The University of Pennsylvania School of Veterinary Medicine MIDAS team will develop hierarchical models for the spatio-temporal dynamics of infectious disease, specifically an evidence-based hierarchical family of paradigmatic model frameworks for the temporal and spatial spread of two directly transmitted diseases of representative species of farm animals: avian influenza (AI) in poultry and Foot and Mouth Disease (FMD) in cattle, pigs, and sheep.
Extending and refining previous work, the group proposes to create models of AI and FMD at the local, county, state, and national levels. These models will take into account relevant spatial differences in farm density, size, and system; animal movement within and between states; and the consequences should certain wildlife be more than "spill-over" hosts for the diseases of interest.
The researchers will also determine the appropriate scale for each of these models, given the availability of existing data—few U.S. farm locations or areas have been mapped to the level of detail needed for existing models. To devise their models, the scientists will also use data based on zip codes, counties, or a national grid. They will employ modeling methods developed in the ecological literature (Patch/Gravity Models). The gold standard for comparative purposes will be the behavior of existing models that use individual U.S. farm data for regions where relevant data exists.
With respect to modeling response systems, the team will use the local and national paradigmatic models to examine, compare, and devise control strategies based on existing tools, such as quarantine, culling, vaccines, and therapy. The researchers will investigate how geographical differences and different systems of animal management and commerce affect the implementation and outcome of programs for prevention and control.
The group also aims to understand what constitutes an optimal control strategy for each paradigmatic group. This will involve an attempt to define "success" with respect to disease control and investigate how the choice of end point—the magnitude and the duration of the epidemic, the speed of recovery, the social acceptability of control methodologies—modulates the choice of strategy.
Co-Principal Investigator: Matthew Keeling, Ph.D., of the University of Warwick in the United Kingdom