We are a multidisciplinary research group 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 backed by rigorous analysis.
Our work covers a broad array of methods and pathogens, but is grounded in the underlying philosophy of Bayesian inference. This means that we focus on the integration of sources of data across biological, social, and spatial scales, using models that can account for the information and uncertainty associated with these different sources. Because of this, our work is informed by ideas and data from an array of fields including infectious disease epidemiology, molecular genotyping/genomics, spatial statistics, data science, environmental epidemiology, clinical medicine, and others.
[12/09/2021] The Atlantic The Pandemic of the Vaccinated Is Here
[11/18/2021] The Atlantic The Pandemic’s Next Turn Hinges on Three Unknowns
[10/16/2021] The Wire Science Explained: The Various Sources of Uncertainty in COVID-19 Studies
[10/15/2021] MIT Technology Review Reimagining our pandemic problems with the mindset of an engineer
The Backstory: Rising Cases of Measles Despite High Vaccination Coverage This explainer is about my recent paper published in PNAS. Even though US measles vaccination rates were on average higher than 95%, which is the WHO vaccination coverage target and threshold for maintaining herd immunity for measles, there were 1,282 cases of measles in 31 states in 2019.
I just finished teaching my first course at Michigan, which was called Theory and Applications of Spatial Epidemiology. At the end of the term, one of the students asked for suggestions for further reading.
Note: This post is part of a series on reproducibility. With any luck, it will make sense on its own. But it may be helpful to start from the beginning.