic acid/lactate used nowadays in the meat products range from 1.8 to 2% (w/w). These amounts decrease to % (w/w) in case diacetate is also included in the EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

107 formulation. Acetate/diacetate is usually added at 0.1% (w/w). Organic acids are in most of the cases added in form of sodium or potassium salts through aqueous concentrated solutions, commercially available as standardised products. EXPERT CONSULTATION ON: the use of organic acid based antimicrobials in cheese DATE: February 2016 VIA: Face to face interview EXPERT: Researcher from the Food Technology Programme at IRTA (Dr. Xavier Felipe) SUMMARY: see below IRTA: Institut de Recerca i Tecnologia Agroalimentàries. Lactate or diacetate are not used as antimicrobials for fermented dairy products such as cheese. EXPERT CONSULTATION ON: Reduced oxygen atmosphere packaging in RTE meat products DATE: January 2016 VIA: Face to face interview EXPERT: Technical staff form the Food Packaging area of the Food Technology Programme at IRTA (Ms. Elsa Lloret) SUMMARY: see below IRTA: Institut de Recerca i Tecnologia Agroalimentàries. MAP is the main packaging type used in commercial sliced/diced cooked meat products. The target gas composition is usually 30% CO 2 plus 70% N 2 (no O 2 ), though 20% CO 2 plus 80% N 2 may also be found. The ratio between product and gas volume in the head space of the packaging varies among the different types of products and the expert consulted could not provide particular figures. Vacuum packing is less frequently used for specific products (e.g., intended to be post-lethally treated, thick products, sausages etc.). EXPERT CONSULTATION ON: Reduced oxygen atmosphere packaging in cheese DATE: February 2016 VIA: Face to face interview EXPERT: Researcher from the Food Technology Programme at IRTA (Dr. Xavier Felipe) SUMMARY: see below IRTA: Institut de Recerca i Tecnologia Agroalimentàries. MAP is the usual method to package cheese although he does not know the percentage of CO 2 that is usually applied. Vacuum is more rarely used. In some cases, a film wrapping the cheese portion is used EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

108 Appendix C Protocol for literature/systematic review 1. Protocol Code A1.1-v6 ( ) 2. Review questions addressed and characteristics of the questions 1. Which RTE foods have been assessed in terms of risk of listeriosis to human health? SR: Yes Specific, OPEN-FRAMED question. Key elements: Population = humans including susceptible groups, (YOPI) Outcome = food items (categories and subcategories) 2. Which is the risk estimate measure in the RTE foods assessments from previous question? SR: Yes Specific, OPEN-FRAMED question. Key elements: Population = humans, including susceptible groups (YOPI) Outcome = risk of listeriosis (any measure of risk: probability of illness, infection, death) 3. Which steps within the food chain (till consumption) have been taken into account in the RA? SR: Yes Key elements: Population = food items Outcome = food chain steps, processing conditions 4. Which variables have been included in risk assessment of listeriosis in RTE foods? SR: Yes Specific, OPEN-FRAMED question Key elements: Population = humans, including susceptible groups (YOPI) Outcome = selected predictive models, risk characterization models, consumption patterns 5. Which quantitative approach (stochastic or deterministic and its value) has been adopted for the variables from the previous question)? SR: Yes Specific, OPEN-FRAMED question. Key elements: Population = humans, including susceptible groups (YOPI) Outcome = types of variables, calculation methods, variable representation 6. Is any uncertainty reported for the variables identified in question Nº 4? SR: Yes Specific, CLOSED-FRAMED question. Key elements: Population = humans, including susceptible groups (YOPI) Outcome = first/second-order models 7. Which quantitative approach (stochastic or deterministic and its value) has been adopted for the uncertainties from the previous question)? SR: Yes Specific, OPEN-FRAMED question Key elements: Population = humans, including susceptible groups (YOPI) Outcome = first/second-order models 8. How exposure assessment model and dose-response model are connected? SR: Yes Specific, OPEN-FRAMED question Key elements: Population = humans, including susceptible groups (YOPI) Outcome = exposure assessment model; integrated dose-response and exposure assessment models 9. How close is the risk estimate (question 2) from the actual risk observed? SR: No Specific, OPEN-FRAMED question. Often, microbial risk assessments do not provide data on risk incidence on the current year or validation EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

109 process. Consequently, this question may imply literature search of primary results (actual incidence of listeriosis). 10. Is any step or the overall risk assessment validated? SR: Yes Specific, CLOSED-FRAMED question. Key elements: Population =food items, humans, including susceptible groups (YOPI) Outcome = validation data set (comparison between observations and estimation provided by the risk assessment 11. Has any country from the EU provided a risk estimate of listeriosis from consumption of RTE foods? SR: No Specific, OPEN-FRAMED question. No selection criteria will be established. Any risk estimate (whatever its methodology or nature) will be collected. SR: Systematic Review; YOPI: Young, Old, Pregnant and Immunocompromised. 3. Eligibility criteria Inclusion factors: Type of reference: peer-review scientific articles, reports of recognised organisations and governmental institutions, PhD thesis (dissertations), proceedings/abstracts Contents: the selection process aimed at identifying scientific studies dealing with complete and formal risk assessment (RA) following the CAC and FAO/WHO guidelines (i.e. including hazard characterization, exposure assessment, risk characterization) associated with L. monocytogenes in foods, for general and/or susceptible population groups. Since the aim of the present review was to search for existing microbial risk assessments on listeriosis and L. monocytogenes in RTE foods, few restrictions were implemented (no restrictions concerning language, time period, type of document). Likewise, different approaches: qualitative, semi-quantitative and quantitative (deterministic or stochastic) were considered. Other related studies not considering the abovementioned steps (i.e. not finalising in a formal risk characterization), were excluded for the analysis, though they were identified with specific keywords and further used for retrieving detailed information, which will be mainly used for the modelling approach. Approaches: Qualitative, semi-quantitative and quantitative (stochastic/point-estimate) studies. Language: no specific restriction was imposed a priori as sometimes articles in languages others than English include some English parts (e.g. abstract, tables and figures) that may enable the collection of interesting information. Time period: no specific restriction was applied in the search strategies. Exclusion factors: For practical reasons, and taking into account the characteristics of the review questions and the general aim of the literature review, all review questions will be grouped (i.e. lumping ) in a common literature search, whose strategy is described below. In this way, a more sensitive search can be expected (EFSA, 2010). 4. Resources Access to internet (for web searching) and to bibliographic databases: Scopus, WoS and Medline. Software: EndNote (desktop and/or web version), to manage references and documenting the search and selection process. The program allows for searching online databases, import, organize and manage references and database sharing. Access to institutional repositories of journals will be further used to download full text articles EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

110 There might be the need to acquire some articles (if UCO and/or IRTA are not subscribed to the journal) or directly requested to the authors. 5. Searching methodology for research studies For each information source a searching strategy must be developed, tested and implemented. Two main types of information sources are considered: (A) Scientific bibliographical databases (Scopus, Web of Science and Medline) and (B) other sources mainly based on specific web portals or sites, including institutional websites. Scientific bibliographic databases. Search 1 Source: Scopus ( Search strategy for questions nº All questions can be In Advanced search: combined TITLE-ABS-KEY(risk assessment AND ("listeria monocytogenes" OR "Listeria" OR "monocytogenes")) Restrictions No date no language restrictions Search 2 Source: Web of Science (WoS) ( Search strategy for questions nº All questions can be combined Restrictions In Basic search: risk assessment AND ("listeria monocytogenes" OR "Listeria" OR "monocytogenes") select: Topic Timespan: All years. Search language=auto No date no language restrictions Search 3 Source: PubMed (NLM) ( Search strategy for questions nº All questions can be In Search details (query translation according to the system): combined ("risk assessment"[mesh Terms] OR ("risk"[all Fields] AND "assessment"[all Fields]) OR "risk assessment"[all Fields]) AND (("listeria"[mesh Terms] OR "listeria"[all Fields]) OR ("listeriosis"[mesh Terms] OR "listeriosis"[all Fields]) OR monocytogenes[all Fields]) Restrictions No date no language restrictions Other web sources Search 4 Source: Foodrisk.org Risk assessment repository ( Search strategy for questions nº All questions can be Select from the tables shown: combined Hazard: Bacteria + specific hazard: Listeria + commodity: all Restrictions No date no language restrictions Search 5 Source: QMRAwiki EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

111 ( (QMRA)_Wiki) Search strategy for questions nº All questions can be Search box: listeria combined Restrictions No date no language restrictions Search 6 Source: Google scholar ( Search strategy for questions nº All questions can be Search box: "risk assessment" AND (listeriosis OR listeria) AND food combined Restrictions Not include patents Not include cites Search 7 Source: Codex Alimentarius ( Search strategy for questions nº All questions can be Search box: "risk assessment" AND (listeria OR listeriosis OR monocytogenes) combined Restrictions Not include patents Not include cites Search 8 Source: WHO ( Search strategy for questions nº All questions can be combined Through web sections: Programes Food Safety and Zoonoses Areas of work: Microbiological risks Activities: Joint FAO/WHO expert meetings on microbiological risk assessment (JEMRA) Restrictions Through search box: (risk assessment) AND (listeria OR listeriosis) Not include patents Not include cites Search 9 Source: FAO ( Search strategy for questions nº All questions can be Through web sections: combined Scientific advice Microbiological risks and JEMRA Risk assessments: Listeria monoctogenes in ready-to-eat foods Restrictions Through search box: (risk assessment) AND (listeria OR listeriosis) Not include patents Not include cites Search 10 Source: Food and Drug Administration (FDA) ( Search strategy for questions nº EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

112 All questions can be combined Restrictions Through web sections: FDA Food Sience & Research (Food) Risk & Safety Assessment Topics: Listeria monocytogenes Through search box: "risk assessment" AND ("listeria" OR "listeriosis" OR "monocytogenes") Not include patents Not include cites Search 11 Source: Food Standard Agency UK (FSA) ( Search strategy for questions nº Through web sections: All questions can be Home Science & policy Food poisoning Listeria combined Restrictions Through search box (anytime, all nations, entire website): "risk assessment" AND (listeria OR listeriosis OR monocytogenes) Not include patents Not include cites Search 12 Source: New Zealand Food Safety Authority ( Search strategy for questions nº Through web sections: All questions can be Home Science risk Programmes Hazard risk management combined Listeria strategy and documents Science & Risk Risk assessment Check headings: Risk ranking, risk profiles, quantitative and qualitative risk assessments sections Restrictions Through search window: "risk assessment" AND (listeria OR listeriosis OR monocytogenes) Not include patents Not include cites Search 13 Source: Health Canada ( Search strategy for questions nº All questions can be combined Through web sections: Home > Food & Nutrition > Food Safety > Food-Related Illnesses > Food Specific Information Restrictions Through searching window: "risk assessment" AND (listeria OR listeriosis OR monocytogenes) Not include patents Not include cites Search 14 Source: WorldCat ( Search strategy for questions nº Search box: All questions can be "risk assessment" AND (listeriosis OR listeria OR monocytogenes) combined Restricted to: Thesis/dissertations EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

113 Restrictions Thesis/dissertations Besides the formal search through bibliographic databases, during the screening of full text and specially when collecting relevant information from selected studies, the reference list at the end of relevant studies as well as citations of key articles and reports will be screened. The screening of references and citations will enable the clarification of some confusing duplicates (e.g., the same study published in different forms). 6. Methodology for selecting retrieved studies 6.1. Output of the search from bibliographic database (i.e. Scopus and WoS) References will be automatically imported (including citation information and abstract). No restriction will be implemented at this stage. Restrictions will be applied and recorded for removing duplicate references, references out of the scope, etc. using the Endnote tools. Automatic importation of the entire retrieved references will be recorded to specific EndNote files, which will be called A1rawNAMEOFBIBLIODATABASE (i.e., A1rawScopus, A1rawWoS, A1rawPubMed ). The number of references retrieved with each search will be recorded and reported, together with the name of the person performing the task. The EndNote files will be merged into one (as A1rawALL ) and: Repeated references will be searched automatically with the Endnote tool ( Find duplicates option) and saved into a new EndNote file/group (A1rawduplicates) with the duplicates removed. The number of detected duplicates will be recorded. Further repeated references will be searched manually and moved to the same file as above (A1rawDUPLIC) A rough screening through the references (title and/or abstract) will be done to classify them as included (if apparently interesting) or excluded (when there is an evidence from title and abstract that it is out of the scope of the search). This item will be compiled with a customised field 12 within the EndNote file, which will allow a rapid selection of potentially interesting references. In case the screening of the title and abstract of the study do not provide a clear insight of the definitive eligibility status, the study will be selected for the full text revision. A copy of the file with preselected references will be created with the following name: A1preselectionINSTITUTION (i.e., A1preselectionUCO and A1preselectionIRTA in order to keep only the included references that need to be downloaded and revised. For excluded references, the exclusion reason will be recorded (e.g. not dealing with Listeria monocytogenes, not being a full risk assessment or both). Moreover, for a number of excluded references, key words will be used to identify and classify the contents or data that can be useful for further activities of the project (i.e., L. monocytogenes prevalence/concentration, storage conditions, dose-response model, exposure assessment model, predictive model, etc.). The studies will be reviewed by two independent reviewers and in case of disagreements a discussion involving at least a third researcher will be carried out until reaching a consensus. The full-text of pre-selected references was downloaded for refining the selection of studies and their further review. The full articles of these references will be downloaded and saved in a specific file. The name of the file (usually pdf or word) would correspond to 1stAUTHOR_YEAR_JOURNALCAPITALS+VOL_1 st PAGE (e.g. Peg_2002_IJFM44_235) 12 Check EndNote help for instructions, type fields, adding in the search box of the Index EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

114 6.2. For searches from other sources (which do not enable automatic import) After the screening of the included studies retrieved from bibliographic databases, the relevant studies retrieved from other sources will be added manually to the EndNote file, after checking for duplicates. The references will be downloaded and saved with the name AUTOR_YEAR_INFOABOUTTHEPUBLICATIONSOURCE_ppPAGES (e.g. FDA_2003_TechReport_pp105) 7. Examining full text for the eligibility of studies, characterization of selected studies, information and data collection A second-level screening will be performed examining the full text of the references remaining after the previous screening. Some studies may be further excluded in this process. For the final selected studies analysed, the information will be collected and recorded into an Excel file. The type of information (fields) together with the data dictionary used to collect information from the selected studies, are described below: Type of information Field Field description Answer # (if applicable) and description User and search activity B1 Activity - Internal code within the project management B2 Protocol - Internal code within the project management B3 User collecting info - Name B4 Referee (if applicable) - Name Bibliographic information (EndNote Export) A1 Ref. # - Number of the reference (internal use) A2 Type of document 1 Report 2 Scientific article 3 Book/book chapter 4 Abstract in conference proceedings A3 Authors - Authors of the studydocument A4 Year of publication - Year of the publication of the documentstudy A5 Title - Title of the study document (article, report, etc.) A6 Journal / Book / Conference - Name of the publication A7 Volume pages - Number A8 URL - URL text A9 Abstract - Text A10 Included/excluded - Text Content of the reference Scope C1 RA Framework 1 Institutional RA 2 Research RA (aiming to perform a RA) 3 Case study within a specific aim C2 Category of food 1 Meat 2 Fish 3 Dairy 4 Produce 5 Composite (e.g. meals) C3 Subcategory of food 11 Meat: Raw 12 Meat: Cooked 13 Meat: Fermented or dry cured 14 Meat: Other products 21 Fish: Smoked 22 Fish: Cured / salted (gravad) 23 Fish: Other products EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

115 Type of information Field Field description Answer # (if applicable) and description 31 Dairy: Milk (raw or not) 32 Dairy: Cheese in general 33 Dairy: Soft/semi-soft cheese 34 Dairy: Raw milk cheese 35 Dairy: Fresh cheese 36 Dairy: Other products 44 Produce: Leafy vegetables 45 Produce: Fruits 50 Other: Composite food 0 Unknown/not available/not applicable C4 Further category (if applicable) - Text C5 Country/region (author s - Text affiliation) C6 RA elements included 1 HI 2 HC/DR 3 EA 4 RC Approach & Technical aspects C7 Approach I 1 Qualitative 2 Semi-quantitative 3 Quantitative C8 Approach II 1 Stochastic (probabilistic) 2 Deterministic (point estimate) 0 Not applicable C9 Approach III (further info) - Text if applicable C10 Variability/ uncertainty 1 1st order management 2 2nd order 0 Unknown/not available/not applicable C11 Peer-review level 1 Public consultation for comments 2 Sci referees (journal peer-reviewed article) 3 Conference abstract Relevant info about Hazard characterization dose response C12 DR model type 1 Exponential 2 Weibull-Gamma 3 Logistic 4 Linear 5 Multiple 0 Unknown/not available/not applicable C13 DR model base 1 Based on animal model 2 Based on epidemiology data 3 FAO and WHO (2004c) 4 FDA/FSIS (2003) 0 Unknown/not available/not applicable C14 Endpoint 1 Death 2 Illness/infection (invasive listeriosis in general) 3 Stillbirth C15a Population 1 General 2 Elderly 3 Pregnant 4 Young (prenatal/infant) 5 Inmunocompromised 6 High risk population 7 Low risk population 0 Unknown/not available/not applicable C15b Country of epidemiological data - Text EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

116 Type of information Field Field description Answer # (if applicable) and description C16 Variability uncertainty 1 1st order management 2 2nd order 0 Unknown/not available/not applicable Relevant info about exposure assessment (EA) C17 Steps of the food chain 1 Primary production 2 Food Production 3 Distribution 4 Retail 5 Consumer storage 6 Other 0 Unknown/not available/not applicable C18 Inputs (factors) 1.1 Retail storage temperature 1.2 Consumer storage temperature 2.1 Retail storage time 2.2 Consumer storage time 3.1 Retail distribution temperature 3.2 Consumer distribution temperature 4.1 Retail distribution time 4.2 Consumer distribution time 5 Food characteristics (ph, aw,...) 6 Extrinsic factors (MAP,...) 7 Processing treatments (heat, pressure,...) 8 Transfer/partitioning/mixing 9 Lag 10 EGR 11 Log increase 12 Competing microbiota 13 Max. Density Population (MDP) C19 Inputs data type 1 Point estimate 2 Distribution 3 Both depending on the input 0 Unknown/not available/not applicable C20 Predictive model/s type 1 G/NG 2 Growth rate 3 Lag 4 Inactivation (thermal or non-thermal) 5 Transfer and other 0 Unknown/not available/not applicable C21 Primary predictive model/s 1 Baranyi 2 Gompertz 3 Other 0 Unknown/not available/not applicable C22 Secondary predictive model/s 1 Polynomial 2 Ratkowsky-type 3 Cardinal parameters 4 Gamma 5 Others (specify) C23 Target population (area) - Text (country, region,...) C24 Consumption data source - Text C25 Consumption endpoint 1 Serving size 2 Frequency 3 Other C26 Inputs data type 1 Point stimate 2 Distribution 3 Both depending on the input EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

