We shall develop an evidence-based hierarchical family of paradigmatic model frameworks for the temporal and spatial spread of two directly transmitted infections of farm animals: Avian Influenza (Al) and Foot and Mouth Disease(FMD. With respect to modeling disease systems we shall create both local (within herd/flock), county, state and national models of Al and FMD. The heirarchical strategy is important because the simpler models can be completed more quickly and parameterized more readily and are therefore more likely to be available for use than the more complicated models. Their behavior can more easily be generalized to other systems and can inform response measures in a strategic (if not tactical) sense. The more complicated models are essential tactical instruments but will take longer to complete and their behavior may not be so easily generalized to other infections. Our proposal represents a compromise between the need to provide useful strategic information about a range of potential threats - and the requirement that we can inform tactical decisions about prevention, response and recovery. Among other things, we shall investigate the appropriate level of granularity (scale) for each of these models. There are few regions in the USA where farms locations are mapped to level of detail required for most existing types of models for spatial and temporal spread at state or national scales. We shall devise models (metapopulation/ Patch/ Gravity models) based on other spatial resolutions (eg data based on zip codes, counties, or a national grid system). We shall test and calibrate these approximations using detailed spatial stochastic models developed using detailed and extensive GIS data bases on farm location in Pennsylvania and then extend their use to other regions in the USA. We shall use the models to devise and refine strategies for prevention, response and recovery. Avian Influenza and Foot and Mouth Disease are potential agents of bio/agroterrorism in addition to being devastating animal epidemic diseases in their own right which have have dramatic adverse effects on the well-being (social, economic and psycholgical) of human populations. The models proposed here will assist in planning strategies for prevention, control and response. In the case of Avian Influenza especially, controlling the infection in birds is a pre-emptive strategy for control in people.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project--Cooperative Agreements (U01)
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Special Emphasis Panel (ZGM1-CBCB-2 (MI))
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Sheeley, Douglas
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University of Pennsylvania
Other Clinical Sciences
Schools of Veterinary Medicine
United States
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Smith, G (2013) Food- and water-borne disease: using case control studies to estimate the force of infection that accounts for primary, sporadic cases. Epidemics 5:77-84
Smith, Gary (2012) Preferential sexual transmission of pseudorabies virus in feral swine populations may not account for observed seroprevalence in the USA. Prev Vet Med 103:145-56
Pelletier, Sky T K; Rorres, Chris; Macko, Peter C et al. (2012) Models of highly pathogenic avian influenza epidemics in commercial poultry flocks in Nigeria and Ghana. Trop Anim Health Prod 44:1681-7
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Rorres, Chris; Pelletier, Sky T K; Smith, Gary (2011) Stochastic modeling of animal epidemics using data collected over three different spatial scales. Epidemics 3:61-70
Smith, G (2011) Models of macroparasitic infections in domestic ruminants: a conceptual review and critique. Rev Sci Tech 30:447-56
Rorres, C; Pelletier, S T K; Bruhn, M C et al. (2011) Ongoing estimation of the epidemic parameters of a stochastic, spatial, discrete-time model for a 1983-84 avian influenza epidemic. Avian Dis 55:35-42
Smith, G; Dunipace, S (2011) How backyard poultry flocks influence the effort required to curtail avian influenza epidemics in commercial poultry flocks. Epidemics 3:71-5
Rorres, Chris; Pelletier, Sky T K; Keeling, Matt J et al. (2010) Estimating the kernel parameters of premises-based stochastic models of farmed animal infectious disease epidemics using limited, incomplete, or ongoing data. Theor Popul Biol 78:46-53
Tildesley, Michael J; Keeling, Matt J (2009) Is R(0) a good predictor of final epidemic size: foot-and-mouth disease in the UK. J Theor Biol 258:623-9

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