EXTERNAL SCIENTIFIC REPORT

Transcription

1 EFSA supporting publication 2014:EN-571 EXTERNAL SCIENTIFIC REPORT Development of a risk assessment methodological framework for potentially pandemic influenza strains (FLURISK). (CFP/EFSA/AHAW/2011/01). 1 M. De Nardi 1, A. Hill 2, S. von Dobschuetz 3, 4, O. Munoz 1, R. Kosmider 2, T. Dewe 2, K. Harris 2, G. Freidl 5, K. Stevens 3, K. van der Meulen 7, K. D.C. Stäerk 3, A. Breed 2, A. Meijer 5, M. Koopmans 5, A. Havelaar 5, S. van der Werf 8, J. Banks 2, B. Wieland 3, K. van Reeth 7, G. Dauphin 4, I. Capua 1 and the FLURISK consortium*. 1 Istituto Zooprofilattico Sperimentale delle Venezie (Project Coordinator), Legnaro, Padova, Italy, 2 Animal Health and Veterinary Agency (AHVLA), Surrey, United Kingdom, 3 Royal Veterinary College (RVC), London, United Kingdom, 4 Food and Agricultural Organization of the United Nations (FAO), Rome, Italy, 5 National Institute for Public Health and the Environment (RIVM), Laboratory for Infectious Diseases Research, Diagnostics and Screening (IDS), Bilthoven, the Netherlands, 6 Department of Viroscience, Erasmus Medical Center, Rotterdam, the Netherlands, 7 Laboratory of Virology, Faculty of Veterinary Medicine, Ghent University, Belgium, 8 Institut Pasteur, Paris, France * Also in the FLURISK Consortium: W. Dundon 1, R. Bassan 1, I. Monne 1, N. Micoli 1, G. Cattoli 1, I. Brown 2, L. Kelly 2, S. Brookes 2, M. Wooldridge 2, D. Pfeiffer 3, V. Enouf 8, JC. Manuguerra 8. ABSTRACT The fact that an influenza virus of swine origin unexpectedly became the most recent human pandemic virus and that a low pathogenicity avian influenza virus H7N9 is currently causing human health concerns in Asia highlights deficiencies in current influenza pandemic preparedness. With the aim to systematically assess the potential public health threat posed by animal influenza viruses, the European Food Safety Authority (EFSA) has commissioned the FLURISK project. The project s main objective is the development and validation of a methodological influenza risk assessment framework (IRAF) capable of assessing the pandemic potential of new influenza viruses or viral subtypes emerging in animals. The project developed a prototype spatial epidemiological model which includes both virological and epidemiological components and data input is either generated through different specific activities of the project (global surveys, literature reviews, expert elicitation, etc.) or derived from existing databases (e.g. FAO s EMPRES-i database; animal species population density by production system from the FAO s Gridded Livestock of the World). The output of the IRAF model is a list of ranked animal viruses according to their potential to infect humans. Opportunity maps are generated to highlight high risk regions. The project has identified a number of scientific gaps concerning both epidemiological and virological factors potentially influencing the jump between 1 Question No EFSA-Q (CFP/EFSA/AHAW/2011/01). Any enquiries related to this output should be addressed to AHAW@efsa.europa.eu Suggested citation: M. De Nardi, A. Hill, S. von Dobschuetz, O. Munoz, R. Kosmider, T. Dewe, K. Harris, G. Freidl, K. Stevens, K. van der Meulen, K. D.C. Stäerk, A. Breed, A. Meijer, M. Koopmans, A. Havelaar, S. van der Werf, J. Banks, B. Wieland, K. van Reeth, G. Dauphin, I. Capua and the FLURISK consortium, Development of a risk assessment methodological framework for potentially pandemic influenza strains (FLURISK). EFSA supporting publication 2014:EN-571, 110 pp. Available online: European Food Safety Authority, 2013

2 animal species and from animals to humans. As a result of the gap analysis, priority research needs have been identified and will be put forward to the scientific community. [Copyright notice of the author(s)], 2013 KEY WORDS Animal influenza virus, zoonotic risk, risk assessment, model, surveillance, gaps, one health DISCLAIMER The present document has been produced and adopted by the bodies identified above as author(s). In accordance with Article 36 of Regulation (EC) No 178/2002, this task has been carried out exclusively by the author(s) in the context of a grant agreement between the European Food Safety Authority and the author(s). The present document is published complying with the transparency principle to which the Authority is subject. It cannot 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. EFSA supporting publication 2014:EN-571 The present document has been produced and adopted by the bodies identified above as author(s). In accordance with Article 36 of Regulation (EC) No 178/2002, this task has been carried out exclusively by the author(s) in the context of a grant agreement between the European Food Safety Authority and the author(s). The present document is published complying with the transparency principle to which the Authority is subject. It cannot 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. 2

3 SUMMARY The overall objective of the EFSA call (CFP/EFSA/AHAW/2011/01) is the development and validation of a methodological influenza risk assessment framework (IRAF) capable of assessing the pandemic potential of new influenza viruses or viral subtypes emerging in animals. To fulfill project objectives, FLURISK brings together internationally recognized research institutes and reference laboratories, international agencies and universities from veterinary and human medicine areas fostering cross-disciplinary expertise and collaborations. The project consortium was formed by six European partners, Istituto Zooprofilattico Sperimentale delle Venezie (IZSVe, Italy, Project Coordinator), Royal Veterinary College (RVC, United Kingdom), Animal Health and Veterinary Laboratory Agency (AHVLA, United Kingdom), National Institute for Public Health and the Environment (RIVM, The Netherlands), Pasteur Institute (IP, France) and Ghent University (UGent, Belgium) which have established networks, complementary knowledge, the scientific infrastructure and the expertise to fulfill the EFSA call objectives. The consortium work benefited from advisors/observers from the Influenza Division of the Centre for Disease Control and Prevention (CDC) and international bodies such as the Food and Agriculture Organization of the United Nations (FAO), World Organization for Animal Health (OIE), the World Health Organization (WHO) and the European Centre for Disease Prevention and Control (ECDC). Medical and veterinary virologists, epidemiologists and modelers were organized in multi-disciplinary working groups and panel of experts active in the following four highly integrated Work Packages (WP) with clearly defined tasks, milestones and deliverables and involving several partners each. 1) Influenza virus epidemiology and surveillance programmes review and assessment (WP leader: RVC) 2) Risk assessment framework development and validation (WP leader: AHVLA) 3) Identification of scientific gaps and research priorities (WP leader: IZSVe) 4) Management and coordination (WP leader: IZSVe) Activities in WP1 were designed to provide WP2 with the background information and data needed to develop the Influenza Risk Assessment Framework. The main objectives were to review animal influenza etiology and epidemiology and to assess current surveillance, monitoring and control strategies in both animal and human populations. With the aim to provide a comprehensive overview on the information available, numerous sources have been used and a huge amount of data was reviewed and consolidated by the FLURISK consortium. Three literature reviews were conducted to identify virological and epidemiological risk factors for an animal influenza virus to jump species and infect humans. In addition, two global surveys (targeting veterinary services and public health sector) were implemented with the support of FAO, OIE and WHO in order to collect information on influenza viruses surveillance and control systems currently implemented worldwide. The FLURISK consortium decided to describe viruses at strain level, i.e. more detailed than subtype level, since different strains of the same subtype may have varying species-specificity and behavior. Therefore, in order to gather data on occurrence and distribution of the animal influenza strains that were selected by the consortium to develop and validate the IRAF, several public epidemiological and genetic databases were consulted and relevant information collected and standardized. The genetic diversity of influenza A viruses, their complex epidemiology and continued fast evolution of strains make consolidation of information on these viruses extremely difficult. While the emergence of new influenza viruses has been an undeniable threat to human health and prosperity for many years, the EFSA supporting publication 2014:EN-571 The present document has been produced and adopted by the bodies identified above as author(s). In accordance with Article 36 of Regulation (EC) No 178/2002, this task has been carried out exclusively by the author(s) in the context of a grant agreement between the European Food Safety Authority and the author(s). The present document is published complying with the transparency principle to which the Authority is subject. It cannot 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. 3

4 virological factors and transmission mechanisms facilitating the virus spread and the species jump are far from being thoroughly understood. The project reviews (Annex 1 a, b, c, g) have highlighted the lack of studies on epidemiological risk factors for cross species transmission of influenza viruses and this is the more evident for swine infuenza viruses. On the contrary there is abundance of papers focusing on the intrinsic viral characteristics that aim at characterizing the zoonotic potential of animal influenza viruses. Several adaptive traits have been recognized in domestic animals and a significance of these adaptations for the emergence of zoonotic influenza has been hypothesised. Nonetheless, despite several decades of research, a comprehensive view of interspecies transmission is still lacking. This has been hampered by the intrinsic difficulties of the subject and the complexity of correlating environmental, viral and host factors and their role in facilitating interspecies transmission. The fact that most of the studies have been focused on avian influenza viruses adapting to humans and on avian species (especially HPAI H5N1) and the use of non-standardized laboratory experimental methodologies across different laboratory settings represent other major challenges. In terms of the epidemiological and virological data and information needed to populate the IRAF model (Annex 1f), there are huge data gaps regarding outbreaks of non-oie notifiable animal influenza viruses, e.g. low pathogenic avian influenza viruses that are not of the H5 or H7 subtype or that are detected in wild birds, swine influenza viruses and canine influenza viruses. This is the more evident for specific subtypes that do not generate research interest (e.g H6N1 and H13N8) for which there is hardly any information available. The surveys on national animal influenza surveillance and control strategies implemented by FLURISK (Annex 1d) confirmed this deficiency and identified the global need for increased surveillance that specifically targets pandemic influenza viruses in different animal species and production systems. Regarding influenza surveillance in humans (Annex 1e), a stronger collaboration with the veterinary sector, associated with a higher capacity to identify a broad range of animal influenza subtypes, would be advisable. WP2 main objectives were the development and validation of the risk assessment framework model and the development of an user-interface to facilitate its practical use. The project proposes a prototype risk assessment framework to spatially rank Influenza A viruses circulating in animal populations for their potential to jump the species barriers to humans. The precise risk question answered is What is the relative likelihood of influenza A virus x infecting one or more humans (compared to viruses y, z etc ) given its current presence and location in an animal population(s)?. There are two critical factors in any zoonotic transfer of pathogens: the ability of the virus to infect humans (a function of its genetic/biochemical characteristics), and the opportunity for it to do so (a function of the amount of human exposure and immunity). Considering this, the IRAF model is unique in that the ranking of viruses is dependent not only on the inherent qualities of the pathogen but also on the epidemiological factors contributing to zoonotic infection. The inclusion of population data, animal production systems, and geographic location of the viruses in question, makes this prototype model the first truly risk-based assessment of the zoonotic potential of influenza A viruses globally. A key component of the spatial epidemiological model is the transmission coefficient (i,j). (i,j) is a summary term incorporating both host species-specific (e.g. number and type of contacts between human and animals in given time period, shedding rate) and virus-specific (e.g., ability to attach to human receptors, human immune response to infection) factors. This term is split into a speciesspecific ( (j)) and virus-specific (V(i)) components where V(i) is a virus characteristic score, representing the intrinsic ability of virus i to infect a human, and (j) is the species-specific component, which weights the transmission component according to the typical characteristics of human exposure given infection in animal species j. EFSA supporting publication 2014:EN-571 The present document has been produced and adopted by the bodies identified above as author(s). In accordance with Article 36 of Regulation (EC) No 178/2002, this task has been carried out exclusively by the author(s) in the context of a grant agreement between the European Food Safety Authority and the author(s). The present document is published complying with the transparency principle to which the Authority is subject. It cannot 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. 4

5 The novel methodology developed here provides a scanning tool to prioritise the risk of currently circulating (avian) influenza viruses to infect humans. This model is unique in that the ranking of viruses is dependent not only on the inherent qualities of the pathogen but also on the epidemiological factors contributing to zoonotic infection. The inclusion of population data, animal production systems and geographic location of the viruses in question, makes this prototype model the first truly riskbased assessment of the zoonotic potential of influenza A viruses globally. Due to the huge gaps in scientific knowledge and data availability, the model currently focuses solely on Avian Influenza viruses and the baseline output of the model is the opportunity map, which indicates the global suitability for a generic avian influenza virus to jump the species barrier from poultry into humans. The map provides useful information on the high-risk areas for zoonotic infection from poultry. In general, data gaps remain the single most important hurdle to effective utilisation of the model. Throughout the project, WP1 and WP2 activities have contributed to identify relevant scientific gaps that were communicated to WP3 where a gap analysis was performed. This contributed to identify the future research needs and awareness activities (Annex 3) which are believed to be of primary importance not only because they are functional to generate the required data and information to fully exploit the IRAF model potentialities but also for promoting specific research activities undoubtedly relevant in the realms of risk analysis and public health. Every effort should be made to develop a standardised and transparent approach to zoonotic disease data collation and analysis, particularly for high-risk diseases such as avian influenza. Also to this purposes, the project developed the triage system (Annex 2), that is a list of criteria (based on subtype and phenotypic/epidemiological characteristics) that aim at identifying and selecting, out of all samples that reach a veterinary laboratory, the isolates believed to have zoonotic potential and that, as a consequence, should be sequenced as a priority; the final objective being to reduce the time between the isolation of an influenza virus and the generation of the genetic information specifically required as data inputs for the IRAF model. The triage system also provides indication on the minimum epidemiological data necessary to parameterise the model. EFSA supporting publication 2014:EN-571 The present document has been produced and adopted by the bodies identified above as author(s). In accordance with Article 36 of Regulation (EC) No 178/2002, this task has been carried out exclusively by the author(s) in the context of a grant agreement between the European Food Safety Authority and the author(s). The present document is published complying with the transparency principle to which the Authority is subject. It cannot 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. 5

6 TABLE OF CONTENTS Abstract 1 Summary 3 Table of contents 6 Background as provided by [EFSA] 8 Terms of reference as provided by [EFSA] 9 Introduction and Objectives of FLURISK WORKPACKAGE 1 (WP1): Influenza virus epidemiology and surveillance programmes review and assessments Introduction and objectives of WP Activity 1: Literature reviews on potential risk factors for, and evidence of, species jumps Influenza A virus genetic adaptations to animal reservoirs and their potential role in interspecies transmission: a literature review Influenza at the animal-human interface: a systematic review of the literature of virological or serological evidence of human infection with swine or avian influenza viruses other than A(H5N1) Jump and spread Epidemiological perspectives on animal influenza viruses overcoming species barriers and transmitting within animal populations Activity 2: Review and evaluation of ongoing monitoring, surveillance and control systems Review and evaluation of ongoing monitoring, surveillance and control systems for influenza viruses in animal populations Review and evaluation of ongoing monitoring, surveillance and control systems for influenza viruses in humans, including the detection of animal-origin viruses Activity 3: Epidemiological analysis of animal influenza viruses in space and time Activity 4: Review and compilation of data related to populations, trade and environment relevant to the IRAF development in WP WORKPACKAGE 2 (WP2): Risk assessment framework development and validation Introduction and objectives of WP Activity 1: Development and validation of the risk assessment framework Derivation of framework model from first principles Case study Parameter estimation Parameter estimation of virus score V(i) Identification of intrinsic factors responsible for the ability of a virus to infect humans Virus score development Weight of factors Questionnaire design and expert elicitation Data analysis and results for virus score Model implementation Practicalities of running the model Identification of viruses to run through the risk assessment framework model 60 reached in the present document, without prejudice to the rights of the authors. 6

7 Baseline risk and ranking viruses Sensitivity analysis Case study results Opportunity score maps Virus risk results Sensitivity analysis Validation Validation of virus score Validation of risk results Discussion Activity 2: User interface WORKPACKAGE 3 (WP3): Identification of scientific gaps and research 85 priorities 3.1. Introduction and objectives of WP Activity 1: identification of scientific gaps through WP1 and WP Results: gaps and research needs identified from the literature 86 reviews Results: gaps and research needs identified from other activities in 96 WP Results: gaps and research needs identified from other activities in 98 WP Activity 2: summary of future research needs and recommendations 100 Conclusions 101 Recommendations 103 References 104 Annexes list 107 Annex 1a: Genetic adaptation of influenza A viruses and their potential role in interspecies transmission: a literature review Annex 1b: Influenza at the animal-human interface: a systematic review of the literature of virological or serological evidence of human infection with swine or avian influenza viruses other than A(H5N1) Annex 1c: Jump and Spread Epidemiological perspectives on animal influenza viruses overcoming species barriers and transmitting within animal populations Annex 1d: Current influenza surveillance in animals: What is our ability to detect emerging influenza viruses with zoonotic potential? Annex 1e: Characterization of influenza surveillance in humans and at the humananimal interface Annex 1f: Epidemiological Report for FLURISK: Description of eleven viruses selected to validate the influenza risk assessment framework (IRAF) Annex 1g: Influenza virus infection of marine mammals Annex 2: Triage system Annex 3: Future research needs and recommendations Glossary/Abbreviations 108 reached in the present document, without prejudice to the rights of the authors. 7

8 BACKGROUND AS PROVIDED BY [EFSA] Legal framework Article 36 of the European Parliament and Council Regulation (EC) No 178/2002 laying down the general principles and requirements of food law, establishing the European Food Safety Authority and laying down procedures in matters of food safety foresees the possibility to financially support a networking of organisations operating in the fields within the EFSA s mission. On the 20th December 2006 the Management Board, acting on a proposal from the Executive Director, drew up a list of competent organisations designated by the Member States that may assist EFSA, either individually or in networks, with its mission. This list was updated on 18th December 2008 by the Management Board. Article 5 of the Commission Regulation (EC) No 2230/2004 laying down detailed rules for the implementation of European Parliament and Council Regulation (EC) No 178/2002 with regard to the network of organisations operating in the fields within the EFSA s mission specifies that the financial support to the networking organisations shall take the form of grants awarded in accordance with the EFSA s financial regulation and implementing rules. The present Call for proposal and guide for applicants (hereinafter referred to as the Call ) is governed by the rules of the Financial Regulation applicable to the general budget of the European Union (Council Regulation No 1605/2002 of 25 June 2002, hereinafter referred to as Financial Regulation ), as amended, and its implementing rules (Commission Regulation No 2342/2002 of 23 December 2002, hereinafter referred to as Implementing Rules ), as amended. Context and scientific background of the proposal In November 2009, the Commission requested scientific opinions from EFSA s Animal Health and Welfare (AHAW) panel on the animal health implications of the pandemic H1N1 virus and the possibility of future monitoring for emergence of influenza viruses with a pandemic potential from the animal reservoir, respectively (EFSA, 2010; EFSA, 2011). Considering that the pandemic H1N1 influenza virus contains gene segments from pig, bird and human influenza viruses, the AHAW panel pointed out that, a better scientific understanding is required of influenza viruses to protect public and animal health. The latest scientific data on biological properties of the virus, transmissibility, host susceptibility and epidemiology has identified factors that could be monitored in animals and that would suggest a risk of emergence of a new pandemic influenza strains. The EFSA opinion on monitoring for the emergence of possible new pandemic strains of influenza in animals concluded that current monitoring of the influenza gene pool in humans has been useful as an alert for the emergence of new human influenza strains of public health significance. Interpretation of the origins and the pandemic potential of influenza viruses do require knowledge of the influenza gene pools in pigs and birds, as well as other animal species. Currently, there is an incomplete view of the influenza virus strains circulating among pigs and birds in the EU, as well as at the global level. Thus, the AHAW panel recommended that there should be long-term support in the EU and globally for a passive monitoring network in pigs and birds in order to promote greater reached in the present document, without prejudice to the rights of the authors. 8

9 understanding of the evolution of influenza viruses at the global level. Maximum benefit can only be obtained by applying an integrated approach involving the medical and veterinary networks, including development of harmonised tools and approaches, exchange of virus strains and of sequence data and enhancing the coordination and dissemination of the findings from the human, swine and avian networks. One of the recommendations from the International Meeting on Influenza Interspecies Transmission in Castel Brando, February 2011, was to develop a methodological risk assessment framework for new/emerging flu virus towards their categorisation and prioritisation of actions (e.g. vaccines). The risk assessment framework represents a structured approach to define and describe the data elements and methodological approaches considered necessary to develop specific risk assessment modelling tools and decision support systems that could be applied to monitor emergence of new pandemic influenza strains. This is in line with the EFSA opinion on monitoring for the emergence of possible new pandemic strains of influenza in animals that supports the need to develop a methodological risk assessment framework that could encompass the various types of information available on epidemiology and virology of influenza in both animals and man. Such a framework could be used to develop integrated decision support tools that could assist in planning and implementing measures to prevent or reduce an emerging pandemic threat. It would also support the assessment and classification of the WHO pandemic phases 1-3, which are based on evaluating the influenza status in animals. The idea of a risk assessment framework has been further discussed with OFFLU (the OIE/FAO network of expertise on animal influenza) as well as with other scientific organisations competent in this field. The EFSA grant project should bring together subject matter experts and risk assessors from both public and veterinary health with multidisciplinary and complementary backgrounds in virus characterization/genetics, epidemiology, clinical knowledge, risk monitoring and risk management. Furthermore, EFSA will establish a group of experts to provide scientific steering for the grant project. The task of the group is to assure a close integration of the risk assessment framework with other approaches and tools being used and developed in human and animal health. The group should also provide information on approaches and tools used for monitoring influenza viruses and facilitate access to data collected on influenza viruses. TERMS OF REFERENCE AS PROVIDED BY [EFSA] This contract/grant was awarded by EFSA to: IZSVe Istituto Zooprofilattico Sperimentale delle Venezie Contractor/Beneficiary: Istituto Zooprofilattico Sperimentale delle Venezie, IZSVE (Project Coordinator) Viale dell Università 10, Legnaro Italy reached in the present document, without prejudice to the rights of the authors. 9