117 Type of information Field Field description Answer # (if applicable) and description C27 Variability uncertainty 1 1st order management 2 2ond order 0 Unknown/not available/not applicable Relevant info about Risk Characterization C28 Type of output (endpoint) 1 Risk per (X) serving/s 2 Risk per (X) habitant/s 3 Risk per annum (annual risk) 4 Other C29 Simulation method - Text C30 Software used - Text C31 Variability/uncertainty 1 1st order 2 2nd order 0 Unknown/not available/not applicable C32 Sensitivity analysis 1 Yes 2 No 8. Methodology for reporting search process and results and processing the retrieved information 8.1. Reporting search process and results The report form will include the following information: User: Data: Activity: Protocol: Results of bibliographic database search # DataBase Num. of retrieved results (raw) with the provided search strategy Endnote file name (of automatic Comments Pre-selection process: Name of the merged EndNote file: Number of total references (should be the sum): Name of the file gathering duplicate references: Number of automatic duplicates found by EndNote: Number of extra duplicates, detected manually: Name of the file without detected duplicates: Name of the file gathering excluded references: Number of references to screen title and abstract Name of the EndNote file: Further duplicates Included --> preselected (file) Excluded EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

118 Number by exclusion criterion 1 Not dealing with LM (1-NO LM) Number by exclusion criterion 2 Not dealing with any RA element (2-NO RA) 1-NO LM & 2-NO RA Further classification: Number by classification criterion 1 Prevalence and/or concentration data (1-LEVELS) Number by classification criterion 2 Experiment about an specific intervention strategy (2-CHALLENGE) Number by classification criterion 3 Storage conditions data (e.g. time/temp) (3-STORAGE) Number by classification criterion 4 Predictive model of LM behaviour (4-PM) Number by classification criterion 5 Food consumption data (5-CONSUMPTION) Number by classification criterion 6 Dose-Response data (6-DR) Number by classification criterion 7 Cross contamination (7-CROSS CONTAMINATION) Number by classification criterion 8 Risk assessment methodology (8-RA METH) Number by classification criterion 9 Exposure assessment (9-EA) Number by classification criterion 0 0-free text Results from sources others than bibliographic databases # Source Num. of retrieved results Name of the (raw) with the provided search strategy EndNote file where they are manually included Comments EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

119 8.2. Processing retrieved information In the first instance, the retrieved information through the excel file will be analysed and bibliometric information will be generated to classify the references. Bibliometric information provides a global overview of the characteristics of the risk assessment models available for L. monocytogenes/ listeriosis in RTE food. Basically, the information was grouped into: the scope of the study; the approach and technical aspects of the risk assessment; hazard characterization / dose-response features; exposure assessment features; risk characterisation features. Moreover, relevant details retrieved from each selected and reviewed study was collected in an excel file with different fields, covering aspects such as: scope, approach and technical aspects, hazard characterization/dose-response information, exposure assessment and risk characterization EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

120 Appendix D Bibliometric information from the reviewed Risk Assessment studies Table D.1: Characteristics of the revised references regarding the scope of the risk assessment Scope Number of references % RA Framework 1-Institutional RA 8 17% 2-Research RA (aiming to perform a RA) 30 64% 3-Case study within an specific aim 12 26% Category of food 1-Meat 25 53% 2-Fish 14 30% 3-Dairy 17 36% 4-Produce 11 23% 5-Composite (e.g. meals) 3 6% Sub-category of food 11-Meat: Raw 2 4% 12-Meat: Cooked 18 38% 13-Meat: Fermented or dry cured 5 11% 14-Meat: Other products 2 4% 21-Fish: Smoked 11 23% 22-Fish: Cured / salted (gravad) 3 6% 23-Fish: Other products 3 6% 31-Dairy: Milk (raw or not) 5 11% 32-Dairy: Cheese in general 2 4% 33-Dairy: Soft/semi-soft cheese 8 17% 34-Dairy: Raw milk cheese 2 4% 35-Dairy: Fresh cheese 1 2% 36-Dairy: Other products 3 6% 44-Vegetable: Leafy vegetables 8 17% 45-Vegetables: Fruits 1 2% 50-Other: Composite food 3 6% Unknown/not available/not applicable 2 4% Table D.2: Characteristics of the revised references regarding the approach and technical aspects of the risk assessment Approach & Technical aspects Number of references % Approach I 1-Qualitative 3 6% 2-Semi-quantitative 3 6% 3-Quantitative 42 89% Approach II 1-Stochastic (probabilistic) 41 87% 2-Deterministic (point estimate) 1 2% Not applicable 5 11% Variability/ uncertainty management 1-1st order 14 30% 2-2nd order 2 4% 1,2-1st and 2nd order 25 53% Unknown/not available/not applicable 6 13% EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

121 Approach & Technical aspects Number of references % Peer-review level 1-Public consultation for comments 8 17% 2-Sci referees (journal peer-reviewed article) 40 85% 3-Conference abstract 0 - Table D.3: Characteristics of the revised references regarding the Hazard Characterization/Dose- Response information Relevant info about HC-DR Number of references % DR model type 1-Exponential 37 77% 2-Weibull-Gamma 6 13% 3-Logistic 2 4% 4-Linear 1 2% 5-Multiple 1 2% Unknown/not available/not applicable 5 11% DR model base 1-Based on animal model 4 9% 2-Based on epidemiology data 15 32% 3-FAO and WHO (2004c) 15 32% 4-FDA/FSIS (2003) 10 21% Unknown/not available/not applicable 5 11% Endpoint 1-Death 14 30% 2-Illness/infection (invasive listeriosis) (a) 41 87% 3-Stillbirth 1 2% Population 1-General 18 38% 2-Elderly 13 28% 3-Pregnant 4 9% 4-Young (prenatal/infant) 8 17% 5-Immunocompromised 2 4% 6-High risk population 21 45% 7-Low risk population 19 40% Unknown/not available/not applicable 1 2% Geographical area (b) Australia 2 4% Canada 4 9% Europe 1 2% France 1 2% Germany 2 4% Sweden 1 2% The Netherlands 1 2% United Kingdom 1 2% United States 31 66% Unknown/not available/not applicable 4 9% Variability/ uncertainty management 1-1 st order 23 49% 2-2 nd order 18 38% Unknown/not available/not applicable 6 13% (a): Infection Illness dose-response model accounts for infection and/or illness endpoint. Country: origin of the epidemiological data used for the dose-response model EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

122 Table D.4: Characteristics of the revised references regarding the Exposure Assessment information Relevant info about EA Number of references % Steps of the food chain 1-Primary production 8 17% 2-Food Production 18 38% 3-Distribution 20 43% 4-Retail % 5-Consumer storage 35 74% 6-Other 3 6% Inputs (factors) 1.1-Retail storage temperature 23 49% 1.2-Consumer storage temperature 36 77% 2.1-Retail storage time 22 47% 2.2-Consumer storage time 35 74% 3.1-Temperature during transportation to retail 7 15% 3.2- Temperature during transportation from retail to home 14 30% 4.1- Time for transportation to retail 6 13% 4.2- Time for transportation from retail to home 15 32% 5-Food characteristics (ph, a w,...) 10 21% 6-Extrinsic factors (MAP,...) 3 6% 7-Processing treatments (heat, pressure,...) 7 15% 8-Transfer/partitioning/mixing 10 21% 9-Lag 6 13% 10-EGR 12 26% 11-Log increase 4 9% 12-Competing microbiota 3 6% 13-Max. Density Population (MDP) 11 23% Unknown/not available/not applicable 10 21% Inputs data type 1-Point estimate 3 6% 2-Distribution 20 43% 3-Both depending on the input 22 47% Unknown/not available/not applicable 2 4% Predictive model/s type 1-G/NG 1 2% 2-Growth rate 33 70% 3- Lag 6 13% 4-Inactivation (thermal or non-thermal) 3 6% 5-Transfer 6 13% Unknown/not available/not applicable 13 28% Primary predictive model/s 1-Baranyi 5 11% 2-Gompertz 1 2% 3-Other 10 21% Unknown/not available/not applicable 31 66% Secondary predictive model/s 1-Polynomial 2 4% 2-Ratkowsky-type 21 45% 3-Cardinal parameters 4 9% 4-Gamma 2 4% 5-Others 2 4% Unknown/not available/not applicable 18 38% Target population (geographical area) (a) Australia 2 4% Belgium 1 2% Brazil 1 2% EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

123 Relevant info about EA Number of references % Canada 2 4% Spain 2 4% European Union 1 2% France 3 6% Germany 1 2% Italy 2 4% Korea 2 4% The Netherlands 4 9% Sweden 1 2% United Kingdom 1 2% United States 19 40% Unknown/not available/not applicable 6 13% Consumption Data source National survey 12 26% National survey (FDA/FSIS, 2003) 11 23% National survey (FDA/FSIS, 2003) and expert elicitation 2 4% National survey with reference 10 21% Published article 6 13% Unknown/not available/not applicable 6 13% Consumption endpoint 1-Serving size 39 83% 2-Frequency 32 68% 3-Other 1 2% Unknown/not available/not applicable 5 11% Inputs data type 1-Point estimate 4 9% 2-Distribution 23 49% 3-Both depending on the input 14 30% Unknown/not available/not applicable 6 13% Variability/Uncertainty management 1-1 st order 36 77% 2-2 nd order 24 51% Unknown/not available/not applicable 6 13% (a): Geographical area and countries targeted by the exposure assessment Table D.5: Characteristics of the revised references regarding the Risk Characterization information Relevant info about RC Number of references % Type of output (endpoint) 1-Risk per (No) serving/s 21 45% 2-Risk per (No) habitant/s 6 13% 3-Risk per annum (annual risk) 27 57% 4-other 9 19% Simulation method Monte Carlo 26 55% Latin Hypercube 9 19% Bootstrap 1 2% Unknown/not available/not applicable 12 26% Software 23 49% Aladin 2 4% Analytica 2 4% Excel 1 2% i-risk 1 2% EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

124 Relevant info about RC Number of references % JAGS 2 4% Matlab 1 2% R 4 9% Risk Ranger 1 2% SAS 1 2% VBA 2 4% Unknown/not available/not applicable 12 26% Sensitivity analysis 1-Yes 23 49% 2-No 24 51% Application (exploitation of results) Scenarios, cases, evaluation of interventions, ALOP/FSO link, etc EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

125 Appendix E Characteristics of the Listeria monocytogenes risk assessment studies Table E.1: General characteristics (scope, framework, approach) of the reviewed L. monocytogenes risk assessment studies Reference (country) (a) Framework/ approach (b) Variability/ uncertainty Farber et al. (1996) (CA) R/Qt 1st order Buchanan et al. (1997) (US) Bemrah et al. (1998) (FR,UK,CA) Notermans et al. (1998) (NL,GE) FAO (1999) (US) I/Ql na Lindqvist and Westoo (2000) (SW) Sumner and Ross (2002) (AU) Chen et al. (2003b) (US,CA) Category: sub-category of food Stages (c) Population (d) Output (risk characterization) Meat: Pâté R,C HR,LR Risk per annum Dairy: Soft/semi-soft cheese R/Qt 1st order Fish: Smoked fish R G Risk per serving R/Qt 1st order Dairy: Raw milk cheese PP,FP,R HR,LR Risk per annum R/Qt 1st order Meat, Fish, Dairy products, Vegetables R LR Risk per annum Fish: Smoked fish, cured/salted fish, other fish products PP,FP,D,R G Risk per serving R/Qt 1st order Fish: Smoked and gravad salmon and trout R HR,LR Risk per annum R/S na Fish: Smoked fish, other fish products (shellfish, sushi) FP,R G,E,P,Y,I Risk per annum R/Qt 1st & 2nd order Di Luch (2003) (IT) R/Qt 1st order FDA/FSIS (2003) (US) I/Qt 1st & 2nd order Gallagher et al. (2003) (US) Meat: Cooked meat Fish: Smoked fish Dairy: Soft/Semi-soft cheese, other dairy products Produce: Leafy vegetables Composite food RTE foods in school catering (Meat, Fish, Dairy, Produce) Meat: Cooked meat, fermented/dry cured meat Fish: Smoked fish, Cured/salted fish, other fish products Dairy products: milk, cheese, soft/semi-soft cheese, fresh cheese, other dairy products Produce: Leafy vegetables, fruits Composite food (23 subcategories) R HR Risk per serving R,School cafeteria R,C HR,LR G,E,Y Risk per serving Risk per serving, Risk per annum I/Qt 1st & 2nd order Meat: Cooked meat (deli meats) FP,D,R,C G,E,P Risk per annum EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

126 Reference (country) (a) Framework/ Variability/ approach (b) Category: sub-category of food Stages (c) Population (d) Output (risk uncertainty characterization) FAO and WHO (2004b) Meat: Fermented/dry cured meat Risk per serving, I/Qt 1st & 2nd order D,R,C HR,LR (US) Fish: Smoked fish Dairy: milk, other dairy products Risk per habitant Risk per serving, Sanaa et al. (2004) (FR) R/Qt 1st order Dairy products: Soft/Semi-soft cheese, raw milk cheese PP,FP,D,R,C HR,LR Risk per annum Meat: Raw meat, cooked meat, fermented/dry cured Sumner et al. (2005) (AU) R/Ql,S na FP,R G Risk per annum meat Yang et al. (2006) (US) R/Qt 1st order Meat: Cooked meat (deli meats) R,C LR Risk per serving Domenech et al. (2007) (ES) Giovannini et al. (2007) (IT) Perez-Rodriguez et al. (2007) (ES) R/Qt 1st order Fish (raw cod) PP,FP,D,R,C G Risk per serving, Risk per annum, lost/serving, lost/year I,R/Qt 1st & 2nd order Meat: Fermented/dry cured meat (dry cured ham) R,C HR,LR Risk per serving R/Qt 1st & 2nd order Meat: Cooked meat (deli meats) R,C E Risk per annum Fosse et al. (2008) (FR) R/S na Meat: Raw meat PP,R HR,LR Risk per annum Oh et al. (2009) (KO) R/Qt 0 Produce: Leafty vegetables (fresh cut vegetables) D,R,C G Risk per annum Pouillot et al. (2009) (FR) R/Qt 1st & 2nd order Fish: Smoked fish (cold-smoked salmon) R,C E,P,I,LR Pradhan et al. (2009) (US) R/Qt 1st & 2nd order Ross et al. (2009) (AU) R/Qt 1st order Williams et al. (2009, 2010) (US) Meat: Cooked meat (deli meats: ham, turkey and roast beef) Meat: Cooked meat (luncheon meats, cooked sausages, pâtés) Risk per serving, Risk per annum R,C G,E,Y Risk per annum FP,D,R,C R/Qt 1st order Dairy: Soft cheese R,C P HR,LR Risk per serving, Risk per habitant Risk per serving, Risk per annum Carrasco et al. (2010) (ES) R/Qt 1st & 2nd order Produce: Leafy vegetables FP, D, R, C HR, LR Risk per annum Endrikat et al. (2010) (US) R/Qt 1st & 2nd order Meat: Cooked meat Fish: Smoked fish Daity products: Soft/Semi-soft cheese Franz et al. (2010) (NL) R/Qt 1st & 2nd order Produce: Leafy vegetables Garrido et al. (2010a) (ES) R/Qt 1st order Meat: Cooked meat Fish: Smoked fish R,C G,E,Y Risk per annum FP,D,R,Salad bars R,C G HR,LR Risk per serving, Risk per annum Risk per serving, Risk per annum EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

127 Reference (country) (a) Hicks Quesenberry et al. (2010) (US) Mataragas et al. (2010) (GR,NL) Framework/ approach (b) Variability/ uncertainty Category: sub-category of food Stages (c) Population (d) Output (risk characterization) I/Qt 1st order Meat: Cooked meat (poultry deli meat) FP,D,R,C G,E,Y Risk per annum R/Qt 2nd order Meat: Cooked meat, fermented/dry cured meat FP,R,C E Risk per annum Pradhan et al. (2010) (US) R/Qt 1st & 2nd order Meat: Cooked meat (ham and turkey) FP,R,C E Risk per annum Tromp et al. (2010) (NL) R/Qt 1st & 2nd order Produce: Leafy vegetables Busschaert et al. (2011) (BE) FP,D,R,6,Sal ad bars R/Qt 1st & 2nd order Meat: Cooked meat (deli meats) R,C HR Latorre et al. (2011) (US) R/Qt 1st order Dairy products: Milk (raw) PP,D,R,C G,E,Y Pradhan et al. (2011) (US) R/Qt 1st & 2nd order Meat: Cooked meat (deli meats) R,C E FDA and HC (2012) (US,CA) I/Qt 1st & 2nd order Dairy products: Soft/Semi-soft cheese (raw milk cheese, pasteurised milk cheese, cambembert-like cheese) FP,D,R,C Chen et al. (2013) (US,CA) R/Qt 1st & 2nd order Dairy products: Soft/Semi-soft cheese R,C G,E,Y Ding et al. (2013) (CH,KO,JA) FDA (2013) (US) I/Qt 2nd order Gallagher et al. (2013) (US) Gkogka et al. (2013) (NL,CH) G,Y HR,LR Risk per serving, Risk per annum 4,sensitivity analysis Risk per serving, Risk per annum Risk per annum, Risk per habitant Risk per serving, Risk per contaminated serving DALY annual, DALYs per eating occasion R/Qt 1st & 2nd order Produce: Leafy vegetables (lettuce) D,R,C HR,LR Risk per annum Meat: Cooked meat Dairy products: Cheese Composite food R,C HR,LR Risk per serving R/Qt 1st & 2nd order Meat: Cooked meat (RTE turkey, ham and roast beef) FP,D,R,C HR,LR Risk per serving R/Qt 1st & 2nd order Meat: Cooked meat (deli meats) R,C HR,LR Risk per habitant, Risk per annum Horigan et al. (2014) (UK) R/Ql na Meat: Cooked meat PP,FP,D,R,C G Overall risk Sant'Ana et al. (2014) (BR,US) Stasiewicz et al. (2014) (US) R/Qt 1st & 2nd order Produce: Leafy vegetables D,R,C G Risk per serving, Risk per habitant R/Qt 1st & 2nd order Dairy products: Milk (pasteurized) D,R,C G Risk per annum EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

128 Reference (country) (a) Tenenhaus-Aziza et al. (2014) (FR) Framework/ approach (b) Variability/ uncertainty Category: sub-category of food Stages (c) Population (d) Output (risk characterization) R/Qt 1st & 2nd order Soft/Semi-soft cheese FP,D,R,C HR Risk per habitant Vásquez et al. (2014) (BE) R/Qt 1st & 2nd order Smoked fish R,C HR Risk per annum Giacometti et al. (2015) (IT) R/Qt 1st & 2nd order Milk (raw and boiled) PP,D,R,C HR,LR Risk per annum na: not applicable. DALY: disability-adjusted-life-year, a measure of burden of disease. This time-based measure combines years of life lost due to premature mortality and years of life lost due to time lived in states of less than full health. (a): Corresponds to the author/s affiliation; AU: Australia, BE: Belgium, BR: Brazil, CA: Canada, CH: China, ES: Spain, FR: France, GE: Germany, GR: Greece, IT: Italy, JA: Japan, KO: Korea, NL: the Netherlands, SW: Sweden, UK: United Kingdom, US: United States. (b): I: Institutional ; R: Research ; Qt: Quantitative; Ql: Qualitative; S: Semi-quantitative. (c): PP: Primary production; FP: Food Production; D: Distribution; R: Retail; C: Consumer storage. (d): G: General population; E: Elderly; P: Pregnant; Y: Young (prenatal/infant); I:Immunocompromised; HR:High risk population; LR:Low risk population EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

