Modeling the Impact of Social Determinants of Health on COVID-19 Transmission and Mortality to Understand Health Inequities

The COVID-19 pandemic has highlighted drastic health inequities, which are particularly pronounced in cities such as Chicago, Detroit, New Orleans, and New York City. Reducing COVID-19 morbidity and mortality will likely require increased focus on social determinants of health given their disproportionate impact on populations most heavily affected by COVID-19. A better understanding of how factors such as financial hardship, housing instability, health care access, and incarceration contribute to COVID-19 transmission and mortality is needed to inform policies around social distancing and testing and vaccination scale-up. This proposal will build upon an existing agent-based model of COVID-19 transmission (CityCOVID) for the city of Chicago. Using multiple sources of existing data, including local COVID-19 contact tracing surveys and public health surveillance, we will apply machine learning methods to quantify the impact of social determinants of health on COVID-19 transmission dynamics and generate a more granular synthetic population with which to evaluate intervention approaches. The extended CityCOVID model will provide a more realistic model to guide local policy and intervention development.