10 The Secretary of State for Environment, Food and Rural Affairs, AHVLA Smith Square, 17 SW1P 3JR London United Kingdom Royal Veterinary College, University of London, RVC Royal College Street, 4 NW1 0TU London United Kingdom National Institute of Public Health and the Environment, RIVM Antonie van Leeuwenhoeklaan BA Bilthoven The Netherlands Institut Pasteur, IP rue du Docteur ROUX, Paris cedex 15 France Universiteit Gent, UGent Sint Pietersnieuwstraat, Gent Belgium FLURISK-Final report Contract/grant title: FLURISK - Development of risk assessment methodological framework for potentially pandemic influenza strains Contract/grant number: EFSA/AHAW/2011/01 INTRODUCTION AND OBJECTIVES OF FLURISK Influenza infections of animals have recently become of great concern for animal and human health. The reasons for this lie in the recognition of changes that have occurred in influenza ecology, epidemiology and host range over the past fifteen years or so. The fact that an influenza virus of swine origin unexpectedly became the most recent human pandemic virus and that a low pathogenicity avian influenza virus H7N9 is currently causing serious human health concerns in Asia highlights deficiencies in current influenza pandemic preparedness. The overall objective of the EFSA call (CFP/EFSA/AHAW/2011/01) is the development and validation of a prototype methodological influenza risk assessment framework (IRAF) capable of assessing the pandemic potential of new influenza viruses or viral subtypes emerging in animals. The central questions addressed by FLURISK are: reached in the present document, without prejudice to the rights of the authors. 10

11 What is the current knowledge on the influenza virus etiology and epidemiology in pigs, birds and other animals (i.e. cats, dogs, horses)? What are the scientific gaps still present that need to be urgently identified and addressed through focused research priorities? What are the scientific community and institutional stakeholders doing in terms of influenza virus surveillance, monitoring and control? What is the evidence for animal to human transmission of animal influenza viruses and how is this identified by existing surveillance? What are the characteristics that an animal influenza A virus must possess to be potentially pandemic? How can we grade the pandemic risk posed by a given animal influenza A virus? How can these data and this risk assessment framework be used to better prepare for the next human pandemic? To address these questions FLURISK brought together internationally recognized research institutes and reference laboratories, international agencies and universities from veterinary and human medicine areas fostering cross-disciplinary expertise and collaborations. The project consortium was formed by six European partners (IZSVe, RVC, AHVLA, RIVM, IP, UGent) that have established networks, complementary knowledge, the scientific infrastructure and the expertise to fulfill the EFSA call objectives. Medical and veterinary virologists, epidemiologists and modelers were organized in multi-disciplinary working groups and panel of experts active in the following four highly integrated Work Packages (WP) with clearly defined tasks, milestones and deliverables and involving several partners each. 1. Influenza virus epidemiology and surveillance programmes review and assessment 2. Risk assessment framework development and validation 3. Identification of scientific gaps and research priorities 4. Management and coordination In the following Figure the overall FLURISK project organization is shown. The consortium work benefited from advisors/observers from the Influenza Division of the Centre for Disease Control and Prevention (CDC) and international bodies such as the Food and Agriculture Organization of the United Nations (FAO), World Organization for Animal Health (OIE), the World Health Organization (WHO) and the European Centre for Disease Prevention and Control (ECDC). reached in the present document, without prejudice to the rights of the authors. 11

12 Project organization The overall objectives of WP1 were to review animal influenza virus etiology and epidemiology and to assess current surveillance, monitoring and control strategies that are applied globally with regards to animal and human influenza viruses. The WP activities contributed to identify the epidemiological and virological elements/risk factors to be considered for the development and validation of the IRAF and to provide actual data accordingly. Specifically, epidemiological and virological data of selected isolates of animal influenza virus were gathered and used as candidates for the validation of the risk assessment framework performed in WP2. The WP also developed the user-friendly interface functional to the utilization of the tool. Throughout the project, WP1 and WP2 activities have contributed to identify relevant scientific gaps to be communicated to WP3 where gap analysis was performed and future research priorities compiled. The project benefited from the collaboration with the United Nations Food and Agricultural Organization (FAO), Rome, Italy which allowed the access of population, trade and environmental data and with the Influenza Division of the Centre for Disease Control and Prevention (CDC) which developed an influenza risk assessment tool to evaluate influenza A virus that are not circulating in the human population. Furthermore, the past and current participation of project partners in projects generating data on the etiology and epidemiology of Influenza virus (i.e. ESNIP3) have also guaranteed the access to databases and relevant data some of which are not yet publically available. reached in the present document, without prejudice to the rights of the authors. 12

13 1. WORKPACKAGE 1 (WP1): INFLUENZA VIRUS EPIDEMIOLOGY AND SURVEILLANCE PROGRAMMES REVIEW AND ASSESSMENTS 1.1. Introduction and objectives of WP1 The overall objectives of Work Package 1 (WP1) are to review animal influenza etiology and epidemiology in both animals and humans and assess current surveillance, monitoring and control strategies. WP1 activities were designed to provide WP2 with the variables needed for the development of the Influenza Risk Assessment Framework (IRAF). Specific WP1 activities are: 1. Literature reviews on potential risk factors for, and evidence of, species jumps; 2. Review and evaluation of ongoing monitoring, surveillance and control systems; 3. Epidemiological analysis of animal influenza viruses in space and time; 4. Review and compilation of data related to populations, trade and environment relevant to the IRAF development in WP2. Any scientific, knowledge or data gaps have been highlighted and are presented in the WP3 section of this report Activity 1: literature reviews on potential risk factors for, and evidence of, species jumps The main objective of FLURISK is the development of an epidemiological and virological evidence-based influenza risk assessment framework to assess influenza A virus strains circulating in the animal population according to their potential to cross the species barrier and cause infections in humans. With the purpose of identifying virological and epidemiological risk factor data to include in the IRAF, three literature reviews were conducted and key findings are summarized here. The Introduction, Materials/Methods, Results, Discussion and References of this reviews are included in Annex 1 (a, b, c). Dr. Sasan Fereidouni (Friedrich-Loeffler-Institut, Germany) shared a literature review on Influenza virus infection of marine mammals with the FLURISK consortium. The review emphasizes the fact that infection of seals and other sea mammals with influenza A and B viruses have been reported on several occasions in different countries. Furthermore antigenic and phylogenetic analysis show that the most probable route of introduction of influenza A viruses is represented by direct contact between birds and marine mammals. Dr. Fereidouni s work is not intended to be a FLURISK outcome but it is included in this report since his work contributes to review animal influenza aetiology and epidemiology. This review is included in Annex 1g. reached in the present document, without prejudice to the rights of the authors. 13

14 Influenza A virus genetic adaptations to animal reservoirs and their potential role in interspecies transmission: a literature review Title: Genetic adaptation of influenza A viruses and their potential role in interspecies transmission: a literature review Summary: Several adaptive traits have been identified in influenza viruses infecting domestic animals and a significance of these adaptations for the emergence of zoonotic influenza, such as shift in receptor preference and mutations in the replication proteins, has been hypothesised. Nonetheless, and despite several decades of research, a comprehensive understanding of the conditions that facilitate interspecies transmission is still lacking. This has been hampered by the intrinsic difficulties of the subject and the complexity of correlating environmental, viral and host factors. Finding the most suitable and feasible way of investigating these factors in laboratory settings represents another challenge. The majority of the studies identified through this review focus on only a subset of species, subtypes and genes, such as influenza in avian species and avian influenza viruses adapting to humans, especially in the context of highly pathogenic avian influenza (HPAI) H5N1. Further research applying a holistic approach and investigating the broader influenza genetic spectrum is urgently needed in the field of genetic adaptation of influenza A viruses. Conclusions: When replicating in animals other than their natural reservoir, influenza A viruses may encounter intra-host impediments at each stage of their viral cycle: attachment, replication, release and shedding, and counteraction of the host immune response. Due to their genetic characteristics, these viruses are prone to adapt to new hosts through mutations and reassortment. This review aims at highlighting key virological mechanisms likely capable of influencing the replication, transmission and spread of influenza A viruses (IAVs) in different animal species and their potential significance for crossing the species barrier to infect humans. It also served to identify relevant scientific gaps in the current state of the art. Several adaptive traits have been recognized in domestic animals and a significance of these adaptations for the emergence of zoonotic influenza, such as shift in receptor preference and mutations in proteins involved in influenza replication, has been hypothesised. Great attention has been paid to the genetic modifications found in IAVs after replication in domestic gallinaceous species in the field and their role in interspecies transmission. This phenomenon has also been reproduced in laboratory settings, mostly in quail. The role of turkeys in this regard remains to be clarified. In contrast, mammalian species, such as pigs, cats, dogs and horses, have been largely overlooked in this research field. Despite longstanding efforts in influenza research, there is still a great gap in knowledge on whether and how genetic changes affect interspecies transmission. Complexities both in the field and in laboratory settings have conditioned scientific achievements. Most research so far has been performed in vitro and it has been seen that in vivo and in vitro experimental results can sometimes be contrasting. This problem might be difficult to surpass, as there is still a lack and/or shortage (either scientifically or financially hampered) of relevant animal models. Nevertheless, novel laboratory methods, such as swine respiratory explants, could reached in the present document, without prejudice to the rights of the authors. 14

15 help overcome some of these gaps. Adding obstacles when approaching these viruses in the laboratory, is the fact that experimental results can be difficult to compare between studies, due to high variation in methodology, or because these can disagree, as different viral strains are used. In fact, it is fundamental to highlight that adaptive motifs are not to be applied as a pattern to all influenza subtypes, since their presence may produce varying effects in different subtypes or strains. Efficient transmission of influenza viruses among a specific host is a polygenic trait (of still unknown nature and probably variable according to strains/subtypes), depending on functional balance between the different viral proteins (e.g. HA and NA) and all the viral cycle steps. Overall these viral characteristics interplay with host (such as adaptive and innate immunity or even behavioural pattern) and environmental factors (artificial, such as rearing systems, or natural). These interactions are yet to be characterized and disentangling this poses a great challenge to the scientific community. A clear evidence of the difficulties of understanding this complex machinery is the unexpected emergence of zoonotic avian H7N9 influenza in China. Genetic analysis has recognized several known adaptive motifs in this strain, but their contribution to the zoonotic phenotype of this virus, as well as their association with the still unclear animal source of infection and host factors, are still to be explored. Several areas for future research have been identified within this review. Regarding viral factors, there is still a great difficulty in defining the right setting for an eventual reassortment. This setting may be characterized and limited by timing conditions (the timing of entry in the cell of two different strains), the host (e.g. type of host and anatomical site within this, immunological status) and the framework within which the host can be found (e.g. dynamics of population). In addition, viral properties could also have an influence on such an event; certain HA and NA subtypes and/or other genes could be more prone to reassortment and/or adaptation. Drawing similarities and differing characteristics between species, especially pigs and humans, would help to disentangle the complex picture of influenza. For example, more detailed receptor studies should be carried out in different species, including animal experimental models such as ferrets, in order to describe thoroughly the complexity of glycan distribution and characteristics (e.g. length). This information shall be then translated in the development of adequate receptor binding assays. Another interesting source of information could be obtained by comparing pandemics and large influenza outbreaks. This could help to understand which might have been the viral, host or epidemiological characteristics/triggers/drivers missing for a pandemic successfully originating from these outbreaks (e.g. swine influenza H1N1 outbreak in Fort Dix in 1976 and H1N1pdm09). It would also be extremely informative to investigate the host, environmental and viral determinants of earned endemicity of only a small subset of influenza subtypes in the domestic animal population (e.g. HPAI H5N1 and H9N2 in poultry; H3 subtype establishment in a broad range of mammals; confined canine influenza circulation mostly to kennels and animal shelters; the extinction of H7N7 from the equine population). To conclude, the information and gaps highlighted in this review are essential in the development of the FLURISK risk assessment framework. The virological factors and transmission mechanisms facilitating the virus spread and the species jump are far from being thoroughly understood. More research efforts (also guided by the identified gaps) are needed reached in the present document, without prejudice to the rights of the authors. 15

16 and the analysis of laboratory data and results should be clearly contextualized also taking into consideration the association with key epidemiological and environmental factors. For Introduction, Materials/Methods, Results, Discussion and References of this review please refer to full text version in Annex 1a Influenza at the animal-human interface: a systematic review of the literature of virological or serological evidence of human infection with swine or avian influenza viruses other than A(H5N1) Title: Influenza at the animal-human interface: a systematic review of the literature of virological or serological evidence of human infection with swine or avian influenza viruses other than A(H5N1) Summary: The work presented here aims at describing available evidence for animal (all possible species) to human influenza virus transmissions. Therefore, we conducted a review of the literature and evaluated the evidence by diagnostic methods used. We stratified the results according to level of evidence, by describing virologically confirmed cases and serologically confirmed cases. Virologically confirmed cases were persons in whom presence of an animal influenza virus had been detected by cell culture isolation, egg culture isolation, detection of viral RNA by reverse transcriptase PCR, (partial) sequencing, or combinations thereof. Serologically confirmed cases were cases in which antibodies to influenza viruses had been detected, either by hemagglutination inhibition assay or by virus neutralisation assay. Conclusions: Virologically confirmed cases (Freidl et al., manuscript submitted for publication): The evidence for virologically confirmed infections of humans with avian or swine influenza viruses (AIV and SIV, respectively) is listed in Annex 1b (Part 1). A total of 263 cases of human infection with non-a(h5n1) avian influenza viruses (AIVs), of which 252 were caused by natural infection, have been described (per July 2013). The majority of these (n = 234) were influenza A(H7Nx) subtype strains, but human infections have also been reported for viruses with H6, H9 and H10 subtype HA s (Figure 1). Regarding human infections with SIV, a total of 386 cases were identified in the published and partly grey literature documented in the English language, all with viruses with HA1 or HA3 subtype hemagglutinins. The majority of identified cases (n=333) were naturally infected by a SIVvariant (abbreviated v ), named A(H3N2)v (Figure 1). Recognized in US pigs in 2010, this variant combines seven genes from the contemporary North American A(H3N2) SIV-lineage and retrieved the M-gene of the A(H1N1)pdm09 virus. The remaining 61 virologically confirmed human cases were caused by different circulating SIV or reassortants thereof, and five persons were experimentally infected with SIV [Annex 1b]. The majority of AIV- and SIV-infected patients had animal-exposure. Full details are discussed in Annex 1b reached in the present document, without prejudice to the rights of the authors. 16

17 Number of naturally infecteed humans Number of naturally infected humans FLURISK-Final report a) LP H6N1 H7N2 HP H7N3 LP H7N3 HP H7N7 LP H7N7 LP H7N9 H9N2 H10N7 Total # infected Avian influenza virus subtype b) H1N1 H1N2 H3N2 H3N2v Total # infected Swine influenza virus subtype Figure 1. a) Humans naturally infected with avian influenza virus subtypes other than A(H5N1). The A(H7N9) bar represents the latest number of cases as of May 30 th, b) Humans naturally infected with swine influenza virus subtypes. The A(H3N2)v bar represents the latest number of cases as of June 28 th, Serologically confirmed cases: As influenza viruses may infect multiple animal species, and from there transmit to other species, carefully designed studies are needed to quantify the human health risk associated with such exposures. Serological monitoring is a powerful tool to identify subclinically infected individuals and allows an estimation of the incidence and prevalence of cross-species influenza infections providing more objective information about morbidity and mortality rates of infections caused by such a virus. Ideally, such studies should include active monitoring of virus circulation in the animal populations to which humans were exposed to during the period covered by the exposure assessment a study design that is rare. Without reached in the present document, without prejudice to the rights of the authors. 17

18 such information, interpretation of negative serological data is not informative as it can be interpreted as to that a virus was not present or a virus was present but did not transmit to humans. Therefore, interpretation of positive serological findings in humans without evidence on virus circulation in animals remains qualitative. Likewise, serological surveys should ideally include non-exposed background/ control population in addition to animal-exposed individuals of interest. Another pitfall is that serological data need to be interpreted with caution, due to lack of standardization between laboratories, and because of cross-reactivity of antibodies between and within virus subtypes. For the detection of human antibodies against animal influenza viruses, the WHO published two guidelines (WHO (2013b), WHO (2011)). One targets the detection of avian influenza virus A(H5N1) or antibodies against this subtype in humans (2007) and another manual advises on detection of animal influenza viruses in general in animals and humans (2002). Despite these existing guidelines, methods used in serological screening studies vary between different studies impairing comparability. Keeping this in mind, seroprevalence studies aiming at the detection of antibodies against avian viruses in humans must be interpreted critically concerning the methods used. Seroprevalence studies in exposure groups have reached different conclusions with respect to evidence for seroconversion to a range of influenza virus subtypes, ranging from no evidence, to strong evidence based on case control studies (Annex 1b- Part 2), suggesting that transmission of avian viruses to humans, especially among poultry-exposed risk groups such as poultry workers, farmers, hunters and veterinarians, seems to occur more often than assumed. In 2006, Gill et al. (2006) found first evidence of transmission of avian influenza from wild birds directly to hunters and wildlife professionals, detecting antibodies against H11N9. In another serosurvey targeting antibodies against H4, H5, H6, H7 and H9 by means of microneutralization assay, Gray et al. (2008) found increased antibody titers against H5 in people handling live birds, increased H7 antibody levels in wild bird hunters and also elevated antibody titer against H6 and H7 among poultry workers in Iowa, compared to non-exposed controls. In contrast, there was no evidence for antibodies against H4 and H9 viruses. A problem in the use of serological data for assessing possible animal-human transmissions is the knowledge that there is some cross reactivity between antibodies triggered by human infection or vaccination. Therefore, studies simply reporting a seroprevalence without comparison with a control group are of limited value. Even with a controlled study, the question remains whether the animal exposed group was indeed exposed to viruses. There are almost no studies that describe this factor, apart from epidemiological studies conducted in course of an outbreak investigation. Nevertheless, a few studies that have included serum samples from controls have indeed found evidence for increased seroprevalence of antibodies to animal influenza subtypes in humans. The percentage of positives typically is low (few %), but the data does suggest undetected human infections, at least for viruses of subtypes H7 and particularly high for H9. In studies in the US among exposure groups, the seroprevalence of antibodies to a range of avian influenza subtypes was elevated. There is insufficient standardisation between studies to allow direct comparison of seropositivity rates across countries, but the data suggests that there is much more ongoing transmission of avian influenzaviruses to humans than desirable. This, if confirmed, should be a cause of concern for public health officials. For swine influenza viruses, the evidence for infection of humans is quite convincing, despite issues with cross reactivity. Here, the percentage seropositives in exposed persons typically differs substantially from that in non-exposed controls. The lack of a strong species barrier reached in the present document, without prejudice to the rights of the authors. 18

19 between pigs and humans has been confirmed by other data such as the high degree of exogenous RNA in swine influenza viruses. For Introduction, Materials/Methods, Results, Discussion and References of this review please refer to full text version in Annex 1b (Part 1 and 2) Jump and spread Epidemiological perspectives on animal influenza viruses overcoming species barriers and transmitting within animal populations Title: Jump and Spread Epidemiological perspectives on animal influenza viruses overcoming species barriers and transmitting within animal populations Summary: Drivers and risk factors for influenza virus transmission across species barriers are poorly understood, despite the ever present threat to human and veterinary health, potentially on a pandemic scale. Here we review the epidemiological risk factors associated with influenza viruses transmitting between species and spreading within animal populations. A total of 19 papers were identified as showing evidence of epidemiological risk factors for influenza virus transmission from animals to humans. Circumstantial or observational evidence of risk factors for viruses being transmitted between animal species was found in 15 publications including proximity to infected species, ingestion of infected material, or potential association with a species known to carry influenza virus. A greater number of studies have been published which identify risk factors related to the spread of virus within a single-species animal population (n=33), particularly involving poultry. Only thirteen of these reported a statistical measure of epidemiological risk factors, five case-control or crosssectional studies and eight models from spatio-temporal analyses. This review has identified a significant gap in knowledge regarding the epidemiological risk factors for influenza viruses transmitting between animal species. Highly pathogenic H5N1 is a highly evolved virus in many populations and has acquired a number of distinct features. Studies on risk factors for spread within animal populations are heavily biased towards this virus and although this knowledge could prove useful in planning risk management and disease control programmes particularly in Asia, it may not directly correlate to less evolved viruses which could still present equivalent or higher pandemic threat. Conclusions: While the emergence of new influenza viruses has been an undeniable threat to human health and prosperity for many years, progress in understanding how, where and why new viruses emerge has been limited. Part of the reason for this is that disease emergence is a complex process, but also this review has highlighted the lack of studies on epidemiological risk factors for cross species transmission of influenza viruses. More progress has been made in identifying the epidemiological factors associated with the spread of influenza viruses within animal populations, particularly for HPAI H5N1 in poultry reached in the present document, without prejudice to the rights of the authors. 19