129 Table E.2: Relevant information about hazard characterization from the reviewed L. monocytogenes risk assessment studies Reference (country) (a) Type of Parameters for DR Model base (c) End- Parameters for Variability/ model (b) point (d) non-susceptible/general population (e) susceptible population (e) uncertainty Farber et al. (1996) (CA) WG ED (CA) I NP (f) NP 1 st order Buchanan et al. (1997) (US) Exp ED (GE) I r= (g) 1 st order Bemrah et al. (1998) (FR,UK,CA) WG ED (CA) I α=0.25, b=2.14, β= α=0.25, b=2.14, β= st order Notermans et al. (1998) (NL,GE) Exp AM (mouse) D,I NP - 1 st order FAO (1999) (US) na na (US) I na na na Lindqvist and Westoo (2000) (SW) Exp Exp WG ED (GE) ED (SW) ED (CA) I I I r= (Buchanan et al., 1997) r= α=0.25, b=2.14, β= α=0.25, b=2.14, β= Sumner and Ross (2002) (AU) na na (AU) D,I na na na Chen et al. (2003b) r= & Exp ED (US) I - (US,CA) (Buchanan et al., 1997) 1 st order Di Luch (2003) (IT) WG ED (CA) I α=0.25, b=2.14, β= st order FDA/FSIS (2003) (US) Multiple ED (US), AM (mouse) D,I data NP data NP 2 nd order Gallagher et al. (2003) (US) Exp (FDA/FSIS, 2003) (US) D,I NP (FDA/FSIS, 2003) - 2 nd order FAO and WHO (2004b) r= , r= , Exp ED (US) I (US) (Md, P5-P95) (Md,P5-P95) 1 st order Sanaa et al. (2004) (FR) Exp (FAO and WHO, 2004b) (US) I r= r= st order Sumner et al. (2005) (AU) - ED (AU) I - - na Yang et al. (2006) (US) Exp (FDA/FSIS, 2003) (US) D NP (FDA/FSIS, 2003) - 1 st order Domenech et al. (2007) (ES) Exp (FDA/FSIS, 2003) (US) D,I NP (FDA/FSIS, 2003) recalibrated - 1 st order Giovannini et al. (2007) (FAO and WHO, 2004b) Exp (IT) (US) I NP (WHO and FAO, 2001) NP (WHO and FAO, 2001) 2 nd order Perez-Rodriguez et al. (2007) (ES) Exp, linear EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors. 1 st order (FDA/FSIS, 2003) (US) I - r=pert ( , , ) 2 nd order

130 Reference (country) (a) Type of Parameters for DR Model base (c) End- Parameters for Variability/ model (b) point (d) non-susceptible/general population (e) susceptible population (e) uncertainty Fosse et al. (2008) (FR) na na (EU) I na na na Oh et al. (2009) (KO) Exp (FAO and WHO, 2004b) (US) I r= (h) Pouillot et al. (2009) (FR) Exp (FAO and WHO, 2004b) r= , , I (FR) (Mean, Md,CI5-95) - 2 nd order Pradhan et al. (2009) (US) Exp (FDA/FSIS, 2003) (US) D,I NP (FDA/FSIS, 2003) - 2 nd order Ross et al. (2009) (AU) Exp (FAO and WHO, 2004b) (US) I r= r= st order Williams et al. (2009, AM - primate and a=0.65, b= 5 (primate) Logistic D,S ) (US) guinea pig (pregnant) a=0.65, b= (guinea pig) 1 st order Carrasco et al. (2010) (ES) WG ED (CA) I α=0.25, b=2.14, β=1e15.26 α=0.25, b=2.14, β= nd order Endrikat et al. (2010) (US) Exp (FDA/FSIS, 2003) (US) D,I NP (FDA/FSIS, 2003) recalibrated - 1 st order Franz et al. (2010) (NL) Exp ED (US) I r= (Chen et al., 2006) - 1 st order Garrido et al. (2010a) (FAO and WHO, 2004b) Exp (ES) (US) I r= r= st order Hicks Quesenberry et al. (2010) (US) Exp (FDA/FSIS, 2003) (US) D NP (FDA/FSIS, 2003) recalibrated - 1 st order Mataragas et al. (2010) r=pert( , , Exp ED (US) I - (GR,NL) ) 2 nd order Pradhan et al. (2010) (US) Exp FDA 2003 (US) D - NP (FDA/FSIS, 2003) recalibrated 2 nd order Tromp et al. (2010) (NL) Exp, Logistic ED (NL) and AM D,I r= (Chen et al., 2006) NP (Williams et al., 2009) 1 st order r= (Chen et al., 2003a) r= (Buchanan et al., 1997) Busschaert et al. (2011) r= & Exp, WG ED (US) I - (BE) r= (Albert et al., 2008) 2 nd order WG α=0.25, b=2.14, β= (Buchanan et al., 1997) Latorre et al. (2011) (US) Exp (FAO and WHO, 2004b) (US) I r = r= (perinatal population), r= (elderly) 1 st order EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

131 Reference (country) (a) Type of DR model (b) Model base (c) Endpoint (d) Parameters for non-susceptible/general population (e) Parameters for susceptible population (e) Variability/ uncertainty Pradhan et al. (2011) (US) Exp (FDA/FSIS, 2003) (US) D - NP (FDA/FSIS, 2003, recalibrated) 2 nd order FDA and HC (2012) (FAO and WHO, 2004b) Exp (US,CA) (US) I r= r= nd order Chen et al. (2013) (FAO and WHO, 2004b) Exp I r= r= (perinatal) (adults 5-59year) (US,CA) (US) r= (adults 60year) 2 nd order Ding et al. (2013) (FAO and WHO, 2004b) r= , r= , Exp I (CH,KO,JA) (US) (Md, CI95) ) (Md, CI95) 1 st order FDA (2013) (US) Exp (FAO and WHO, 2004b) (US) I r= r= nd order Gallagher et al. (2013) (FAO and WHO, 2004b) Exp (US) (US) I r= r=1.05e-12 2 nd order Gkogka et al. (2013) (FAO and WHO, 2004b) r=pert( , , r=pert , , Exp I (NL,CH) (US) ; Truncate (0, 1) ; Truncate (0, 1) 2 nd order Horigan et al. (2014) (UK) na na (UK) I na na na Sant'Ana et al. (2014) r=pert( , , Exp ED (US) I - (BR,US) ) (Mataragas et al., 2010) 2 nd order Stasiewicz et al. (2014) (US) Exp (FDA/FSIS, 2003) (US) D, I NP (FDA/FSIS, 2003, recalibrated) - 2 nd order Tenenhaus-Aziza et al. (FAO and WHO, 2004b) Exp (2014) (FR) (U) I - r= st order Vásquez et al. (2014) r= (Buchanan et al., Exp ED (U) I - (BE) 1997) 1 st order Giacometti et al. (2015) (FAO and WHO, 2004b) Exp (IT) (US) I r= r= st order (a): Country abbreviations: AU: Australia; BE: Belgium; BR: Brazil; CA: Canada; CH: China; ES: Spain; FR: France; EU: European Union; GE: Germany; GR: Greece; IT: Italy; JA: Japan; KO: Korea; NL: the Netherlands; SW: Sweden; UK: United Kingdom; US: United States; U: Unspecified. (b): Exp: Exponential; WG: Weibull-Gamma. (c): Reference of the model and country source of the epidemiological data. (d): Illness/infection; D: Death; S: Stillbirth. (e): CI95: 95% Confidence Interval, lower limit-upper limit; Md: Median; na: non-applicable; NP: figures not provided, P5-P95: 5th Percentile-95th Percentile. (f): NP: figures not provided. If available, the original reference is cited. (g): Not considered in the publication. (h): Unknown due to the language of the publication (Chinese) EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

132 Table E.3: Exposure assessment data (hazard levels related data) from the reviewed L. monocytogenes risk assessments studies Reference (a) PREDICTIVE MODEL (i) PATHOGEN LEVELS at retail (i) TIME-TEMPERATURE (i) Approach to estimate level at consumption Prevalence Concentration Retail storage Transportation Home storage Farber et MT:GR,Lag Meats: 4.4% CFU/g al. (1996) 2M:Polynomial (Farber et al., 1995) Dairy products: (Farber and - (d) - 4 and 8 C up to 60 days (CA) (h) Factors: t,t 1.2% Peterkin 1991) Buchanan et al ( (1997) 20.38) CFU/g (c) (US) Bemrah et al. (1998) (FR,UK, CA) Noterman s et al. (1998) (NL,GE) FAO (1999) (US) MT:increase in concentration (Peeler and Bunning, 1994) - - MT:GR Factors:t,T,pH,a w, lactate 66.80% Cold smoked fish: 10-60% Cooked fishery products: <1% RTE cooked shrimp: 4.8% 10.2, 2.5, (M,Md,Rg); CFU/g Meat: 3.9( ) CFU/g (c) Fish: ( ) CFU/g (c) Cheese: ( ) CFU/g (c) Salads: 1.92( ) CFU/g (c) NP (e) (sic rarely exceed 10 3 CFU /g at the time of consumption) EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

133 Reference (a) PREDICTIVE MODEL (i) PATHOGEN LEVELS at retail (i) TIME-TEMPERATURE (i) Approach to estimate level at consumption Prevalence Concentration Retail storage Transportation Home storage Lindqvist and 15.8( ) 2.37( ) Westoo - % (c) CFU/g (c) (2000) (SW) Sumner and Ross (2002) (AU) Chen et al. (2003b) (US,CA) Di Luch (2003) (IT) FDA/FSIS (2003) (US) Gallagher et al. (2003) (US) FAO and WHO (2004b) (US) - - MT:GR,I 2M:Ratkowsky MT:GR,I,Tr Factors:t,T,sanitation, postlethality MT:GR 1M:other (f) 2M:Ratkowsky Factors:t,T 1.82% (Md) (Gombas et al., 2003) 2.5%: Binomial(1,0.02 5) Multiple values (depending on the food category) 2% Beta(0.29,2.68,- 1.69,6.1) Gamma(0.33,2.96) -1.70; log CFU/g (Gombas et al., 2003) LogN(15.58,52.16) ; CFU/g Multiple values (depending on the food category) N(-9,3.5); CFU/g log T(ºC): 0.5,3.4,6.9 (P 5,P 50,P 95 ) (Audits Intl/FDA, 1999) t(d): data with post-retail storage times for different food categories (TableIII-5) t(d): frankfurters and deli meats (AMI, 2001) NP (FDA/FSIS, 2003) - NP (FDA/FSIS, 2003) Multiple values Multiple values NP (Willocx et al., 1993; Notermans et al., 1997; O'Brien, 1997; Sergelidis et al., (depending on the (depending on the 1997; Johnson et al., 1998; MLA, 1999) food category) food category) EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

134 Reference (a) PREDICTIVE MODEL (i) PATHOGEN LEVELS at retail (i) TIME-TEMPERATURE (i) Approach to estimate level at Prevalence Concentration Retail storage Transportation Home storage consumption Sanaa et 10% (Ross et al. (2004) al., 2004) (FR) Sumner et al. (2005) (AU) Yang et al. (2006) (US) Domenec h et al. (2007) (ES) MT:GR,Tr 1M:other (f) 2M:Ratkowsky Factors:t,T,Tr,EGR MT:GR Factors:t,T,cooking MT: GR Lag; 1M: Logistic; 2M: Cardinal (factors: t, T, ph, log increase) LogN( ,6.188); CFU/g (FDA/FSIS, 2003; Gombas et al., 2003) (0, 476, ) (Min,M,Max); CFU/serving Estimation based on the concentration in milk and the effect of ripening (multiple equations/distribut ions) T(ºC): Pert(14,24,42) t(h): LogN( ,1.331) T(ºC): N(4,2.17) t(d): 0.8 U(0,14)+0.2 U( 14,21) (Pierre, 1996) T(ºC): N(5.7,3.3, truncate(1,10)); t(d): U(1,8) - T(ºC): BetaPert(4, 10, 25) (Evans, 1998 ) t(h):gamma(5.24,8.1 7)/60 (Evans et al., 1991) T(ºC): U(2,5); t(d): U (1,2) T(ºC) N(3.096,2.381) (Audits Intl/FDA, 1999) t(d) unopened: LogN(3.189, 4.377) t(d) opened: LogN(5.141, 4.041) T(ºC): Extvalue(3.6446, ) (Audits Intl/FDA, 1999) t(h): Exp((21-PD)/3) (Nauta et al., 2003) T(ºC) N(6.59,2.23,truncate (0.5,)); t(d): U (0.125; 7) Giovannin i et al. (2007) (IT) Perez- Rodriguez et al. (2007) (ES) Fosse et al. (2008) (FR) MT:GR 2M:Polynomial Factors:t,T,a w MT:GR 2M:Ratkowsky Factors:t.T,Tr,EGR,MDP 4.08% ( %) (M,95%CI) 0.04(0.02,0.04); T(d): 1-30; T(ºC): 4- MPN/g (c) 10 - T(d): 1-30; T(ºC): 4-10 NP NP - - T(log days): 0.36( ) b EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

135 Reference (a) PREDICTIVE MODEL (i) PATHOGEN LEVELS at retail (i) TIME-TEMPERATURE (i) Approach to estimate level at Prevalence Concentration Retail storage Transportation Home storage consumption Oh et al ,-4.49,-3.37 TM:G/NG (2009) 0.2% (M,P Factors:t,T,washing 5,P 95 ); Log - g - g - g (KO) CFU/g MT:GR Pouillot et 1M:Exp without lag T(ºC): N(4.6,2.2) T(ºC): N(7.0,3.0) al. (2009) 1% NP - 2M:Ratkowsky t(d): 32 t(d): 32 (FR) Factors:t,T,CM Pradhan et al. (2009) (US) Ross et al. (2009) (AU) Williams et al. (2009, 2010) (US) Carrasco et al. (2010) (ES) Endrikat et al. (2010) (US) MT:G/NG,GR 2M:Ratkowsky Factors:t,T,Lac,Diac,Lag,EGR,MDP MT:GR 2M:Gamma Factors:t,T,pH,a w,nitrite,cm MT:- Factors:- MT:GR 1M:Baranyi 2M:Ratkowsky Factors:t,T,MAP,washing 1M:other (f) 2M:Ratkowsky Factors:t,T,packaging,GI NP (FDA/FSIS, 2003) NP (FDA/FSIS, 2003) NP (FDA/FSIS, 2003) NP (FDA/FSIS, 2003) Processed deli meats: 4.47,2.62 Pâtés: 1.20, (1.14,86.78); NP (ACM, 1998; Microtech, 1998; Cooked MPN/g (c) Audits Intl/FDA, 1999) sausages/frankfur ters: 2.77,1.70 (M,SD) % (Sergelidis al., 1997) et 0.09(0.01,0.36) % (c) - NP Retail-sliced: LogN(-11.1,4.04) Prepackaged: LogN(-11.9,3.39); LogMPN/g (Draughon, 2006) NP (FDA/FSIS, 2003) T(ºC): 5.16(- 0.31,14.95) (c) - - NP (FDA/FSIS, 2003) T(ºC), mean change in transportation store-home: 0.97 t(h) Triangular(0,1, 2.5) (Audits Intl/FDA, 1999) NP (FDA/FSIS, 2003) NP (FDA/FSIS, 2003) T(ºC): N(6.78; 2.56; Truncate(1; 11.3)) (Carrasco et al., 2007) t(h):t(12; U(72; 96); U(192; 288)) (FDA/FSIS, 2003) T(ºF) Logistic(40.15,3.193) & FDA/FSIS (2003), AMI (unpublished), Audits International/FDA (1999) t(d) retailsliced:w(1.14,7.78), prepackaged: W(1.14,18.39) EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

136 Reference (a) PREDICTIVE MODEL (i) PATHOGEN LEVELS at retail (i) TIME-TEMPERATURE (i) Approach to estimate level at Prevalence Concentration Retail storage Transportation Home storage consumption MT:GR Franz et 250(224,276) 1M:Baranyi al. (2010) - (M(95%CI)); Different profiles - T(ºC): N(3.50,0.33) 2M:Ratkowsky (NL) CFU/g Factors:t,T Smoked Smoked salmon: Garrido et al. (2010a) (ES) Hicks Quesenbe rry et al. (2010) (US) Mataraga s et al. (2010) (GR,NL) Pradhan et al. (2010) (US) Tromp et al. (2010) (NL) MT:GR 1M:other (f) 2M:Cardinal Factors:t,T MT:GR 1M:Exp Factors:t,T MT:GR Factors:t,T MT:GR;Lag 2M:Ratkowsky Factors:t,T,Lag,EGR MT:GR 1M:Baranyi 2M:Ratkowsky Factors:t,T,MPD salmon: 10.7% Smoked trout: 25% Vacuum-packed (VP) meat: 2.4% Non-VP (NVP) meat: 8.5% Prepackaged: 0.17% Retail sliced: 1.39% Beta(645,33,18 0) (FDA/FSIS, 2003) Prepackaged: 0.4% (Cum( ,3.6) Retail sliced: 2.7% (Cum(- 8.58,3.6) N(1.01,0.71) Smoked trout: N(1.35,1.4) VP meat: N(1.16,0.71) NVP meat: N(1.51,0.88); log CFU/g T(ºC) N(5.44, 2.32) -1.42(-1.96,0.70); log CFU/g (c) t(d): 0-60 (Nauta et al., 2003) - NP (FDA/FSIS, 2003) - - Poison(250) T(ºC): N(5.1, 0.27) - T(ºC): N(7.9±2.96) (Garrido et al., 2010b) t(d)vp: 5.11(1.07,9.33); days (c) t(d)nvp: 3.37(1.00,6.75); days (c) T(ºF): logistic(40.15,3.193) (Pouillot et al., 2010) t(d) retail sliced: W(1.830,7.777) t(d)prepackaged: W(1.137,18.390) t(d): 0.02(0.00,0.05) t(d): 9.31(1.46,32.69) days (c) days (c) NP (FDA/FSIS, 2003; Pradhan et al., 2009) Figure provided (Franz et al., 2010) EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

137 Reference (a) PREDICTIVE MODEL (i) PATHOGEN LEVELS at retail (i) TIME-TEMPERATURE (i) Approach to estimate level at Prevalence Concentration Retail storage Transportation Home storage consumption Busschaer Normal MT:GR t et al. distribution(fda20 2M:Ratkowsky NP NP (FDA/FSIS, 2003) - NP (FDA/FSIS, 2003) (2011) 03, Factors:t,T,EGR (BE) Busschaert2010) Latorre et al. (2011) (US) Pradhan et al. (2011) (US) FDA and HC (2012) (US,CA) Chen et al. (2013) (US,CA) Ding et al. (2013) (CH,KO,JA) MT:GR 2M:Ratkowsky Factors:t,T,MPD MT:GR,Lag 2M:Ratkowsky Factors:t,T,Tr,Lag,EGR MT:GR,Lag,Inactivation, partitioning/mixing 2M:Ratkowsky,Cardinal,MPD,RLT Factors:t,T,pH,a w,tr,lag,egr, MPD MT:- Factors:t,T,Log increase MT:GR,Tr 1M:Gompertz 2M:Ratkowsky Factors:t,T,Tr,washing 1.3 to 35.3% (Latorre et al., 2009) 0.4% (Gombas et al., 2003) % (Gombas et al., 2003) 5.88% -1.26(-1.4,-0.29) log CFU/ml (c) 10 CFU/g (Gombas et al., 2003; USDA/FSIS, 2009; Endrikat et al., 2010) 51.3(26.2,104.2) (M(95%CI)); CFU/cheese T(-1.39,- 1.15,0.70); log CFU/g (Gombas et al., 2003) 1.22(0.13,2.59); log CFU/g (c) T(ºC): T(26.2,4.4,14.5, Truncate(0,)) t(d): U(1,7) - - NP (Ecolab, 2008) - t(d): 6-10,0.5,15-45 (Most likely,min,max) (FDA/FSIS, 2003) T(ºC) supermarket:7-14 T(ºC) traditional market:15-25ºc t(h): 24 T(ºC): Depends on T of storage at retail and arrival at home (equation provided) t(h): T(0.3,0.6,3.2) - - T(ºC):T(25.0,2.8,17.2,Trunc ate(0,)) t(d): Pert(0.5,2.5,8.5) T/t: (USDA/FSIS, 2009) T(ºC) room:u(15,30) T(ºC) refrigerator: table provided (Pouillot et al., 2010) t(d): (Ecolab, 2008) T(ºC): 3.4,2.4 C (Mean,SD) (Ecolab, 2008) T(ºC) Pert(2,U(4,8.32),U(15, 25)) t(h): U(4,10) + U(0,24) + Pert(12,48,168) EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

