Characterizing epidemics in metapopulation cattle systems through analytic models and estimation methods for data-driven model inputs

Abstract

We have analytically discovered the existence of two global epidemic invasion thresholds in a directed meta-population network model of the United States cattle industry. The first threshold describes the outbreak of disease first within the core of the livestock system while the second threshold describes the invasion of the epidemic into a second class of locations where the disease would pose a risk for contamination of meat production. Both thresholds have been verified through extensive numerical simulations. We have further derived the relationship between the pair of thresholds and discovered a unique dependence on the network topology through the fractional compositions and the in-degree distributions of the transit and sink nodes. We then addressed a major challenge for epidemiologists and their efforts to model disease outbreaks in cattle. There is a critical shortfall in the availability of large-scale livestock movement data for the United States. We meet this challenge by developing a method to estimate cattle movement parameters from publicly available data. Across 10 Central States of the US, we formulated a large, convex optimization problem to predict the cattle movement parameters which, having minimal assumptions, provide the best fit to the US Department of Agriculture's Census database and follow constraints defined by scientists and cattle experts. Our estimated parameters can produce distributions of cattle shipments by head which compare well with shipment distributions also provided by the US Department of Agriculture. This dissertation concludes with a brief incorporation of the analytic models and the parameter estimation. We approximated the critical movement rates defined by the global invasion thresholds and compared them with the average estimated cattle movement rates to find a significant opportunity for epidemics to spread through US cattle populations.