|
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. Anna Hotton is a Research Assistant Professor in the Section of Infectious Diseases and Global Health at the University of Chicago Department of Medicine. She earned her B.S. degree at Cornell University and her MPH and Ph.D. at the School of Public Health at the University of Illinois at Urbana-Champaign. As staff scientist at the Chicago Center for HIV Elimination, Hutton studied the relationship between social factors and viral spread. Her C3.ai DTI-funded project aims to adapt that work to COVID-19, using machine learning to identify data elements that are most important to include in modeling to better simulate various scenarios of disease spread and virtually test how different public health or social policy strategies can help mitigate the disease. Jonathan Ozik is a Computational Scientist at Argonne National Laboratory and Senior Scientist in the Consortium for Advanced Science and Engineering at the University of Chicago, where he develops applications of large-scale agent-based models -- including models of infectious diseases, healthcare interventions, biological systems, water use and management, critical materials supply chains, and critical infrastructure. He also applies large-scale model exploration across modeling methods, including agent-based modeling, microsimulation and machine/deep learning. He leads the Repast project for agent-based modeling toolkits and the Extreme-scale Model Exploration with Swift (EMEWS) framework for large-scale model exploration capabilities on high performance computing resources.
|
