We developed a spatially explicit agent-based model (ABM), DeerLandscapeDisease (DLD), to investigate the effects of landscape structure, disease transmission, and management alternatives on dynamics of chronic wasting disease (CWD) in white-tailed deer (Odocoileus virginianus). We fitted biased random walk models to data from GPS-collared deer to simulate movements of individual deer and deer groups in an agricultural landscape with fragmented forest patches and a forest-dominated landscape. We estimated behavioral and demographic parameters from field data and published literature of deer ecology. We considered both direct and indirect transmission routes and assumed that bioavailability of infectious pathogens deposited in the environment decreased exponentially over time. We tuned transmission parameters to match observed trajectories of CWD prevalence in Wisconsin, and assumed that infection probability during an encounter was equal for all age classes. Thus, infection prevalence varied with sex- and age-specific behavior.

DLD simulations demonstrated significant effects of landscape structure, social behavior and transmission mode on temporal changes in prevalence. Prevalence rose faster and reached higher levels in fragmented forest landscapes due to aggregation of deer within small forest patches. Furthermore, simulation results suggested that CWD might be driven through a mix of frequency- and density-dependent processes, potentially facilitating coexistence of CWD and deer populations. These results demonstrate the utility of ABMs and the importance of including spatial and behavioral heterogeneity when modeling disease transmission.