Title: Geospatial distribution and dynamics of swine diseases in the United States

Advisors: Dr. Gustavo Machado, faculty fellow and associate professor in the Department of Population Health and Pathobiology & Dr. Chris Jones, faculty fellow and senior research scholar in the Center for Geospatial Analytics

Abstract: This defense examines how geospatial modeling contributes to understanding swine disease distribution and transmission in the United States by integrating spatial epidemiology, machine learning, and population modeling. We first investigate the spatial and spatiotemporal dynamics of porcine reproductive and respiratory syndrome virus transmission to identify the environmental, demographic, and network-related factors associated with outbreak risk. We then develop a spatially explicit machine-learning framework using high-resolution aerial imagery to detect swine farms, classify production types, and estimate population sizes. Finally, we use the Pest or Pathogen Spread (PoPS) model to simulate the spread of African swine fever virus under multiple movement restriction scenarios, quantify the relative contribution of direct and indirect transmission pathways, and evaluate how incomplete movement restrictions may undermine outbreak control efforts.