The central goal of our research is to develop and apply quantitative tools to more accurately
forecast and manage infectious disease.
Research in the lab blends molecular and field data with large-scale GIS, statistical, and mathematical modeling techniques. An emerging paradigm in epidemiology is the use of models to understand feedbacks across multiple scales (e.g., individual to population- or herd-level, local to global). Historically, modeling in epidemiology has primarily relied on statistical or compartmental models that focus on a single scale.
These phenomenological models, however, cannot account for dynamic feedbacks between the environment, host demography, or the rapid evolution of pathogen traits like drug resistance. That is, they miss important dynamical changes because of their coarse resolution. These models often struggle to accurately capture or predict patterns observed in epidemiological data.
Motivated by these concerns, research in the lab seeks to simultaneously confront and refine models with data to make general yet mechanistic and broadly applicable predictions. Thus, we work with multiple host-pathogen systems, including vector- and water-borne pathogens, amphibian chytrid, and helminths.
To address these questions we collaborate with a multi-disciplinary team of experts — including the UW-Madison Extension and Soil-Health Initiative, the UW Meat Science and Animal Biologic Discovery program, and the UW-Global Health Institute — to find more sustainable solutions to manage pathogen evolution and prevent cross-species spillovers.
In collaboration with: Dr. Clay Cressler, and Dr. André deRoos
In collaboration with: Dr. Erin Silva at UW Extension Collaborative Hub and Dr. Steve Ricke at UW Microbiolics Research Institute.
