We are a multidisciplinary research group in the Dept. of Epidemiology at the University of Michigan School of Public Health, led by Prof. Jon Zelner,
Our work is focused on unraveling the drivers of infectious disease transmission as well as socially and spatially disparate outcomes in infection, morbidity and mortality. This work covers a broad array of pathogens ranging from tuberculosis to influenza, diarrheal disease, COVID-19, and others. Methodologically, our work sits at the interface between infectious disease data and statistical and simulation models. We are motivated by a strong commitment to global and domestic health equity anchored in rigorous analysis.
Why EpiBayes?
Our work covers a broad array of methods and pathogens, but is grounded in the philosophy and tools of Bayesian inference. This means that we focus on integration of sources of data across biological, social, and spatial scales, using models that account for the information - and uncertainty - associated with different sources of information.
Because of this integrative approach, our work touches on an array of fields including infectious disease epidemiology, social epidemiology, molecular genotyping/genomics, spatial statistics, data science, environmental epidemiology, clinical medicine, and others.