Putting it together: The potential role of modeling to explore the impact of FMD

Transcription

1 Putting it together: The potential role of modeling to explore the impact of FMD Mo Salman Animal Population Health Institute Colorado State University And Melissa McLaws European Commission for the Control of Foot-and-Mouth Disease (EuFMD), Rome, Italy New Delhi, February 2012

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3 Information

4 Information Decision-making

5 and disease measurement should be described and analyzed with the aim to derive conclusions about the observed events and procedures. Epidemiological indices can be used for assessing specific intervention strategies in terms of effectiveness. Specific disease measurements can also be used to determine the source of a disease and if a trend change has occurred. Disease measurements, however, require careful assessment of the available data in order to derive the appropriate conclusion.

6 and disease measurement Epidemiological indices: Incidence Prevalence Epidemic curves

7 What is a model? A representation of a system, developed for the purpose of studying that system; Includes assumptions about how the system works for simplification; Imitates the behavior of complete biosystems in a different medium so that the behavior of these models can be studied faster, more easily and less expensively than real biosystems Enable us to ask What if? questions

8 What is Model? Comes in many shapes, sizes and styles; It is a representation of the real world, developed so it can be studied to help us understand the real world; A model always has a purpose, or scope; Initial conditions, or input parameters must be defined. Solar system Weather prediction

9 Laws of simulation (Dillon) 1. simulation, like statistics, cannot prove anything 2. simulation, like statistics, can nearly prove anything 3. once started, simulation of a system will continue until available funds are exhausted

10 Models are Poor substitutes for reality Bottom line! Tools to accumulate what is currently known about a disease/epidemic into an educated guess about what may happen Interpretation of a model s output by a person who knows the input parameters and assumptions of the model is critically important

11 Why use simulation models in animal health? As part of the retrospective analysis of data; Contingency planning; Resource planning; Training and preparations; Surveillance targeting; Real time decision support.

12 Issues with simulation modeling Assumptions are made to overcome lack of knowledge Level of detail Needed Available Human behavior unpredictable Biological phenomena are linked with uncertainty.

13 dependent: Epidemiological knowledge Models in animal health Quantity/quality of data Poor Poor Explanatory hypothesis development Good Simplified representation of past events with data assumptions. Good Empirical/analytical hypothesis testing Good representation (simulation) of past events. Can be used predictively IF future is predictable

14 What is NAADSM North American Animal Disease Spread Model This is only a name Works anywhere in the world Does more than create models 14

15 What is NAADSM A software based framework for the development of animal disease models and for simulating those models Based on previous software models and simulation engines developed for the study of FMD Frequently updated and improved to reflect new or additional needs of users 15

16 The NAADSM Development Team Canadian Food Inspection Agency Colorado State University Animal Population Health Institute Department of Agricultural and Resource Economics Ontario, Ministry of Agriculture, Food, and Rural Affairs United States Department of Agricultural University of Guelph Department of Computer and Informational Science 16

17 What is NAADSM NAADSM is capable of running on: Microsoft Windows Super computers Grid computers Parallel systems Macintosh computers (with the use of Parallels for the Mac) And more 17

18 Purpose and design Primary Purpose of NAADSM: Strategic planning Mitigation of animal disease Mitigation of the effects of animal disease Study of the efficiency and effectiveness of mitigation strategies 18

19 Purpose and design NAADSM is: A state-transition model Natural progression through the disease states Mitigation strategies may preempt these A spatially explicit model Disease progresses over areas of interest Inherently temporal as a result A Monte Carlo simulation tool Allows stochastic simulation of the models built 19

20 Key objectives for NAADSM development Production of a sound conceptual framework and a practical modeling tool for the evaluation of proposed disease control strategies and preparedness planning Ease of use New users should be able to understand, build, and use models relatively quickly Transparency Models users should have a clear understanding about how the model operates If a model is to be used in the development of emergency response or regulatory programs, it is essential users and decision makers know the assumptions and limitations of the model 20

