Chronic wasting disease (CWD) is a transmissible spongiform encephalopathy that has spread in North American cervids for at least the last 50 years. Since its initial discovery in Colorado in 1967 (Williams and Young 1980), it has affected at least eight cervid species including white-tailed deer (Odocoileus virginianus), spread to at least 26 U.S. states (Rivera et al. 2019), and been called the greatest contemporary threat to free-ranging deer herds (Gillin and Mawdsley 2018). Once CWD is introduced, it is known to cause localized population declines that threaten the long-term sustainability of deer herds and associated hunting activities. As a result, state and federal agencies have allocated a large amount of financial and staff-time resources to manage CWD but have experienced limited success.One of the most rational and effective ways to mitigate the effects of a persistent disease like CWD is to perform proactive surveillance efforts that can either prevent, or detect and eradicate, disease prior to its establishment. However, a combination of human activities that increase the risk of disease translocation, complex disease dynamics, and imperfect observation makes assessing the comprehensive risk of CWD introduction by empirical study prohibitive. Furthermore, once CWD is introduced and becomes established it has been difficult to maintain long-term monitoring programs at a level that produces accurate spatial predictions and improves our ability to understand the causal mechanisms that promote disease spread and persistence.I developed several distinct modeling approaches to perform proactive and reactive CWD surveillance and monitoring given limitations in data availability, disease observation, and long-term financial and staff-time resources. I determined that expert elicitation techniques are valuable tools to help inform comprehensive risk assessments given that appropriate methods are employed to limit expert biases. I determined that increasing the complexity of disease models can help predict the locations of CWD occurrence and may elucidate mechanisms that promote localized spread, long-distance spread, and persistence of disease. Finally, I utilized important covariate relationships that impact disease detection to help inform the strategic and efficient long-term use of resources. Important findings from my dissertation include: 1) a comprehensive approach to estimate the spatially-explicit risk of disease exposure and amplification that is adaptable to localized disease hazards; 2) CWD extent estimates that are specific to the localized landscape and that include more disease detections than existing distance benchmark approaches; 3) a statistical modeling framework that incorporates more of the complex disease ecology and produces more accurate spatial estimates of disease occurrence; and finally, 4) the estimation of important covariate relationships that affect disease spread and persistence as well as disease detection. This dissertation and associated research findings will be broadly useful to disease management endeavors and contribute to improved decision making that maximizes the value of resources spent to acquire and learn from disease observation data.
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