Appendix 31

Appendix 31

Epidemiological models for global surveillance of Foot-and-Mouth disease

Andrés M. Perez1*, Mark C. Thurmond1, Tim E. Carpenter1, Thomas W. Bates2, Wesley O. Johnson3 , Brett A. Melbourne1, Shagufta Aslam1, Rebecca B.Garabed1, Young-ku Choi3, Adam J. Branscum3 , Miryam L. Gallego1, and Paul W. Grant1

1The FMD Modeling and Surveillance Laboratory, Department of Medicine and Epidemiology, School of Veterinary Medicine, University of California, Davis, CA 2Lawrence Livermore National Laboratory, Livermore, CA 3Department of Statistics, University of California, Davis, CA

Abstract:

This paper summarizes some of the epidemiological models we have developed to describe the global temporal-spatial distribution and risk of FMD and to identify factors that predict changes in FMD status or movement, as applicable to real-time global FMD surveillance. Regional models focused on the time, space (geographic location), and time-space evolution of FMD in specific countries. Countries being studied include Iran, Pakistan, Afghanistan, Turkey, Nepal, Mongolia, Colombia, Bolivia, and Argentina. Country-specific models were validated using methods of cross-validation and expert opinion, as solicited from veterinarians in each country. The results of risk models are projected to assess when and where FMD can be expected and to forecast globally changing risks of FMD. These models could be applied via an FMD web-portal currently under development for realtime global risk surveillance of FMD to characterize changes in time, place, and transmission of FMD, and to identify new anomalous and unexpected FMD cases or precursor events. Real-time global risk surveillance for FMD, utilizing prediction, forecasting, and anomaly detection models, can improve our awareness and assessment of FMD globally and provide fundamental information to enhance biosecurity in free areas and control strategies in infected areas.

Introduction:

It is becoming increasingly apparent that, if we are to control and perhaps eradicate FMD on a global scale, a comprehensive understanding of the global distributions and changes in risk and movement of the disease must be developed. Currently, we lack of information relating to where FMD can be expected to be found and the new or emerging risks of FMD world wide. Key goals of our laboratory are to disseminate information on FMD relating to outbreaks and isolates, make available modeled distributions of FMD in regions where the information is uncertain, and project or predict risk of infection in FMD-free areas of the world. In this paper we summarize some of the models and approaches explored by our group in the past year.

Methods and Results:

BioPortal system: The FMD BioPortal is a web-based system designed for the real-time capture and dissemination of data, diagnostic results, and FMD-related risk information to and from countries, agencies, and laboratories. The aim of the portal is to offer a secure and confidential mechanism for rapid transfer of data, analyses, and maps needed to assess changes in FMD risk for specified geographic locations and times. FMD-related data may be obtained automatically or by ‘hand’ upload from websites of the international organizations, such as OIE, FAO, or the WRLFMD, as well as from individual country or agency databases. The database outputs are reformatted into a standardized structure via a data adapter specific to a particular messaging system. The data can be subjected to a variety of epidemiological and statistical analytic and mapping methods, including spatio-temporal clustering, anomaly detection, and spread or movement prediction.

Temporal models: The goal of temporal models is to provide an estimate, during some time i, of the expected number of FMD outbreaks in a given region of the world in time i+1. The mathematical process that best models the time series depends on the behavior of the infectious disease (trend, sporadic, seasonal, annual, and secular cycles). We are currently examining the potential application of different Bayesian models that alternatively use autoregressive, random mixed effects, stochastic processes, or the combination of these models to predict the number of FMD outbreaks in Iran. The number of outbreaks reported by Iran from January 1996 through December 2001 is used to select the model that best fit the distribution of the data, using estimates of the mean absolute prediction error and deviance information criterion. Then the selected model is used to predict the number of outbreaks in Iran for the next year, and the resulting prediction is compared with the actual observed number of outbreaks (as part of validation).

Spatial models: Spatial models were used to estimate the most likely distribution of FMD throughout an area under study, when incomplete information is available. We used a combination of smoothing techniques and elicitation of expert opinion to recover real or more realistic outbreak distributions, which were missed when data were grouped at a province level in Iran (actual outbreak locations were missing). A spatial scan statistic was run to estimate high FMD risk areas of Iran, and then compared estimates for the distribution of the susceptible population. The resulting relative risk estimates were validated through correlation tests with a subset of data with actual locations. The Rs>0.8 indicates a very close fit of the smooted spatial distribution model to the actual data of FMD cases, as described by experts in field. For Pakistan, which has not reported FMD outbreaks via OIE since 2001, the expected probability of having at least one outbreak was modeled by estimating the spatial correlation among reported outbreaks and the use of covariates hypothesized to be related with FMD prevalence (livestock and human population densities). The model was validated by comparing the expected probability with the true location of samples, from which FMDV was isolated, submitted by Pakistan to the world reference laboratory after 2001.

