Abstract: In Chicago and elsewhere across the U.S., Latinx and Black communities have experienced disproportionate morbidity and mortality from COVID-19, highlighting drastic health inequities. Testing and vaccination efforts need to be scaled up within communities disproportionately affected by economic vulnerability, housing instability, limited healthcare access, and incarceration. Agent-based models (ABMs) can be used to investigate the complex processes by which social determinants of health influence population-level COVID-19 transmission and mortality, and to conduct computational experiments to evaluate the effects of candidate policies or interventions. Through partnerships between the University of Chicago, Argonne National Laboratory, the Chicago Department of Public Health, and the Illinois COVID-19 Modeling Task Force, we combined multiple data sources to develop a locally informed, realistic, and statistically representative synthetic agent population, with attributes and processes that reflect real-world social and biomedical aspects of transmission. We built a stochastic ABM (CityCOVID) capable of modeling millions of agents representing the behaviors and social interactions, geographic locations, and hourly activities of the population of Chicago and surrounding areas. Transitions between disease states depend on agent attributes and exposure to infected individuals through co-location, placed-based risks, and protective behaviors. The model provides a platform for evaluating how social determinants of health impact COVID-19 transmission, testing, and vaccine uptake and testing optimal approaches to intervention deployment. We discuss implications for public health interventions and policies to address health inequities.