21 State Transition Model NAADSM is a state transition model Natural progression Susceptible Infection Naturally immune Pre-emptive slaughter Natural progression Pre-emptive slaughter Vaccine immune Vaccination Pre-emptive slaughter Latent Natural progression Pre-emptive slaughter Detection & destruction Destroyed Pre-emptive slaughter Natural progression Clinically infectious Pre-emptive slaughter Subclinically infectious Natural progression Figure modified from Harvey et al.,

22 NAADSM is a unit-based state transition model Each unit (herd or flock) in NAADSM has a disease state Upon infection, a unit will enter a predictable cycle The length of time that a unit spends in a state is variable, depending upon user-defined distributions Application of control measures may interrupt the natural state transition Naturally immune Natural progression Natural progression Pre-emptive slaughter Pre-emptive slaughter Detection & destruction Clinically infectious Natural progression Pre-emptive slaughter Susceptible Vaccine immune Destroyed Pre-emptive slaughter Natural progression Vaccination Infection Pre-emptive slaughter Pre-emptive slaughter Subclinically infectious Latent Natural progression 22

23 Spatially Explicit Each unit in a NAADSM simulation has a specific geolocation (latitude/ longitude) Distances between units are considered 23

24 Number of iterations NAADSM produces a range of possible outcomes Each iterative run of a simulation produces a different result set One single iteration or realization is of little value Size of simulated outbreaks in the Iowa-based population (1000 iterations) Without vaccination With vaccination A simulation completion consists of many iterations The aggregation of these result sets gives us much more information Number of herds infected 24

25 Limitations The design of the NAADSM framework is: Intended for diseases of livestock and poultry Primarily centered toward trans-boundary animal diseases, where a pathogen is introduced into a previously unexposed population Capable of other situations however Requires more advanced techniques Intended for non-mobile populations 25

26 Limitations The design of the NAADSM framework is: Intended for non-fatal diseases But is capable of fatal diseases Intended for non-endemic disease Advanced techniques can help however Not for vector-borne diseases Not able to simulate population dynamics (e.g., births, deaths, business failures, etc.) 26

27 NAADSM and existing outbreak FMD data The outbreak field data can be used to apply techniques in modeling and simulation for assessing and/or supporting specific control strategies; Surveillance data can be also used to assess alternative approaches for reducing the impact of FMD; such as alternative vaccination strategies, surveillance zones etc.

28 NAADSM and existing outbreak FMD data The modeling concepts however should mimic natural occurrence occurring of the disease as much as possible; Modeling requires reliable data and other information to reflect the actual situation in the field; Modeling has not been widely used in FMD-endemic countries, disease spread models have tremendous potential to assist in making decisions about the best use of limited resources available for disease control.

29 Examples of projects using NAADSM in the United States & Canada FMD Evaluation of vaccination strategies in the U.S. Provide vaccine estimates for the North American FMD Vaccine Bank Provide carcass disposal estimates HPAI and others Has been used to model HPAI and Exotic Newcastle Disease Capacity building Support PANAFTOSA and participating countries in the application of NAADSM 29

30 Examples of projects using NAADSM in the United States & Canada Economic impact estimates Regional economic impact of FMD in Southwest Kansas Benefit cost analysis of the National Animal Identification System Economic impact analysis of pseudorabies virus Economic impact of regionalization in the case of HPAI Model comparison AusSpread, InterSpread Plus, and NAADSM QUADS comparison team evaluated the abilities of each with respect to the others results, etc. 30

31 NAADSM is transparent and accessible Description of the simulation process and assumptions made by NAADSM has been published in the peer-reviewed literature (Harvey et al. 2007) Further detail is available online The NAADSM application is freely available via the Internet from Updates User s guide ListServ Development team Training opportunities Complete source code for the NAADSM application is available for modification and use Source code is free, subject to the terms of the GNU General Public License 31

32 Conclusions should be described and analyzed, and so turned into information useful for decision makers This information can be used in models to study the spread of disease, risk factors and alternative control strategies including economic impact Usefulness of models is directly related to quality of information that goes in (garbage in-garbage out) and assumptions must be clearly explained and understood NAADSM is an example of a model that (with some amendments) could be useful to study FMD and FMD control in endemic countries