A flexible spatial framework for modeling spread of pathogens in animals with biosurveillance and disease control applications

Abstract

Biosurveillance activities focus on acquiring and analyzing epidemiological and biological data to interpret unfolding events and predict outcomes in infectious disease outbreaks. We describe a mathematical modeling framework based on geographically aligned data sources and with appropriate flexibility that partitions the modeling of disease spread into two distinct but coupled levels. A top-level stochastic simulation is defined on a network with nodes representing user-configurable geospatial -patches. Intra-patch disease spread is treated with differential equations that assume uniform mixing within the patch. We use U.S. county-level aggregated data on animal populations and parameters from the literature to simulate epidemic spread of two strikingly different animal diseases agents: foot-And-mouth disease and highly pathogenic avian influenza. Results demonstrate the capability of this framework to leverage low-fidelity data while producing meaningful output to inform biosurveillance and disease control measures. For example, we show that the possible magnitude of an outbreak is sensitive to the starting location of the outbreak, highlighting the strong geographic dependence of livestock and poultry infectious disease epidemics and the usefulness of effective biosurveillance policy. The ability to compare different diseases and host populations across the geographic landscape is important for decision support applications and for assessing the impact of surveillance, detection, and mitigation protocols.