20 in Asia. HPAI H5N1 is now a highly evolved virus in many animal populations and has acquired a number of distinct features. The knowledge gained in this review should be very useful in planning and conducting risk management and disease control programmes for this virus in Asia, but may not directly correlate for less evolved viruses which nevertheless may still present equivalent or higher pandemic threat. A number of studies investigating the spatial distribution and co-occurrence of relevant host species (e.g. poultry, pigs and humans) have been conducted to identify geographical hot spots, but the myriad of other potentially relevant factors that show spatial variation leaves plenty of progress to be made for this approach. Carefully designed prospective studies, along with detailed analyses of available data from influenza outbreaks, could greatly improve our knowledge in this area and support our ability to manage risk and conduct successful control programmes in the future. For Introduction, Materials/Methods, Results, Discussion and References of this review please refer to full text version in Annex 1c Activity 2: Review and evaluation of ongoing monitoring, surveillance and control systems The review and description of the animal and human influenza virus surveillance strategies and control programmes that are currently in place in the veterinary and public health sectors has been split into two parts: one on surveillance in animal populations and one on surveillance in humans. In the IRAF, results of the review in animal populations are used for the mapping of influenza surveillance coverage by geographic area and of the variation in coverage worldwide. For Introduction, Materials/Methods, Results, Discussion and References of these reviews please refer to their full text versions in Annex 1 (d, e) Review and evaluation of ongoing monitoring, surveillance and control systems for influenza viruses in animal populations Title: Current influenza surveillance in animals: What is our ability to detect emerging influenza viruses with zoonotic potential? Summary: The survey on national animal influenza surveillance strategies aimed to assess the current ability to detect potentially human-pathogenic influenza viruses in animals at regional and global levels. Information on 587 animal influenza surveillance system components was received for 99 countries from Chief Veterinary Officers (CVOs) and complementary sources such as an OFFLU survey, information generated by the ESNIP 3 project and the OIE World Animal reached in the present document, without prejudice to the rights of the authors. 20

21 Health Information Database WAHID. Results showed that less than 1% of the surveillance system components analysed specifically investigated pandemic influenza viruses and exclusively in domestic pigs. This reveals the global need for increasing surveillance that targets pandemic influenza viruses in different animal species. Most surveillance efforts in animals have the objective of safeguarding animal health rather than providing data for pandemic preparedness. Here we recommend on how existing components could be adapted to capture also information on potential human-infectivity of animal influenza viruses and how surveillance efforts may be improved in general. In order for an influenza surveillance system to have high capacity to inform human pandemic risk, several integrated components of active-representative and risk-based surveillance should be in place, ideally in the framework of a sustainable national surveillance system. With these components higher risk subtypes of influenza A need to be targeted, i.e. H2, H5, H6, H7, and H9, in poultry and pigs from extensive as well as intensive production systems, regardless of their importance for animal production and health. The case definition should be based on virology and we strongly recommend genetic sequencing of all remarkable isolates. Submission of virus sequences into public genetic databases in real time is as important as timely communication of results to the national and international communities. Conclusions: This survey aimed to assess the current ability to detect influenza viruses with zoonotic potential in animals at regional and global levels. We identified the global need for increased surveillance that specifically targets pandemic influenza viruses in different animal species. Most surveillance efforts in animals have the objective of safeguarding animal health rather than providing data for pandemic preparedness. For countries with limited resources, we recommend to adapt existing surveillance to also capture influenza viruses with zoonotic potential. If resources are available, however, targeted surveillance system components should be implemented to survey specific animal populations that would otherwise not be targeted by routine surveillance. In addition research will help addressing specific questions and filling surveillance gaps. Recommendations from the study can be summarized by thematic area as follows: 1. Routine surveillance adapted to also detect animal influenza viruses with zoonotic potential Routine testing of poultry or pigs for purposes other than pandemic preparedness can and should be used to screen also for potentially pandemic viruses. A prerequisite is that laboratory protocols are adapted to also test for less common influenza viruses or viruses with little economic consequences for the poultry and pig sectors. Webby et al. (2003) suggested to prioritize H2, H5, H6, H7, and H9 subtypes of influenza A as these are most likely to be transmitted to humans. The triage system developed by the FLURISK project (Annex 2) further facilitates identifying and selecting isolates that should be sequenced with priority. In routine surveillance, it is important not to neglect certain production systems. Backyard and intensive systems usually represent separate environments in which influenza viruses may evolve very differently. In addition, countries should put more reached in the present document, without prejudice to the rights of the authors. 21

22 effort into swine influenza (SI) surveillance in general, especially in backyard and free-range pigs, an area that currently seems to be neglected. 2. Targeted surveillance to detect animal influenza viruses with zoonotic potential Those countries only implementing one or few animal influenza surveillance system components are encouraged to diversify surveillance efforts in order to target more influenzas and/or animal populations. Countries are further encouraged to implement surveillance system components that specifically investigate pandemic influenza viruses. From a pandemic risk point of view, poultry and pigs are the animal species that should be targeted with priority. Those countries that vaccinate against influenza are urged to closely monitor vaccinated populations in order to detect silent influenza circulation and closely monitor virus evolution under vaccine pressure. It is further recommended to design an exit strategy as vaccination should be considered as one tool in influenza control, but should not be relied upon indefinitely. 3. Specific research In order to capture virus diversity in different domestic species it is important to target also those animal populations or production systems that are not undergoing routine surveillance. We encourage countries and institutions to implement specific components, e.g. through research projects, to survey pigs, domestic cats or dogs (if not done routinely) and to gather baseline information as a minimum. FLURISK suggests and prioritizes influenza research gaps in the WP3 part of this document. Scientific value would be added by implementing influenza surveillance also in species other than pigs and poultry, especially when these are in close contact with domestic pigs and humans in low biosecurity settings. Target species of interest are domestic cats and dogs. Regarding influenza surveillance in poultry, quail and turkeys should be given some attention for their role as bridging species and amplifiers for wild waterfowl viruses. There are also some general considerations countries should take into account and implement in their day-to-day surveillance activities: I. Laboratory investigations Laboratory methods such as ELISA, PCR or virus isolation constitute the case definition of choice to confirm a positive influenza case in the context of surveillance aiming at early detection of potentially pandemic animal influenza viruses. Any current infection with influenza viruses can only be confirmed through antigen or virus detection and any serological positives from suspect outbreaks should be followed up accordingly. Countries are further encouraged to regularly select a subset of viruses for genetic characterization through sequencing. Selection should be based on criteria such as reached in the present document, without prejudice to the rights of the authors. 22

23 invasion of new hosts or new geographic territories but also be representative for endemic strains in order to trace their evolution over time. Guidelines for selecting appropriate isolates can be extracted from the FLURISK triage system (Annex 2). As part of the routine laboratory investigation of viruses suspected to have zoonotic potential, screening for human-pathogenicity should be performed. This includes molecular markers known or suspected to enhance pandemic potential as discussed recently by Munoz et al.. The FLURISK triage system provides a detailed list of virological or phenotypic characteristics (Annex 2): - Receptor preference; - Presence of known mutations in the ribonucleoprotein complex (PB1, PB2, PA, NP); - Re-assortment within virus; - Stalk deletion in NA; - Phylogenetic relatedness of the HA to HAs of strains circulating in humans. II. Governance and collaboration Governments should give their political support to national as well as regional surveillance and include such activities in national budgets in order to provide sustainability and continuity. Governments are further encouraged to identify and approach potential funding agencies to support the surveillance of viruses with limited economic consequences for poultry and pigs. Since the aim is detection of viruses with zoonotic potential, such agencies may be approached under the human or one health umbrella. Collaboration should be strengthened between private (livestock production) and public sectors to jointly implement and fund surveillance and establish procedures for the communication of results and the instigation of control actions. Such a framework, however, needs to consider and address potential trade implications of influenza virus detection in animals. For example, following H1N1pdm09 the USA developed standard operating procedures for data sharing between the pig industry and the US Department of Agriculture, safeguarding the identity of individual farmers. We further urge national governments to ensure and encourage the link between veterinary services and public health officials and define regular communication. Routine operating procedures should be put in place, officials adequately trained and communication as well as actions regularly tested in simulation exercises. III. Data sharing and reporting Countries are urged to report their surveillance results and facilitate international access to important or particular findings, including genetic sequences, in a timely manner. Regional networks need to be implemented or sustained, which should be facilitated by international organisations. An example constitutes OFFLU, an reached in the present document, without prejudice to the rights of the authors. 23

24 international network of expertise on animal influenza viruses implemented by OIE and FAO where countries can share and discuss their findings. Publishing animal influenza virus sequences in public genetic databases and sharing live virus with the international research community is of utmost importance, especially for emerging viruses suspected to have pandemic potential. Such collaboration is prerequisite for pandemic prevention and control, which needs to be coordinated at international level. Governments should ensure that adequate and timely feedback is provided from research activities and surveillance performed by the private sector to those who are involved in preparedness and control, e.g. CVOs or national epidemiology units. Even if this was not an objective of the assessment, the dataset generated through this study could be used for further analysis such as component analysis to further investigate regional patterns. It is important to keep in mind that this survey has been implemented following a period of increased financial investment into animal influenza surveillance related to the recent H5N1 HPAI and H1N1pdm09 events. The situation has to be considered volatile, with surveillance efforts largely depending on the emergence of animal influenza strains with high economic or public health impact. A similar survey carried out at a different point in time will likely result in a diverse picture. In order to reflect the current situation, we recommend repeating this kind of survey in regular intervals, e.g. every 5 years. For Introduction, Materials/Methods, Results, Discussion and References of this review please refer to full text version in Annex 1d Review and evaluation of ongoing monitoring, surveillance and control systems for influenza viruses in humans, including the detection of animal-origin viruses Title: Characterization of influenza surveillance in humans and at the human-animal interface Summary: Surveillance of seasonal influenza is based on gathering data related to clinical syndrome identification (i.e. Influenza-like illness (ILI), acute respiratory infection (ARI) or severe acute respiratory infection (SARI)), which are identified through sentinel sites (i.e. general practitioners, primary health care centers and/or hospitals). A subset of these patients is sampled and virological diagnosis is attempted. Results on virological surveillance originate or are collected by National Influenza Centres (NICs) and National Influenza Reference Laboratories (NILs). NICs are the backbone of World Health Organization (WHO) Global Influenza Surveillance and Response System (GISRS). The sampling strategy for virological surveillance is nationally defined and can be systematic, random or based on convenience scheme. WHO recommends a systematic approach rather than a convenience approach, and reached in the present document, without prejudice to the rights of the authors. 24

25 the sampling of all SARI patients. Overall, the method will depend on the number of samples that a laboratory can process in a certain period of time. As part of the influenza risk assessment framework (IRAF) development and the FLURISK project tasks, ongoing influenza virus monitoring, surveillance and control systems, both in animals and in humans were characterized. Institut Pasteur coordinated the characterization of influenza A surveillance in humans and contacted National Influenza Centres ( and Influenza Reference Laboratories worldwide asking them to compile a questionnaire focused on surveillance systems for human influenza and on influenza surveillance at the human-animal interface. One hundred and twenty three (123) countries were contacted and a total of 147 questionnaires were sent to heads of laboratories (some countries having several reference laboratories). Forty one (41) countries responded to the survey (33.3%) and a total of 46 questionnaires were received (31.3%). Although this survey addressed countries worldwide, the data gathered are not representative at a global level due to several reasons: not all countries have NICs or NILs; the response coverage in the Americas and Africa has been extremely low (9.1% and 12.5% respectively, probably partially due to: the impossibility of contacting countries within these regions more than once as a consequence of lack of coordination with the respective WHO regional offices, and the fact that at least half of these countries were contacted only through air mail, in contrast to Oceania and Europe, where only 1 NIC out of 5 and 8 NICs out of 40 were contacted through air mail respectively); the overall response coverage has been lower than expected (33.3%); no responses were received from Central Asia; less than 30% of countries responded in Southern and Western Asia; and, for the responses received, not all countries answered to all questions. Data analysis evidenced that 97.3% of countries have a network of sentinels in place for the surveillance of human influenza and possibly a long-term sustainability. In contrast, few countries in Europe (4) and none in Oceania, perform active surveillance for the introduction of animal influenza viruses into the human population. On the other hand, more than half of Asian (6) countries have such a system in place. Taking into account both active and passive surveillance at the human-animal interface, most countries are able to identify only the most common avian zoonotic subtypes (i.e. H5, H7, H9). Furthermore, even if a high percentage of NICs/NILs reported to have collaborations on going with the veterinary sector, only few of these would send unsubtypable influenza A viruses to veterinary institutes and/or laboratories, and a few countries in Europe (4/23) do/would not alert the veterinary sector in case of detection of a human infection with an animal influenza virus. In addition, a web search and a search in PubMed were run in order to find alternative/additional sources for data on human influenza surveillance and at the animalhuman interface. An integrated analysis of survey data (from NICs/NILs and alternative sources) was attempted, but as quantity (not all fields of the questionnaire are covered by alternative sources, especially surveillance in the animal-human interface) and quality (not always up to date, need of interpretation) of data for each country varies greatly depending on the source, this has not been possible, so data from these sources is presented separately. Nonetheless, it is important to mention that all countries for which alternative sources were found (i.e. 4 countries in Africa, 11 in America, 8 in Asia, 1 Europe, 1 Oceania) possess a network of sentinels for the surveillance of human influenza. It is also worth mentioning that according to the latest GISRS NIC survey conducted in 2010, 103 of 104 (99%) responding NICs collected seasonal specimens. reached in the present document, without prejudice to the rights of the authors. 25

26 Conclusions: A stronger collaboration of human influenza surveillance institutions with the veterinary sector for subtyping of influenza viruses or increased in-house capacity for identifying a broader range of animal influenza subtypes would be advisable. In addition, difficulties in retrieving human-animal interface surveillance data and approaches as part of this survey partially reflect the current gap in this area. Due to lack of HA and NA specificity deriving from possible cross-reaction with antibodies arising from human influenza vaccination or infection, research on ad hoc serologic diagnostics tests to apply at the human-animal interface is also desirable. This may be achieved for instance by strengthening the implementation of capacity building programmes within the One Health framework including exchange of personnel between veterinary and public health institutes. Interdisciplinary approaches under a One Health umbrella are being advocated by several organizations, such as the WHO, FAO and OIE and taken forward by ongoing projects such as PREDEMICS and ANTIGONE (ANTIcipating the Global Onset of Novel Epidemics), both funded by the European Commission FP7 programme. Both projects promote exchange of ideas, expertise, staff and information between human and veterinary medical sectors and other pertinent disciplines. It is also regarded as important to promote active surveillance at the human-animal interface, targeting, as a result of careful epidemiological assessment, groups of people amongst the general population which are at higher risk of being infected with animal influenza viruses (e.g. people working in close and frequent proximity to poultry or swine), in order to increase the probability of detecting mild cases and the initial jump of animal influenza viruses into the human population. In addition, integration of general practitioners not settled in hospitals and clinics may help identify influenza cases presenting with only mild symptoms, including those originating from animal to human infection. It will also be advisable to conduct awareness campaigns amongst staff involved in sentinel networks of possible zoonotic origin of influenza infection in cases seeking medical care, in order to include animal contact as a selection criteria for virological diagnosis. Furthermore, it is paramount to raise awareness amongst people with occupational exposures to animal reservoir of influenza viruses (e.g. use of personal protective equipment), especially amid swine workers to prevent bi-directional transmission. Surveillance at the human-animal interface should always be performed during outbreaks in the animal population to identify any cases of human infections and evaluate the potential for human-to-human transmission of animal influenza viruses. Conversely, increased awareness and focus on surveillance of ILI in people with occupational exposure to influenza virus animal reservoirs may result in the identification of human cases of zoonotic infections that could trigger investigations in the animal populations. This could be especially important for the detection of influenza viruses that do not result in significant symptoms in the animal population (e.g. H7N9) but could pose a major health threat for humans. In order to complement the implementation of cohort and case-control studies at the animalhuman interface to assess the role of zoonotic transmission in human influenzas, it might be suggested to encourage internet-based self-reporting for gathering information on symptomatic influenza patients not seeking medical attention. Data collected by these methods should include key epidemiological information related to the human-animal interface (e.g. occupation, outdoor activities or animal contacts in the 3-4 days preceding the onset of symptoms, etc.). reached in the present document, without prejudice to the rights of the authors. 26

27 The impact of seasonal influenza in Africa and the Eastern Mediterranean Region is still largely unknown and much remains to be done to upgrade influenza surveillance in this region. Thus, regional gaps in influenza surveillance should be addressed as well. Overall, it is extremely important in surveillance approaches to consider that influenza A viruses constitute important zoonotic viruses with a complex epidemiology and continuous genetic evolution. Subtype or species of origin alone are therefore not suitable as a basis for influenza virus characterization and we need to retain a much broader perspective when designing influenza surveillance. A multidisciplinary and global collaborative effort is fundamental. For Introduction, Materials/Methods, Results, Discussion and References of this review please refer to full text version in Annex 1e Activity 3: Epidemiological analysis of animal influenza viruses in space and time WP1 was tasked with the review and consolidation of information on eleven currently or historically circulating animal influenza virus strains that could be used to validate the IRAF. For this purpose, seven avian influenza viruses, three swine influenza viruses and one equine influenza virus were chosen by the FLURISK consortium. The relevant information and references, comprising both virological and epidemiological data, has been summarized in the FLURISK Epidemiological Report (Annex 1f). Title: Epidemiological Report for FLURISK: Description of eleven viruses selected to validate the influenza risk assessment framework (IRAF) Summary: In order to test, fine-tune and validate the IRAF, eleven historically or currently circulating animal influenza virus strains were selected for which isolates with full genome sequence are available and information on epidemiological risk factors can be traced. The decision was taken to select strains rather than subtypes, since different strains of the same subtype may have different species-specificity and behavior. The FLURISK epidemiological report describes these eleven selected animal influenza virus strains, including their global distribution and evolution in space and time, major outbreaks, virological and epidemiological features as well as the rationale behind their selection. For each of these strains several isolates have been selected on which the IRAF can be run. These are being described here in further detail with regards to virological features and epidemiological context. Rationale and selection criteria will also be described and can be used to test whether the model reflects the expert s thought process, and if not why not; the latter being a function of both inaccuracy of the model and/or the model capturing information not readily available to experts and/or lack of necessary information on the chosen virus strains. reached in the present document, without prejudice to the rights of the authors. 27

28 While acknowledging existing data gaps, the FLURISK epidemiological report aims at providing a comprehensive overview of epidemiological and virological properties of the eleven selected validation strains or their isolates that may act as risk factors for animal influenza viruses overcoming the species barrier and infecting humans. Conclusions: The FLURISK Influenza Risk Assessment Framework is being developed using a standard disease transmission model, assuming Poisson contact rates and incorporating both speciesspecific and virus-specific factors. The output will be a list of animal-origin influenza viruses ranked according to their potential to jump the species barrier and cause human infection. In order to validate the IRAF, eleven currently or historically circulating viruses have been selected according to their ability to infect humans. The decision was taken to select strains rather than subtypes, since different strains of the same subtype may have different speciesspecificity and behaviour. For this purpose, seven avian influenza viruses, three swine influenza viruses and one equine influenza virus were chosen and assigned to one of three categories: Category A contains viruses infecting humans, Category B consists of viruses unlikely to infect humans, but with some potential to do so and Category C contains viruses not infecting humans. Since the IRAF is run by isolate, a number of validation isolates have been identified for each of these strains. Currently there is no systematic definition for an 'influenza virus strain' and historically strains have been defined using different criteria, e.g. the pathogenicity and clade concept for H5N1 HPAI, the acquired M gene from the H1N1pdm09 for the H3N2v, etc.. The different FLURISK validation strains were therefore defined according to very different, individual criteria. For the future, it may be interesting and useful to review characteristics that could be used to systematically define influenza strains, such as genetic similarity, host/species specificity, pathogenicity, time and place. When assigning validation virus strains to one of the categories, it was acknowledged that categorisation remains arbitrary to some extent because some of the pertinent scientific information may still be missing. Some strains that have not yet done so may cause human infection in the future, hence we are likely to classify them wrongly with the knowledge of today. The categorisation of strains may also be confounded by epidemiological factors, e.g. intense human-animal contact. Viruses unlikely to jump the species barrier may jump given sufficient opportunity and viruses with supposedly high capacity to jump may not have done so simply because of missing opportunity. Epidemiological information and virological features that constitute risk factors for a species jump from animals to humans were identified through systematic literature reviews within the FLURISK project framework. Such risk factor information has been collected for the validation strains and their isolates and is presented in this report. However, care has to be taken in the interpretation of virological and epidemiological risk factors since most of these have either been studied in the context of avian influenza, in particular H5N1 HPAI, or in the context of H1N1pdm09 and we cannot be certain that these factors involve similar risks in virus strains for which they have not been studied so far. Nevertheless, it would be of interest to capture if genetic markers identified by FLURISK literature reviews as virological risk factors are present in the genepool of viruses circulating in certain geographic areas. In relation to this, animal influenza reference laboratories expressed an interest in receiving guidance on which virological properties to screen for in animal influenza viruses. To these purposes, the FLURISK project generated the Triage system (see Annex 2), that is a list of reached in the present document, without prejudice to the rights of the authors. 28

