Agent-based Modeling of COVID-19 to Support Public Health Decision Making

Jonathan Ozik, Ph.D.
The University of Chicago

Anna Hotton, Ph.D., MPH, BS
The University of Chicago

ABSTRACT:
The COVID-19 pandemic has highlighted the need for detailed modeling approaches that can capture the myriad complexities of emerging infectious diseases. In response, our group has developed CityCOVID, an agent-based model capable of tracking COVID-19 transmission in large, urban areas. Through partnerships between Argonne National Laboratory, the University of Chicago, 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 model all 2.7 million individual residents of Chicago, as they go to and from 1.2 million different places according to their individual hourly schedules. The places include locations such as households, workplaces, schools, and hospitals, and, as individuals congregate with other individuals in these places over the course of their daily routines, they are exposed to potential infection from other infectious people who are also at those places. Transitions between disease states depend on agent attributes and exposure to infected individuals, placed-based risks, and protective behaviors. This detailed modeling approach allows us to implement very specific and realistic mitigation strategies that are being considered by stakeholders, and which have been evolving over the course of the pandemic. We continue to apply CityCOVID to examine the impact of non-pharmaceutical interventions, SARS-CoV-2 variants of concern, vaccination deployment strategies, and to understand the impacts of social determinants of health on disease outcomes. In this presentation we will describe CityCOVID, including how the synthetic population was developed, what agent-based modeling and high-performance computing technologies were required, and our efforts in supporting local public health stakeholders in understanding, responding to and planning for the current and future population health emergencies.