138 Reference (a) PREDICTIVE MODEL (i) PATHOGEN LEVELS at retail (i) TIME-TEMPERATURE (i) Approach to estimate level at Prevalence Concentration Retail storage Transportation Home storage consumption FDA (2013) (US) Gallagher et al. (2013) (US) Gkogka et al. (2013) (NL,CH) Horigan et al. (2014) (UK) Sant'Ana et al. (2014) (BR,US) Stasiewic z et al. (2014) (US) MT:GR 1M:Yule pure birth 2M:Gamma Factors:t,T,nitrite,aw,Lac,Diac,Tr,Lag,EGR,Log increase,mpd MT:GR,Tr 2M:Ratkowsky Factors:t,T,GI,Tr,Lag,EGR MT:GR 2M:Ratkowsky Factors:t,T,EGR,MPD MT:- Factors:t,T,Tr MT:GR 2M:Ratkowsky Factors:t,T MT:GR,Lag 1M:Baranyi 2M:Ratkowsky (Pradhan et al., 2009) Factors:t,T,MPD 0.42% 0.42% (FSIS, 2009; FSIS, 2009 ) Beta(49,3471); % Different qualitative levels depending the species on multin(-9.228, 2.923); CFU/g LogN(-9.22,2.92); log CFU/g T(ºC) different scenarios:-3.33 to18.33 (Ecolab, 2008) NP (FDA/FSIS, 2003) - Conc of positive samples: -1.42(- 1.96,0.70); log - - CFU/g (c) NP (Ecolab, 2008) - NP NP NP 0.22% log CFU/g 9.57(7,12); ºC (c) t(h):gamma(5.24,8.1 7)/60 (Nauta et al., T(ºC): Pert(7,12,20) 2003) NP NP NP NP NP T(ºC): Laplace(4.06,2.31) t(d) deli meat:w(2.08,8.33) t(d) soft cheese: W(1.34,18.7) t(d) deli salad:w(1.34,7.49) Information for time to first consumption is also provided (Pouillot and Delignette- Muller, 2010) T(ºC): Logistic((40.15,3.193) 32) 5/9 (Hicks Quesenberry et al., 2010) t(d) Retailsliced:W(1.83,7.77) t(d) Prepackaged:W(1.14,18.39) (Pouillot et al., 2010) T(ºC): N(5.99,1.83,Truncate (0.15)) (Nauta et al., 2003) t(d): Pert(0,5,15) T(ºC): Pert(3.04,6,10.8) (Silva et al., 2008) t(h):u(0,192) EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

139 Reference (a) PREDICTIVE MODEL (i) PATHOGEN LEVELS at retail (i) TIME-TEMPERATURE (i) Approach to estimate level at Prevalence Concentration Retail storage Transportation Home storage consumption Tenenhau MT:GR,Tr s-aziza et 1M:Logistic with delay 3.5% log CFU/g T(ºC): 4 - T(ºC): 4 al. (2014) 2M:Cardinal (FR) Factors:pH,a w,processing,tr Vásquez et al. (2014) (BE) Giacomett i et al. (2015) (IT) MT:GR 1M:Exp 2M:Ratkowsky Factors:t,T,CM,MPD MT:GR 1M:Baranyi Factors:t,T,boiling 0.75% (FASC, 2010) 0.54% Figure with cumulative logistic distribution provided N(-3.656,0.888); log CFU/g - - T(ºC):4 or 11(worst case) (Giacometti et al., 2012b) t(h):22.5 T(ºC):4 or 30(worst case) (Giacometti et al., 2012b) t(h):0.5 T(ºC): N(7.18,2.78, Truncate(0,15)) (De Vriese et al., 2004) t(d): Exp distribution (Afchain et al., 2005) T(ºC): 4 or 12(worst case) (Giacometti et al., 2012b) t(h)=t(0.5, 24, 120) / 68 (a): Corresponds to the author/s affiliation. (b): Information corresponding to retail level. (c): Parameters calculated from distribution/table data provided in the publication M,P5,P95 are provided. (d): Not considered in the reference. (e): NP: Not Provided, i.e. data from the cited reference were used without explicitly providing figures. (f): Other than Baranyi and Gompertz. (g): Unknown due to the language of the publication (Chinese). (h): Country abbreviations: AU: Australia; BE: Belgium; BR: Brazil; CA: Canada; CH: China; ES: Spain; FR: France; EU: European Union; GE: Germany; GR: Greece; IT: Italy; JA: Japan; KO: Korea; NL: the Netherlands; SW: Sweden; UK: United Kingdom; US: United States; U: Unspecified. (i): Other abbreviations: CI95: 95% Confidence Interval, lower limit-upper limit; CM: Competing Microbiota; Cum: Cumulative; Diac: Diacetate; EGR: Exponential Growth Rate; Exp: Exponential; GI: Growth inhibitor; I: Inactivation; LAB: Lactic Acid Bacteria; Lac: Lactate; LogN: LogNormal distribution; M: Mean; MAP: Modified Atmosphere Packaging; Md: Median; MPD: Max. Population Density; N: Normal distribution; P5-P95: 5th Percentile-95th Percentile; PT: Processing treatment; Rg: Range; SD:Standard Deviation; T:temperature; t: time; T:Triangular distribution; Tr: Transfer; U:Uniform distribution EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

140 Table E.4: Exposure assessment data (consumption related data) from the reviewed L. monocytogenes risk assessments studies Reference (a) Serving size Frequency intake Country/source (servings per capita/year) Farber et al. (1996) (CA) (d) 100g 55 CA/national survey Buchanan et al. 55g 20 GE/published article (Teufel and (1997) (US) Bendzulla, 1993) 31g 50 FR/national survey (Centre Bemrah et al. Interprofessionel de Documentation et (1998) (FR,UK,CA) d'information LaitieÁres) Notermans et al. (1998) (NL,GE) 100g 200 (meat products), 50 (fish products), 100 (cheese), 100 (salads) NL/national survey FAO (1999) (US) 50g - - Lindqvist and Westoo (2000) Trigen(50,100,175,10,90); g - SW/national survey (Nodrisk Ministerrad Kobenhavn, 1998; SLV, 1998) (SW) Sumner and Ross - (b) - - (2002) (AU) Chen et al. (2003b) (US,CA) 50g - US/ national survey (FDA/FSIS, 2001) Di Luch (2003) (IT) FDA/FSIS (2003) (US) Gallagher et al. (2003) (US) FAO and WHO (2004b) (US) Sanaa et al. (2004) (FR) Sumner et al. (2005) (AU) Yang et al. (2006) (US) Domenech et al. (2007) (ES) g (different amounts depending on the school (kindergarten or primary/high school) and course) Serving sizes for 23 food subcategories provided (Table III-3) IT/national survey (Servizio Igiene Alimenti Nutrizione) Data not shown (depends on the type of food) US/national survey (USDA and ARS, 1998; USDHHS and NCHS, 1998) Data not shown - US/national survey (FDA/FSIS, 2003) Data not shown Data not shown US/national survey (multiple sources) 27 - FR/unpublished data (French reports) - Weekly (cooked sausages, salami,deli meats, fresh sausage); monthly (pâté/terrines) AU/national survey (ABS, 1999) Weibull(1.306,41.27) - US/ national survey (USDA and ARS, 1998; USDHHS and NCHS, 1998) EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

141 Reference (a) Serving size Frequency intake Country/source (servings per capita/year) Giovannini et al. 28g - US/own stimation (2007) (IT) Perez-Rodriguez et 59g Data not shown US (FDA/FSIS, 2003) al. (2007) (ES) Fosse et al. (2008) (FR) Oh et al. (2009) Poisson(20.9) 0.25 KO/national survey (KO) (population <3years) FR/national survey Pouillot et al. (2009) (FR) 4.57 (3-14 years) 6.96 (15-64 years) 7.01 (<65years) Pradhan et al. (2009) (US) Data not shown Data not shown US/national survey (FDA/FSIS, 2003) and expert elicitation Ross et al. (2009) (AU) 15,28-25,84 (processed meats) 42, , 140 (cooked sausages & frankfurters) 7, 40-56, 140 (pâté) (in g; values indicate Min,Rg, Max) - AU/national survey 31, 246, 246 (P 50,P 95,P 99 ); g annual servings (perinatal population) US/national survey (FDA/FSIS, 2003) Williams et al. (2009, 2010) (US) Carrasco et al (25.30,122.73); g (c) Weibull( ; ;Shift(3.0003)) ES/ published article (Carrasco et al., (2010) (ES) 2007) Endrikat et al. - - US/national survey (2010) (US) Franz et al. (2010) Loglogistic (6.51,81.71,2.47); g Equation provided (depends on the number of NL/national survey (NL) visitors and salads with leafy green vegetables) 48.41(5.08,111.85) (c) (ham); g 87.38(8.99,296.97) (c) (ham); ES/national survey Garrido et al (16.47,89.93) (c) (salmon); g 14.85(1.00,46.69) (c) (salmon); (2010a) (ES) 31.80(10.64,60.93) (c) (trout); g 18.65(2.00,60.06) (c) (trout); Hicks Quesenberry Data not shown Data not shown US/national survey (FDA/FSIS, 2003) et al. (2010) (US) Mataragas et al. Pert (0, 50, 100); g 6 EU/ published article (Mataragas et al., (2010) (GR,NL) 2008) and expert opinion Pradhan et al. Data not shown Data not shown US/national survey (FDA/FSIS, 2003) and (2010) (US) expert elicitation Tromp et al. Log logistic(6.51,81.71, 2.47); g Equation provided (depends on the number of NL/national survey EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

142 Reference (a) Serving size Frequency intake Country/source (servings per capita/year) (2010) (NL) visitors and salads with leafy green vegetables) Busschaert et al. (2011) (BE) Data not shown Data not shown US/national survey (FDA/FSIS, 2003) Data not shown , , and (annual servings US/national survey (FDA/FSIS, 2003) for the intermediate, perinatal, and elderly Latorre et al. populations, respectively). (2011) (US) 0.91 (serving per capita per/day for dairy producers and farm workers) Pradhan et al. (2011) (US) 56,119,648 (Md,P 1,P 99 ); g Data not shown US/national survey (FDA/FSIS, 2003) FDA and HC (2012) (US,CA) Chen et al. (2013) (US,CA) Ding et al. (2013) (CH,KO,JA) FDA (2013) (US) Gallagher et al. (2013) (US) Gkogka et al. (2013) (NL,CH) Horigan et al. (2014) (UK) Sant'Ana et al. (2014) (BR,US) Stasiewicz et al. (2014) (US) Tenenhaus-Aziza et al. (2014) (FR) Vásquez et al. (2014) (BE) 40,11,115 (Elderly) 37,11,100 (Pregnant) 42,12,117 (General and immunocompromised); (Mean,P 5,P 95 ); g T(10, 28, 85) Perinatal T(10, 28, 85) 60 years T(10, 28, 168) Intermediate-age; g - US & CA/national survey Eating occasions per year: Perinatal years Intermediate-age US/national survey (FDA/FSIS, 2003) 20g (calculated) 0.11(0.00,0.78) (c) KO/national survey (Korea Food and Drug Administration) Empirical cumulative distribution provided in a Data not shown US/national survey (FDA/FSIS, 2003) figure 62,68 (Mean,SD); g Data not shown US/national survey (FDA/FSIS, 2003) T(10, 15, 20); g 4730/ T(10, 15, 20) NL/national survey - Data not shown UK/national survey (Canning, 2005) Pert(25,50,75); g 6.23(1.00,30.00) (c) BR/published article (Perez et al., 2008) BE/national survey (Devriese et al., 2004) EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

143 Reference (a) Serving size Frequency intake Country/source (servings per capita/year) Giacometti et al. (2015) (IT) T (100,250,1000); ml Servings per year IT/published article (Giacometti et al., 2012a) Md: Median; Rg: Range; SD: Standard Deviation; Min: Minimum; Max: Maximum; P x: Xth Percentile; T: Triangular distribution (a): Corresponds to the author/s affiliation. (b): Not considered. (c): Parameters calculated from distribution/table data provided in the publication M, P5, P95 are provided. (d): Country abbreviations: AU: Australia; BE: Belgium; BR: Brazil; CA: Canada; CH: China; ES: Spain; EU: European Union; FR: France; GE: Germany; GR: Greece; IT: Italy; JA: Japan; KO: Korea; NL: the Netherlands; SW: Sweden; UK: United Kingdom; US: United States EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

144 Appendix F NUSAP system F.1 Instructions for expert elicitation on dose-response L. monocytogenes quality assessment (NUSAP system) Project: Reference: Leader: Closing gaps for performing a risk assessment on Listeria monocytogenes in ready-to-eat (RTE) foods: activity 2, a quantitative risk characterization on L. monocytogenes in RTE foods; starting from the retail stage OC/EFSA/BIOCONTAM/2014/02 CT1 University of Cordoba (UCO) EXPERT ELICITATION Date: 29/09/ EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

145 Instructions for experts in dose-response modelling In the framework of the project above, the consortium UCO-IRTA would be very grateful to consider your scientific contribution in order to finally select the most appropriate dose-response model of Listeria monocytogenes. For this selection, a Numeral Unit Spread Assessment Pedigree (NUSAP) system was applied (Boone et al., In the NUSAP system, four pedigree criteria are evaluated, in this case with regards to dose-response models of Listeria monocytogenes published so far. These criteria are: (i) proxy, (ii) empirical criterion, (iii) methodological rigour, and (iv) validation. Below is an explanation of these criteria. Proxy criterion: evaluates the closeness of resemblance between the input parameter available from the data source and the actual variable that would be required in the model (Boone et al., 2009). Van der Sluijs et al. (2005) defined proxy criterion as a tool to evaluate how good or close the quantity that we measure is to the actual quantity about which we seek information. For the purpose of this project, the UCO-IRTA consortium considered proxy criterion in terms of time and geographical closeness. Empirical criterion evaluates the degree to which direct observations were used to estimate the input parameter. A higher pedigree score for empirical basis was attributed to input parameters obtained from field data compared with indirect, modeled data or data obtained by expert judgment (Boone et al., 2009; van der Sluijs et al., 2005). The methodological rigour refers to the norms used in the collection and checking of data and the degree of acceptance of these norms by the peer community of the relevant discipline. Lastly, the validation criterion evaluates the degree to which one was able to cross-check the data against independent sources. For the purpose of the present risk assessment, this cross-check degree was divided/described in four levels (see Table F.1): validation with more than one human data source, validation with one human data source, validation with animal data or in-vitro experiments and no validation. For each dose-response model of Listeria monocytogenes published up to now, UCO-IRTA consortium is scoring each of the four pedigree criteria, extracting information from these models (Annex I provides a guide with the type of information extracted from the dose-response models and score scale). The total score for each dose-response model could be either the sum of the four pedigree criteria scores, or the average, or the weighted average of the pedigree criteria scores. UCO-IRTA deems that the latter would be the most adequate approach, as the relative importance of every pedigree criteria must be reflected in some way, such as the weight in the final average. As an expert, UCO-IRTA consortium would ask you to fill in the following cells with a particular weight (percentage) for each pedigree criterion, so that the total weight sums up 100%. Also, if you consider it necessary, you could indicate your level of expertise on this matter from 0% (no expertise at all on this matter) to 100% (high level of expertise on this issue); if you leave the cell blank, we will assume that your level of expertise is 100%. Table 1. Weights to be assigned to pedigree criteria for Listeria monoctogenes dose-response models and expertise level. Pedigree criteria Proxy Empirical basis Methodological Time Geograp rigour Weight (%) Validation Expertise (%) EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

146 Table F.1 can be consulted to have an overview of the items evaluated in each pedigree criteria for scoring purposes Comments or suggestions: REFERENCES Boone, I, der Stede, YV, Bollaerts, K, Vose, D, Maes, D, Dewulf, J, Messens, W, Daube, G, Aerts, M, and Mintiens, K NUSAP method for evaluating the data quality in a quantitative microbial risk assessment model for Salmonella in the pork production chain. Risk Analysis, 29, van der Sluijs, JP, Risbey, JS, Ravetz, J Uncertainty assessment of voc emissions from paint in The Netherlands using the NUSAP system. Environmental Monitoring and Assessment, 105, EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

147 Table F.1: Pedigree criteria and scoring scale guide (a)(b) Scores Time Score: 4 Exact measure of the desired quantity (e.g., measurements from the same geographically representative area as that being investigated) Data from the last 5 years (measured, if not available publication date). Pedigree criteria Proxy Empirical basis (c)(d) Methodological rigour (c) Validation Space Large sample direct Best available (method) practice in wellestablished measurements, recent data, discipline (accredited method controlled experiments for sampling/diagnostic test) Data from more than 1 European country. Score: 3 Good fit or measure (e.g., measurements used from another geographical area but representative) Data from the Data from 1 last 10 years European (measured, if country. not available publication date). Score: 2 Well correlated but not measuring the same thing (e.g., large geographical differences, less representative) a) Data obtained from epidemiological studies in humans naturally infected (i.e. outbreaks, sporadic cases). b) Data obtained from at least 2 independent sources. c) Data obtained from at least three subpopulation groups (at least, susceptible). Small sample, direct measurements, less recent data, uncontrolled experiments, low nonresponse rate a) Data obtained from studies in surrogate animals with physiological or immune characteristics human related. b) Data obtained from one independent source. c) Data obtained from two subpopulation groups (at least, susceptible). Very small sample modeled/derived data/indirect measurements, structured expert opinion a) Explicit variability and uncertainty included in dose-response estimate. b) Goodness-Of-Fit (GOF) indices included. Statistical analysis indicating model robustness. c) At least, 2 well-defined quantitative DR endpoints (at least, illness). d) Publication: SCI article; national, European or international report (with peer-review). Reliable method common within established discipline, best available practice in immature discipline (sampling/diagnostic test) a) Explicit estimation of variability or uncertainty. b) Statistical analysis indicating acceptable model fit. c) Provision of one well-defined quantitative DR endpoint: illness. d) Publication as a report (without peerreview). Acceptable method but limited consensus on reliability Compared with independent measurements of the same variable over long domain, rigourous correction of errors a) Validation with more than one independent human data source (epidemiological data). Compared with independent measurements of closely related variable over shorter period a) Validation with one independent human data source (epidemiological data). Compared with measurements not independent, proxy variable, limited domain More than 10 Data from US, a) Data obtained from studies in a) Output provided: one point-estimate a) Validation with animal data sources/ in EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