29 criteria (based on subtype and phenotypic/epidemiological characteristics) which aim at identifying and selecting, out of all samples that reach a veterinary laboratory, the isolates believed to have zoonotic potential and that, as a consequence, should be sequenced as a priority; the final objective being to reduce the time between the isolation of an influenza virus and the generation of the genetic information specifically required as data inputs for the IRAF model. The triage system provides indications on those virological properties to be screened for in animal influenza viruses and indication on the minimum epidemiological data necessary to parameterise the model. In addition, the Genetic Module of FAO s EMPRES-i database could be explored as a platform to visualize genetic markers in the context of epidemiological information pertaining to associated outbreaks. In the attempt to depict the strain circulation as accurate as possible different data sources were used to create graphs and maps in this report. Data includes animal influenza outbreak events, positive animal influenza surveillance events as well as animal influenza virus isolation events. However, each of the data sources used has some limitations and biases. The virus strain distribution maps and graphs featured in this report reflect therefore the best knowledge we have on the occurrence of the validation strains, but cannot be regarded as complete. While acknowledging the above-mentioned data gaps, the FLURISK epidemiological report aims at providing a comprehensive overview of epidemiological and virological properties of the eleven selected validation strains or their isolates that may act as risk factors for animal influenza viruses overcoming the species barrier and infecting humans. For Introduction, Materials/Methods, Results, Discussion and References of this review please refer to full text version in Annex 1f Activity 4: Review and compilation of data related to populations, trade and environment relevant to the IRAF development in WP2 In close collaboration with WP2, WP1 reviewed population, trade and environmental data to identify relevant data for the IRAF. This activity was jointly coordinated by the project consortium and the United Nations Food and Agricultural Organization (FAO), Rome, Italy. The following information has been compiled for the IRAF: Human population density; Population densities for animal species of concern - data available at FAO for swine and poultry (chicken and duck); Human-animal contact pattern/intensity - animal population density maps by species and production system (chicken, ducks and pigs; intensive vs extensive) have been provided by FAO s EPT+ project in June 2013, ahead of publication. For data sources used in the IRAF and their references please refer to the WP2 part of this report. reached in the present document, without prejudice to the rights of the authors. 29

30 WP1 further developed the following recommendations for the frequency of updating all data that is used to run the IRAF: Virus distribution and genetic information are both highly variable and should ideally be updated for each model run; Population density data are less variable and can be updated every 5 to 10 years (depending also on data availability); Implementation of influenza surveillance and control systems in animal populations continuously changes according to financial and political support, evolving disease situations, etc. Mechanisms should be developed and responsibilities defined for storing and updating such information in regular intervals. reached in the present document, without prejudice to the rights of the authors. 30

31 2. WORKPACKAGE 2 (WP2): RISK ASSESSMENT FRAMEWORK DEVELOPMENT AND VALIDATION 2.1. Introduction and objectives of WP2 In our modern and globalised society the interface between animals, humans and zoonotic pathogens is an increasingly important area for scientific research and surveillance. Zoonotic diseases know few boundaries and are potentially able to spread efficiently and quickly through the various international trade and human travel networks. In recent years, there have been several examples whereby diseases have either emerged and/or spread to new areas (e.g. SARS, West Nile Virus). One of the most notorious group of zoonotic pathogens are Influenza A viruses. Zoonotic transmission of a virus to which humans are naïve has been a recurring theme in the development of past influenza pandemics, including the 1918 Spanish flu pandemic and the 1967 pandemic, plus intermittent outbreaks in various countries (e.g. H7N7 in the Netherlands in 2003) [7-9]. These pandemics focused attention on avian influenza viruses, hence the scientific community was unprepared in 2009 for the unexpected emergence and worldwide spread of the swine-origin Influenza A virus (H1N1). The latter outbreak has challenged the ethos of Influenza A pandemic preparedness and highlights the importance of taking a broader approach to the problem. One such approach is to formalise the process by which countries/international organisations identify animal influenza viruses that are thought to warrant precautions against a potential human pandemic. A risk assessment tool has been developed in 2010 by the United States Centre for Disease Control and Prevention (CDC) to evaluate influenza A viruses that are not circulating in the human population but are circulating in the animal populations [10]. The objective is to identify appropriate risk mitigation strategies (e.g. developing a vaccine) based on a standardized set of factors to evaluate animal influenza A viruses in terms of their relative potential to cause a future human pandemic. The European Union, through the European Food Safety Authority (EFSA), is also keen to develop a more formal approach, specifically concentrating on the identification of animal influenza strains that have the potential to make the species jump into humans (the first step in any pandemic). Numerous reviews have been conducted to assess the driving factors behind the emergence and subsequent spread of recent (and ancient) influenza pandemics [7, 11-12], but the science is hampered by a very complex environmental and evolutionary system of interaction and feedback loops between virus(es), animal species and humans. The development of a pandemic is a chaotic and unpredictable process, where key factors will contribute varying amounts to the generation of different pandemics. However, the first key stage that will be important in any pandemic will be the exposure of susceptible humans to an animal influenza virus capable of human infection (with perhaps an intermediate stage of evolution through other animal species, e.g. avian mixing with swine influenzas); this has been the focus of this risk assessment framework. The task of developing a risk assessment tool to identify the relative likelihood of animal influenza A viruses to cause a human pandemic (yet alone predict the next pandemic) is therefore a formidable one. So far, no human pandemic strain of influenza has successfully been predicted, or indeed has any pandemic been attributed to any specific factor(s) such as climate, farming practices or virus genetic reassortment [7, 13-14]. The interaction between reached in the present document, without prejudice to the rights of the authors. 31

32 such factors is such that even minute permutations can lead to chaotic and unpredictable effects on virus characteristics and host susceptibility. Any attempt to produce a virus prioritisation framework should therefore be considered as a tool to objectively evaluate current (limited) scientific knowledge, rather than as an advancement of either scientific knowledge and/or ability to predict the next pandemic. However, by formalising the process of identifying potentially important factors for the ability of influenza A viruses to cross the species barrier from animals to humans, we hope to provide a relatively objective basis for identifying higher risk scenarios, and to formally identify gaps in data requirements and scientific knowledge that would be needed to improve the identification of high(er) risk viruses. There are two critical factors in any zoonotic transfer of pathogens: the ability of the pathogen to infect humans (a function of its genetic/biochemical characteristics), and the opportunity for it to do so (a function of the amount of human exposure and the innate resistance (immunity) of the human population). We therefore propose a prototype spatial risk assessment framework to rank Influenza A viruses circulating in animal populations for their potential to jump the species barriers to humans. The output will be a list of ranked animal viruses that could have the potential to infect humans in certain areas of the globe and, hence, may have pandemic potential. The precise risk question answered is What is the relative likelihood of influenza A virus x infecting one or more humans (compared to viruses y, z etc ) given its current presence and location in an animal population(s)?. To this aim, there are two main activities: the development of the risk assessment framework model and a user-interface to facilitate its practical use Activity 1: Development and validation of the risk assessment framework Derivation of framework model from first principles While zoonotic transmission of influenza A (or any disease) is inevitably complex and multifactorial, there are certain a priori assumptions we can make regarding a species jump, based on the physical attributes of disease transmission. First, there must be an influenza virus circulating in one or more animal species, which have sufficient contact with humans that virus transmission is possible by at least one route. Second, if we assume that the human dose-response relationship for the animal influenza virus is monotonically increasing, then we can assume greater exposure to the virus increases the chance of human infection. Hence, anything that increases human-animal interaction and/or human exposure to a contaminated environment also increases the chance of human infection. Third, we can also assume that the greater the animal and/or human population density, the more opportunities (contacts/exposures) there are for the virus to successfully initiate the first human infection (once human interactions with livestock, such as farming practices, are taken into account). These rather simple assumptions imply that there are some minimal spatial requirements that should be captured in the model framework. Finally, the virus must be capable of infecting a human; hence we assume there are distinct genetic characteristics (independent of animal host species) that affect the relative ability of the virus to cause human infection. That is, that the virus contains all of the relevant machinery to infect a human and bypass the innate reached in the present document, without prejudice to the rights of the authors. 32

33 immune response. The literature reviews conducted within WP1 have highlighted broadly similar themes/factors in terms of the epidemiological and virological elements that are important for cross-species transmission of influenza from animals to people. Our a priori assumptions are very similar to those made in classical disease transmission models [15]. Hence, with a few more assumptions we can derive a standard disease transmission model. We assume that at time t, within a distinct geographical area, there is a closed population of humans (of number H) and animals of a single species j (of number A(j)), where within a predefined period of time t the populations are homogenously mixed (i.e. all humans have contact with all animals). Therefore, assuming there is no recovery from infection during the period t, the number of new infections of virus i occurring in the human population, ID(i,j,t), because of contact with infected animal species j, is given by, (1) where (i,j) is the transmission coefficient for virus i between species j and humans and n(i,j,t) is the number of outbreaks within species j with virus i during time t. In order for an accurate representation of the absolute risk, n(i,j,t) would be the prevalence of infection within the animal population. However, this is unlikely to be known with accuracy at the beginning of an outbreak, and hence, we use numbers of outbreaks (where the farm is defined as the epidemiological unit and each infected farm is defined as an outbreak) as a more realistic method to weight the risk according to the intensity of infection. This therefore means that ID(i,j,t) is not a measure of absolute risk, but it can be used to assess the relative risk of different viruses against each other. In order to more realistically estimate the transmission of infection from infected livestock to susceptible humans we differentiate between intensive and extensive livestock production, such that is also dependent on k, where k represents either extensive or intensive livestock production. That is, we now denote the transmission coefficient as (i,j,k) and the number of infected livestock as A(j,k). The formula in Equation 1 represents an incidence rate of new infections over time. From the introduction we have defined risk as the probability that one or more humans will be infected during the time period t. This is equivalent to the definition of epidemiological risk. This is usually derived from the incidence rate assuming contact with Poisson intensity [16]. From Equation 1 the unit of (i,j,k) is the proportion of exposed humans that are infected. By definition the presence of farmed animals also implies the presence of humans, and we define the ratio of humans to livestock as w(j,k), i.e. the number of exposed humans is equal to A(j,k)w(j,k). Therefore, the risk of one or more human infections in a given area, R(i,j,t), is given by reached in the present document, without prejudice to the rights of the authors. 33. (2) The transmission coefficient (i,j,k) is a summary term incorporating both host speciesspecific (e.g. number and type of contacts between human and animals in given time period, shedding rate) and virus-specific (e.g., ability to attach to human receptors, human immune response to infection) factors. In the absence of further information, we split this term into a species-specific and virus-specific component:

34 , (3) FLURISK-Final report where V(i) is a virus characteristic score, representing the intrinsic ability of virus i to infect a human, and (j,k) is the species-specific component, which weights the transmission component according to the typical characteristics of human exposure given infection in animal species j. For example, the amount of virus aerosolised by chickens maybe very different to that of pigs, and the number of contacts between humans and animals may also be very different under intensive and extensive production systems Case study The model suggested in Equation 2 is applicable on a global scale, for any species where the general assumptions will hold. The aim of the model is to broadly capture the spatial risk of human/animal interaction for a range of viruses, and so it is deliberately aimed at a low resolution of detail. However, even at this low resolution, data input can be problematic. Therefore, we have chosen a case study that is of current interest and high profile infection of domestic chickens and hence also where we have the best access to relevant data. Of primary concern is the level of surveillance conducted for a species; unfortunately our original intention of including swine influenza within the model was abandoned due to a lack of reliable global swine influenza surveillance information (sufficient for realistic parameterisation of the model). The intention of using the chicken case study is to a), highlight the type of result the model will produce and b), determine the data requirements for such a model. These are described in more detail in the next section Parameter estimation The parameter estimates used in the current version of the model are given in Table 2.1. Table 2.1: Parameter estimates for current version of the model using avian influenza as the case study. Parameter Description Value Reference A(j,k), j = chicken Global poultry population density at resolution of 0.5 pixels (between 5- See Figures 1a-b FAO, unpublished k = intensive 80km 2 depending on location on data or extensive globe) (j,k) Epidemiological component of See Section transmission parameter w(j, intensive) Contact ratio for intensive chicken production Uniform(4*10-5, 1*10-3 ) Authors estimate w(j, extensive) Contact ratio for extensive chicken production Uniform (0.02,1) Authors estimate V i Virus score dependent on virus 0-1 (See Section ) n(i,j,t) Number of outbreaks during time t See Section Population densities, H and A chicken ; contact intensities w int and w ext reached in the present document, without prejudice to the rights of the authors. 34

35 Chicken population densities, A(chicken,intensive) and A(chicken,extensive); contact intensities w(chicken, intensive) and w(chicken,extensive) The model proposed in Equation 2 is applicable for a theoretical population of homogenously mixed animals and humans. The smaller the area of the closed population of humans and animals, the more we are likely to represent the ideal of homogeneous mixing. At a global level, the highest-resolution data we have for both extensive and intensive chicken population densities (see Figure 2.1 and Figure 2.2) is at 5km 2, from Geonetwork (Food and Agriculture Organization of the United Nations - FAO) (unpublished data). An example of the methods used to produce these estimates for population density by production type is given by Van Boeckel et al. [17]. In order to modify the number of available chickens that would expose humans to influenza, we differentiate between intensive and extensive chicken production. Research suggests that for every chicken that is reared in an extensive system there is at least one human who would be within reasonable contact with the chickens for influenza exposure to occur [18]. In intensive systems, where there are perhaps tens of people maintaining a flock of tens or hundreds of thousands of birds behind closed doors, the effective susceptible population of humans is much reduced. Therefore, a ratio of thousands to ten thousands of birds for every human is more likely. For the purposes of this study, the ratios of human:chicken have been set according to the authors estimate of between 1000 and birds:human for intensive production, and 1 to 50 birds:human for extensive production. These figures are highly uncertain and more research would be required to better parameterise these factors. reached in the present document, without prejudice to the rights of the authors. 35

36 Figure 2.1: Intensive chicken population density (FAO, unpublished data) bodies identified above as author(s). In accordance with Article 36 of Regulation (EC) No 178/2002, this task has Safety Authority and the author(s). The present document is published complying with the transparency principle 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. 36

37 Figure 2.2: Extensive chicken population density (FAO, unpublished data). bodies identified above as author(s). In accordance with Article 36 of Regulation (EC) No 178/2002, this task has Safety Authority and the author(s). The present document is published complying with the transparency principle 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. 37

38 Transmission coefficient, (j,k) (j,k) was generated from published reports of avian influenza outbreaks [19-29] in chickens that resulted in human infections. The total number of humans P infected in an outbreak can be expressed as the product of the number of infected animals I of species j (in this case, chickens) in production system k (intensive and extensive), the number of susceptible humans S, and the transmission parameter (j,k) : These data were gathered from the above reports. Only humans that were exposed during normal contact with birds (e.g. farmers and farm workers) were included, and not those taking part in post-influenza-detection interventions (e.g. government workers engaged in systematic culling). The number of infected birds in epidemiological investigations involving large farm sizes is not usually specified, so the number of birds on infected farms was used as a proxy for the number of infected birds. Denominator data for outbreaks in backyard flocks were difficult to obtain, and so were simulated when necessary (see Table 2.2. (4) Table 2.2). For a given outbreak m, the mean number of human infections per infected bird is approximated by a gamma distribution:, (5) Hence. (6) There was a large amount of variation between the values of m (j,k), such that overall distributions for (j,k) were unable to be fitted. Instead, the final distributions for (j,k) was reached in the present document, without prejudice to the rights of the authors. 38

39 formed by randomly sampling from the distributions for m (j,k) generated from each outbreak. The values for H m, I m and S m from each study are given in Table 2.2. Table 2.2: Parameter estimates for the estimation of the transmission parameter (j). Outbreak References Parameter H m: Number of I m : Estimate for the S m : Estimate humans infected number of infected for the number with avian birds in outbreak m of susceptible influenza during humans outbreak m* exposed during avian influenza outbreak m* Intensive production Netherlands, 2003 (H7N7) [9, 20] 20 6,096,898 1,400 Canada, 2004 [24, 30] 2 1,147, (H7N3) Japan, 2005 [23] 20 5,700, (H5N2) United [19] 1 47, Kingdom, 2006 (H7N3) Extensive production Thailand, 2004 [25-27] 14 Binomial 1019 Poisson (H5N1) ( , ) (4), truncated at min=1 and max=11 Egypt, [28-29] Poisson (15.2) (H5N1) Poisson(4.6), truncated at min=1 * Excluding those engaged in interventions reached in the present document, without prejudice to the rights of the authors. 39

40 cases with known exposure to backyard chickens Number of outbreaks in chicken population and selection of case study virus strains Number of outbreaks reported for virus i The presence of avian influenza outbreaks in chickens has been identified as a risk factor for human infection [31]. The number of outbreaks was used in this model as the most accessible factor to describe the intensity of infection of each virus i on a global scale. A relatively complete and maintained dataset is EMPRES-i, collated and published by FAO ( This is a web-based application that aims to facilitate collation and analysis of, and access to, animal disease information. Data are received from a wide range of sources including country or regional project reports, non-governmental organisations and government ministries of agriculture and health. OpenFluDB, on the other hand, is a publicly available influenza-specialized database developed by the Swiss Institute for Bioinformatics (SIB) that contains genomic and protein influenza virus sequences. Isolates are submitted together with a minimum of epidemiological information such as host species and geographical location at country or admin 1 level. For the case study we have chosen to assess the risk of human infection from previous avian flu outbreaks, such that there may also be some validation of the model results. Data on H5N1 outbreaks in poultry were extracted from the EMPRES-i database, including all outbreaks recorded in the six months up to the first record of human infection from particular outbreaks (May 2004 for H5N1, 2002 for H7N7 and 1997 for H9N2). There are over 4000 records for H5N1 during the six months prior to May 2004 (during an outbreak in Viet Nam), of which a small sample of records is shown in Table 2.3, including only relevant fields. A similar table is presented for H9N2 in Table 2.5, which includes all records in 1996/1997. It is clear from visual inspection that the quality of data is highly variable between these two databases, especially for location information for H9N2. This drastically reduces the number of complete records that can be used to populate the model. This is the major data issue/gap identified in the model; a much more standardised system of data collation of virus isolates would greatly assist in populating the model with data, increase its accuracy, and speed up the process of data collation. Greater standardisation of data would arguably have many other benefits beyond the scope of this model. This data gap and others are further described and assessed in the discussion. The longitude and latitude data from records for domestic chicken infections can be taken to assess the intensity of infection in a three-to-six month period. Specifically, a spatial kernel density model 2 is applied to the data to weight the spatial intensity of infection (and normalised to one over the maximum under-reporting factor (see later)). Where geographical data are missing, we fill in with dummy data by randomly sampling coordinates within five degrees of the bounding box delineated by the minimum and maximum coordinates contained in the records for each virus. 2 We used the 2d binned kernel estimator within the R package KernSmooth. reached in the present document, without prejudice to the rights of the authors. 40

41 reached in the present document, without prejudice to the rights of the authors. 41

42 Table 2.3: Sample of records from H5N1 dataset from EMPRES-i (relevant fields only) Id Observation Latitude Longitude Admin 1 Admin Country Animal species date 1 (ID) /05/ Chiang Mai 2859 Thailand chicken /05/ Vinh Long 3388 Viet Nam quail /04/ Phetchabun 2892 Thailand chicken /04/ Phetchabun 2892 Thailand chicken /04/ Uttaradit 2924 Thailand chicken,geese /04/ An Giang 3326 Viet Nam chicken,duck /04/ Chonburi 2861 Thailand chicken /04/ Khon Kaen 2866 Thailand chicken /03/ Kampong 793 Cambodia chicken Cham /03/ Takeo 814 Cambodia chicken /04/ Kyooto 1671 Japan crow Table 2.4: H5N1 dataset including clade information from the EMPRES-i Genetic Module (relevant fields only). source latitude longitude Country Admin 1 Observation date Clade Species description OPENFLU China Fujian bird OPENFLU China OPENFLU Thailand OPENFLU China Gull OPENFLU China Hunan Province OPENFLU Japan Kyoto-fu Insect OPENFLU Thailand Cats EMPRESi/OPenFluDB Thailand Nakhon 08/11/ pigeon Pathom OPENFLU Thailand Chicken OPENFLU China Anhui Sheng Chicken OPENFLU Indonesia Chicken OPENFLU Indonesia Chicken OPENFLU Indonesia Propinsi Banten like Chicken reached in the present document, without prejudice to the rights of the authors. 42

43 Table 2.5: H9N2 dataset (relevant fields) from OpenFluDB. NAME COLLECT SPECIES COUNTRY ADMIN 1 LAT LONG _ YEAR A/Chicken/Beijing/1/ Chicken China A/Chicken/Beijing/1/ Chicken China A/Chicken/Beijing/2/ Chicken China Beijing Shi A/Chicken/Guangdong/11/ Chicken China Guangdong Sheng A/Chicken/Hebei/1/ Chicken China Hebei Sheng A/Chicken/Heilongjiang/10/ Chicken China Heilongjiang Sheng A/Chicken/Hong Kong/G9/ Chicken Hong Kong A/Chicken/Korea/ / Chicken South Korea A/Chicken/Korea/38349-p96323/ Chicken South Korea A/Chicken/Korea/MS96/ Chicken South Korea A/Chicken/Shandong/6/ Chicken China A/Chicken/Shenzhen/9/ Chicken China A/Chicken/Sichuan/5/ Chicken China Sichuan Sheng A/Chicken/Tianjing/1/ Chicken China A/Chicken/Tianjing/2/ Chicken China A/Duck/Hong Kong/Y280/ Duck Hong Kong A/Duck/Hong Kong/Y439/ Duck Hong Kong A/Duck/Nanjing/1/ Duck China 1997 Pigeon Hong Kong A/Pigeon/Hong Kong/Y233/97 A/Quail/Hong Kong/G1/ Quail Hong Kong A/Quail/Shanghai/8/ Quail China A/chicken/Guangdong/5/ Chicken China Guangdong Sheng A/chicken/Guangdong/6/ Chicken China Guangdong Sheng A/chicken/Korea/AI-96004/ Chicken South Korea A/chicken/Korea/MS96-CE6/ Chicken South Korea A/chicken/Osaka/aq48/ Chicken Japan A/chicken/Shandong/7/ Chicken China A/chicken/Yanggam/AI96004/ Chicken South Korea Kyonggi-do A/duck/Hokkaido/31/ Duck Japan Hokkaido A/duck/Hong Kong/W213/ Duck Hong Kong A/duck/Nanjing/2/ Duck China A/parakeet/Chiba/1/ Parakeet Japan A/Muscovy duck/fujian/cl/ Duck China Fujian A/duck/Malaysia/91/ Duck Malaysia A critical factor missing is information on the genetic sequencing of the virus, which is required to assign the characteristics that would ultimately determine the virus score V(i). This gap is now being addressed by the EMPRES-i Genetic Module which links disease outbreak information stored in EMPRES-i with genetic sequence information stored in OpenFluDB (Claes et al. 2013, Manuscript in preparation). However, genetic sequences are not routinely reached in the present document, without prejudice to the rights of the authors. 43

