Abstract for: Responding to the COVID-19 Pandemic: Epidemiological Modeling on Speed Dial

As the COVID-19 pandemic unfolded, the modeling community mounted an impressive response to support decision makers with forecasts of how the disease could progress. Providing decision makers with timely answers to critical questions required a new set of tools. Forecasting models at the national and state levels have been common, but few have accounted for SARS-CoV-2 transmission at city or smaller scales. CityCOVID is a novel, large-scale, agent-based model that simulates the movements, contacts, and disease transmission outcomes of every person in the Chicago area as they go about their daily activity schedules. The model is calibrated regularly to available data on COVID-related hospitalizations and deaths, effectively adapting model parameters to current trends. CityCOVID is being used to understand the impacts of various strategies for shutting down/reopening and social distancing before they are implemented, and also to investigate the effects of disease-related developments, such as the emergence of COVID-19 variants. This talk describes the results from the modeling efforts, the ongoing interactions with public health departments in Chicago and Illinois to answer relevant COVID-related questions, thoughts on how agent-based modeling and system dynamics can work together to best inform decision-making, and concludes with lessons learned.