148 Scores Score: 1 Pedigree criteria Proxy Empirical basis (c)(d) Methodological rigour (c) Validation Time Space years old study. Canada or NZ. surrogate animals with (mean, median, percentile, range). vitro experiments. physiological or immune b) Some kind of statistical measures characteristics no human related included in the model. or data obtained in-vitro c) Although a quantitative outcome is experiments (cell lines). b) Data obtained from one provided, the definition of the endpoint is not clear, or it is just called subpopulation group listeriosis. (susceptible). d) Publication as conference proceeding. Weak correlation (e.g., very large geographical differences, low representativeness) More than 20 years old. Data from other countries. One expert opinion, rule-ofthumb estimate a) Dose-response models derived mathematically from annual number of cases and exposure to the pathogen. b) Subpopulation groups not discernible, global estimation. Preliminary methods with unknown reliability a) Qualitative models, models based on literature data. b) Statistical analysis not included in the model. c) Qualitative estimation of illness or other endpoint. d) Model not published in any scientific media. Weak, very indirect validation a) No validation available. (a): In black: general information extracted from the approach of Boone et al. (2009); in blue: specific information with regards to dose-response models specifically detailed for the present project. (b): In some cases, a combination of two levels of achievements are provided for a pedigree criterion, for example, dose-response models based on both surrogate animal studies and surveillance data adjusted to exposure to the pathogen (item Primary source of data within the pedigree criterion Empirical basis). In these cases, the score corresponding to the maximum scored item is applied; in this example, the score would be 3 or 2, depending on the closeness of the physiological and immune characteristics of the surrogate animals to humans (dose-response models only based on surveillance data adjusted to exposure to the pathogen could have a score of 1). (c): Every item was scored separately, and the average was used as OS. For example, for the criterion Empirical basis, the items primary source of data, number of independent sources and number of subpopulations groups were scored individually and averaged. To calculate the average, when there was lack of information to score a specific item, that item was not considered in the calculation of the mean. For example, for the criterion Empirical basis, if a dose-response model was derived mathematically through anchoring the annual number of cases to exposure information to the pathogen (score: 1), it is nonsense to provide any goodness-of-fit index (item Statistical analysis of the pedigree criterion Methodological rigour). In this case, the item Statistical analysis would not be included in the calculation of the criterion Methodological rigour. (d): The item Number of subpopulation groups from which data were analysed refers to different subpopulation groups of any species (i.e. humans or surrogate animals, or even cell lines) in terms of immunological status. In the cases of studies in animal surrogates, the item Geographical origin of primary data of the Proxy criterion and the item Number of independent sources for the primary source of data were not applicable, and so withdrawn from the calculations. In the case of dose-response models based on surveillance data adjusted to exposure to the pathogen (item Primary source of data within the pedigree criterion Empirical basis), the item Statistical analysis of the Methodological rigour criterion was not applicable, and so withdrawn from the calculations EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

149 Table F.2: Objective Scores assigned to the dose-response models of the studies reviewed Reference Proxy Empirical basis Methodological rigour Validation Time Space Primary N Nº V/U (a) GOF (b) End-P (c) Publication source nature sources subpopulat. Buchanan et al. (1997) NA Notermans et al. (1998) 2 NA 2 NA Linqdvist and Westoo NA (2000) Chen et al. (2003b) NA FDA/FSIS (2003) Gallagher et al. (2003) FAO and WHO (2004b) NA Saana et al. (2004) NA Yang et al. (2006) Domenech et al. (2007) Giovannini et al. (2007) NA Pérez-Rodríguez et al (2007) Pouillot et al. (2009) NA Pradhan et al. (2009) Ross et al. (2009) NA Endrikat et al. (2010) Franz et al. (2010) NA Garrido et al. (2010a) NA Hicks Quesenberry et al (2010) Mataragas et al. (2010) NA Pradhan et al. (2010) Tromp et al. (2010) Latorre et al. (2011) NA Pradhan et al. (2011) FDA and HC (2012) NA Chen et al. (2013) NA Ding et al. (2013) NA EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

150 Reference Proxy Empirical basis Methodological rigour Validation Time Space Primary N Nº V/U (a) GOF (b) End-P (c) Publication source nature sources subpopulat. FDA (2013) NA Gallagher et al. (2013) NA Gkogka et al. (2013) NA Horigan et al. (2014) 4 3 NA Sant Ana et al. (2014) NA Stasiewicz et al. (2014) Tenenhaus-Ariza et al NA (2014) Vásquez et al. (2014) NA Giacometti et al. (2015) NA Chen et al. (2006) NA Williams et al. (2009) 3 NA Smith et al. (2008) 4 NA 3 NA van Stelten et al. (2011) 4 NA 3 NA Pouillot et al. (2015) NA NA: Not applicable (a): V/U: Variability/Uncertainty. (b): GOF: Goodness of Fit. (c): End-P: Endpoint EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

151 Table F.3: Weights assigned to Pedigree Criteria (%) by Expert Elicitation Reference Proxy Empirical Methodological Validation Selfassessment Time Space basis rigour Expert Expert Expert Expert Expert Expert Expert EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

152 Quantitative characterization on listeria monocytogenes in ready-to-eat foods Appendix G Time-temperature profiles retrieved from Frisbee database ( Table G.1: Metadata of the time-temperature profiles of heat-treated meat products at retail (a) Record Total storage time (min) Stage/step of cold chain Country Food product Packaging Packaging material Accuracy of data collecting equipment (± C) Position of data collecting equipment ,110 Retail display France Cordon bleu AP 0.5 Top of the food ODM ,190 Retail display France Cordon bleu AP 0.5 Top of the food ODM Retail display France Cordon bleu AP 0.5 Top of the food ODM ,210 Retail display France Cordon bleu AP 0.5 Top of the food ODM ,855 Retail display France Cordon bleu AP 0.5 Top of the food ODM ,490 Supermarket Greece Cooked turkey VP 0.1 Inside the food RP slices ,960 Supermarket Greece Cooked turkey VP 0.1 Inside the food RP slices ,540 Supermarket Greece Cooked turkey VP 0.1 Inside the food RP slices ,420 Supermarket Greece Cooked turkey VP 0.1 Inside the food RP slices ,480 Supermarket Greece Cooked turkey VP 0.1 Inside the food RP slices ,550 Supermarket Greece Cooked turkey VP 0.1 Inside the food RP slices ,970 Supermarket Greece Cooked turkey VP 0.1 Inside the food RP slices ,880 Supermarket Greece Cooked turkey VP 0.1 Inside the food RP slices ,290 Supermarket Greece Cooked turkey VP 0.1 Inside the food RP slices ,640 Supermarket Greece Cooked turkey VP 0.1 Inside the food RP slices ,810 Supermarket Greece Cooked turkey slices VP 0.1 Inside the food RP ,032 Retail warehouse, Supermarket France 4 slices of cooked ham MAP Set of two packages of 4 slices of ham/per package 0.9 Other: between 2 packages Retail France 4 slices of MAP Set of two packages of 4 slices 0.9 Other: between ODM EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors. Data source ODM

153 Quantitative characterization on listeria monocytogenes in ready-to-eat foods Record Total storage time (min) Stage/step of cold chain Country Food product Packaging Packaging material Accuracy of data collecting equipment (± C) Position of data collecting equipment warehouse, cooked ham of ham/per package 2 packages Hypermarket ,874 Retail France 4 slices of MAP Set of two packages of 4 slices 0.9 Other: between ODM warehouse, Hypermarket cooked ham of ham/per package 2 packages ,602 Retail France 4 slices of MAP Set of two packages of 4 slices 0.9 Other: between ODM warehouse, Hypermarket cooked ham of ham/per package 2 packages ,512 Retail France 4 slices of MAP Set of two packages of 4 slices 0.9 Other: between ODM warehouse, Hypermarket cooked ham of ham/per package 2 packages Retail France 4 slices of MAP Set of two packages of 4 slices 0.9 Other: between ODM warehouse, Hypermarket cooked ham of ham/per package 2 packages ,602 Retail France 4 slices of MAP Set of two packages of 4 slices 0.9 Other: between ODM warehouse, Hypermarket cooked ham of ham/per package 2 packages ,176 Retail France 4 slices of MAP Set of two packages of 4 slices 0.9 Other: between ODM warehouse, Supermarket cooked ham of ham/per package 2 packages ,124 Retail France 4 slices of MAP Set of two packages of 4 slices 0.9 Other: between ODM warehouse, Hypermarket cooked ham of ham/per package 2 packages Retail France 4 slices of MAP Set of two packages of 4 slices 0.9 Other: between ODM warehouse, Hypermarket cooked ham of ham/per package 2 packages Retail France 4 slices of MAP Set of two packages of 4 slices 0.9 Other: between ODM warehouse, Hypermarket cooked ham of ham/per package 2 packages ,602 Retail France 4 slices of MAP Set of two packages of 4 slices 0.9 Other: between ODM warehouse, Hypermarket cooked ham of ham/per package 2 packages Retail France 4 slices of MAP Set of two packages of 4 slices 0.9 Other: between ODM warehouse, Hypermarket cooked ham of ham/per package 2 packages ,124 Retail France 4 slices of MAP Set of two packages of 4 slices 0.9 Other: between ODM EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors. Data source

154 Quantitative characterization on listeria monocytogenes in ready-to-eat foods Record Total storage time (min) Stage/step of cold chain warehouse, Hypermarket Retail warehouse, Hypermarket ,802 Hypermarket, Retail display ,554 Hypermarket, Retail display ,062 Supermarket, Retail display ,100 Hypermarket, Retail display ,048 Hypermarket, Retail display ,934 Hypermarket, Retail display ,318 Hypermarket, Retail display ,190 Hypermarket, Retail display ,916 Hypermarket, Retail display ,138 Hypermarket, Retail display Hypermarket, Retail display ,730 Hypermarket, Retail display ,174 Hypermarket, Retail display Hypermarket, Retail display Hypermarket, Retail display Country Food product Packaging Packaging material Accuracy of data collecting equipment (± C) France France France France France France France France France France France France France France France France Position of data collecting equipment cooked ham of ham/per package 2 packages 4 slices of cooked ham 4 slices of cooked ham 4 slices of cooked ham 4 slices of cooked ham 4 slices of cooked ham 4 slices of cooked ham 4 slices of cooked ham 4 slices of cooked ham 4 slices of cooked ham 4 slices of cooked ham 4 slices of cooked ham 4 slices of cooked ham 4 slices of cooked ham 4 slices of cooked ham 4 slices of cooked ham 4 slices of cooked ham MAP MAP MAP MAP MAP MAP MAP MAP MAP MAP MAP MAP MAP MAP MAP MAP Set of two packages of 4 slices of ham/per package Set of two packages of 4 slices of ham/per package Set of two packages of 4 slices of ham/per package Set of two packages of 4 slices of ham/per package Set of two packages of 4 slices of ham/per package Set of two packages of 4 slices of ham/per package Set of two packages of 4 slices of ham/per package Set of two packages of 4 slices of ham/per package Set of two packages of 4 slices of ham/per package Set of two packages of 4 slices of ham/per package Set of two packages of 4 slices of ham/per package Set of two packages of 4 slices of ham/per package Set of two packages of 4 slices of ham/per package Set of two packages of 4 slices of ham/per package Set of two packages of 4 slices of ham/per package Set of two packages of 4 slices of ham/per package 0.9 Other: between 2 packages 0.9 Other: between 2 packages 0.9 Other: between 2 packages 0.9 Other: between 2 packages 0.9 Other: between 2 packages 0.9 Other: between 2 packages 0.9 Other: between 2 packages 0.9 Other: between 2 packages 0.9 Other: between 2 packages 0.9 Other: between 2 packages 0.9 Other: between 2 packages 0.9 Other: between 2 packages 0.9 Other: between 2 packages 0.9 Other: between 2 packages 0.9 Other: between 2 packages 0.9 Other: between 2 packages Data source ODM ODM ODM ODM ODM ODM ODM ODM ODM ODM ODM ODM ODM ODM ODM ODM EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

155 Quantitative characterization on listeria monocytogenes in ready-to-eat foods Record Total storage time (min) Stage/step of cold chain ,572 Supermarket Netherl ,864 Supermarket Netherl Country Food product Packaging Packaging material Accuracy of data collecting equipment (± C) Cooked ham ands slices Cooked ham ands slices VP: vacuum-packaged; MAP: modified atmosphere packaged; AP: air packaged; RP: research project; ODM: own data measurements. (a): Chilled processed RTE; recommended food storage conditions: 2-4 C; data collecting equipment: data logger. Position of data collecting equipment MAP 0.1 Inside the food RP MAP 0.1 Inside the food RP Data source Table G.2: Metadata of the time-temperature profiles of heat-treated meats during transportation from retail to home (a) Record Total storage time (minutes) Country Food product Packaging Packaging material Accuracy of data collecting equipment (± C) Position of data collecting equipment France Cordon bleu AP 0.5 Top of the food ODM France Cordon bleu AP 0.5 Top of the food ODM France Cordon bleu AP 0.5 Top of the food ODM France Cordon bleu AP 0.5 Top of the food ODM France Cordon bleu AP 0.5 Top of the food ODM France Cordon bleu AP 0.5 Top of the food ODM France Cordon bleu AP 0.5 Top of the food ODM France Cordon bleu AP 0.5 Top of the food ODM France Cordon bleu AP 0.5 Top of the food ODM France Cordon bleu AP 0.5 Top of the food ODM France 4 slices of cooked ham MAP Set of two packages of 4 slices 0.9 Other: between 2 ODM of ham/per package packages France 4 slices of cooked ham MAP Set of two packages of 4 slices 0.9 Other: between 2 ODM of ham/per package packages France 4 slices of cooked ham MAP Set of two packages of 4 slices 0.9 Other: between 2 ODM of ham/per package packages France 4 slices of cooked ham MAP Set of two packages of 4 slices 0.9 Other: between 2 ODM of ham/per package packages France 4 slices of cooked ham MAP Set of two packages of 4 slices of ham/per package 0.9 Other: between 2 packages ODM France 4 slices of cooked ham MAP Set of two packages of 4 slices of ham/per package 0.9 Other: between 2 packages EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors. Data source ODM

156 Quantitative characterization on listeria monocytogenes in ready-to-eat foods Record Total storage time (minutes) Country Food product Packaging Packaging material Accuracy of data collecting equipment (± C) Position of data collecting equipment France 4 slices of cooked ham MAP Set of two packages of 4 slices 0.9 Other: between 2 ODM of ham/per package packages France 4 slices of cooked ham MAP Set of two packages of 4 slices 0.9 Other: between 2 ODM of ham/per package packages France 4 slices of cooked ham MAP Set of two packages of 4 slices 0.9 Other: between 2 ODM of ham/per package packages France 4 slices of cooked ham MAP Set of two packages of 4 slices 0.9 Other: between 2 ODM of ham/per package packages France 4 slices of cooked ham MAP Set of two packages of 4 slices 0.9 Other: between 2 ODM of ham/per package packages France 4 slices of cooked ham MAP Set of two packages of 4 slices 0.9 Other: between 2 ODM of ham/per package packages France 4 slices of cooked ham MAP Set of two packages of 4 slices 0.9 Other: between 2 ODM of ham/per package packages France 4 slices of cooked ham MAP Set of two packages of 4 slices 0.9 Other: between 2 ODM of ham/per package packages France 4 slices of cooked ham MAP Set of two packages of 4 slices 0.9 Other: between 2 ODM of ham/per package packages France 4 slices of cooked ham MAP Set of two packages of 4 slices 0.9 Other: between 2 ODM of ham/per package packages France 4 slices of cooked ham MAP Set of two packages of 4 slices 0.9 Other: between 2 ODM of ham/per package packages France 4 slices of cooked ham MAP Set of two packages of 4 slices 0.9 Other: between 2 ODM of ham/per package packages France 4 slices of cooked ham MAP Set of two packages of 4 slices 0.9 Other: between 2 ODM of ham/per package packages France 4 slices of cooked ham MAP Set of two packages of 4 slices 0.9 Other: between 2 ODM of ham/per package packages France 4 slices of cooked ham MAP Set of two packages of 4 slices 0.9 Other: between 2 ODM of ham/per package packages France 4 slices of cooked ham MAP Set of two packages of 4 slices 0.9 Other: between 2 ODM of ham/per package packages France 4 slices of cooked ham MAP Set of two packages of 4 slices 0.9 Other: between 2 ODM of ham/per package packages France 4 slices of cooked ham MAP Set of two packages of 4 slices 0.9 Other: between 2 ODM Data source EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

157 Quantitative characterization on listeria monocytogenes in ready-to-eat foods Record Total storage time (minutes) Country Food product Packaging Packaging material Accuracy of data collecting equipment (± C) of ham/per package Position of data collecting equipment packages France 4 slices of cooked ham MAP Set of two packages of 4 slices 0.9 Other: between 2 ODM of ham/per package packages France 4 slices of cooked ham MAP Set of two packages of 4 slices 0.9 Other: between 2 ODM of ham/per package packages France 4 slices of cooked ham MAP Set of two packages of 4 slices 0.9 Other: between 2 ODM of ham/per package packages France 4 slices of cooked ham MAP Set of two packages of 4 slices 0.9 Other: between 2 ODM of ham/per package packages France 4 slices of cooked ham MAP Set of two packages of 4 slices 0.9 Other: between 2 ODM of ham/per package packages France 4 slices of cooked ham MAP Set of two packages of 4 slices 0.9 Other: between 2 ODM of ham/per package packages France 4 slices of cooked ham MAP Set of two packages of 4 slices 0.9 Other: between 2 ODM of ham/per package packages Netherlands Cooked ham slices MAP 0.1 Inside the food RP Netherlands Cooked ham slices MAP 0.1 Inside the food RP Netherlands Cooked ham slices MAP 0.1 Inside the food RP Netherlands Cooked ham slices MAP 0.1 Inside the food RP Hungary Cooked ham slices VP 0.1 Inside the food ODM Hungary Cooked ham slices VP 0.1 Inside the food ODM Hungary Cooked ham slices VP 0.1 Inside the food ODM Hungary Cooked ham slices VP 0.1 Inside the food ODM Hungary Cooked ham slices VP 0.1 Inside the food ODM VP: vacuum-packaged; MAP: modified atmosphere packaged; AP: air packaged; RP: research project; ODM: own data measurements. (a): Chilled processed RTE; recommended food storage conditions: 2-4 C; data collecting equipment: data logger. Data source EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

