NWRC Research Scientists: Dr. Kim Pepin

Disease. I quantify processes that are important in wildlife disease transmission and invasive species spread, such as contact structure, demography, animal movement, and environmental transmission. I leverage an understanding of these processes for management applications. Example applications include:

• Predicting geographic spread, hotspots, or prevalence of disease
• Inferential methods of disease surveillance data for optimizing disease risk assessment
• Developing designs and conducting adaptive disease surveillance
• Determining optimal disease control strategies (e.g., wildlife vaccination, removal, and other techniques)
• Monitoring and estimating disease elimination
• Methods for interpreting serology data across within and between-host scales for risk assessment

I work on a variety of zoonotic or wildlife-livestock disease systems, often focusing on the reservoir host species. Examples include influenza A, rabies in wild carnivores, African swine fever in wild pigs, bovine tuberculosis in deer, SARS-CoV-2 in wild mammals, and chronic wasting disease in deer.

Invasive or pest species. I develop quantitative tools for risk assessment, evaluating management actions, and guiding control of invasive/pest species and their damage, especially of wild pigs. I also design field studies to identify and understand key ecological processes that are essential for planning science-based management solutions and identifying the best targets for management. Example applications include:

• Developing modeling tools for risk assessment of new pest or disease control products where risk assessment methods are challenging to establish (e.g., transmissible vaccines, gene drive products)
• Methods and user-friendly tools for inferring management efficacy based on management data
• Inferring population density or damage before and after management
• Evaluating population dynamics under different management strategies
• Estimating the amount of damage prevented by management
• Mapping elimination probability
• Identifying how resource allocation can be optimized in space and time