44 generated for all virus isolates (the need for genetic sequencing is often determined by animal health, rather than public health, drivers). We do not address this issue within this project, except to suggest some relevant decision criteria ( triage system ; Annex 2) that might be considered by the isolating/reference laboratories. These criteria would enable at least partial sequencing of isolates of public health interest, and provide the relevant characteristics that would determine the virus score of each isolate. In the interim, in order to further differentiate within subtypes, we have been provided with a limited H5N1 dataset that includes information on clades. While this does not necessarily differentiate viruses with different viral characteristics as described in Section 2.2.4, it does allow us to differentiate between viruses in a real-life manner, and hence identify any issues in doing so. Thus, our use of clades in this prototype should be considered a proxy categorisation, which ideally should be replaced by virus scores, strain, isolates, or some other identifier. Clade information is available for 474 H5N1 records within the larger H5N1 dataset (a sample is presented in Table 2.4). Longitude and latitude information is missing for many of the records and again dummy coordinates are assigned for missing values where necessary. In order to preserve a reasonable number of records for each category of virus we differentiated viruses only by the first number of the clade (i.e. clades 2.1.2, etc were collated under a category of Clade 2 viruses). This resulted in three separate virus categories that included domestic chicken outbreaks: Clade 1, 2 and 9. The number of assumptions required to manipulate real-life data into the model largely invalidates the accuracy of assigning risk to actual clades 1, 2 and 9. Hence, to avoid confusion, we have simply defined three virus strains (H5N1_1, H5N1_2, and H5N1_3) and assigned virus scores to each. We therefore produce results for three hypothetical viruses, although they have the advantage of being drawn from realistic outbreak data. Under-reporting factor for country C The local number of infected birds will be hard to estimate in an outbreak situation. There will almost certainly be an under-reporting factor, URF, where not all infected farms/animals are identified. Naturally this under-reporting factor is by definition unknown, but we can make a rough estimate by taking available information on the sensitivity of avian influenza surveillance systems and identifying the probability of detecting an infected holding. As discussed above, the most likely information available in an outbreak situation will be the number of infected holdings in a particular region. We therefore determined an underreporting factor at the farm level as the most accessible parameter to estimate. In any given spatial cell the true number of infected farms, N I (j), will be given by where URF C (j) is the under-reporting factor for species j in the country (C) within which the pixel is located, and N D (j) equals the number of detected, infected farms within the cell. Thus: (7) (8) reached in the present document, without prejudice to the rights of the authors. 44

45 If the probability of detecting an infected farm, P D (j), is equivalent to N D (j)/n I (j), then We can make an indirect estimate of URF C (j) for countries reporting the type of surveillance (active and/or passive) that they carry out for avian influenza. For the case study we assess only surveillance in chickens, hence the generalised notation for species (j) is dropped for the specific calculations below. Numerous studies have developed scenario tree models to assess the sensitivity of surveillance systems for avian influenza [32-34]. These were identified by a systematic literature review. Searches were performed in Google Scholar, PubMed and CAB Direct, using the search terms [ scenario tree OR sensitivity AND surveillance AND avian influenza ]. Only those articles calculating or simulating surveillance system sensitivity (SSe) for avian influenza in domestic birds, using scenario tree methodology, were included. To avoid bias in the data, where multiple surveillance scenarios were analysed within a single article (for example, when a surveillance system changed over time), only the most recent representation was included. Data on SSe, type of surveillance, species and sector of interest, reference population, sampling strategy, and design prevalence were captured in Microsoft Excel Surveillance system sensitivity (SSe) is the probability of detecting at least one infected farm, given that infection exists within the country or region. The scenario tree models are based on a generalised equation for SSe, which can be given as: (9), (10) where P I is the design prevalence of the study (equivalent to the probability of infection at farm level), and N S is the number of farms sampled within the study. Rearranging this equation gives:. (11) Remembering that P D, N I and N D represent the probability of detecting an infected farm, the number of infected farms, and the number of detected farms, respectively (P D = N D /N I and P I = N I /N S ), simplifying Equation 11 for N D results in:. (12) That is, N D must be less than or equal to N S : the number of detected farms must be less than the number tested. This is a boundary condition that must be met to produce realistic estimates of SSe and P D. Given data on SSe, N S and P I then we can estimate URF c for chicken surveillance from the following distribution: reached in the present document, without prejudice to the rights of the authors. 45

46 . (13) The equations represent an approximation of the full mathematics, assuming that the number of herds sampled is much less than the number of flocks in a country (a generally accepted boundary condition for using this approximation is less than 10% of total flocks sampled). If the sampling percentage of flocks is greater than around 10%, then a Hypergeometric derivation of URF C would be appropriate (e.g. in the case of passive surveillance, where we assume all flocks in a country are effectively sampled ). However, this requires detailed information on the total number of flocks within a country, which is not known for many countries around the globe. Should these data become available, then a Hypergeometric derivation may well be appropriate, especially for the passive surveillance estimate. A distribution was generated for each study from Equation 13. We were able to produce overall distributions of URF C for each of three categories of surveillance (active surveillance for avian influenza, passive surveillance for high-pathogenicity avian influenza (HPAI), and passive surveillance for low-pathogenicity avian influenza (LPAI)) by resampling from the distributions from applicable studies (e.g. the overall distribution for HPAI passive surveillance was generated from resampling from the URF C distributions for Spain, New Zealand, Nigeria and Denmark). The distributions were truncated at a maximum of An exponential distribution (the best fit), was then fitted to the overall distributions for each surveillance type (in order to optimise the code for running the model) using the dfittool in MATLAB R2012b (The MathWorks Inc., Natick, MA, 2000). Ultimately, passive surveillance for LPAI was discarded, as it is uncommonly reported by countries, and highly insensitive. The final distributions for URF C are given in Figure 2.3. There is a clear difference in the distributions for active and passive surveillance; the most likely value for the under-reporting factor for active surveillance is within the range of 2-4, while for passive it is the region of The passive surveillance distribution is bimodal due to truncation, with a significant second most likely value around 1000, which is orders of magnitude higher than the peak for active surveillance. This intuitively makes sense, as passive surveillance is dependent on self-reporting. Each country in the world was then assigned the relevant distribution (passive surveillance for HPAI and/or active surveillance for HPAI/LPAI) according to the category of surveillance reported by each to the World Organisation for Animal Health (OIE) over the preceding three years, and to the FLURISK consortium via the surveillance survey detailed in WP1 (See Section 1.3). Information on avian influenza was available from either or both sources for 169 of the of the 242 countries identified by WP1. Of these, only 26 countries presented the same information via both sources, and 65 countries had a direct conflict between the information from the two sources. Conflicts included 25 countries that had reported specific LPAI surveillance to the OIE, but according to their survey results, were not undertaking any surveillance for LPAI. Additional information was provided by the OIE in the absence of survey results for either or both HPAI and LPAI in 24 cases, and was provided by the survey in the absence of OIE reports in 47 cases. In order to be able to update this information in the future, it was decided to classify countries according to their OIE status, except where missing information could be supplied from the survey results (i.e. when the two sources were reached in the present document, without prejudice to the rights of the authors. 46

47 directly contradicting, the OIE status was used). Countries reporting both types of surveillance were assigned to the active surveillance category, and those with no known surveillance type were not included in the model. reached in the present document, without prejudice to the rights of the authors. 47

48 Figure 2.3: Distributions for URF C for active and passive avian influenza surveillance bodies identified above as author(s). In accordance with Article 36 of Regulation (EC) No 178/2002, this task has Safety Authority and the author(s). The present document is published complying with the transparency principle 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. 48

49 Parameter estimation of virus score V(i) Identification of intrinsic factors responsible for the ability of a virus to infect humans The identification of the intrinsic factors associated with an initial species jump from animals to humans is challenging, as it is limited by the extent of current scientific understanding. For example, the mechanisms by which specific viral mutations occur, and their impact, are not fully understood. Further, we are considering an unpredictable, natural process for which it is not possible to account for all uncertainties. This also needs to be balanced by identification of factors for which data are available in order to characterize and input strains into the RAF. Furthermore, the virus score should only include factors associated with the initial jump into humans and not consider factors deemed important for human-to-human transmission that would be used to characterize pandemic potential after the initial jump. To focus the identification process, intrinsic factors were considered for each stage of the virus life cycle, in terms of their possible importance for making the species jump. These stages were attachment and fusion to the (human) host cell, escape from the host s immune response, successful viral replication, and viral release and shedding [35-36]. Using a literature review and extensive discussions within the project team, five intrinsic factors were identified. Each factor was described by two mutually exclusive characteristics as outlined in Table 2.6. In total, therefore, 32 (5 2 ) different virus strains can be characterized within the RAF. The first factor identified is associated with the preference the virus has to attach to the sialic acid (SA) receptor on cells of the host s respiratory tract. This is a key step in the virus lifecycle: if the virus is not able to attach readily to host cells, it will not readily enter them. Broadly, avian and equine viruses preferentially bind to 2-3 SA receptors in the respiratory tract and human and swine viruses prefer 2-6 SA receptors. These preferences are used to define the two characteristics associated with receptor preference. Attachment and fusion, replication, and release and shedding are all affected by the next three factors: presence of known mutations in the ribonucleoprotein complex (RNP), re-assortment within the virus, and whether there is a deletion within the stalk region of the neuraminidase (NA) viral surface protein. These factors were included as it is broadly considered that adaptive mutations that increase polymerase activity may increase replication and progeny virus formation and/or allow adaptation to mammalian importin-, a cell protein that mediates viral transport into the cell nucleus, amongst other phenotypic consequences. Regarding reassortment, the acquisition of a gene segment from another lineage/subtype circulating in a different population could facilitate the virus crossing the species barrier; the extent of this ability is dependent on the origin of the donor virus and the segment exchanged. To address this, two characteristics were defined: no evidence of re-assortment or acquisition of any gene segment from mammals other than swine or humans, or birds other than gallinaceous poultry; and acquisition of any segment from a swine, human or gallinaceous reached in the present document, without prejudice to the rights of the authors. 49

50 poultry virus strain. The last factor, phylogenetic relatedness of haemagluttinin (HA) surface protein to strains circulating in the human population, aims to assess the host immune s response to the virus. Those virus strains which are closer related to strains already circulating in the human population (defined by the project s expert virologists as having >85% homology), may instil an effective immune response in the host. In contrast, those strains which are divergent from those currently circulating will be entering a naïve host and a longer time span may be required for immunity to the strain(s) to develop. Based on the factors outlined in Table 2.6, the minimum data requirement for characterizing strains within the virus score (and the risk assessment framework) is genome sequencing. This is routinely undertaken for new and novel virus strains but it is acknowledged that the time it takes to undertake this analysis may influence the rapidity by which the RAF tool could be used. It is hoped that the triage system developed for this project (see Annex 2) can prioritise virus sequencing so that this data is collected in a routine and timely fashion by international laboratories, including both novel and endemic virus isolates Virus score development The virus score (V(i)) represents the probability (or fitness) of a virus strain, i, jumping the species barrier given certain intrinsic properties or factors. An important criterion of the virus score with respect to the risk assessment framework is that it is independent of the host species. The hypothesis, therefore, is that the intrinsic factors influential in a strain jumping the species barrier are independent of whether the virus resides in a swine or avian species. The virus score is given by the following equation: where F(x,i) is the value of the factor x of virus i, W(x,i) is the weight of factor x of virus i, and b(x) is the constant associated with factor x. (14) reached in the present document, without prejudice to the rights of the authors. 50

51 Table 2.6: Definitions of the characteristics for each factor (F(x)) Weight of factors To parameterize the weights associated with each factor, a questionnaire was designed utilizing conjoint analysis methods [37]. This is a common method employed in consumer preference surveys in which the consumer is provided with a choice of products and are asked to select the preferred product. From these choices, inference can be made as to which combination of factors is most influential on the overall product choice. Experts (or subjects ) preferences are measured in terms of individual attributes of the product, and levels are assigned to each attribute. Extending this to the virus characterization score, conjoint reached in the present document, without prejudice to the rights of the authors. 51

52 analysis was used to ascertain experts preferences for which virus strains with a given set of characteristics (as defined in Table 2.6) are more likely to jump the species barrier to humans. Conjoint analysis measures trade-offs between several different features negating the need for asking successive questions about one characteristic (feature) at a time. In other words, it can effectively consider interactions between characteristics. Using utility theory [37], the responses from conjoint analysis can be assessed to understand how the expert makes decisions given the choices provided. Using this conjoint analysis approach with the data in Table 2.6, there are 32 hypothetical virus strains (profiles). The full profile approach (e.g. 32 virus strains) typically yields too many attributes to consider within an expert elicitation. In this instance, an orthogonal fractional design matrix is an efficient way of testing the effects of factors on respondent preferences by using a small sample to represent the profiles of interest. The orthogonal matrix is used as it is balanced with each level of one attribute combined the same number of times with each level of the other attributes. An orthogonal design ensures all pairs of levels are represented and the attributes can be evaluated separately. Non-orthogonal matrixes may lead to unclear conclusions of expert preferences. Given this, the Taguchi orthogonal array for five factors with 2 levels yields 8 virus profiles as outlined in Table 2.7. Table 2.7: Orthogonal array matrix from the full virus strain profile ; (-1 denotes characteristic 1 is present and 1 denotes characteristic 2 is present). Virus Profile Factor 1 Factor 2 Factor 3 Factor 4 Factor Questionnaire design and expert elicitation There are several approaches for implementing a conjoint analysis using an orthogonal array matrix. In this study, two different approaches were used, namely, discrete pairwise choices and ranking. Discrete pairwise choice is a technique commonly used for measuring respondents choices and allows for internal consistency checks; experts with consistent judgment will provide greater predictive value. In applying this approach to this study the 8 hypothetical virus strains yielded 28 pair-wise combinations. For each pair-wise combination, the expert was asked, Please tick which virus strain (A or B) is more likely to jump the species barrier from animals to humans? To check for consistency within the experts responses, 7 profiles were repeated, yielding 35 pair-wise questions. The 28 pair-wise combinations were randomized to prevent any bias in the presentation of the questions. Further, for the duplicated questions, the order of Strain A and Strain B was reversed. reached in the present document, without prejudice to the rights of the authors. 52

53 In addition, the experts were asked to rank the 8 virus profiles in terms of their likelihood for jumping the species barrier (most likely =1 to least likely =8). It was aimed that this would serve, to some degree, as a validation of the analysis from the pairwise choice. A pictorial representation of the ranking question is summarized in Figure 2.4. The 35 pair-wise choice questions and one ranking question were collated with an introduction in a questionnaire designed in Survey Monkey (SurveyMonkey Inc., Palo Alto, Claifornia, USA, In order to make the questionnaire as userfriendly as possible, the characteristics associated with the hypothetical strains were represented pictorially. This allows for a more efficient and visual guide as to the key differences between pair-wise strains. After extensive piloting of the questionnaire by scientists from the project team institutes, the final version was distributed to all of the virologists within the project team and 23 international experts whom were identified by the project team. The criteria for selecting the experts were individuals with expertise/knowledge (as demonstrated by publications) on the factors associated with species jump. After input from the project team and consultation with WHO colleagues, 7 experts were identified within Europe, 12 within North America and 4 from Asia. Individuals were given 2 weeks to respond and a reminder was distributed. Experts were not explicitly identified upon submitting a completed survey but were allowed the opportunity to comment and be contacted with a summary of the results from the study. Initially, the response rate was low, and colleagues within the project team directly contacted the identified experts to gain their engagement with the project. This assisted in gaining further responses. At the end of the elicitation process, the response rate was 43% for the international experts, which was supplemented by 4 team virologists and 6 additional virologists providing 21 responses in total to analyze. Of these, however, only 18 responses (3 from the project team, 4 additional scientists and 11 experts) were included in the final analysis due to incomplete responses in 3 questionnaires. reached in the present document, without prejudice to the rights of the authors. 53

54 Figure 2.4: Ranking question; Please rank the following 8 virus strains in relation to their potential to jump the species barrier from animals to humans (1- most likely, 8 = least likely). bodies identified above as author(s). In accordance with Article 36 of Regulation (EC) No 178/2002, this task has Safety Authority and the author(s). The present document is published complying with the transparency principle 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. 54

55 Data analysis and results for virus score Ranking The responses from the experts are summarised in Table 2.8. In order to analyse the expert s responses, a linear regression model was fitted within R. (15) where y i,k is the rank of strain i by expert k, F 1,2,3,4,5 is the value factor x = {1,2,3,4,5} and W 1,2,3,4,5 is the weight of each respective factor. The value of each factor was denoted by -1 if characteristic 1 was present and by 1 if characteristic 2 was present. The latter are defined in Table 2.7 where Strain A is equivalent to profile 1, Strain B is equivalent to profile 2 etc. Using all the expert s data, part-worths 3 for each factor were estimated by summing the absolute values of the regression coefficients and dividing each factor s respective absolute value coefficient by the total. Specifically, the part-worths are 0.36 (Attachment Factor 1), 0.25 (Mutations- Factor 2), 0.24 (Re-assortment Factor 3), 0.11 (Stalk length- Factor 4) and 0.03 (Phylogentic relatedness Factor 5). Clearly the first three factors dominate with a decreasing, ordered, relative importance for the remaining factors. Pairwise analysis As mentioned previously, there were 35 questions asking the expert to select either Strain A or Strain B as more likely to jump the species barrier. Seven of these questions were duplicated to assess the consistency (and hence reliability) of the expert. Those experts that were inconsistent on >2 occasions were excluded from the analysis; 7 experts had one inconsistency and 21 experts had no inconsistencies, therefore, all experts were included in the analysis. To better understand the variation in the responses between the experts, a heatmap was produced (see Figure 2.5). Along the x-axis are the experts and along the y-axis are the 28 questions. For each question, an expert may either chose Strain A (red) or Strain B (green). 3 When multiple attributes are combined together, they describe the total worth of the virus strain. The utility values for the separate parts of the virus strain are part-worths. reached in the present document, without prejudice to the rights of the authors. 55

56 Table 2.8: Summary of the 18 responses to the ranking question. Expert 1 Expert 2 Expert 3 Expert 4 Expert 5 Expert 6 Expert 7 Expert 8 Expert 9 Expert 10 Expert 11 Expert 12 Expert 13 Expert 14 Expert 15 Expert 16 Expert 17 Expert 18 Strain A Strain B Strain C Strain D Strain E Strain F Strain G Stra in H Figure 2.5: Heat map illustrating the variation between and within experts answers. bodies identified above as author(s). In accordance with Article 36 of Regulation (EC) No 178/2002, this task has Safety Authority and the author(s). The present document is published complying with the transparency principle 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. 56

57 It can be seen that between experts there is complete consistency (agreement) for certain questions (e.g. 4, 7, 22) and slightly more variation for others (e.g. 2, 27). These pairwise choice data were analyzed using a discrete-choice logit model within R. Using this model, the probability of choosing strain A over Strain B is a function of the utility of strain A divided by the function of the utilities for both Strain A and Strain B, as given by: Here, the two characteristics of probabilities are satisfied: Further, the utility of each choice depends linearly on the five factors: To implement the model within R, the data extracted from the questionnaires was reformatted. Specifically, for each expert, the characteristics for each strain within each question was inputted alongside the choice the expert made for each question (i.e. Q16 Strain A Choice= no, Q16 Strain B Choice= yes ). A discrete-choice logit model defined by where C is the choice of expert k for question q, β o is the intercept/constant and F 1 is factor 1 and w 2 is weight associated with factor 2 etc. Within this analysis, Factor 1 is assigned a -1 if characteristic 1 is present or 1 if characteristic 2 is present. Using this model, the part-worths were estimated using the same approach as for the ranking analysis as follows: 0.33 (factor 1), 0.26 (factor 2), 0.29 (factor 3), 0.07 (factor 4) and 0.05 (factor 5). Based on this, it can be seen that the most influential factors are those relating to attachment (factor 1), mutations (factor 2) and re-assortment (factor 3) within the virus. Both stalk length (factor 4) and phylogenetic relatedness (factor 5) were considered to have very little influence on the overall probability of a strain jumping the species barrier. This is in alignment with the conclusions from the ranking analysis. Indeed, there is very little difference in the conclusions between both analyses. For the estimation of the overall virus score for each virus strain, the results from the pair-wise comparison are used as it is considered a more robust method. (16) reached in the present document, without prejudice to the rights of the authors. 57

