Informed disease management

The CWD modeling project began in 2014 in collaboration Josh Millspaugh (University of Montana), Matt Gompper (University of Missouri, now New Mexico State University) and Missouri Department of Conservation. We developed an agent-based modeling framework for assessing the efficacy of harvest-based disease surveillance in white-tailed deer populations of Missouri. We also developed a spatially-explicit, agent-based model of chronic wasting disease transmission dynamics. This model is being used to assess the rate of CWD spread in Missouri, as well as to evaluate alternate management strategies to limit the spread of CWD. The CWD Modeling Framework was subsequently (2019-2021) adapted to simulate Michigan white-tailed deer populations and then applied to assess alternate harvest strategies for their impact on CWD spread. Currently, I am collaborating with Atle Mysterud (University of Oslo) and Hildegunn Viljugrein (Norwegian Veterinary Institute) to apply the modeling framework to reindeer populations, and we are evaluating surveillance and harvest strategies to better mitigate the threat of newly introduced CWD in the reindeer populations of Norway. The CWD modeling framework is also being used to inform CWD management in Indiana (in collaboration with Purdue University) and Texas (supported by the Safari Club International Foundation). Summary of the work done so far https://gist.github.com/anyadoc/4340276f3cfdc87ce145ca1275199941

This project was supported by a Modeling Access Grant, Center for Modeling Complex Interactions, University of Idaho. I collaborated with Ryan Long (University of Idaho) to develop an agent-based model of bighorn sheep population dynamics. This model can be used to investigate pneumonia dynamics in bighorn sheep populations and guide research questions. The goal of this work was to support the Idaho Department of Fish and Game in developing locale-specific disease management strategies for bighorn sheep populations.

The objective of this collaborative, OneHealth research project is to use viral genome sequencing, dog demography and epidemiological modeling to better understand the mechanisms of persistence and dispersal of canine rabies, and find focused, efficient strategies for interrupting dog-to-dog transmission of rabies virus in resource-limited settings.