Speaker: Dr. Alison C. Ketz, University of Wisconsin, Madison
The dynamics of chronic wasting disease (CWD) are driven by spatially and temporally varying processes that operate on multiple scales, leading to complicated population effects. This complexity necessitates a flexible modeling approach that can structurally incorporate relevant ecological and epidemiological processes, to forecast disease impacts on host populations.
To fill this need, presented here is a spatiotemporal integrated population model (IPM) that assimilates disease dynamics within a host population model and simultaneously estimates key demographic and epidemiological parameters. The model utilizes data from numerous sources, including age-at-harvest, cross-sectional surveillance, and survival information from marked individuals to assess population impacts of CWD on white-tailed deer (Odocoileus virginianus) in the CWD-endemic region of south-central Wisconsin, USA.
Previous studies indicate declines of CWD-affected cervid populations in the Rocky Mountains, but due to strikingly different environmental, behavioral, demographic, and density patterns, a data-driven assessment for the midwestern USA is needed. Additionally, IPMs frequently use mark-recapture data to obtain survival and disease rates, alternatively, this modeling approach jointly estimates survival and force-of-infection using a time-to-event continuous time framework that allows these processes to vary over time and across ages of individuals in the host population. This novel method provides a rigorous tool for increasing understanding of disease and population dynamics, which are critical to mitigate disease impacts.