58 Using the regression coefficients and the defined characteristics of each of the 32 virus strains included within the risk assessment framework, a virus score can be estimated using the following equation: where Score k is the score of virus strain i (k 1, 2, 3, 32), F i,k is value of factor i (i 1, 2,.. 5) of virus strain i, W x is the estimated regression coefficient for factor x and b is the estimated regression intercept. Using the standard errors of the regression coefficients, upper and lower virus scores can also be estimated (i.e. uncertainty range). Finally, these scores were normalised by the maximum of the error bar as depicted in Figure 2.6 and Table 2.9. (17) Figure 2.6: Normalised virus scores for each of the 32 virus profiles. Based on this analysis, it can be concluded that the virus strain most likely to jump the species barrier (akin to V(i)=1) is that with a receptor binder for 2-6 SA, presence of known mutations in the RNP complex, acquisition of any segment(s) from a gallinaceous poultry or reached in the present document, without prejudice to the rights of the authors. 58

59 swine virus, a full-length stalk, and strains phylogenetically related to strains currently circulating in the human population. In contrast, a strain that is considered least likely to jump (i.e. V(i)=0) has a receptor binding affinity for 2-3 SA, no known mutations in the RNP complex, no reassortment or acquisition of any segment(s) from a bird species other than gallinaceous poultry or mammals other than swine, a short-length stalk, and is not phylogenetically related to strains currently circulating in the human population. reached in the present document, without prejudice to the rights of the authors. 59

60 Virus profil e Table 2.9: Final virus scores for 32 virus profiles. Normalised score Factor 1: receptor binding preference Factor 2: presence of mutations in RNP Factor 3: reassortment within virus Factor 4: stalk deletions in NA Factor 5: phylogenetic relatedness of HA a2,6 None None Full stalk Different a2,6 None None Full stalk Related a2,6 None None Short stalk Different a2,6 None None Short stalk Related a2,6 None Acquisition Full stalk Different a2,6 None Acquisition Full stalk Related a2,6 None Acquisition Short stalk Different a2,6 None Acquisition Short stalk Related a2,6 Mutations None Full stalk Different a2,6 Mutations None Full stalk Related a2,6 Mutations None Short stalk Different a2,6 Mutations None Short stalk Related a2,6 Mutations Acquisition Full stalk Different a2,6 Mutations Acquisition Full stalk Related a2,6 Mutations Acquisition Short stalk Different a2,6 Mutations Acquisition Short stalk Related a2,3 None None Full stalk Different a2,3 None None Full stalk Related a2,3 None None Short stalk Different a2,3 None None Short stalk Related a2,3 None Acquisition Full stalk Different a2,3 None Acquisition Full stalk Related a2,3 None Acquisition Short stalk Different a2,3 None Acquisition Short stalk Related a2,3 Mutations None Full stalk Different a2,3 Mutations None Full stalk Related a2,3 Mutations None Short stalk Different a2,3 Mutations None Short stalk Related a2,3 Mutations Acquisition Full stalk Different a2,3 Mutations Acquisition Full stalk Related a2,3 Mutations Acquisition Short stalk Different a2,3 Mutations Acquisition Short stalk Related Model implementation Practicalities of running the model The final model has been written in the open-source software R , taking advantage of several GIS and spatial analysis packages available: Raster - o Used to aggregate input data and produce final raster output. reached in the present document, without prejudice to the rights of the authors. 60

61 Maptools - o Import of data. Rgdal - o Import of data. BigMemory - o Large matrices/rasters produced within model, BigMemory packages (BigMemory, BigAnalytics) use base C scripting to perform calculations that R is not able to complete. SpatialKernel - o Used to compute kernel density for number of outbreaks per pixel. MLogit - o Used to fit discrete choice logic model for virus score pairwise analysis. A user interface has also been developed using Excel VBA, which provides an accessible platform to change parameter values and run baseline and alternative scenarios (see Section 2.3). Ten thousand iterations were run to produce the current results. First and foremost, the tool must be practical to use on a regular basis, and within a timeframe appropriate for intervention. Current consensus suggests that the time between runs must be no longer than 2-3 months if new viruses of potential threat are to be identified in a timely manner. Hence the model was designed from the start to be quick to run and relatively easy to understand. Whilst the graphical model results are instantly intuitive, there remains a relatively high level of expertise to use and interpret the model and the results which are produced. In addition, the results of the model will be produced at a global level and hence there is no rationale for several bodies/laboratories to run the model independently from each other. Hence, it makes sense that the model is implemented by veterinary epidemiologists or risk analysts, in a central location, within EFSA or some other organisation that has technical GIS and virology resources available. The results can then be disseminated through relevant channels Identification of viruses to run through the risk assessment framework model While the model has been developed to require the minimal amount of information possible, still one of the primary issues facing the use of the tool is the availability of relevant data. There will be minimum data requirements to run a virus through the model, including at least partial sequencing data and good knowledge of the distribution of the virus in animal species across the globe, and precise epidemiological information for all outbreaks (location, date, and host species). It is understood that sequencing data may not be available immediately after a virus has first been isolated, and also depends on a laboratory with sufficient equipment being sent a sample of the virus. Hence, along with the implementation of the risk assessment framework, there must also be a system in place to prioritise the sequencing of potentially high-risk isolates, based on phenotypic characteristics that can be collected in real time. As such, a triage system to prioritise viruses to be sequenced in reference laboratories, based on five decision criteria, has been developed (See Annex 2). It is hoped that this will at least initiate reached in the present document, without prejudice to the rights of the authors. 61

62 discussion of how to standardise data produced by laboratories for epidemiological and risk assessment purposes, and to enhance the awareness of the zoonotic potential of viruses, not just their animal health relevance Baseline risk and ranking viruses In order to relatively rank viruses there should be a baseline for which to compare it against. The worst-case (albeit unrealistic) scenario is a virus that has a virus score (V i = 1) and has infected all domestic chickens across the globe (n(i,j,t) = 1). The maximum possible risk score (R(i,j,t)) that this worst-case scenario can produce will then depend on the maximum possible value of A(j). This value is then taken as the baseline from which all other viruses are compared, and it also provides an indication of the opportunity or suitability for a generic avian influenza virus to make the species jump Sensitivity analysis A highly non-linear model such as that in Equation 2 tends to result in unreliable sensitivity analyses by using standard statistical methods (e.g. rank correlation coefficient, correlation coefficient). Instead, we simply produce scatter plots of each parameter against the final risk score produced across all pixels with non-zero chicken population density and risk scores Case study results Opportunity score maps All risk maps presented below are generated by applying Equation 2 to all pixels across the globe that are estimated to contain domestic chickens. The opportunity maps shown in this section are generated by assuming that V i = 1 and n(i,j,t) = 1. The opportunity maps therefore give an indication of where the inherent epidemiological risk lies, without consideration of individual influenza A viruses or their location (baseline risk or opportunity for the species jump to occur; see Section ). The median global opportunity map is shown in Figure 2.7. The opportunity score is shown on a log scale due to the wide range of values that the score may take. There is a wide range of uncertainty in, and hence a useful visual description of this uncertainty is given using postage stamp maps (commonly used to present meteorological forecasts) shown in Figure 2.8. More detailed maps of particularly interesting regions are shown in Figures Note the much more clustered regions of high opportunity in Europe due to the larger dependence on intensive chicken production. The maximum opportunity score from the median results was O = log 10 (0) = 1; this is used as the baseline for the virus risk score. reached in the present document, without prejudice to the rights of the authors. 62

63 Figure 2.7: Global opportunity map (50 th percentile). Opportunity score (O) given on log scale. bodies identified above as author(s). In accordance with Article 36 of Regulation (EC) No 178/2002, this task has Safety Authority and the author(s). The present document is published complying with the transparency principle 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. 63

64 Figure 2.8: Opportunity postage stamp maps showing range of wide uncertainty. Opportunity score (O) given on log scale. reached in the present document, without prejudice to the rights of the authors. 64

65 Figure 2.9: Opportunity map for Africa. Opportunity score (O) given on log scale. reached in the present document, without prejudice to the rights of the authors. 65

66 Figure 2.10: Opportunity map for Europe. Opportunity score (O) given on log scale. reached in the present document, without prejudice to the rights of the authors. 66

67 Figure 2.11: Opportunity map for Asia. Opportunity score (O) given on log scale Virus risk results Three hypothetical viruses have been constructed, based on real geographical data for the H5N1 outbreak of , but with each virus having been assigned an arbitrary virus score (H5N1_2, the minimum virus score possible, H5N2_1 a medium score and H5N1_3 the maximum virus score possible). Hence, the results presented here are theoretical, but provide a realistic assessment of the type of results that are possible with this model. A visual presentation of the distribution of risk for all three strains is given in Figure 2.12, and a summary is presented in Table reached in the present document, without prejudice to the rights of the authors. 67

68 H5N1_1 This virus was recorded in three outbreaks with a relatively wide geographical distribution spanning several human population centres, with a medium-level virus score (mean 0.53). H5N1_2 This virus was the most commonly isolated virus of the three, but was given the lowest virus score possible (mean 0.03) and covered only one major human population centre (Bangkok). H5N1_3 This virus had only one outbreak associated with it, which occurred near a heavily populated area in China. The virus score was the highest possible (mean 0.94). In Table 2.10 viruses are ranked on the average risk per pixel, which is estimated by summing the risk scores for each pixel containing that virus and dividing by the total number of pixels with a risk score. The peak risk for a pixel indicates the maximum risk in a single pixel containing that virus. The virus score, as explained previously, gives an indication of the innate ability of the virus to cause human infection, regardless of the epidemiological situation within its spatial distribution. Table 2.10: Summary of virus risk (R(i,j,t), as per Equation 2; parentheses 5 th and 95 th percentiles respectively). Strain Virus score (Vi) (5 th ;95 th percentiles) Peak risk score for a pixel Average risk per pixel Rank (by avg. risk/pixel) H5N1_ (0.44;0.63) H5N1_ (0.01;0.07) H5N1_ (0.88;0.99) reached in the present document, without prejudice to the rights of the authors. 68

69 Figure 2.12: Risk map for three viruses H5N1_1-H5N1_3. Outbreak locations are marked by black crosses; grey areas represent countries with unknown surveillance type. reached in the present document, without prejudice to the rights of the authors. 69

70 Sensitivity analysis The results of the sensitivity analysis for H5N1_3 are shown in Figure 2.13 (similar results were obtained for H5N1_1 and H5N1_2). There is little correlation between many of the parameters and the eventual risk score, bar for the extensive chicken population density and the contact ratio between humans and extensive chickens. There is a smaller trend visible for the extensive chicken transmission parameter. There is little evidence of any positive correlation between the intensive production parameters, the virus score and the final risk score. The range considered for V(i) is small relative to the possible range of values V(i) may take (0-1), but a further sensitivity analysis allowing V(i) to range between zero and one showed only slightly more evidence for a positive correlation (not shown), suggesting the virus score was not one of the more important factors in determining risk Validation Validation of virus score The key aim of the validation of the virus score is to determine the efficacy of the score at being able to sift between those virus strains most likely to jump and those least likely to jump. Three categories of viruses were determined for validation: A (viruses that are more prone to infect humans/viruses that are less prone to infect humans), B (viruses unlikely to infect humans, but with some potential to do so) and C (viruses not infecting humans). Given that the virus score is independent of host species, virus strains from a wide variety of animal species can be used for this validation. To this aim, 123 strains/isolates were included in the analysis of which 42 were classified as Category A strains, 51 were category B and 30 strains were considered Category C strains. These included strains from birds, swine, canines and equines. The selection of the strains was provided in tabular format (each virus was assigned to Categories A, B or C independent of the virus characteristics, but instead on general phenotypic characteristics and whether or not the virus had been confirmed to cause human infection). Using the characteristics of each strain, these could be aligned to one of the 32 virus profiles and a virus score assigned to each virus strain. This analysis is shown in Table 2.11, which highlights the ranking of Category A strains (red), Category B strains (amber) and Category C strains (green). Table 2.11 does not include all the strains but a sample of the 123 strains given the limited variation between isolates of the same strain. It can be seen from Table 2.11 that there is variation in the categorisation of virus strains that are considered less likely to jump the species barrier (i.e. V(i)<0.33). Indeed there are some Category A, B and C strains that are identified as unlikely or least likely to jump the species barrier. However, strains identified as more likely (V(i)>0.39) or likely to jump the species barrier, are all Category A strains. This suggests that the virus score is able to efficiently sift those viruses that are likely to jump the species barrier but is less sensitive to sorting those viruses less likely to jump the species barrier. However, it is important to note that there is some subjectivity associated with characterising the virus strains/isolates to input into the virus score. reached in the present document, without prejudice to the rights of the authors. 70

71 Figure 2.13: Sensitivity analysis for virus H5N1_1, using log scatter plots. bodies identified above as author(s). In accordance with Article 36 of Regulation (EC) No 178/2002, this task has Safety Authority and the author(s). The present document is published complying with the transparency principle 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. 71

72 Table 2.11: Summary of a sample of strains included in the validation analysis and their relative ranking. Category Category A HPAI H5N1 Strain A/Indonesia/CDC1046/2007 Mean score Min Score Max score Category C H6N Category C H11N Category B LPAI H7N7 A/goose/Leipzig/187-7/ Category A HPAI H5N1 A/chicken/Bangladesh/12VIR / Category A HPAI H5N1 A/chicken/Bangladesh/12VIR / Category A HPAI H5N1 A/chicken/Scotland/ Category A H9N2 A/Hong Kong/35820/ Category A H9N2 A/Duck/Hong Kong/702/ Category A HPAI H7N7 A/Netherlands/33/ Category B LPAI H7N7 A/mallard/Sweden/S90735/ Category C H13N Category C H11N Category B LPAI H7N7 A/American black duck/new Brunswick/00344/ Category B LPAI H7N7 A/American green-winged teal/illinois/10os3329/ Category A HPAI H5N1 A/Egypt/3105-NAMRU3/ Category A HPAI H5N1 A/Viet Nam/1203/ Category B CIV H3N8 A/canine/California/ / Category A H9N2 A/chicken/Jiangsu/7/ Category A HPAI H5N1 A/chicken/Bangladesh/1151-9/ Category A H7N9 A/chicken/Zhejiang/DTID-ZJU01/ Category A HPAI H7N7 A/Netherlands/219/ Category B H3N8 A/canine/Pennsylvania/137154/ Category A H9N2 A/chicken/Guangdong/V/ Category A H9N2 A/Japanese Quail/Vietnam/4/ Category A H9N2 A/guineafowl/HongKong/NT101/ Category A H9N2 A/chicken/Attock/NARC-14994/ Category A H9N2 A/chicken/Egypt/S4456B/ Category A HPAI H5N1 A/Hong Kong/483/ Category A HPAI H5N1 A/Thailand/676/ Category A H7N9 A/Pigeon/Shanghai/S1069/ Category A H7N9 A/Environment/Hangzhou/34/ Category A H9N2 A/chicken/Pakistan/UDL-03/ Category A H7N9 A/Shanghai/1/ Category A H1N1pdm09 A/California/04/ reached in the present document, without prejudice to the rights of the authors. 72

73 Category A H1N1pdm09 A/Netherlands/602/ Category A A/Minnesota/12/ Category A H3N2v A/Indiana/10/ Category A H3N2v A/swine/NY/A / Robustness of validation In undertaking the validation analysis, it was evident that there may be inherent subjectivity in defining the characteristics of the virus strain being profiled, in terms of both the characteristics it possesses and the category of validation (likely to infect humans etc ). For example, with regards to the profiling of strains, it appears that some experts interpreted the question on the homology relatedness (Factor 5) in an opposite manner to which we intended; that is, they may have thought of a strain with a highly related homology as riskier due to the ability of the strain to spread through a human population easier, whereas the question was targeted at assessing the importance of potential cross-immunity that would prevent the species jump. Whilst every effort was made to try and ensure the transparency and objectivity of questions, it is inevitable that some subjectivity of experts views and interpretation will remain. Of perhaps more importance, given their high relative weighting compared to Factor 5, there is uncertainty associated with how some strains are characterised particularly regarding Factors 2 (presence of adaptive mutations) and 3 (re-assortment). This is because there may not be definitive evidence to determine precisely whether a virus strain complies with characteristic 1 or 2 of Factor 2 or 3, for example. It is a subjective opinion of the virus profiler as to which characteristic the virus should take. The impact of this was explored by asking additional project virologists to define specific virus strains including those that were less and more ambiguous to define. Specifically, for the reproducibility analysis, experts from RIVM, AHVLA, IP and IZSVe were asked to characterise factors 1, 2 and 3 for a subset of the validation strains. In this exercise, re-assortment within the virus (Factor 3) was further defined to assist in characterization. Characteristic 2 is assigned if the strain has originated through (further) reassortment between subtypes when compared to strains endemic in the specific animal population and the novel (genetically different) gene/s derive from gallinaceous poultry, human and/or swine strains (e.g. European avian-like H3N2 emerged as a reassortant virus around 1984 and it is currently endemic in the European pig population, thus a strain belonging to this lineage would not be regarded as a reassortant). If the isolate is a reassortant carrying a gene/s similar to a virus isolated from gallinaceous poultry, human and/or swine strains, but the donor virus is not an endemic or a frequently circulating strain in the donor animal population (e.g. an H9N2 isolate carrying a gene similar to an H5N2 isolated from a chicken) the assignment to characteristic 1 or 2 is dubious. Reassortments between lineages and clades are not taken into account due to the intrinsic difficulties in defining these events. Each expert was provided with a table outlining their respective mutations and reassortments so that all individuals had the same evidence available. This was to assist in determining the variation in interpreting and defining the virus characteristics for the three factors. reached in the present document, without prejudice to the rights of the authors. 73

74 In total, five virologists completed the questionnaire to characterize the 3 factors for the 16 isolates which could be compared to the original characterization for the validation analysis above (denoted IZSVe (2)). A summary of the results are shown in Table It can be seen from this table that there is some variation in the characterization as highlighted by the grey squares. This variation is distributed across the factors and strains. Of note, for Factor 2, three virologists (IP, RIVM (2) and IZSVe (2)) varied from the other virologists but all agreed with each other. The impact that this divergence of characterization has on the virus score was also analysed as summarized in Table It can be seen that there is variation in the rankings between the experts as may be expected by the variation in the characterization of the strains. The biggest impact of this is displayed in the rankings of the Category B and C virus strains. For a few experts, the rankings are broadly in line with expected (i.e. Category A>Category B>Category C). However, for 3 experts the category C virus strains are ranked highly with a value of 0.63, akin to a Category A virus strain. The reason for this is that Factor 1 and Factor 2 are characterized as having α2-6 binding and acquisition of any segment from a swine or gallinaceous poultry virus strain. This results in a higher ranking than a strain with α2-3 binding and no reassortment or acquisition of any segment from a mammal other than swine or gallinaceous poultry (0.09). This reliability analysis demonstrates the importance of being able to precisely define the virus characteristics and, for virus strains where there is ambiguity to derive a virus score for the virus characteristic combinations. In doing so, the virus score could be calibrated with virus strains of known characteristics using expert knowledge. It is acknowledged that there is subjectivity in implementing the virus score as characterization is not always an easy task. However, it is important to note that despite the variation highlighted in Table 5, all Category A viruses are ranked highly suggesting that the virus score would potentially over-estimate the risk of a new and emerging strain (i.e. class it as Category A rather than B or C). In addition to the subjective profiling of strains is the subjectivity of the selection of strains into each category. This can only be based on (limited) relevant expert knowledge and so by definition is extremely subjective. Hence, the results of the validation can only be approximate and must be treated with caution; both characterisation of viruses in the questionnaire and categorisation of current virus strains are vulnerable to the same subjective biases. reached in the present document, without prejudice to the rights of the authors. 74

75 Table 2.12: Summary of the results from the reliability analysis (ISZVe (2) completed the original validation analysis) where 1 is characteristic 1 and 2 is characteristic 2. Table 2.13: Summary of the rankings of the 16 isolates from the reliability analysis. bodies identified above as author(s). In accordance with Article 36 of Regulation (EC) No 178/2002, this task has Safety Authority and the author(s). The present document is published complying with the transparency principle 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. 75

76 Validation of risk results There is no reliable way to validate the overall risk assessment framework; there are so many variables that could affect the infection of a human with a novel avian 4 influenza that agreement/non-agreement with any specific incident will be spurious. In addition, we do not predict human infection, but simply rank the ability of avian influenza viruses in chickens to jump into humans. Therefore, again, any one observed species jump cannot be said to confirm or deny the accuracy of the model. While this is an unsatisfactory answer to the issue of validation, it reflects the real-world situation if there was confidence in what factor(s) caused a species jump, this risk assessment framework tool would not be necessary. What can be done is to observe known historical cases of human H5N1 infection (of which the majority are still chicken-to-human and not human-to-human) and compare the geographical location of these infections. A recent map published by the World Health Organisation plots the known cases of human H5N1 infection over the past year (see Figure 2.14). These cases occur in areas identified as high risk by the opportunity maps (Figure 2.7), and have both large human and large chicken populations. Again, this cannot be considered absolute validation of the model, but considering that the opportunity maps are independent of individual viruses and their inherent abilities, does indicate again that the epidemiological situation is of critical importance to the risk of zoonotic avian influenza infections. 4 We validate against avian influenza as this is the current case study, but ultimately we would want to validate for all animal influenzas. reached in the present document, without prejudice to the rights of the authors. 76

77 Figure 2.14: Human H5N1 infections from 1st January th April bodies identified above as author(s). In accordance with Article 36 of Regulation (EC) No 178/2002, this task has Safety Authority and the author(s). The present document is published complying with the transparency principle 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. 77

