Mathew Kiang

I am an assistant professor in the Department of Epidemiology and Population Health. My research lies at the intersection of computational epidemiology and social epidemiology. Methodologically, my work revolves around combining disparate data sources in epidemiologically meaningful ways. For example, I work with individual-level, non-health data (e.g., GPS, accelerometer, and other sensor data from smartphones), traditional health data (e.g., survey, health systems, or death certificate data), and third-party data (e.g., cellphone providers or ad-tech data). To do this, I use a variety of methods such as joint Bayesian spatial models, traditional epidemiologic models, dynamical models, microsimulation, and demographic analysis. Substantively, my work focuses on socioeconomic and racial/ethnic inequities. For example, recently, my work has examined inequities in COVID-19 vaccine distribution, cause-specific excess mortality, and drug poisonings. I have an NIDA-funded R00 examining equitable ways to improve treatment for opioid use disorder across structurally disadvantaged groups and am Co-I on a NIDA-funded R21 examining ways to use novel data sources (such as social media) to predict surges in opioid-related mortality.