158 Quantitative characterization on listeria monocytogenes in ready-to-eat foods Table G.3: Metadata of the time-temperature profiles of heat-treated meats during home storage (a) Record Total storage time (minutes) Country Food product Packaging Packaging material Accuracy of data collecting equipment (± C) Position of data collecting equipment Data source ,400 Greece Cooked turkey slices VP NR 0.1 Inside the food RP ,130 Greece Cooked turkey slices VP NR 0.1 Inside the food RP ,220 Greece Cooked turkey slices VP NR 0.1 Inside the food RP ,540 Greece Cooked turkey slices VP NR 0.1 Inside the food RP ,160 Greece Cooked turkey slices VP NR 0.1 Inside the food RP ,240 Greece Cooked turkey slices VP NR 0.1 Inside the food RP ,400 Greece Cooked turkey slices VP NR 0.1 Inside the food RP ,110 Greece Cooked turkey slices VP NR 0.1 Inside the food RP ,050 Greece Cooked turkey slices VP NR 0.1 Inside the food RP ,420 Greece Cooked turkey slices VP NR 0.1 Inside the food RP ,460 Greece Cooked turkey slices VP NR 0.1 Inside the food RP ,950 Greece Cooked turkey slices VP NR 0.1 Inside the food RP ,390 Greece Cooked turkey slices VP NR 0.1 Inside the food RP ,040 Greece Cooked turkey slices VP NR 0.1 Inside the food RP ,370 Greece Cooked turkey slices VP NR 0.1 Inside the food RP ,550 Greece Cooked turkey slices VP NR 0.1 Inside the food RP ,490 Greece Cooked turkey slices VP NR 0.1 Inside the food RP ,030 Greece Cooked turkey slices VP NR 0.1 Inside the food RP ,570 Greece Cooked turkey slices VP NR 0.1 Inside the food RP ,560 Greece Cooked turkey slices VP NR 0.1 Inside the food RP ,250 Greece Cooked turkey slices VP NR 0.1 Inside the food RP ,464 France 4 slices of cooked ham MAP set of two packages of 4 slices 0.9 Other: between two ODM of ham/per package packages France 4 slices of cooked ham MAP set of two packages of 4 slices 0.9 Other: between two ODM of ham/per package packages France 4 slices of cooked ham MAP set of two packages of 4 slices 0.9 Other: between two ODM of ham/per package packages France 4 slices of cooked ham MAP set of two packages of 4 slices 0.9 Other: between two ODM of ham/per package packages ,064 France 4 slices of cooked ham MAP set of two packages of 4 slices 0.9 Other: between two ODM of ham/per package packages ,748 France 4 slices of cooked ham MAP set of two packages of 4 slices 0.9 Other: between two ODM of ham/per package packages ,198 France 4 slices of cooked ham MAP set of two packages of 4 slices 0.9 Other: between two ODM of ham/per package packages ,758 France 4 slices of cooked ham MAP set of two packages of 4 slices 0.9 Other: between two ODM of ham/per package packages ,156 France 4 slices of cooked ham MAP set of two packages of 4 slices 0.9 Other: between two ODM EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

159 Quantitative characterization on listeria monocytogenes in ready-to-eat foods Record Total storage time (minutes) Country Food product Packaging Packaging material Accuracy of data collecting equipment (± C) Position of data collecting equipment Data source of ham/per package packages ,226 France 4 slices of cooked ham MAP set of two packages of 4 slices 0.9 Other: between two ODM of ham/per package packages ,240 France 4 slices of cooked ham MAP set of two packages of 4 slices 0.9 Other: between two ODM of ham/per package packages ,240 France 4 slices of cooked ham MAP set of two packages of 4 slices 0.9 Other: between two ODM of ham/per package packages ,724 France 4 slices of cooked ham MAP set of two packages of 4 slices 0.9 Other: between two ODM of ham/per package packages ,370 France 4 slices of cooked ham MAP set of two packages of 4 slices 0.9 Other: between two ODM of ham/per package packages ,536 France 4 slices of cooked ham MAP set of two packages of 4 slices 0.9 Other: between two ODM of ham/per package packages ,162 France 4 slices of cooked ham MAP set of two packages of 4 slices 0.9 Other: between two ODM of ham/per package packages France 4 slices of cooked ham MAP set of two packages of 4 slices 0.9 Other: between two ODM of ham/per package packages ,096 France 4 slices of cooked ham MAP set of two packages of 4 slices 0.9 Other: between two ODM of ham/per package packages ,460 France Cordon bleu AP set of two packages of 4 slices 0.5 Top of the food ODM of ham/per package France Cordon bleu AP set of two packages of 4 slices 0.5 Top of the food ODM of ham/per package ,625 France Cordon bleu AP set of two packages of 4 slices 0.5 Top of the food ODM of ham/per package ,625 France Cordon bleu AP set of two packages of 4 slices 0.5 Top of the food ODM of ham/per package ,920 France Cordon bleu AP set of two packages of 4 slices 0.5 Top of the food ODM of ham/per package ,635 France Cordon bleu AP set of two packages of 4 slices 0.5 Top of the food ODM of ham/per package ,300 France Cordon bleu AP set of two packages of 4 slices 0.5 Top of the food ODM of ham/per package France Cordon bleu AP set of two packages of 4 slices 0.5 Top of the food ODM of ham/per package ,408 Hungary Cooked ham slices VP 0.1 Inside the food ODM ,640 Hungary Cooked ham slices VP 0.1 Inside the food ODM ,000 Hungary Cooked ham slices VP 0.1 Inside the food ODM VP: vacuum-packaged; NR: not reported; MAP: modified atmosphere packaged; AP: air packaged; RP: research project; ODM: own data measurements. (a): Chilled processed RTE; recommended food storage conditions: 2-4 C; data collecting equipment: data logger EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

160 Quantitative characterization on listeria monocytogenes in ready-to-eat foods Table G.4: Metadata of the time-temperature profiles of soft and semi-soft cheese at retail (a) Record Food product Stage/step cold Country Accuracy Position Tmin Tmax Tmean chain t/t data 3321 Stilton cheese Retail display UK +/- 0.5ºC Top/front of the product t/t data 3322 Stilton cheese Retail display UK +/- 0.5ºC Middle/front of the product t/t data 3323 Stilton cheese Retail display UK +/- 0.5ºC Bottom/rear of the product t/t data 3331 Blue cheese Retail display UK +/- 0.5ºC Top/front of the product t/t data 3333 Blue cheese Retail display UK +/- 0.5ºC Middle/front of the product t/t data 3334 Blue cheese Retail display UK +/- 0.5ºC Bottom/rear of the product t/t data 3340 Blue cheese Retail display UK +/- 0.5ºC Top/rear of the product t/t data 3341 Blue cheese Retail display UK +/- 0.5ºC Middle/rear of the product t/t data 3342 Blue cheese Retail display UK +/- 0.5ºC Bottom/rear of the product t/t data 3343 Brie Retail display UK +/- 0.5ºC Top/rear of the product (a): Data collecting equipment: thermocouplers; packaging not defined EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

161 Appendix H products Organic acids as preservatives in commercial RTE Search on the frequency of use of organic acids as preservatives in commercial RTE product The search was carried out in the Mintel-Global New Products Database ( provides information from ingredients and packaging about new products launched to the market worldwide. The database compiles information present on the labels of food products. This information was used to search for RTE cooked meat products (sliced meat products, pâté and sausages), smoked and gravad/marinated fish products and soft and semi-soft chesses put on the market in the EU countries during the last 10 years. Information regarding product formulation (i.e. the presence of specific preservatives, such as lactate, acetate/diacetate, nitrite) and packaging was extracted. Table H.1 shows the search parameters applied to each type of product, the number of matches rendered by each search and the number of products selected for the analysis after revising all the retrieved products case by case. Tables H.2 to H.4 show, for each category, the number and percentage of products containing, according to the information provided in the label, the listed formulation parameters and packaging type. Table H.1: Search strings applied Mintel-Global New Products Database and results retrieved Products Search string (Full text search) Sub-Category matches Cooked \"cooked ham\" OR \"turkey breast\" OR \"cooked Poultry meat meat\" OR \"mortadella\" Products, Meat products 1 Products Cooked meat products 2 Cooked meat products 3 Marinated or gravad fish Soft and semi-soft cheeses "pâté" OR "terrine" \"sausage\" OR \"bratwurst\" OR \"weisswurst\" OR \"bockwurst\" OR \"linguica\" OR \"mettwurst\" OR \"keilbasa\" OR \"boterhamwurst\" OR \"frankfurter\" OR \"wiener\" OR \"bologna\" OR \"knackwurst\" OR \"braunschweiger\" OR \"salsiccia\" NOT \"dry\" NOT \"cured\" NOT \"sliced\" NOT \"pepperoni\" NOT \"fuet\" NOT \"chorizo\" NOT \"marinated\" NOT \"raw\" NOT \"salami\" NOT \"roast\" NOT \"ham\" \"marinated\" OR \"gravad\" OR \"smoked\" OR \"gravlax\" NOT \"fishcakes\" NOT \"surimi\" NOT \"prawn\" Individual searches for the following types of cheese were performed: epoisses, langres, limburger, munster, serpa, taleggio, vacherin mont s'or, wynendale, brie, camembert, chevre mould ripened, coulommiers, dunbarra, garrotxa, pouligny-saint-pierre, saga, saint marcellin, bavarian blue, blue castello, blue de graven, cashel blue, banon, fleur de maquis, harzer, robiola, cambozola Poultry Products, Meat Products Poultry Products, Meat Products Matches 1783 products 59 products 4059 products Fish products 2383 products Soft Cheese & Semi-Soft Cheese 1246 products Selected products 1482 All searches were restricted for products where Region matches Europe and Date Published is between January 2006 and January EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

162 Table H.2: Cooked meat products: number of products found associated with each parameter (presence of specific preservatives, type of packaging) and percentage (%) with respect to the total number of products within the category (see Table H.1) as grouped by different time frames Parameter Last 10 years ( ) Last 5 years ( ) Last 2 years ( ) Last year ( ) Sliced meat products Pâté Sausages All Sliced meat products Pâté Sausages All Sliced meat products Pâté Sausages All Sliced meat products Pâté Sausages All Lactate % Acetate/diacetate % Citric acid % Benzoate % Sorbate % Nitrite % Nitrate % Smoke % Sulphites % MAP % Vacuum % Note: Table shows the raw numbers and percentages of cooked meat products containing each formulation parameter and packaging type. Few products contain the packaging type information in their label EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

163 Table H.3: Smoked and marinated fish products: number of products found associated with each parameter (presence of specific preservatives, type of packaging) and percentage (%) with respect to the total number of products within the category (see Table H.1) as grouped by different time frames Last 10 years ( ) Last 5 years ( ) Last 2 years ( ) Last year ( ) Parameters Smoked Marinated All Smoked Marinated All Smoked Marinated All Smoked Marinated All Lactate % Acetate/diacetate % Citric acid % Benzoate % Sorbate % Nitrite % Nitrate % Smoke % Sulphites % MAP % Vacuum % Note: Table shows the raw numbers and percentages of smoked and marinated products containing each physicochemical parameter and packaging conditions. Not all products contain the packaging conditions information in their label EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

164 Table H.4: Soft and semi-soft cheese: number of products found associated with each parameter (presence of specific preservatives, type of packaging) and percentage (%) with respect to the total number of products within the category (see Table H.1) as grouped by different time frames Parameters Last 10 years ( ) Last 5 years ( ) Last 2 years ( ) Last year ( ) Lactate % Acetate/diacetate % Citric acid % Benzoate % Sorbate % Nitrite % Nitrate % Smoke % Sulphites % MAP % Vacuum % Note: Table shows the raw numbers and percentages of soft and semi-soft cheese products containing each physicochemical parameter and packaging conditions. Not all products contain the packaging conditions information in their label EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

165 Appendix I Growth information and lag time modelling of Listeria monocytogenes Table I.1: Growth data, mean and standard deviation (SD) of h 0 of Listeria monocytogenes challenged in different meat categories as adapted from Augustin et al. (2011) Food (physico-chemical parameter = mean ± SD) Vacuum-packed pork pie (ph = 5.94 ± 0.10; a w = ± 0.004) Cooked ham packed under modified atmosphere (ph = 6.08 ± 0.07 a w = ± 0.007) Manufacturer (a) (AM/LAB (b) log CFU/g) Batch (c) Physiological state Laboratory (d) x 0 (log CFU/g) x max (log CFU/g) Lag (h) μ max (h 1 ) h 0 h 0 mean ; h 0 SD PA (1.7/<1.0) PA1 Exponential # ; # # # # # # # # # # # # # Starved cells # # CHA (2.4/1.6) CHA1 Exponential # ; # # CHA2 Exponential # CHA3 Exponential # CHB (<2/1.5) CHB1 Exponential # # # Starved cells # CHC (7.1/4.8) CHC1 Exponential # # EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

166 Food (physico-chemical parameter = mean ± SD) Cooked chicken (ph = 6.30 ± 0.19 a w = ± 0.008) Manufacturer (a) (AM/LAB (b) log CFU/g) Batch (c) Physiological state Laboratory (d) x 0 (log CFU/g) x max (log CFU/g) Lag (h) μ max (h 1 ) h 0 h 0 mean ; h 0 SD # CHC2 Exponential #6 1.8 CHC3 Exponential # CA (3.1/ -) CA1 Exponential # ; # # CA2 Exponential # Starved cells # : no information available. (a): PA, CHA, CHB, CHC, CA: Manufacturers coded according to the source of data (Augustin et al., 2011). (b): AM: Initial counts of aerobic microorganisms; LAB: Initial counts of lactic acid bacteria. (c): PA1, CHA1, CHA2, CHA3, CHB1, CHC1, CHC2, CHC3, CA1, CA2: Batches coded according to the source of data (Augustin et al., 2011). (d): #1, #2, #3, #4, #5, #6, #7, #8: Laboratories coded according to the source of data (Augustin et al., 2011) EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

167 Table I.2: Growth data, mean and standard deviation of h 0 of Listeria monocytogenes challenged in different cheese categories as adapted from Augustin et al. (2011) Source Milk Product in_on (a) Temp ph lag (h) EGR h 0 h 0 mean ; h 0 SD Ryser and Marth (1987) soft cheese In: camembert cheese ; 5.74 Back et al. (1993) soft cheese In: soft cheese Back et al. (1993) soft cheese In: soft cheese Back et al. (1993) soft cheese In: soft cheese Back et al. (1993) soft cheese In: soft cheese Whitley et al. (2000) pasteurized semi-soft cheese In: blue stilton cheese Whitley et al. (2000) pasteurized semi-soft cheese In: blue stilton cheese Whitley et al. (2000) pasteurized semi-soft cheese In: blue stilton cheese Whitley et al. (2000) pasteurized semi-soft cheese In: blue stilton cheese Whitley et al. (2000) pasteurized semi-soft cheese In: blue stilton cheese NS Whitley et al. (2000) pasteurized semi-soft cheese In: blue stilton cheese NS Larson et al. (1996) soft cheese In: camembert cheese Larson et al. (1996) soft cheese In: camembert cheese Larson et al. (1996) soft cheese In: camembert cheese 4 - Larson et al. (1996) soft cheese In: camembert cheese 4 - Larson et al. (1996) soft cheese In: camembert cheese Larson et al. (1996) soft cheese In: camembert cheese Larson et al. (1996) soft cheese In: camembert cheese Larson et al. (1996) soft cheese In: camembert cheese Tiwari et al. (2014) raw semi-soft cheese white semi-soft rind washed cheese Tiwari et al. (2014) raw semi-soft cheese white semi-soft rind washed cheese Tiwari et al. (2014) raw semi-soft cheese white semi-soft rind washed cheese Tiwari et al. (2014) raw semi-soft cheese white semi-soft rind washed cheese Tiwari et al. (2014) raw semi-soft cheese white semi-soft rind washed cheese Tiwari et al. (2014) raw semi-soft cheese white semi-soft rind washed cheese Tiwari et al. (2014) raw semi-soft cheese white semi-soft rind EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

168 Source Milk Product in_on (a) Temp ph lag (h) EGR h 0 h 0 mean ; h 0 SD washed cheese Tiwari et al. (2014) raw semi-soft cheese white semi-soft rind washed cheese Tiwari et al. (2014) raw semi-soft cheese white semi-soft rind washed cheese Tiwari et al. (2014) pasteurized semi-soft cheese white semi-soft rind washed cheese Tiwari et al. (2014) pasteurized semi-soft cheese white semi-soft rind washed cheese Tiwari et al. (2014) pasteurized semi-soft cheese white semi-soft rind washed cheese Tiwari et al. (2014) pasteurized semi-soft cheese white semi-soft rind washed cheese Tiwari et al. (2014) pasteurized semi-soft cheese white semi-soft rind washed cheese Tiwari et al. (2014) pasteurized semi-soft cheese white semi-soft rind washed cheese Tiwari et al. (2014) pasteurized semi-soft cheese white semi-soft rind washed cheese Tiwari et al. (2014) pasteurized semi-soft cheese white semi-soft rind washed cheese Tiwari et al. (2014) pasteurized semi-soft cheese white semi-soft rind washed cheese Rosshaug et al. (2012) pasteurized soft cheese soft blue-white cheese Rosshaug et al. (2012) pasteurized soft cheese soft blue-white cheese Lobacz et al. (2013) possibly soft cheese blue cheese pasteurized Lobacz et al. (2013) possibly soft cheese blue cheese pasteurized Lobacz et al. (2013) possibly soft cheese blue cheese pasteurized Lobacz et al. (2013) possibly soft cheese blue cheese pasteurized Lobacz et al. (2013) possibly soft cheese blue cheese pasteurized Lobacz et al. (2013) possibly soft cheese camembert EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

169 Source Milk Product in_on (a) Temp ph lag (h) EGR h 0 h 0 mean ; h 0 SD pasteurized Lobacz et al. (2013) possibly soft cheese camembert pasteurized Lobacz et al. (2013) possibly soft cheese camembert pasteurized Lobacz et al. (2013) possibly soft cheese camembert pasteurized Lobacz et al. (2013) possibly soft cheese camembert pasteurized Ferrier et al. (2013) pasteurized soft cheese smear soft-cheese (Munster) Ferrier et al. (2013) pasteurized soft cheese smear soft-cheese (Munster) Schvartzman et al. raw soft/semi-soft smear-ripened cheese (2014) cheese Schvartzman et al. raw soft/semi-soft smear-ripened cheese (2014) cheese Schvartzman et al. pasteurized soft/semi-soft mold-ripened cheese (2014) cheese Jordan et al. (2010) raw soft/semi-soft cheese smear semi-softripened cheese : no information available; NS: not specified (a): According to the experimental challenge test, L. monocytogenes was inoculated in the cheese ( in ) or on the cheese surface ("on") EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

170 Appendix J Growth predictive models Table J.1: Growth models implemented in the probabilistic risk model for Listeria monocytogenes (list) and Lactic Acid Bacteria (lab) Model/microorganism/Matrix Model Mathematical function Variables and parameters Source Comment output Primary model/list/lab/all N_list/N_lab dn_list=egr_list*(1-(n_list/nmax_list))*(1- (N_lab/Nmax_lab))*(q_list/(1+q_list)) *dt*n_list EGR_list/lab= exponential growth rate (h -1 ) of listeria and lactic acid bacteria dq_list= EGR_list*q_list*dt Nmax_list/lab = maximum Baranyi and population density of listeria and Roberts (1994) Differential dn_lab=egr_lab*(1-(n_lab/nmax_lab))*(1- (N_list/Nmax_list))*(q_lab/1+q_lab) *dt*n_lab lactic acid bacteria q_list/lab =concentration of a form limiting substrate for starting dq_lab= EGR_lab*q_lab*dt growth of listeria and lactic acid bacteria N_list/lab =concentration of listeria and lactic acid bacteria at a specific time Primary model/list /all N_list At = t - L + (Ln(1 - Exp(-EGR_list * t) + Exp(- EGR_list * (t - L))) / EGR_list) N_list = N0_list + (EGR_list * At) - (1 / m) * Log(1 + ((Exp(m * EGR_list * At) - 1) / (Exp(m * (Nmax_list - N0_list))))) L= EGR_list*h0_list (lag time) t =time m=curvature parameter (1) Baranyi and Roberts (1994) Explicit form Secondary model/list/all EGR_list (EGR_5C*((T-Tmin_list)/(Tref_list-Tmin_list)) ^2) EGR_5C =EGR at 5ºC Tmin_list = minimum temperature for listeria growth Tref_list= reference temperature (5ºC) Secondary model/lab/ heattreated meat EGR_lab (b_lab*(t-tmin_lab)*((aw_product-awmin_lab) *(CO2max_lab-CO2_product)*(NaLmax_lab-NaL)) ^0.5) ^2 b=regression parameter; Tmin_lab=mininum temperature for lab growth a w min_lab=minimum water activity for lactic acid bacteria growth CO2max_lab= maximum USDA/FDA (2003) Devlieghere et al. (2000) Reference temperature (5ºC) EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

171 Model/microorganism/Matrix Model output Mathematical function Variables and parameters Source Comment concentration for lactic acid bacteria growth; NaLmax_lab = lactate maximum concentration for lactic acid bacteria growth a w _product= water activity in product CO2_product=CO2 concentration dissolved in product water phase NaL =lactate concentration in product Secondary model/lab/ fish EGR_lab b_lab*((t-tmin_lab)/(tref_lab-tmin_lab)) ^2*((aw_product-awmin_lab)/(1-awmin_lab)) *(1-10^(pHmin_lab-pH_product))*(1- (NaL/NaLmax_lab))*(1- (phenol_produtc/phenolmax_lab))*(1- (CO2_product/CO2max_lab)) phmin_lab=minimum ph for lactic acid bacteria growth phenolmax_lab= maximum phenol concentration for lactic acid bacteria growth ph_product=product ph; phenol_product= phenol concentration in product Mejlholm Dalgaard (2007) and Reference temperature 25 ºC Secondary model /LAB/ soft and semi-soft cheese EGR_lab b_lab*(((t-tmin_lab)/(25-tmin_lab)) ^2)*(1-10^(pHmin_lab-pH_product))*(1-10^(pH_product- phmax_lab))*((aw_product-awmin_lab)/(1- awmin_lab))*(1-(nal/nalmax_lab))*(1- (NaS/NaSmax_lab)) ^2 NaSmax_lab=maximum concentration of sorbate for lactic acid bacteria growth NaS= sorbate concentration in product Ostergaard al. (2014) et Reference temperature 25 ºC EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is European Food Safety Authority reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.