78 Discussion The novel methodology developed here provides a scanning tool to prioritise the risk of currently circulating (avian) influenza viruses to infect humans. This model is unique in that the ranking of viruses is dependent not only on the inherent qualities of the pathogen, a technique adopted elsewhere [38], but also on the epidemiological factors contributing to zoonotic infection. The inclusion of population data, animal production systems and geographic location of the viruses in question, makes this prototype model the first truly risk-based assessment of the zoonotic potential of influenza A viruses globally. Most reviews [7, 12, 36, 39], including the WP1 literature review, identify similar factors that affect the capability of an influenza virus to infect humans. These include receptor binding, genetic reassortments and phylogenetic relatedness, which have been captured by the virus score model. Several reviews progress through the stages of human infection (exposure/attachment to human cell/replication/release) in an identical manner to the way in which the virus score is derived. As such, we feel confident that the factors that are known to influence the inherent ability of a virus to infect humans are captured in the model. The evidence on epidemiological factors that are involved with transmission of zoonotic influenza has been identified as lacking in the WP1 epidemiological factor review. However, occupational exposure and proximity to an infected animal population were identified as being correlated with human infection [40-42], and there were indicators that working with backyard poultry increased the risk to humans [41]. As such, while there is no conclusive proof of epidemiological risk factors, we can be relatively confident that factors included in the model that will increase the number of contacts between humans and infected birds, and thus exposure to the virus, are important to the risk of human infection. These factors include poultry density (which is strongly correlated with human population density) and the type of interaction between birds and humans (e.g. commercial (intensive) chicken production versus backyard (extensive) farming). The ultimate aim of the project is to use the risk assessment framework as a prioritisation tool for intervention against potentially pandemic virus strains circulating in animal populations; however, it has always been recognised by EFSA and others that this model is a prototype and would require further development to truly be effective as a decision-making aid. We suggest that the model is used to assess viruses of concern every three months by a central organisation (FAO have expressed an interest in taking forward the development/maintenance of the model). A trial run of a year or so would allow any data issues or problems with interpreting the results to be identified, and ideally would be followed by further development of the model to address these issues, include any update in scientific knowledge or modelling methodology, and incorporate other species as data become available. The eventual aim would then be to regularly use the model to inform the decision-making process for tackling avian and other animal-derived influenzas, similar to the six-monthly process of identifying human influenza strains against which to develop vaccines for the oncoming winter (see, for example, As such, the baseline output of the model is the opportunity map, which indicates the global suitability for a generic avian influenza virus to jump the species barrier from domestic chicken into humans. To our knowledge, this is the first time that cross-species transmission of the virus has been quantified in this way, and the map provides useful information on the high-risk areas for zoonotic infection from bodies identified above as author(s). In accordance with Article 36 of Regulation (EC) No 178/2002, this task has been carried out exclusively by the author(s) in the context of a grant agreement between the European Food Safety Authority and the author(s). The present document is published complying with the transparency principle to which the Authority is subject. It cannot 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. 78

79 domestic chicken. While validation of risk assessments is always problematic, records of known human infections with H5N1 influenza virus show that this is most likely to occur in regions with high human and poultry densities, consistent with the areas indicated as the highest opportunity risk by the model. This simple validation approach also shows that the interaction between animals and humans, and the wider epidemiological context in which the virus is present, is paramount when assessing zoonotic risks. The risks of individual viruses are ranked against the maximum value of the opportunity score (O=1). Within this report we have assessed three hypothetical viruses based on realistic geographical data for H5N1 clades. A low, medium and high virus score was assigned to each virus, specifically assigning the low virus score to the most commonly isolated strain, in order to assess the relative importance of the innate virological factor (virus score V(i)) against the epidemiological factors such as chicken population density and the species-specific component of the transmission parameter (β ). The clear conclusion was that the epidemiological inputs to the model were enough to offset a medium virus score, as the medium-virus-score strain (H5N1_1) presented on average a greater overall risk of human infection than the high-virus-score strain (H5N1_3). Indeed, the sensitivity analysis suggests that the virus score is not a majorly important component driving risk; the most dominant parameter being the number of domestic extensively-reared chickens. This presents something of an issue for ranking: virologists would probably be more keen to know the innate ability of a virus to cause human infection (represented by the virus score), but epidemiologically the location of the virus is just as important, if not more so. For the moment the viruses are ranked by relative risk, rather than virus score, although it is suggested that consideration is taken of both virological and epidemiological risk scores when using the model to inform decision-making. Given the validation of the model, where geographical location appears to be a large risk factor for human H5N1 infection, then the inclusion of epidemiological factors within a risk assessment framework should be mandatory. In general, data gaps remain the single most important hurdle to effective utilisation of the model (although standardisation and improved collation of data would resolve many issues). The risk assessment framework has been developed as a scanning tool rather than a detailed model, not only because this is presumed to be of most use for decision-makers in the event of an outbreak of a novel virus, but also because we were aware of the possibility of large unknowns when trying to quantify the relative risks of human infection (especially at the beginning of an outbreak where data will be even more scarce). The model development process was therefore also seen as a key method for identifying these data gaps and other hurdles. While the model was parameterized mostly with real-world data rather than detailed experimental data, the lack of appropriate information was surprising. Indeed, the original intention was to include at least swine influenza within the model as well as avian influenza, but this was not possible due to a) the severe data limitations with regards to swine surveillance and b) the interpretation of the virus factors for swine influenza, when most studies performed on virus capability are on avian influenzas. Of note was the complete lack of quantitative (or even qualitative) data regarding the innate ability for avian/swine/other viruses to infect humans. While next generation sequencing and other virological methods have provided a wealth of data on individual viruses, there is still an absence of knowledge about what genetic characteristics drive human infection with animal influenzas [12]. There is a large literature base on the factors determining zoonotic influenza infection, and several reviews have considered the subject of past zoonotic (pandemic) influenzas with a view to understanding the underlying mechanisms for emergence [11-12]. However, only a few key factors have been identified as being important, such as the receptors found on the virus and how well-matched they are to cells in the human upper respiratory tract. Even then, the predictive power of such factors are very limited as the overall system is more complex than, for example, simply identifying whether a virus has an α2-6 bodies identified above as author(s). In accordance with Article 36 of Regulation (EC) No 178/2002, this task has been carried out exclusively by the author(s) in the context of a grant agreement between the European Food Safety Authority and the author(s). The present document is published complying with the transparency principle to which the Authority is subject. It cannot 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. 79

80 receptor or not. As such, the virus score, based on expert opinion, requires a large amount of further work to update it as and when scientific knowledge improves. Huge variation/uncertainty (of around 7-8 orders of magnitude) was captured in our estimates of the species-specific transmission parameter. The empirical model developed to assess the broad rate of transmission between chickens and humans during an outbreak is relatively sound and proportionate for the type of model we have developed and the type of outbreak data available to estimate it. However, only four outbreaks (of different AI subtypes in Canada, Japan, Netherlands, and the UK) were identified where we could collate the necessary information (number of infected chickens, number of humans exposed, and number of humans infected); denominator data could be simulated for an additional two outbreaks (of H5N1 in Thailand and Egypt) in backyard chickens. Thus, the data are non-representative of virus subtype and geographical location. More diverse outbreaks were reported but none captured the required quantitative information. This highlights the recommendation that simple epidemiological data are collected whenever possible, but also reported diligently and preferably in a standardised way. An additional data hurdle was the type of surveillance systems in place for animal influenza. There was considerable discrepancy between the OIE-reported avian influenza surveillance, and the results of the surveillance survey conducted in WP1. This was remarkable, considering that in both cases (with the exception of a handful of survey results that came from national reference laboratories and other sources), the information came directly from the Chief Veterinary Officer (CVO) of the country in question. The decision to use largely OIE results was made in order to facilitate continual updating of this information in the future, from a standardised source, although it fails to capture seasonal and country-specific variation in the quality of surveillance of a given type. Very little information was available on swine influenza surveillance, which is not reported to the OIE, and comprised only 8% of surveillance components reported via the FLURISK survey. Information on sensitivity of surveillance systems was also reasonably difficult to obtain. Several studies on avian influenza surveillance did not fully report the input and output parameters, such that partial simulation/estimation of some variables was required in order to use the data for this project. Studies on sensitivity of swine influenza surveillance were non-existent. The issue of not having all the required information was a recurring theme throughout the parameter estimation stage it was rare that all epidemiological information was reported in sufficient detail. No more was this apparent than for the outbreak data required to estimate the number of outbreaks, n(i,j,t). FAO s database EMPRES-i is arguably the most complete central database for global animal influenza viruses. Its Genetic Module provides a link between epidemiological outbreak information (stored in EMPRES-i) and genetic sequence information (stored in OpenFluDB, a database maintained by the Swiss Institute for Bioinformatics). However, the quality of the minimum epidemiological data provided by reference laboratories when submitting genetic sequences to OpenFluDB is at best inconsistent, making linkages with more detailed outbreak information in EMPRES-i difficult. For example, the H5N1 dataset used within the project (generated from both EMPRES_i and OpenFluDB data) contained 474 records from the period Of these, only 37 had sufficient information to identify the virus subtype and clade, date and location of the outbreak and the species affected. Even then, the data quality of these few records was variable, e.g. variable coordinate detail (e.g. precise coordinates versus Admin 1 centroid coordinates) being provided. This is surprising considering the evidence linking avian influenza outbreaks in poultry with zoonotic infections [31]. The quality of this data has not been compared against data from other sources, such as HealthMap, although this could be considered in future. Regardless, it is recommended that a standard minimum bodies identified above as author(s). In accordance with Article 36 of Regulation (EC) No 178/2002, this task has been carried out exclusively by the author(s) in the context of a grant agreement between the European Food Safety Authority and the author(s). The present document is published complying with the transparency principle to which the Authority is subject. It cannot 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. 80

81 set of epidemiological data is provided when genetic sequences are submitted to public genetic databases (such as OpenFluDB, GISAID, IRD or GenBank), including isolation month and year, geographic location at least at Admin 1 level and precise host species information. This would not only be useful for the risk assessment framework described in this report, but for many types of further analysis. The triage system suggested in this report is a first step to identifying interesting viruses quickly, and also alludes to the type of data required to conduct sound epidemiological studies of novel influenza outbreaks. The data issues inhibit the development of a quantitative model. However, we believe that our work improves upon previous attempts to systematically describe and assess the zoonotic risk of animal influenzas. The framework developed encapsulates many of the key factors believed to influence the zoonotic risk of (avian) influenza, by simply applying and modifying a 19 th century equation for epidemiological risk [43] in a spatial framework. Specifically, this novel application of a very old formula addresses two fundamental issues - the location of potentially zoonotic viruses, and their opportunity to jump into humans - while also applying a systematic framework for the assessment of novel viruses. Many experienced virologists may be able to broadly recognise a virus innately capable of infecting humans, and epidemiologists where such a virus may cause issues (e.g. in densely populated areas of the globe with dense poultry populations in close proximity), but this framework standardises that thought process. It also quantifies a risk calculation over a vast scale of several orders of magnitude, which people (including experts) are unlikely to be able to intuitively comprehend. The original methodology for the risk assessment framework was to apply a spatial Multi-Criteria Decision Analysis (MCDA) framework to the problem, similar to previous spatially explicit prioritisation models [44-46]. MCDA methods essentially take weighted averages of different factors; this is really all that can be done when combining, for example, epidemiological and economic factors (see Havelaar et al. [47] for a rigorous application of the method). However, with the risk question at hand, the application of basic quantitative epidemiological principles removes the need to make some of the more spurious and simplistic assumptions that are made when using MCDA-type approaches (e.g. assuming that the weighted product of factors is sufficient to replicate a real-world situation where a simple linear model is not appropriate). It could be argued that additional complexity, for example the inclusion of live bird markets, is needed in the risk assessment framework. Live bird markets have previously been implicated as a risk factor in transmission of avian influenza between domestic poultry [48] and from poultry to people [42, 49]. Data on market presence vary considerably by country, and are not publicly available for much of the globe. However, human population density and specifically, the presence of cities or large towns, have previously been used as indicators of market presence [50-51]. Human population density is closely linked to poultry density, and as such, is already represented (albeit implicitly) in the model. Two other limitations of this model should be considered: the inability of the model to distinguish between flock-level and bird-level infection (and indeed the lack of information regarding bird-level prevalence), and its failure to account for variations in susceptibility of the human population. Immunity through vaccination or past infection by identical/related virus strains is an important part of host (human) resistance to infection. Similarity to previously circulating viruses was accounted for in this model as part of the virus score, but other mechanisms affecting human susceptibility (e.g. vaccination, age or gender) were not considered. The literature review in WP1 identified studies indicating skewed distributions of age and/or sex of human cases of H5N1[31, 52], but these were country-specific. Again, insufficient data was available to explore these effects fully; however, they would be worthwhile considerations for a later stage of this project (modelling pandemic spread between people). bodies identified above as author(s). In accordance with Article 36 of Regulation (EC) No 178/2002, this task has been carried out exclusively by the author(s) in the context of a grant agreement between the European Food Safety Authority and the author(s). The present document is published complying with the transparency principle to which the Authority is subject. It cannot 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. 81

82 Whilst our framework is not as simply interpreted as that developed by the CDC [10] (which essentially produces a number ranking a virus between 1 and 10), we think it provides more detailed information on outbreaks, while also trying to use quantitative data from the scientific literature as much as possible (the CDC tool is based solely on expert opinion). The end outputs of the two frameworks are different: the CDC tool assesses the likelihood of a pandemic (animal-to-human as well as human-to-human transmission), whereas the framework presented here assesses the likelihood of initial human infection (animal-to-human transmission) only. In this way we are able to produce a much more detailed and realistic output as we do not need to consider the complexities of human-tohuman transmission, which involves anthropogenic factors such as speed/practicality of vaccine production and global connectivity through air travel. The CDC tool could be used to provide detail on the pandemic potential of a virus, in conjunction with the detailed virological and epidemiological assessment that the FLURISK framework provides on the relative likelihood of avian (and ultimately, other animal-origin) influenza viruses becoming zoonotic. It is worth a final note comparing this model against other models for (avian) influenza transmission between birds and humans. Two similar theoretical models [53-54] have assessed the likelihood of transmission between birds and people for avian influenza and a mutant influenza, but depending on the formulation/parameterization of the models, present different conclusions regarding the effectiveness of culling infected birds and the eventual probability of a pandemic occurring. The method used is stability analysis, which can give useful, quick results on whether an epidemic will continue or die out; however, at the present time it is unlikely that accurate data would be available to populate these detailed transmission models during the first stages of an outbreak. A more detailed simulation model incorporating spatial spread of H5N1 in the USA has been developed [55]; the results give a detailed breakdown of expected human cases across states. All three models mentioned here assume a very similar and classical SIR transmission model, of which our framework is also based on (although the framework uses a summary equation rather than the full differential equations). The previous models are all very detailed, but crucially assume a generic H5N1 or avian influenza virus, whereas our model attempts to assign a more discriminatory value to the ability of animal influenza viruses to infect humans. While impractical as a scanning tool, these more detailed models may be useful once high-risk viruses have been identified through the FLURISK framework, to enable more detailed/localised consideration of the consequences of spread. It is hoped that this work will provide useful information to policy makers on the global risk of animal-to-human influenza transmission. Additionally, several critical data gaps have been identified, the closing of which would be to benefit not just this model, but other work in the realms of risk analysis and public health. Every effort should be made to develop a standardised and transparent approach to zoonotic disease data collation and analysis, particularly for potentially high-risk diseases such as avian influenza. It is suggested that the model is run repeatedly over the next year, in order to identify further data gaps and to appreciate the speed at which relevant virological and epidemiological data becomes available. It is assumed that this process will in itself help streamline data collation and manipulation, and identify priorities for further work. bodies identified above as author(s). In accordance with Article 36 of Regulation (EC) No 178/2002, this task has been carried out exclusively by the author(s) in the context of a grant agreement between the European Food Safety Authority and the author(s). The present document is published complying with the transparency principle to which the Authority is subject. It cannot 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. 82

83 2.3. Activity 2: User interface The development of a user-friendly interface from which to run the model was intended to increase transparency and accessibility for the end-user. While the interface requires a high level of epidemiological expertise to utilise fully, programming expertise is not required. The interface also provides user options that can be manipulated in order to generate specific results, and also to understand the effects on the final results of changing different model parameters. The interface has been developed so that minimal input is required from the user when default settings are chosen, but it does provide the option for them to determine some basic inputs (e.g. animal species of interest, timeframe). The model has an additional functionality for the advanced user, as mentioned previously, so that they can define various technical parameters (e.g. the under-reporting factor, URF, and contact ratios, w e and w i ). It is expected that this latter function will be relevant during the early life of the model, when users are familiarising themselves with it, and during sensitivity analysis; however, we strongly recommend that any results from these advanced user scenario analyses should be discarded. The model and user interface run in two different modes: virus mode and opportunity mode. Opportunity mode represents the worst-case scenario detailed above, and runs independently of circulating virus strains. It is thus based solely on spatial data on animal and human populations and production systems (intensive vs. extensive) and thus, the likelihood and intensity of contact between the animal species of interest and humans. This mode generates maps indicating the relative opportunity for a generic virus to jump the species barrier. Virus mode is the end-function of the model and has been designed to indicate the risks of specific influenza A viruses, currently circulating in animal populations, jumping the species barrier into people. The model is based on the opportunity mode results, as well as data (date, location, species, virus) from actual influenza A outbreaks in animals, and genetic sequencing of the relevant viruses. This mode requires the user to ensure outbreak data are up-to-date and complete; results are generated as risk maps, and tables ranking the viruses. Flow charts for moving through the user interface, for each mode, are shown below. The different parameters of the risk equation, R( i, j, t) 1 e [ ( i, j) H ( Aint ( j) wint ( j) A ext ( j) w ext ( j)) n( i, j, t)], are indicated in red at the appropriate section of the user interface. bodies identified above as author(s). In accordance with Article 36 of Regulation (EC) No 178/2002, this task has been carried out exclusively by the author(s) in the context of a grant agreement between the European Food Safety Authority and the author(s). The present document is published complying with the transparency principle to which the Authority is subject. It cannot 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. 83

84 Open FLURISK model Choose virus or opportunity mode Virus mode Opportunity mode i, w Choose default or userdefined settings for species and contact ratio Default Species: chicken Contact ratio: 1 human to 1 bird under extensive production; birds under intensive production. User-defined Species:? Contact ratio:? Choose number of iterations and region of interest Run the model New results folder opens. Parameters are saved in a separate file Figure 2.15: Flow chart indicating movement through user interface: opportunity mode (this page) and virus mode (following page). * Second-level category used here is clade, but this is limited to H5N1 viruses. Ideally, the second-level category would be virus score, strain, or some other identifier that allows greater specificity than subtype. bodies identified above as author(s). In accordance with Article 36 of Regulation (EC) No 178/2002, this task has been carried out exclusively by the author(s) in the context of a grant agreement between the European Food Safety Authority and the author(s). The present document is published complying with the transparency principle to which the Authority is subject. It cannot 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. 84

85 Open FLURISK model Choose virus or opportunity mode Virus mode Opportunity mode Upload outbreaks file Sets possible date range Choose default or user-defined settings for species and timeframe j, t Sets possible date range Default User-defined Species: chicken Timeframe: within 3 months of last recorded Species:? Timeframe:? Choose viruses by first-level categories (subtype) i Choose viruses of interest within selected timeframe Get list of second-level virus categories available for these subtypes * n, w Choose default or user-defined technical settings Add or remove second-level categories from list Default User-defined URF: refer to section1.2.3 Contact ratio: 1 human to 1 bird under extensive production; birds under intensive production. URF:? Contact ratio:? Choose number of iterations Run the model New results folder opens. Parameters are saved in a separate file bodies identified above as author(s). In accordance with Article 36 of Regulation (EC) No 178/2002, this task has been carried out exclusively by the author(s) in the context of a grant agreement between the European Food Safety Authority and the author(s). The present document is published complying with the transparency principle to which the Authority is subject. It cannot 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. 85

86 3. WORKPACKAGE 3 (WP3): IDENTIFICATION OF SCIENTIFIC GAPS AND RESEARCH PRIORITIES 3.1 Introduction and objectives of WP3 Work Package 3 (WP3) fulfills the EFSA Call Objective: Identify relevant gaps in monitoring of influenza viruses in animals and humans, where data would be needed for the RA framework, and constraints of data sharing through the achievement of the following objectives: Objective 1 - Identification of data gaps: identification, description and evaluation of key data gaps of strategic relevance relating to: extant monitoring of influenza viruses etiology and epidemiology in animals and humans, Influenza Risk Assessment Framework (IRAF) parameterization, knowledge/data sources availability. Objective 2 - Recommendation of future research priorities 3.2 Activity 1 : identification of scientific gaps through WP1 and WP2 Methodology Data and knowledge gaps have been generated as outcomes of the tasks performed in WP1 and WP2 and incorporated into the WP3 working document presented in the project interim reports. The WP3 working document was being up dated throughout the lifespan of the project as new gaps were identified. In addition, the identified scientific knowledge gaps, have been evaluated in context with the WHO Research Agenda for Influenza, 2010 (WHO Public health research agenda for influenza. Available at: int/influenza/resources/research/about/en/index html, 2010) and the OFFLU Agenda for Influenza Research Priorities in Animal Species (An OFFLU Agenda for Influenza Research Research priorities in Animal Species. Available at: int/doc/ged/d11129 PDF, 2011.) In fulfillment of task 3.1, a WP3 working group was established with representatives of each partner of the consortium. Working group members: Istituto Zooprofilattico Sperimentale delle Venezie (IZSVe): Marco De Nardi, Olga Munoz, Giovanni Cattoli and Ilaria Capua Animal Health and Veterinary Laboratories Agency (AHVLA): Andy Hill, Andrew Breed, Richard Irvine, Rowena Kosmider, Sharon Brookes and Ian Brown bodies identified above as author(s). In accordance with Article 36 of Regulation (EC) No 178/2002, this task has been carried out exclusively by the author(s) in the context of a grant agreement between the European Food Safety Authority and the author(s). The present document is published complying with the transparency principle to which the Authority is subject. It cannot 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. 86