Time-space models: These types of models combine the characteristics of the previous temporal and spatial models and are an end goal of modeling and forecasting FMD spread in endemic countries. A spatiotemporal regression model was developed and applied using training data for the annual number of reported FMD outbreaks from 1996-2003 in each province of Turkey. Let Yij denote the number of reported FMD outbreaks in region i for year j , where j=1 corresponds to year 1996, and let µij denote the mean number of FMD outbreaks and xi denote a vector of covariates for region i, and let α be the corresponding vector of regression coefficients. The covariates included in the model were cattle and sheep density and whether the province bordered a body of water. The general Poisson regression model considered was of the form: Yij | µij ~ Poisson(µij), i=1,2,…,66; j=1,2,…,8 log(µij) = f(tij) + fi(tij) + xi T α + θi The functions f(t) and fi(t) were used to model the time trend in FMD outbreaks for each province. To account for spatial correlation, the random effects (θi) were modeled using a conditionally autoregressive structure. A Bayesian approach was used to fit all models. The results of the model will be validated by comparison of the predictions obtained for year 2004 with the data actually observed in field in 2004.

Evaluation and design of eradication programs: Evaluation of past control strategies may lead to better design of future control programs in countries where FMD is endemic. To assess past control strategies in Colombia, we analyzed 20-years of data relating to FMD cases for both serotypes A and O. Smoothing of the time series using moving averages suggested a secular variation of large epidemics of each serotype, with large epidemic occurring every 4-6 years. The total number of FMD outbreaks caused by both serotypes decreased significantly (P<0.01) throughout the 20-years period. However, the dramatic decrease in the number of cases that led to the control of FMD in Colombia occurred only after initiation of the eradication campaign in 1997, which involved international, national, and private organizations. The Colombian experience suggests that a successful eradication program must address a multitude of complex problems that extend beyond the technical issues involved in the selection of vaccines or the surveillance system.

Serotype and phylogenetic models: The probability of that an FMD outbreak is associated with a specific serotype will be modeled using Bayesian models that consider the time and space relation between isolates and information on specific covariates (density of roads, species affected). The models are currently being developed into prototypes using information on a viral infection of salmon in California as the training data. Further models will include assessment of the spatial distribution and factors associated with variation in the homology and divergence of FMDV strains. We expect to start working with FMD-specific information from Iran and Nepal before the end of the year.

Global models: The information collected for each country and the results obtained from national and regional models will be used to create comprehensive global models to predict FMD. The aims of these global models are 1) to quantify the association between economic, political, agricultural, and demographic factors and FMD status at the national and sub-national level and 2) to predict true national and sub-national FMD status globally, using publicly available data and controlling for suspected confounders. Surrogate variables describing political, agricultural, demographic, and economic status can be obtained from publicly available sources of information, such as national governments, the World Bank, and the United Nations. For example, the gross domestic product per capita was used as a surrogate for economic status in a country. Training data for the dependent variable (yearly FMD status) is based on the presence of FMD in each country, as estimated by expert opinion and OIE data. The association between FMD status and the predictor covariates described

above will be evaluated using a Bayesian logistic regression. Figure 1 shows a prototype of the expected outcome of the global models.

Discussion:

Development of reliable models is typically a very time-consuming process. The quality of the output is usually related with the quality of the input and therefore, improving the quality of the information used to design the models is key for the success of the predictions. Modeling infectious diseases in endemic countries allows for better opportunities for validation and improvement of the model design, a major advantage that compensates for the typical lack of information. We expect during the next year to increase collaboration with national and international organizations to improve the quality and dissemination of our maps and models. We also expect to explore serotype-specific and phylogenetic models. The ultimate goal will be to make these models broadly available via the FMD web-based system (FMD BioPortal), currently under development for real-time global risk surveillance of FMD, to characterize changes in time, place, and transmission of FMD, and to identify anomalous and unexpected FMD cases or precursor events.

Conclusion:

Real-time global risk surveillance for FMD, utilizing prediction, forecasting, and anomaly detection models, can improve our awareness and assessment of FMD globally and provide fundamental information to enhance biosecurity in free areas and to develop control strategies in affected regions.

Recommendation:

Expand development of models to project changes in FMD distribution, risk and genomic variation.

Acknowledgements:

The project was funded in part by the U.S. Armed Forces Medical Intelligence Center. We gratefully acknowledge the support received from Dr. David Paton and Jean-François Valarcher, World Reference Laboratory for FMD, Pirbright, UK; Dr. Juan Lubroth, Dr. Giancarlo Ferrari, and Dr. David Ward, Animal Production and Health Division, Food and Agricultural Organization; Dr. Keith Sumption, European Commission for the Control of FMD; and Dr. Paul Kitching, National Centre for Foreign Animal Diseases, Winnipeg, Canada. We also gratefully acknowledge the assistance of Dr. I. Nowrouzian and Dr. S. Bakthiari, School of Veterinary Medicine, University of Tehran, Iran, Dr. S. Akhtar, Aga Khan University, Karachi, Pakistan; Dr. M. Afzal, Pakistani Agricultural Research Council, Islamabad, Pakistan; Dr. G. Gongal, Central Epidemiologic Unit, Nepal; Dr. C. Orozco, APHIS-USDA, Bolivia; Dr. E. Späth, INTA Balcarce, Argentina; Dr. O. Díaz, Instituto Colombiano Agropecuario, Colombia, for assistance in interpreting the reporting system and providing expert opinion about the distribution of FMD in their respective countries.

Figure 1: Prototype of a system to display the results of the global models. Different shades indicate different risk for FMD. The predictions displayed here are presented using a partial set of covariates as predictors and at a relatively high scale of definition. The purpose of this figure is to demonstrate the outcomes that we are expecting to produce and not to present the actual outcomes of the model; model development is still in the design stage.