172 Appendix K Maximum Population Density Modelling Table K.1: Minimum and maximum values for the maximum population density (MPD) of L. monocytogenes and the mathematical correlation between initial concentration and MPD in cooked meat and smoked salmon products observed in different scientific studies Source Food subcategory Min Max Ρ (a) Comment Delignette-Muller et al. (2006) Smoked salmon 7.27 Hwang and Sheen (2009) Smoked salmon Gimenez and Dalgaard (2004) Smoked salmon Besse et al. (2006) Smoked salmon Hwang and Sheen (2011) Cooked meat Low inoculum Hwang and Sheen (2011) Cooked meat High inoculum (a): Pearson correlation coefficient EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

173 A B Trend lines are represented in those cases where a significant fitting was obtained. Figure K.1: Graphical representation of initial (Yinitial) vs. final population (Yfinal) (a) and storage temperature vs. final population (b) of L. monocytogenes taken from different scientific studies for sausage EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

174 A B Trend lines are represented in those cases where a significant fitting was obtained. Figure K.2: Graphical representation of initial (Yinitial) vs. final population (Yfinal) (a) and storage temperature vs. final population (b) of L. monocytogenes taken from different scientific studies for cooked meat EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

175 A B B Trend lines are represented in those cases where a significant fitting was obtained. Figure K.3: Graphical representation of initial (Yinitial) vs. final population (Yfinal) (a) and storage temperature vs. final population (b) of L. monocytogenes taken from different scientific studies for pâté EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

176 A A B B Trend lines are represented in those cases where a significant fitting was obtained. Figure K.4: Graphical representation of initial (Yinitial) vs. final population (Yfinal) (a) and storage temperature vs. final population (b) of L. monocytogenes taken from different scientific studies for smoked and gravad fish EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

177 A B Trend lines are represented in those cases where a significant fitting was obtained. Figure K.5: Graphical representation of initial (Yinitial) vs. final population (Yfinal) (a) and storage temperature vs. final population (b) of L. monocytogenes taken from different scientific studies for soft and semi-soft cheese EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

178 Table K.2: Correlation and regression analysis of the relationship between storage temperature and Maximum Population Density of L. monocytogenes in different RTE food categories Food Subcategory Pearson correlation Spearman s rank correlation Slope (a) Intercept (a) Coefficient of determination (a) Cooked meat (0.01) 5.69 (0.12) 0.29 (1.17) Sausage NA NA NA Pâté (0.02) 7.31 (0.17) 0.25 (1.15) Smoked /gravad 0.45/ / fish Soft semi-soft (1.62) 6.01 (0.23) 117 (1.62) Cheese NA: Not available data. (a): Numbers between brackets correspond to the standard error of parameter and regression EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

179 Appendix L RTE categories Initial population Lactic Acid Bacteria in different Table L.1: Main statistics of initial levels (log CFU/g) of Lactic Acid Bacteria (LAB) at retail in heat-treated meat products Source Min Max Mean Standard deviation IRTA Food Safety Program < Mataragas et al. (2007) 1.6 Mataragas and Drosinos (2007) IRTA: Institut de Recerca i Tecnologia Agroalimentàries. Table K.2. Main statistics of initial levels at retail of Lactic Acid Bacteria (LAB) in smoked and marinated fish products Source Min Max Mean Standard deviation Mejlholm and Dalgaard (2007) Mejlholm and Dalgaard (2007) IRTA Food Safety Program IRTA Food Safety Program 1.5 IRTA: Institut de Recerca i Tecnologia Agroalimentàries. Table K.3. Initial levels (log CFU/g) of Lactic Acid Bacteria (LAB) at retail in cheese products Reference Type of cheese No (log CFU/g) Østergaard et al. (2014) Cream 5.6 Østergaard et al. (2014) Cream 6.7 Østergaard et al. (2015) Cream 6.4 Leong et al. (2014) Fresh 4.63 Leong et al. (2014) Hard 5.7 Leong et al. (2014) Hard 5.04 Leong et al. (2014) Semi-soft 7.74 Leong et al. (2014) Semi-soft 4.85 Leong et al. (2014) Semi-soft 2.7 Leong et al. (2014) Semi-soft 3.78 Leong et al. (2014) Semi-soft 6.25 Leong et al. (2014) Semi-soft 5.73 Leong et al. (2014) Fresh 4.38 Leong et al. (2014) Fresh 4.86 Leong et al. (2014) Semi-soft 4.87 Leong et al. (2014) Semi-soft 4.85 Leong et al. (2014) Semi-soft 4.57 Whitley et al. (2000) Semi-soft 7.73 Whitley et al. (2000) Semi-soft 7.68 Whitley et al. (2000) Semi-soft 7.82 Whitley et al. (2000) Semi-soft 7.83 Kagkli et al. (2009) Fresh 6.35 Personal communication Fresh Personal communication Fresh Personal communication Fresh Personal communication Soft 2.08 Personal communication Soft EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

180 Reference Type of cheese No (log CFU/g) Personal communication Soft 4.33 Personal communication Fresh 3.9 Personal communication Soft 3.31 Personal communication Semi-soft 1 Personal communication Semi-soft 4.87 Personal communication Semi-soft 4.51 Mean 4.83 Median 4.86 Standard deviation 2.05 Maximum 7.83 Minimum EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

181 Appendix M Models: Risk models and simulation settings Three stochastic risk models have been developed in Excel (Microsoft, Redmond) using functions (Palisade, NY) and a customized Visual Basic for Applications (VBA) macro. Each model corresponds with one of the RTE food categories: (i) RTE smoked or gravad fish, (ii) heat-treated meat products and (iii) soft or semi-soft cheese. The risk model output allows the estimation of the number of listeriosis cases per year in the EU population as well as predicted risk for listeriosis per million servings from consumption of a meal containing each of the RTE food subcategories. The model parameters can be introduced or modified in specific cells in the risk model Excel spreadsheets according to the type of intput. For software is required. A brief description of the main risk model spreadsheets and the simulation setup are provided below. Parameters sheet The first one ( parameters sheet) contains all the variables used in the risk model. A description of each variable can be found in column D. The type of variable (0: internal; 1: external), type of model (product, exposure, Listeria growth, Lab growth, etc.), type of food subcategory (cooked meat, sausage and pâté) and scenario were recorded the following four columns (see columns E, F and G, respectively). A filter-data system can be used in the spreadsheet to select different models, scenarios and food subcategories. The column N contains the distributions, equations, values or calculations describing each variable and in column H, I, J and K are the obtained statistical values. Gray-shaded cells in these columns are values that can be modified by Users, while non-shaded cells correspond to internal model calculations that should not be changed. A brief description of the data sources or types of distributions used for describing the model inputs can be found in the column O titled comment. The last column ( Uncertainty ) refers to the uncertainty degree (low, medium or high) assigned to each model input. Data sheet Sheet Data contains the time-temperature profiles used in the risk assessment. There is a macro button which allows to import time-temperature data from other files (e.g. xls, csv, etc.). Risk and input sheets In the sheet risk, output variables are reported (dose, probability of illness, number of cases, etc) in column F. The main input variables used in the model simulation are reported in column F in sheet input. Simulation settings: Features Setting CPU Intel Core i6-4785t PC Memory RAM 8GB (4GB+4GB) Sampling method Latin Hypercube Pseudo-random Number Generator Mersenne Twistter Initial seed 1 Number of iterations 10, ,000 VBA macro: risksim EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

182 Excel Add-in Lis-RA: An Excel Add-in, Lis-RA, for the simulation of the risk assessment model for L. monocytogenes in selected RTE food categories was developed based on a Ribbon interface and VBA. The Lis- RA enables the users to introduce and modify risk model parameters in an more user-friendly system. The same risk model Excel files as those mentioned above can be used with Lis-RA and should be first loaded in Add-in system. Then, scenarios and model parameters can be selected, defined and simulated to finally report the risk model output. The user needs to software (Palisade Corporation) installed on the computer to simulate models. The application is optimized for Excel and 7.5. The application developed for the risk model proves an easy and intuitive way for introducing, selecting data and options for listeriosis risk assessment modeling. User manual and risk model files: A user manual is available to explain the structure and use of the risk model Excel spreadsheet and the main features of the Add-in Lis-RA for the simulation of the risk assessment model for L. monocytogenes in selected RTE food categories. The risk model Excel files for the above-mentioned RTE food categories were named as Listeria_RiskModel_meat_v1.xlsm, Listeria_RiskModel_fish_v1.xlsm and Listeria_RiskModel_cheese_v1.xlsm, respectively. The model(s) and user manual have been made available through the Knowledge Junction. The DoI of the models is /zenodo and it can be retrieved and downloaded using this link: EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

183 Appendix N Assumptions and modelling approach for the Baseline model 1. Prevalence of L. monocytogenes Food-based subcategories for initial prevalence: Cold, cooked meat Sausage Pâté Cold smoked fish Hot smoked fish Gravad fish Soft and semi-soft cheese Scenarios for initial prevalence: Packaging type for heat-treated meat and smoked and gravad fish: Air (normal) and ROP (Reduced Oxygen Packaging) Slicing and nonslicing for heat-treated meat Assumptions for initial prevalence: It is assumed that the microbiological analyses performed in the collected studies are without error. In other words, Listeria is supposed to be detected during the analyses if it is present in the analyzed portion. The sensitivity (probability of detecting Listeria knowing that it is present in the analyzed portion) is thus considered equal to 1. In the same way, specificity (probability that the test is negative knowing that Listeria is not present in the analyzed portion) is considered as equal to 1. In the results section, Listeria prevalence was described through Beta distributions. The Beta distribution was assumed to be a conjugate prior to the Binomial likelihood function in Bayesian inference and, as such, it was used to describe the uncertainty about a binomial probability, given a number of trials n have been made with a number of recorded successes s. In this situation, α is set to the value (s + x) and β is set to (n - s + y), where Beta(x, y) is the prior. Out of samples <10 g only those with positive results for L. monocytogenes were considered in the analysis. The reasoning to exclude negative < 10 g-samples was that the size is not sufficiently representative to reliably determine prevalence in the lot. Information regarding specific L. monocytogenes serovars and differences in processing conditions and product formulations among European regions were not considered. Potential scenarios regarding type of atmosphere and slicing were only considered using the BLS dataset. Modelling approach for initial prevalence: Total number of positives was calculated by summing up the number of positive samples by detection and enumeration methods for each individual RTE food subcategory. However, if a given sample was positive by detection, enumeration results corresponding to the same sample were discarded. Distributions corresponding to BLS data + monitoring data and prevalence data from individual published studies coming from ACT EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

184 Further information about selection criteria for prevalence can be found in the section Modelling prevalence of L. monocytogenes in selected RTE food categories. 2. Initial concentration of L. monocytogenes at retail Food-based subcategories for initial concentration: Cooked meat and sausage Pâté Cold smoked fish Hot smoked fish Gravad fish Soft and semi-soft cheese Scenarios for initial concentration: No scenarios Variables and factors for initial concentration: Sample size (g) Limit of detection and limit of quantification (CFU/g) Type of food Number of samples (n) Year of the survey Country Assumptions for initial concentration: Initial concentration simulated in the model represents for mean concentration variation between lots. To obtain an accurate representation of between-lot variation, count/prevalent data should be generated based on a batch-based sampling, thus differentiating between batches. This information is not usually reported or is overlooked in the studies. In monitoring data, although the type of sampling strategy is recorded, there is not a direct link between analysis outcome and batch. In spite of this limitation, individual concentration data obtained from the studies were assumed to represent different batches since studies usually deal with different sampling times, different types of product, brands, premises, locations and countries which makes probable that individual data come from different batches. Initial concentration was based on monitoring data in Europe from and scientific publications retrieved from ACT1. Moreover, data from the BLS corresponding to fish at retail were used. These data were assumed to be representative for L. monocytogenes levels in RTE products consumed in Europe. Due to the scarce information on the type of matrix analyzed and differences in the terminology used between data sources, certain food subcategories presented biased results and could not be considered separately in order to derive probability distributions of initial concentration, such as cooked meat and sausage. Indeed, Cooked meat and Sausage were assumed to have similar contamination pattern as technological processes, ingredients and composition can be also considered similar. This contamination pattern would be significantly different to that obtained in pâté as technological processes and composition are different with respect to the other RTE products. For soft and semi-soft cheese, description did not allow for further classification. Therefore, it was assumed a single distribution describing initial counts in Semi-soft and soft cheese products EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

185 Data from positive and negative samples (censored data) were considered for building probability distributions: 0 CFU/25g < 0.04 CFU/g. Data from ACT1 where prevalence and concentration of positive samples are not correctly reported and/or are given in different units (i.e. MPN/g; CFU/cm 2 etc.) were not included in the analysis. Modelling strategies for initial concentration: Concentration distribution in the present model describes variation of mean concentrations between lots. For that, a log-normal distribution is used together with a Poisson distribution in a mixture distribution Log-normal x Poisson: First, the lot is simulated with the lognormal distribution returning the mean concentration for the simulated lot. Then, this value is used to estimate the actual concentration in the serving using the Poisson distribution, assuming that cell distributions is homogenous/random in lot. The log-normal distribution (log cell/g) was assessed to be the most suitable distribution to represent mean concentration distribution, and several works support its use. The lognormal distributions as derived in the present work can also explain for nonprevalent samples, even though, given prevalence was separately modelled, lognormal was truncated to values only allowing for positive samples. For that, the largest serving size reported in the EFSA consumption database for each food subcategory was used to determine the minimal theoretical concentration yielding positive servings. For example, if the maximum serving size were 200 g, the minimal theoretical concentration being able to yield positive samples would correspond to 1 cell in 200g, that is, cells per gram. This value was used as limit in the distribution to exclude those values of initial concentration in the simulation with lower concentration values. 3. Serving size Food-based subcategorization for serving size: Cooked meat Sausage Pâté Smoked and Gravad fish Soft and semi-soft cheese Scenarios for serving size: No scenarios Variables and factors for serving size: Grams of consumed product reported by consumers (g) Risk-based subpopulation Days of the survey (day) Eating events Assumptions for serving size: Serving size was assumed to be similar between the types of subpopulations (i.e. adult, pregnant, elderly) and countries considered in the risk assessment therefore, serving sizes from EFSA consumption database were pooled and probability distribution were fitted to them. For heat-treated meat products, a serving size distribution was built for each targeted food subcategory: cooked meat, sausage and pâté EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

186 EFSA consumption database included information for smoked fish but not for gravad fish. The same serving size distribution as that used for smoked fish was assumed for gravad fish No detail was provided in the consumption database about type of cheese within the soft sand semi-soft cheese category, hence serving size distribution only referred the whole cheese category. In the model it was assumed that serving size was independent on the type of cheese. 4. Number of servings Food-based subcategorization for number of servings: Cooked meat Sausage Pâté Smoked and Gravad fish Soft and semi-soft cheese Scenarios for number of servings: No scenarios Variables and factors for number of servings: Number of servings Risk-based subpopulation (individuals) Assumptions for number of servings: The same subcategories considered for serving size, as described in the corresponding section, were considered for number of servings. The total number of servings in EU was assumed to be the sum of the extrapolated number for each country and subpopulation Modelling strategies for number of servings: Number of servings is estimated by extrapolating survey outcomes, for age group and country, to the total population of the country for each risk-based subpopulation considered in this work (adult, pregnant, elderly). The type of extrapolation method was linear and calculation was carried out by applying the value of numbers of serving consumed per individual obtained from the surveys for a specific subpopulation to the total subpopulation in the surveyed country. When no data were available for either subpopulation, estimates were derived from the whole country population. In the case of fish and cheese products, few surveys were available, with several countries without any available information. To derive the total number of servings in cheese, extrapolation from the few countries with available data to the rest of EU population was made. Therefore, a larger uncertainty is expected in the number of servings estimated for this food category. For fish products, number of servings was estimated as apparent consumption as import-export and production data were available for this food category EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

187 5. Time to consumption Food-based subcategorization for time to consumption: Cooked meat Sausage Pâté Smoked and Gravad fish Soft and semi-soft cheese Scenarios for time to consumption: Packaging type: Air (normal) and ROP (Reduced Oxygen Packaging) Variables for time to consumption: Purchase date Use-By-Date (UBD) Minimum time to consumption Assumptions for time to consumption: Time-to consumption refers to the time elapsed from the purchase of products until they are consumed. Baseline database provides information on date (month) of purchase and Use-By-Date (UBD); the former is assumed to be the date of purchase by consumers. The time from purchase to UBD is named as remaining shelf-life, which was calculated by subtraction. The remaining shelf-life was calculated considering type of packaging since this factor is expected to influence shelf-life. Given the scarcity of data, vacuum and modified atmosphere data were pooled in only one data set named reduced oxygen packaging. Nevertheless, it is assumed that largest differences in shelf-life would be between normal (air) and reduced oxygen atmospheres since oxygen can be the most determinant factor affecting sensory and microbial deterioration in these products. In fish and cheese, it was not possible to distinguish between remaining shelf-life of some subcategories such as smoked and gravad fish. Modelling strategies for time to consumption: Time-to-consumption of products was modelled by the exponential distribution, where most eating events occur early after purchase. To define the distribution, information from the remaining shelf-life, i.e. the 99% percentile and a minimum value guess (uniform (0.01; 0.04 months), were used. 6. Time-temperature profiles Food-based subcategorization for time-temperature profiles: Heat-treated meat Smoked and Gravad fish Soft and semi-soft cheese Scenarios for time-temperature profiles: EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

