Date of Award:
Doctor of Philosophy (PhD)
Mathematics and Statistics
The effects of emerging wildlife diseases are global and profound, resulting in loss of human life, economic and agricultural impacts, declines in wildlife populations, and ecological disturbance. The spread of wildlife diseases can be viewed as the result of two simultaneous processes: spatial spread of wildlife populations and disease spread through a population. For many diseases these processes happen at different timescales, which is reflected in available data. These data come in two flavors: high-frequency, high-resolution telemetry data (e.g. GPS collar) and low-frequency, low-resolution presence-absence disease data. The multi-scale nature of these data makes analysis of such systems challenging. Mathematical models serve as valuable tools for forecasting disease spread. To produce meaningful predictions a model must include appropriate mechanisms for both transmission and animal movement and be parameterized with data. Herein, a framework is developed for modeling wildlife disease spread. Model competition is used to select and parameterize appropriate transmission mechanisms given time-series prevalence data. For animal movement a parameterization method for a mechanistic, population-scale wildlife movement model is derived for use with individual telemetry data. Throughout, special attention is payed to computational complexity. Homogenization and other asymptotic methods are used to maintain feasibility of parameterization. In tandem, these two methods determine and parameterize the movement and transmission mechanisms that play a role in wildlife disease spread, taking into account the types of available data and inherent separation of scales between the two processes.
McGahan, Ian, "Methods in Modeling Wildlife Disease from Model Selection to Parameterization With Multi-Scale Data" (2020). All Graduate Theses and Dissertations. 7865.