Michael DeWitt is an applied statistician, infectious disease epidemiologist, and data scientist working in infectious disease research as a member of theWake Forest University School of Medicine. Additionally, he leads the Infectious Disease Epidemiology and Applied Statistics (IDEAS) group. He is interested in using data science and mathematical models to understand how and why certain pathogens emerge and persist in new host population. He is also interested in designing surveillance and control strategies to mitigate the impact of infectious diseases on human and animal health through understanding these infectious disease dynamics.
Prior to infectious disease research, Michael has over ten years of experience in manufacturing, higher education analytics, and health care analytics. His specialties are Bayesian inference, hierarchical modeling, survey design and analysis, simulation and optimization, network analysis, and applied predictive modeling.