188 No scenarios Variables and factors for time-temperature profiles: Time (h) Temperature (ºC) Metadata (food chain, country, measurement location, type of product, etc.) Assumptions for time-temperature profile: Those temperature-time profiles of different RTE products obtained from the FRISBEE project were applied to the present assessment from retail to consumption. Due to the lack of time-temperature profiles for fish in the Frisbee database, those profiles for heat-treated meat products were applied to fish products, considering that both types of product can follow similar cold distribution chains and are located in similar exhibitors at retail and domestic fridge. Currently, no distinction is made between different stages from retail to home, so profiles are applied as a whole during simulation. Modelling strategies for time-temperature profile: During simulation of Listeria monocytogenes growth from retail to food consumption, a profile is picked out randomly for every iteration. To increase the number of simulated different profiles, a noise component is added to temperatures in the profile, consisting of adding or subtracting a value between -1-1 ºC to the simulated temperature. In that way, a new profile is built though similar to the original one. In time-temperature profiles, time is truncated based on time-to-consumption outputs, so that a profile can never exceed the simulated time-to-consumption. Time and mean temperature from the profile are coupled to prevent the simulation from unrealistic combinations of high temperatures and long storage time. These combinations are expected to produce food spoilage which means that product are not is rejected (i.e. not consumed). 7. Simulating growth of L. monocytogenes and Lactic acid bacteria Food-based subcategorization for L monocytogenes and Lactic acid bacteria growth: Cooked meat and Sausage Pâté Smoked and Gravad fish Soft and semi-soft cheese Scenarios for L monocytogenes and Lactic acid bacteria growth: Packaging type: Air (normal) and ROP (Reduced Oxygen Packaging) Variables for L monocytogenes and Lactic acid bacteria growth: Exponential Growth Rate (EGR) (h -1 ) Maximum Population Density (MPD) (log CFU/serving) Lag time (h) h 0 Environmental variables (ph, a w, CO 2, temperature) EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

189 Lactate content (ppm) Initial and final concentration (log CFU/serving) Assumptions for L. monocytogenes growth: Effect of microbiota in food on L. monocytogenes was modelled based on lactic acid bacteria growth since this is a majority microbial group in the targeted RTE products. In addition, acid lactic bacteria are generally considered to be antagonist microorganisms of L. monocytogenes. No other factor is assumed to affect L. monocytogenes MPD Environmental variables in the determinist models for acid lactic bacteria were defined based on expert opinion as well as information available in existing databases on food formulation such as MINTEL. The types of product on which formulation data were taken were assumed to be similar to those included in the BLS so that the model variables derived from BLS study, such as prevalence and concentration were in concordance with the simulated formulation. The effect of changing temperature from retail to home was assessed in L. monocytogenes and lactic acid bacteria by applying specific primary and secondary predictive models. Alternatively, static temperature conditions were assessed as a simpler approach by applying an integrated form of the Baranyi model. The percentage of products with lactate considered in the baseline risk model is an approximation due to the lack of suitable data and corresponded to 8% according to the number of products including lactate in label, reported by MINTEL database. Nonetheless, other scenarios will be simulated. Lag phase was considered for L. monocytogenes and lactic acid bacteria; however, it might be excluded if needed so as to obtain more conservative predictions. Modelling strategies for L. monocytogenes growth: L. monocytogenes growth was modelled assuming a growth random pattern specific to each food subcategory. This random pattern is expected because of the large variation in formulation, food composition, preservatives, strains, microbiota, etc. found within each food category. Contractor strongly believes that using a pure deterministic model to account for listeria growth will not accurately depict the variable behavior shown by L. monocytogenes in the wide range of products considered in the present risk models. Therefore, a stochastic model was developed but including a deterministic effect for temperature and lactate. The rest of factors and variables were considered as systematic variability (uncontrolled variables) which was included as variability in probability distributions. These distributions represent for the variation of EGR at a reference temperature (5ºC) for each considered food subcategory. Values simulated from these distributions were used to estimate growth rate at specific temperatures based on a modified version of Ratkowsky s square-root model as explained in previous documents. The effect of microbiota (lactic acid bacteria) is included in the EGR distributions given that data are mostly based on experiments in naturally contaminated food products. However, lactic acid bacteria growth was also EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

190 modelled to determine the effect on maximum population (MPD) density of L. monocytogenes. According to previous studies, Listeriosis is greatly determined by high doses hence the importance of including this kinetic parameter. To account for the effect of microbiota growth on L. monocytogenes MPD, a modified version of Baranyi and Roberts (1994) was used considering as the hypothesis of microbial interaction the Jameson effect. This model was input by EGR values obtained by the stochastic model for L. monocytogenes as well as the kinetic parameters from deterministic secondary models for acid lactic bacteria. A validated deterministic growth model taken from literature was used for acid lactic bacteria in each targeted food categories. When possible, variables were defined as probability distributions in order to consider variation in food formulation. Alternatively, L. monocytogenes MPD was modelled by a probability distribution describing the effect of natural microbiota and food matrix on the population maximum level reached by the pathogen. The distribution was described based on data collected from scientific literature and databases. Distribution parameters were correlated with temperature to produce reliable estimates of the kinetic parameter (i.e., the higher the temperature, the larger the MPD is). Lag time was also modelled as probability distributions based on h0 reported in literature for the target food categories. The L. monocytogenes growth can be accounted by three modelling approaches which were included as different modelling options in the risk model: Model 0: Effect of static temperature conditions on L. monocytogenes Model 1: Dynamic temperature conditions on L. monocytogenes Model 2: Dynamic temperature conditions and lactic acid bacteria on L. monocytogenes EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

191 Appendix O Statistical analysis for growth data In order to collect growth data for the target food categories, Growth database 13 was accessed. The search was carried out manually, filtering by type of food matrix and microorganism. Then, cordings were saved into a CSV file for each food category including all descriptors reported by Growth. Tables O.1-O.3 include descriptors used by Growth in the three RTE food categories. Table O.1: Factors, co-variables and outputs considered in the statistical analysis of EGR data of heat-treated meat products taken from Growth database Factors Covariables Output Addition of antimicrobial NaCl (%) (0.8, 3.6) logcvar (-10.37, 11.78) preservatives cut CO 2 (%) (20, 100) EGRGrowth (-82.14, 0.687) Co-culture N 2 (%) (56, 70) EGR 5ºC (-2.131, 3.561) Atmosphere Nisin (IU/g) (322.5) log(tegr+z) (-3, 0.436) Type of meat product Acid lactic (ppm) (2500, 2.1x10 4 ) Temperature (-1.5, 90) ph (4.7, 6.72) a w (0.449, 0.996) Table O.2: Factors, co-variables and outputs considered in the statistical analysis of EGR data of smoked and gravad fish taken from Growth database Factors Covariables Output Addition of antimicrobial preservatives NaCl (%) (1.5, 7.3) logcvar (-6.78, 7.7) Cut CO 2 (%) (70, 100) EGRGrowth (-0.014, 0.096) Co-culture O 2 (%) EGR 5ºC (-0.014, 0.069) Atmosphere N 2 (%) (30) log(tegr+z) (-3, ) Smoked/non smoked nisin (IU/g) (180, 1250) Temp (3, 25) ph (6, 6.6) a w ( ) Table O.3: Factors, co-variables and outputs considered in the statistical analysis of EGR data of soft and semi-soft cheeses taken from Growth database Factors Covariables Output Addition antimicrobial preservative NaCl (%) (2.4, 3.8) logcvar (-4.69, 5.8) Cut CO 2 (%) (10, 35) EGRGrowth (-0.914, 0.227) Co-culture N 2 (%) (52, 80) EGR 5ºC (-1.256, 0.024) Atmosphere Nisin (IU/ml) log(tegr+z) (-3.182, 0.196) Type of cheese Temperature (3, 30) ph (4.5, 7.4) a w (0.978, 0.987) EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

192 1. Heat-treated meat products Heat-treated meat growth data collected from Growth database, and then treated for standardization (i.e. transformed into EGR 5C ), were used for the statistical analysis. Using descriptors in Growth database (Table O.1), EGR 5C data were characterized by three two-level factors as shown in Table O.4. The descriptive analysis revealed a high number of missing data when co-variables were considered such as NaCl (34%), lactic acid (87%), acetic acid (93%) and nitrite (41%). The statistical inference derived from this unbalance design could result in biased estimates of the associations between factors and outcomes. The ratio of the highest number of observations to minimum number of observations can be used as measure of departure from a balanced design. A ratio of 4:1 is tolerable, but a ratio of 16:1 should not be (Gosslee and Lucas, 1967). The sample number, in our study, is in the tolerable range. Table O.4: Sample size of the different between-subjects factors submitted to GLM analysis Between-Subjects Factors N Animal species Meat others 149 Pork 466 Poultry 28 Sausage 50 Type of product Cooked meat product 474 Pâté 150 Sausage 69 Type of atmosphere Modified atmosphere 27 Other 498 Vacuum 168 The statistical analysis chosen was a General Linear Model (GLM) analysis. This allowed determine possible associations between factor levels and listeria growth, considering the effect of covariates (ph, a w, NaCl). The different sample sizes together with unequal variance found in on EGR 5C values could affect results increasing the chance of incorrectly rejecting the null hypothesis, thus identifying significant difference when none exists. To overcome this limitation, outliers were identified by using the z-scoring method and removed in order to reduce variability. In addition, the analysis was performed on both on EGR 5C values and their logarithmic transformation by applying log (EGR 5C +z), provided min (EGR 5C +z) To find z value, goal seek function from Solver Microsoft Excel add-in was used on the whole EGR 5C value set. Both outlier removals and logarithmic transformation reduced sample variability, thereby obtaining similar variances (i.e. homoscedasticity) within the factor Type of atmosphere (Figure O.1). Concerning the normality assumption, it is well-known that the F test will not be seriously affected unless the sample sizes are small or the departure from normality is extreme (kurtosis less than -1 or greater than 2) EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

193 The circles and vertical lines represent mean and variability (2 Standard deviation), respectively. Figure O.1: Mean graphs for factor levels Type of atmosphere and Type of product when outliers were removed The initial statistical study evidenced that type of animal did not exert a significant influence on EGR 5C. In addition, the inclusion of this factors with 4 levels would reduce the number of samples for factor combination (i.e. interactions) thus negatively affecting the power of the statistical analysis. Therefore, this factor was excluded for further analysis. The GLM analysis was performed on Type of atmosphere and Type of product and covariables aw, ph and NaCl based on the use of model type III and unweighted means in order to minimize the effect of the unbalanced design. The inclusion of covariables allowed reduce variance in EGR 5C both EGR 5C and log (EGR 5C +z) prior to test for group effect. The test of Levene was not significant (P>0.05) for both EGR 5C and log (EGR 5C +z), indicating that homoscedasticity condition was met. Results for both variables were similar, hence only EGR 5C will be showed. The statistical outcome is presented in Table O.5. In this table, it can be observed that the effect of Type of atmosphere was not significant (P>0.05) while Type of product showed significant differences EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

194 Table O.5: Tests of Between-Subjects Effects obtained from a GLM analysis on Type of atmosphere and Type of product and covariables a w, ph and NaCl Source Type III Sum of Squares Df Mean Square F Sig. Corrected Model Intercept Aw ph 1.32x E NaCl Type of product Type of atmosphere 9.85x E type_product type_atmosphere 1.06x E Error Total Corrected Total Then, a similar analysis was performed without considering covariables in order to determine the effect of these on the final statistical outcome. Results are presented in Table O.6. These showed no significant differences for the two studied factors, which demonstrates that these covariables could account for part of the variability in output (EGR 5C ). Table O.6: Tests of Between-Subjects Effects obtained from a GML analysis on Type of atmosphere and Type of product Source Type III Sum of Squares df Mean Square F Sig. Corrected Model Intercept Type of product Type of atmosphere Type_product Type_atmosphere Error Total Corrected Total When factors were analyzed separately, results were similar, with only Type of product showing significant differences (P<0.05). To determine homogenous groups, Tukey HSD and Scheffe tests were applied specifically on Type of product. In Table O.7, it can be observed that both tests were in agreement with the existence of two homogenous group. These groups corresponded to, on the one hand, sausage and heat-treated meat and, on the other hand, pâté. Table O.7: Homogenous group (P<0.05) reported by Tukey HSD and Scheffe test for Type of product Test Type of product N Subset 1 2 Sausage Tukey HSD Heat-treated meat Pâté Sausage Scheffe Heat-treated meat Pâté EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

195 2. Smoked and gravad fish Smoked and gravad fish growth data collected from Growth database, and then treated for standardization (i.e. transformed into EGR 5C ), were used for the statistical analysis. Using descriptors in the Growth database, EGR 5C data were only characterized by three two-level factors as shown in Table O.8. Table O.8: Sample size of the different between-subjects factors in smoked and gravad fish growth data submitted to GLM analysis Between-Subjects Factors Preservative Type of atmosphere Smoked/gravad N As evidenced by Table O.9 and Table O.10, some factor levels and factor levels combinations had a small sample size giving a rise an unbalanced design. Note that small sample size could result in rejecting null hypothesis which means obtaining a false positive (i.e. Type I error). Nonetheless, EGR 5C and log (EGR 5C +z) were submitted to a GLM analysis applying model type III and unweighted means. Results are shown in Table O.10, in which it can be observed that for the main effects, no significant differences were detected. In the case of factor interactions, sample size was not suitable to obtain reliable estimates of statistics. As conclusion, smoked and gravad fish growth data as reported did not allow a reliable statistical analysis as the limited number of data for some factor level combinations. Table O.9: Sample size and main statistics of the different level factor combinations in smoked and gravad fish growth data submitted to GLM analysis Preservative Atmosphere Smoked/gravad Mean Standard deviation N Total Total Total Total Total Total Total Total Total Total Total Total Total EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

196 Table O.10: Tests of Between-Subjects Effects obtained from a GML analysis on Preservative, Type of atmosphere and Smoked Source Type III Sum of df Mean F Sig. Squares Square Corrected Model E Intercept Preservative Type of atmosphere Smoked Preservative type of atmosphere Preservative smoked/gravad Type of atmosphere smoked/gravad Preservative type of atmosphere Smoked Error Total Corrected Total Soft and semi-soft cheese Treated and standardized soft and semi-soft cheese data were subject to statistical analysis. Due to the limited number of growth records and the lack of detail in several of them, only the factor milk heat treatment (raw and pasteurized) could be analysed (Table O.11). The results for the normality test and variance homogeneity test confirmed that results met the main condition for ANOVA. The statistical analysis results are shown in Table O.12, in which it can be observed that the type of treatment of the sample did not exert a significant effect on listeria growth provided the confidence level is set at As the value of the significance level was 0.06, slightly lower confidence levels could result in a different conclusion. Hence the significance of this factor could not reliably elucidated based on the existing growth data. Table O.11: Sample size of the different between-subjects factors in soft and semi-soft cheese growth data submitted to GLM analysis Milk heat treatment Mean Std. Deviation N Pasteurized Raw Total EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

197 The circles and vertical lines represent mean and variability (2 Standard deviation), respectively. Figure O.2: Mean graphs for factor levels milk heat treatment in soft and semi-soft cheese products Table O.12: Tests of Between-Subjects Effects obtained from a GML analysis on milk heat treatment in Cheese Source Type III Sum of Squares df Mean Square F Sig. Corrected Model Intercept Milk Error Total Corrected Total EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

198 sd CDF Quantitative risk characterization on Listeria monocytogenes in ready-to-eat foods Appendix P Concentration distributions of L. monocytogenes in different scenarios A) Cumulative distribution B) Censored data Bootstrapped values of the two parameters mean Figure P.1: Fitting of the log normal distributions to describe the initial concentration of L. monocytogenes of cold smoked fish at retail (A) and correlation between mean (µ) and standard deviation (SD) values resulting from the bootstrap of simulated data (B) EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

199 sd CDF Quantitative risk characterization on Listeria monocytogenes in ready-to-eat foods A) Cumulative distribution Censored data B) Bootstrapped values of the two parameters mean Figure P.2: Fitting of the log normal distributions to describe the initial concentration of L. monocytogenes of hot smoked fish at retail (A) and correlation between mean (µ) and standard deviation (SD) values resulting from the bootstrap of simulated data (B) EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

200 sd CDF Quantitative risk characterization on Listeria monocytogenes in ready-to-eat foods A) Cumulative distribution Censored data B) Bootstrapped values of the two parameters mean Figure P.3: Fitting of the log normal distributions to describe the initial concentration of L. monocytogenes of gravad fish at retail (A) and correlation between mean (µ) and standard deviation (SD) values resulting from the bootstrap of simulated data (B) EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

201 sd CDF Quantitative risk characterization on Listeria monocytogenes in ready-to-eat foods A) Cumulative distribution Censored data B) Bootstrapped values of the two parameters Figure P.4: Fitting of the log normal distributions to describe the initial concentration of L. monocytogenes of cooked meat at retail (A) and correlation between mean (µ) and standard deviation (SD) values resulting from the bootstrap of simulated data (B) mean EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

202 sd CDF Quantitative risk characterization on Listeria monocytogenes in ready-to-eat foods A) Cumulative distribution Censored data B) Bootstrapped values of the two parameters mean Figure P.5: Fitting of the log normal distributions to describe the initial concentration of L. monocytogenes of pâté at retail (A) and correlation between mean (µ) and standard deviation (SD) values resulting from the bootstrap of simulated data (B) EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

203 0.0e e e e e+08 sd CDF Quantitative risk characterization on Listeria monocytogenes in ready-to-eat foods A) Cumulative distribution Censored data B) Bootstrapped values of the two parameters -5e+08-4e+08-3e+08-2e+08-1e+08 0e+00 Figure P.6: Fitting of the log normal distributions to describe the initial concentration of L. monocytogenes of sausage at retail (A) and correlation between mean (µ) and standard deviation (SD) values resulting from the bootstrap of simulated data (B) mean EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

204 sd CDF Quantitative risk characterization on Listeria monocytogenes in ready-to-eat foods A) Cumulative distribution Censored data B) Bootstrapped values of the two parameters mean Figure P.7: Fitting of the log normal distributions to describe the initial concentration of L. monocytogenes of soft and semi-soft cheese at retail (A) and correlation between mean (µ) and standard deviation (SD) values resulting from the bootstrap of simulated data (B) EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

205 Appendix Q Stochastic model for L. monocytogenes growth A µ= σ= shift= min=0 max = B µ=0.026 σ= shift= min=0 max =0.087 Figure Q.1: Frequency distribution of EGR 5C (1/h) and fitted distribution (Lognormal) for cooked meat and sausage packaged as Reduced Oxygen Packaging (ROP, A) and normal atmosphere (B) EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

206 µ=0.026 σ= shift=0.006 min=0 max=0.097 Figure Q.2: Frequency distribution of EGR 5C (1/h) and fitted distribution (Lognormal) for pâté packaged as normal atmosphere A µ=0.017 σ=0.014 shift= min=0 max = EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.

207 B µ=0.012 σ= shift= min=0 max =0.062 C µ=0.015 σ= shift= min=0 max =0.069 Figure Q.3: Frequency distribution of EGR 5C (1/h) and fitted distribution (Lognormal) for smoked and gravad fish packaged as Reduced Oxygen Packaging (ROP, A), normal atmosphere (B) and ROP + normal atmosphere (C) EFSA Supporting publication 2017:EN-1252 The present document has been produced and adopted by the bodies identified above as authors. This task has been carried out exclusively by the authors in the context of a contract between the European Food Safety Authority and the authors, awarded following a tender procedure. The present document is without prejudice to the rights of the authors.