87 Institut Pasteur (IP): Sylvie van der Werf National Institute of Public Health and Environment (RIVM): Marion Koopmans, Adam Meijer Royal Veterinary College (RVC): Katharina Stärk, Sophie von Dobschuetz University of Ghent (UniGhent): Karen van der Meulen The gaps presented in this report have been generated by the following activites: 1) WP1-Literature reviews on potential risk factors for, and evidence of, species jump from animal to human populations (see also Chapter 1.2. and Annex 1 a, b, c, g): Influenza A virus genetic adaptations to animal reservoirs and their potential role in interspecies transmission Influenza at the animal-human interface: a systematic review of the literature of virological evidence of human infection with swine- or avian influenza viruses other than A(H5N1). Jump and spread Epidemiological perspectives on influenza jumping species barriers and spreading in animal populations Influenza virus infection of marine mammals 2) WP1- Review and evaluation of ongoing monitoring, surveillance and control systems (see also Chapter 1.3. and Annex 1 d,e) 3) WP1- Epidemiological analysis of animal influenza viruses in space and time since 2005, including maps and graphs (epidemiological report) (see also Chapter 1.4. and Annex 1f) 4) WP1-Review and compilation of data related to populations, trade and environment relevant to the IRAF development in WP2 (see also Chapter 1.5) 5) WP2 IRAF development and validation (see also Chapter 2.2) All identified gaps contributed to identify the future research needs and awareness activities which are included in Annex 3 Results Results: gaps and research needs identified from the literature reviews The following gaps and research needs were identified in each literature review. Please refer to the Annexes 1a, 1b, 1c for the full list of references. bodies identified above as author(s). In accordance with Article 36 of Regulation (EC) No 178/2002, this task has been carried out exclusively by the author(s) in the context of a grant agreement between the European Food Safety Authority and the author(s). The present document is published complying with the transparency principle to which the Authority is subject. It cannot 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. 87

88 A. Jump and spread Epidemiological perspectives on influenza jumping species barriers and spreading in animal populations: gaps and research proposals We identified a significant gap in knowledge regarding the epidemiological factors most associated with cross species transmission. Published studies are lacking which quantify the role of risk factors for transmission of influenza viruses between animal species. All papers identified by our review described observational or circumstantial evidence rather than reporting any statistical measure of risk factors identified. Literature searches disclosed a greater number of publications relating to viral spread within animal species than for transmission between different species. Also, most work was conducted on poultry. Much of the knowledge on the risk factors for spread of influenza viruses within animal populations and transmission of virus from animals to humans is restricted to H5N1 HPAI in poultry populations. Very little information is published on swine-to-human transmission and risk factors related to this species-interface. Again most of the current literature relates to poultry. Future research on emerging viruses, like influenza A(H7N9), is needed to shed more light on factors that are implicated in influenza virus transmission and spread. A number of studies investigating the spatial distribution and co-occurrence of relevant host species (e.g. poultry, pigs and humans) have been conducted to identify geographical hot spots for inter-species contact and consequential pathogen transmission, but the myriad of other potentially relevant factors that show spatial variation leaves a vast number of unknown relationships. Exposure routes/levels and dose-response data for routine levels of human-animal interaction are largely unknown. Future research needs - Conduct observational epidemiological studies (e.g. case-control, cohort and cross sectional studies), along with detailed analyses of available virological, serological and epidemiological data from influenza outbreaks in animals to investigate: prevalence of infection of animal influenza virus monitored at regular intervals stratified by virus clades (when possible) incidence of infection of animal influenza virus stratified by virus clades and in association with risk factors (when possible) patterns of virus spread within and between farms stratified by virus clades (when possible) and in association with risk factors risk factors for transmission and spread of animal influenza viruses between animal species - Conduct longitudinal epidemiological studies (e.g. cohort studies, cross sectional studies), at the animal-human interface to investigate: risk factors for transmission of animal influenza viruses from birds to humans stratified by virus clades (when possible) and in association with risk factors bodies identified above as author(s). In accordance with Article 36 of Regulation (EC) No 178/2002, this task has been carried out exclusively by the author(s) in the context of a grant agreement between the European Food Safety Authority and the author(s). The present document is published complying with the transparency principle to which the Authority is subject. It cannot 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. 88

89 risk factors for transmission of animal influenza viruses from animals (mammals) to humans - Investigate and quantify the impact of trade and marketing activities in the transmission chain of avian and swine influenzas and virus /disease persistence along the market chain. This should also include economic drivers. - Investigate socio-economic and behavioral factors influencing animal-human interaction patterns in the different production systems and their relation to transmission risk and risk of pandemic emergence. Strong emphasis should be on: - avian influenza (H7, H9 and other subtypes with zoonotic potential rather than HPAI H5N1) - swine influenza (H3, H1 subtypes with higher priority) canine and equine influenza - production systems characterized by mixed animal species and in production systems of varying degree of biosecurity - consider environmental, socio-economic and anthropogenic factors as potential drivers of disease - field studies implemented in regions with dense animal and human populations (e.g. China, South East Asia, Egypt) B. Virological and serological evidence of human infection with animal influenza viruses: gaps identified in the literature reviews and research recommendations a) SEROLOGY Virologic techniques, e.g. virus culture, PCR and sequencing provide solid evidence of infection, whereas serologic methods can help reaching a diagnosis when the virus is already cleared. Antibodies triggered by infection can be detected for months, and therefore, serological methods, such as the hemagglutination inhibition (HI) or microneutralization (MN) assay, are valuable tools when acute phase clinical specimens are unavailable or when a laboratory is not equipped for virus isolation or RT-PCR detection. Thereby, more objective information about morbidity and mortality rates of infections caused by such a virus in contrast to only considering hospitalized cases can be obtained. A downside is that serological methods may lack H- and N- subtype specificity due to cross-reaction of antibodies triggered by animal influenza virus infection with human subtypes or vaccination. Serologic evidence needs therefore to be interpreted with caution due to the existence of cross-reactive antibodies, hence providing less solid evidence than direct detection of the infecting virus itself. The antibodies kinetics is not thoroughly clear. The absence of a normal antibody level following A(H7N7) infection has been documented but infection of humans with A(H5N1) viruses have resulted in clear serologic responses. A four-fold antibody titer rise (or greater) between acute phase and convalescent phase serum samples usually classifies an individual as sero-positive, meaning that the patient truly underwent influenza virus infection. Although the majority of studies comply with this WHO definition, some studies apply other methods or define their own cut-offs (as to when a person is regarded as sero-positive), which complicates comparability between different studies. Work has been done by the CONSISE consortium to improve standardization ( bodies identified above as author(s). In accordance with Article 36 of Regulation (EC) No 178/2002, this task has been carried out exclusively by the author(s) in the context of a grant agreement between the European Food Safety Authority and the author(s). The present document is published complying with the transparency principle to which the Authority is subject. It cannot 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. 89

90 A major gap is represented by the lack of standardization between laboratories. Despite HI and MN assay being gold standards for influenza antibody detection, standardization is limited and variation between different laboratories can be high. In addition, during the recent pandemic, it became clear that antibodies that cannot be detected by MN or HI may be present and play a role in population susceptibility to infection. Bearing in mind the above limitations, there is serological evidence for exposure to avian influenza virus HA subtypes 4, 5, 8, 11 and 12, in addition to the subtypes for which virological confirmation has been described (H5, 6, 7, 9, 10). Future research needs - Develop research strategies and improve serological test performance (in term of sensitivity and specificity) including detection of non-neutralising antibodies, to fully understand kinetics and cross reactivity of influenza antibody response in humans infected with different subtypes of AIV and thus how serologic laboratory techniques can be used to address and quantify zoonotic risk. - Develop SOPs and standards for the use of serological assays and promote the standardization of serological assays across laboratories. This has to be done also considering the activities and protocols developed by the CONSISE consortium ( b) SWINE INFLUENZA Regarding SIV, there is ample evidence of human infection with A(H3N2), A(H1N1) and A(H1N2) subtypes, as well as reassortants deriving from these endemic SIV lineages. Nevertheless, the true number of human SIV-cases is probably higher than the reported cases since clinical symptoms of SIV are indistinguishable from seasonal influenza. The swine-human barrier for interspecies transmission is not high, as new pandemic influenza viruses have rapidly transgressed into the pig population. Pigs were assumed to play an important role by acting as intermediate hosts or mixing vessels for human, avian and swine originating strains, due to the presence of avian- and human influenza-specific receptors in the tracheal epithelium. However, recent research refuted this assumption by demonstrating that the sialicacid-receptor distribution in the porcine respiratory tract is similar to that of humans, leading to the conclusion that humans are equally likely to constitute mixing vessels as swine. Cocirculation of different influenza virus strains in pigs might facilitate the generation of new variants potentially posing a threat for public health. For instance, A(H1N1)pdm09 is known to have been present in pig herds for months prior to unfolding as a human pandemic. The fact that influenza viruses can circulate unnoticed in pig populations warrants close surveillance in this animal species. Future research needs - Conduct epidemiological surveys at regular intervals in densely populated animals and human areas to determine swine influenza virus prevalence and different subtypes circulation patterns bodies identified above as author(s). In accordance with Article 36 of Regulation (EC) No 178/2002, this task has been carried out exclusively by the author(s) in the context of a grant agreement between the European Food Safety Authority and the author(s). The present document is published complying with the transparency principle to which the Authority is subject. It cannot 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. 90

91 - Promote swine workers sensitization and awareness programs (awareness of risk factors and use of personal protective equipment (PPE)) to limit the chance of infection and prevent workers from becoming bridging populations between swine and community contacts and vice versa. c) ANIMAL INFLUENZA VIRUS INFECTION AND EXPOSURE The factors that determine whether an animal influenza virus infection in a human may trigger a pandemic among humans are poorly understood. As a consequence, any human infection with a non-human influenza virus needs special attention. Reassortment is not a necessary prerequisite for human infection and there is clear documentation of direct transmission and human disease caused by animal influenza viruses, in particular influenza viruses of avian and swine descent, such as avian A(H5N1), A(H9N2) and various H7-subtypes, as well as European avian-like swine A(H1N1). The majority of AIV- and SIV-infected patients had animal-exposure. Apart from A(H5N1) infections, avian- and swine influenza viruses generally present with mild clinical symptoms in humans. Therefore, the chance is high that human infection will often occur unnoticed and that the true number of human infections is likely to be higher than the number of cases identified in this review. Future research needs - conduct targeted surveillance in animal populations to monitor evolution and circulation of potentially novel viruses with yet unknown public health risks to be prepared for a potentially emerging influenza virus of animal origin in humans - promote epidemiological studies with active serological and virological monitoring of virus circulation in the animal populations to which humans were exposed - conduct targeted surveillance in humans professionally exposed to animals with possible influenza virus infections C. Influenza A virus genetic adaptations to animal reservoirs and their potential role in interspecies transmission: a literature review Introduction Several scientific gaps and areas for future research have been identified within this review. Efficient transmission of influenza viruses among a specific host is a polygenic trait (of still unknown nature and probably variable according to strains/subtypes), depending on functional balance between the different viral proteins (e.g. HA and NA) and all the viral cycle steps. Overall these viral characteristics interplay with host (such as adaptive and innate immunity or even behavioral pattern) and environmental factors (artificial, such as rearing systems, or natural). These interactions are yet to be characterized and disentangling this poses a great challenge to the scientific community. bodies identified above as author(s). In accordance with Article 36 of Regulation (EC) No 178/2002, this task has been carried out exclusively by the author(s) in the context of a grant agreement between the European Food Safety Authority and the author(s). The present document is published complying with the transparency principle to which the Authority is subject. It cannot 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. 91

92 With regards to viral factors, there is still a great difficulty in defining the right setting for an eventual reassortment. This setting may be characterized and limited by timing conditions (the timing of entry in the cell of two different strains), the host (e.g. type of host and anatomical site within this, immunological status) and the framework within which the host can be found (e.g. dynamics of population). In addition, viral properties could also have an influence on such an event; certain HA and NA subtypes and/or other genes could be more prone to reassortment and/or adaptation than others. Also drawing similarities and differing characteristics between species, especially pigs and humans, would help to disentangle the complex picture of influenza. For example, more detailed receptor studies should be carried out in different species. It was shown that some important mutations for crossing the species barrier may be already present in the viral population circulating in the donor host as dominant traits, or within the pool of strains, and are later selected during replication in the recipient host. Different authors agree on the possibility that the original host can act as a transient reservoir of pre-adapted mutations, as these mutations might be present temporarily in the donor host. The knowledge and understanding of the intrinsic processes leading to interspecies adaptation of AIV to swine and horses hosts, of SIV to humans and EIV to dogs is clearly lacking and all of these interspecies adaptation events deserve further investigation, as each may unveil fundamental adaptation strategies which may appear in future zoonotic strains. Furthermore, the studies so far have been mostly focused on HPAI H5N1 or H1N1pdm09, so the subtypes and species targeted by research studies should be wider. Scientific gaps and research needs/recommendation Gaps have been classified and summarized in the following categories: 1. Hosts and viral adaptation for interspecies transmission 2. Viral proteins 3. Receptor preference and distribution 4. Interaction with the host s immune response 5. Experimental methods 1) Hosts and viral adaptation for interspecies transmission Several authors have proposed quails and turkeys as disease amplifiers or as bridging species for the transmission of AIVs from wild waterfowl to chickens and other gallinaceous poultry. There are a number of factors supporting the hypothesis that quail play an important role in the adaption of AIVs to domestic avian and, potentially, to mammalian species. In contrast the role of turkeys as a bridging species is more ambiguous. According to currently available evidence, some authors argued that turkeys are more susceptible than chickens to both domestic and wild bird AIVs, and to SIVs, therefore potentially facilitating the adaptation process of IAVs to other species. The Swine Influenza Virus (SIV) epidemiology is a complex and unresolved issue. During the last 15 years, several novel reassortant viruses have emerged and established stable lineages in swine. The H1N1pdm09 virus has become endemic in swine populations worldwide and it continues to reassort with the established SIVs. Three subtypes - H1N1, H1N2 and H3N2- circulate worldwide. Nonetheless, their genetic constellation and history differs between continents and geographic regions with varying lineages of each subtype circulating in different parts of the world. In addition, there has been limited work related to adaptation of AIVs to swine as well as SIVs bodies identified above as author(s). In accordance with Article 36 of Regulation (EC) No 178/2002, this task has been carried out exclusively by the author(s) in the context of a grant agreement between the European Food Safety Authority and the author(s). The present document is published complying with the transparency principle to which the Authority is subject. It cannot 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. 92

93 adaptation to humans. If, which and why (considering epidemiological and biological factors) certain species can act as bridging or/and mixing vessel species for wild bird, domestic poultry and mammalian influenza virus is still unclear. H3N8 Equine Influenza Virus (EIV) has transmitted to swine and established itself in dogs. However multiple questions still surround EIV, as well as canine influenza virus (CIV) virology and epidemiology. In fact, the amount of data generated regarding virological factors related to adaptation of these subtypes is extremely limited. All equine and canine strains carry a recognized mammalian genetic marker in the PB2 protein and other novel strategies could be circulating unveiled amongst these hosts. Future research needs - Sensitise the scientific community and the veterinary services on the importance of including regularly turkeys and swine in risk based influenza surveillance efforts - Conduct adaptation studies to clarify the role of turkey and swine as a bridging species or as a disease amplifier and to identify the modifications (e.g. NA stalk deletion in gallinaceous poultry) arising in these species which render a virus more prone to jump to humans - Implement in vitro (swine, avian- especially turkey and quail- and human explants) studies of influenza viruses co-infection and possible reassortment (timing, infected cell types, infectious doses, genes involved) - Compare through reverse genetics the swine, equine and canine influenza NP, PA, PB1 and PB2 genes and selection of host-species signature amino acids - Understand the influenza virus adaptive pathways existing within dogs and equine population and their significance in terms of risk for the human population or for other animals 2) Viral proteins Studies on proteins other than HA and PB2, on the interplay between proteins and their possible roles in more than one stage of the viral cycle are relatively few. Being viral adaptation to different hosts a complex event, probable polygenic modifications are needed, especially if taking into account that different viral proteins interact with others and may cover a role in more than one stage of the cycle. Thus it is important to focus the attention on IAV s genome as a whole. Future research needs - Investigate: the role of NA in entry, release, replication (associated genetic adaptations) HA ph stability, mutations correlated with changes in this characteristic and its role in species jump. the effect of NA stalk deletion in different species (other than avian) and to which extent this can be associated with a viral host jump. the role of M in aerosol and droplet transmission bodies identified above as author(s). In accordance with Article 36 of Regulation (EC) No 178/2002, this task has been carried out exclusively by the author(s) in the context of a grant agreement between the European Food Safety Authority and the author(s). The present document is published complying with the transparency principle to which the Authority is subject. It cannot 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. 93

94 3) Receptor preference and distribution Influenza A Viruses (IAV) HA binds to host receptors containing glycans with terminal sialic acids (SAs) with either α2-6 or α2-3 linkage to galactose. Human and swine influenza virus (SIV) strains preferentially bind to α2-6 SAs, while avian and equine influenza viruses (AIVs, EIVs) prefer α2-3 SAs. Nevertheless the situation is more complex than this: an important glycan characteristic is topology. Short α2-3 and α2-6 glycans assume a cone-like structure, and long α2-6 glycans an umbrella-like topology. The predominant topology in the human upper respiratory tract is umbrella-like, thus various authors suggested that an AIV needs to acquire preference versus this type of receptors in order to transmit among humans. Adaptation to human long-branched glycans could determine one of the many barriers SIVs encounter when crossing into humans. Future research needs - In depth characterization of receptor characteristic (especially length, and other characteristics such as fucosylation, etc., e.g. by mass spectrometry) and distribution present in the respiratory and intestinal tracts of quails, chickens, turkeys, swine, horses and dogs. Special attention on characterization of receptors present in the swine respiratory tract and their comparison with available data on receptors present in humans, correlating these results with possible differences between human and swine adapted HAs. 4) Interaction with the host s immune response Reassortment is an efficient pathway for IAVs to gain functions in new hosts. Continuous circulation of H1N1/1918 and H3N2/1968 pandemic viruses in the human population has driven adaptive changes in these strains: H1N1 seasonal strain carries a truncated PB1-F2, while H3N2 has a full-length protein, yet has undergone adaptive modifications. An in vitro and in vivo study evidenced how PB1-F2 proteins encoded by PB1 genes directly originating from the avian reservoir were capable of inducing lung inflammation in contrast to seasonal human adapted proteins. It remains to be seen if an avian PB1 segment can endow an IAV with an advantage for the initial transmission to a new host. Overall, numerous different avian-mammalian influenza gene combinations might be a path for an AIV to breach the mammalian species barrier. Classical swine H1N1 lineage is characterized by a truncated PB1-F2 protein as well. On the other hand, most European and all Asian and American H1N2 and H3N2 SIVs have a full-length protein. The role of innate (and the capacity of an influenza virus to modulate it) and adaptive (mucosal and not, i.e. cross-immunity) host immune response on influenza dynamics and pandemic emergence is a critical step that requires further investigations. This should be investigated both in animals and humans aiming to address the capacity with which IAVs are able to counteract the immune response and replicate in the host cells, and, thus, promote possible necessary interspecies adaptations. This research could contribute to clarify differential species susceptibilities to strains and further species barriers. bodies identified above as author(s). In accordance with Article 36 of Regulation (EC) No 178/2002, this task has been carried out exclusively by the author(s) in the context of a grant agreement between the European Food Safety Authority and the author(s). The present document is published complying with the transparency principle to which the Authority is subject. It cannot 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. 94

95 Future research needs - Investigate the function of PB1-F2 in a strain and host correlated manner - Compare the innate immune response arising after infection with different influenza A viruses in swine, equine and canine appropriate experimental surrogates and capacity of IAV to counteract this response (particularly addressing the role of NS1 and PB1-F2 in subtypes other than H1N1/1918 and HPAI H5N1) - Characterize the local/mucosal immunity and its role in intra-subtype and inter-subtype crossprotection both in animals and humans 5) Experimental methods It has recently been proven that several mutations in the PB2 gene, which have been evidenced as fundamental for adapting AIV replication to human cells, are also significant for replication of AIVs in pig cell lines. Nonetheless, it is important to highlight that these findings were obtained only through in vitro experiments using continuous cell lines (as most of the experiments regarding this subject). A total of 193 mutations and genomic markers have been identified (See Annex 1a) through experimental methods and categorized as follows (according to the degree of scientific knowledge available for the specific mutation): Category A: only field data (genomic markers with unknown consequences) Category B: only experimental data Category C1: experimental data available, but only in vitro Category C2: experimental data available, but only in vivo Category D: experimental data available both in vitro and in vivo but further studies with different animal models are needed Category E: contrasting results between different in vitro and/or in vivo studies bodies identified above as author(s). In accordance with Article 36 of Regulation (EC) No 178/2002, this task has been carried out exclusively by the author(s) in the context of a grant agreement between the European Food Safety Authority and the author(s). The present document is published complying with the transparency principle to which the Authority is subject. It cannot 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. 95