Our work focuses on improving health by finding better ways to intervene in disease processes. We work on all sorts of diseases, and all sorts of outcomes, in a One Health context. Across all these systems, one thing remains the same: we want to be able to tell people the best way to meet their goals.

For this research, we rely heavily on two groups of methods: data analysis and disease modeling.

Data Analysis: Understanding how diseases change over time, space, and context is the key to finding how to stop them. We use the tools of data analysis (i.e., statistics, mapping, and data science) to better understand the dynamics of disease.

Modeling: “Model” is a broad term that covers a lot of different areas, and we get into quite a few. Our research uses simulation modeling, risk analysis, and Bayesian network models to explore the potential outcomes of disease systems, and to predict the long-term impact of interventions.


  • We are working to find better methods for controlling West Nile Virus in endemic regions. Our data analysis has identified factors driving high-risk periods, the efficacy of insecticide spraying, and the characteristics of a good mosquito trap site. Our modeling has identified processes driving transmission to improve the timing and location of interventions.
  • We are studying the ways in which people and animals are exposed to ticks and tick-borne disease. Our data analysis has combined the results of multiple knowledge, attitudes, and practices surveys to build improved communication and education programs. Our modeling has predicted the spread of tick vectors across the state of Illinois under climate change, so that we can target interventions over time.