Most pathogens that cause disease in humans, domestic animals, or wildlife can infect multiple species, and pathogen spillover is driven by the spatial and temporal intensity of infection in the reservoir host. Our work explores the ecological and evolutionary factors that determine infectious disease dynamics in reservoir hosts and cross-species transmission risk. We combine spatiotemporal field studies, meta-analysis, epidemiological models, immunology, and machine learning to better understand how pathogens spread within and between populations and species and how environmental change will alter these infection dynamics. Much of our research focuses on zoonotic pathogens in bats and birds, but we are fundamentally driven by questions. Some current topics of interest include: 

• How do anthropogenic diet shifts influence infection dynamics?
• What are the effects of land conversion on pathogen sharing?
• How does urbanization interact with migration to shape disease?​
• ​What are the key seasonal and environmental drivers of immunity?
• Do competence data enhance predictive models of reservoir hosts?

Our work is united around using models to develop field-testable predictions. We aim to better understand how environmental change affects infectious disease risks and to generate robust predictions for guiding surveillance and management. We maintain a supportive lab environment that values collaborative science, quantitative rigor, creativity, and diversity.