The CWD modeling project began in 2014 in collaboration Josh Millspaugh (University of Montana), Matt Gompper (University of Missouri, later 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 in white-tailed deer populations. This model was 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. I also collaborated with Atle Mysterud (University of Oslo) and Hildegunn Viljugrein (Norwegian Veterinary Institute) to apply the modeling framework to reindeer populations, and we assessed surveillance and harvest strategies for 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 a USDA-APHIS grant). Current work on assessing public practices like baiting, supplemental feeding and carcass movements for their impact on the establishment and spread of CWD in regional deer populations is supported by a grant from the Safari Club International Foundation. Summary of the work done so far https://gist.github.com/anyadoc/4340276f3cfdc87ce145ca1275199941
I discuss how models can support informed wildlife disease management.
Given the interconnectedness of animal health, environmental health and human health/well-being, it is necessary to investigate the ecological context of animal disease systems that have public health, conservation or economic implications. Such host-pathogen systems are highly complex and heterogeneous, and often, our understanding of such systems is fraught with uncertainties. Our aim is to develop and use models to elucidate host-pathogen dynamics in such systems, and translate the insights gained into actionable outcomes for effective and meaningful management of diseases. At present, I continue to work on Kyasanur Forest Disease, canine rabies, and leptospirosis. I am also developing a conceptual framework for informing policies on emerging zoonoses.
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.
With Matt Gompper (New Mexico State University). Raccoon roundworm _Baylisascaris procyonis_ can cause clinical infections with high morbidity and mortality in several vertebrate species including humans, and therefore poses a public health as well as conservation threat. The objective of this modeling project is to develop a tool to compare and contrast interventions for effective surveillance and management of raccoon roundworms.
With Claudia Munoz-Zanzi (University of Minnesota), Meghan Mason (St. Catherine University), Matt Gompper (New Mexico State University). We are using an agent-based approach to simulate transmission dynamics of host-adapted Leptospira strains in a multi-host system. One of the main objectives of this model is to evaluate alternate interventions aimed at reducing human infection risk in small-scale communities like urban slums.
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.