Agent-based Modeling to Understand
Social Determinants of Health as
Drivers of COVID-19 Epidemics
and Test Interventions to Reduce
Health Inequities
April 1, 1 pm PT/4 pm ET Anna Hotton, Research Assistant Professor
Department of Medicine, University of Chicago Jonathan Ozik, Computational Scientist
Argonne National Laboratory
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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.
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For those who missed the previous C3.ai DTI Colloquium, you can catch it on our YouTube channel Colloquium Playlist, where videos of all weekly talks are available.
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C3.ai DTI Colloquium: March 18, 2021 Building Structure Into Deep Learning Zico Kolter, Associate Professor
Department of Computer Science
Carnegie Mellon University
Despite their wide applicability, deep learning systems often fail to exactly capture simple "known" features of many problem domains, such as those governed by physical laws or those that incorporate decision-making procedures. In this talk, I will present methods for these types of structural constraints — such as those associated with decision making, optimization problems, or physical simulation — directly into the predictions of a deep network. Our tool for achieving this will be the use of so-called "implicit layers" in deep models: layers that are defined implicitly in terms of conditions we would like them to satisfy, rather than via explicit computation graphs. l discuss how we can use these layers to embed (exact) physical constraints, robust control criteria, and task-based objectives, all within modern deep learning models. I also highlight several applications of this work in reinforcement learning, control, energy systems, and other settings, and discuss generalizations and directions for future work in the area.
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About the C3.ai Digital Transformation Institute
Established in March 2020 by C3 AI, Microsoft, and leading universities, the C3.ai Digital Transformation Institute is a research consortium dedicated to accelerating the socioeconomic benefits of artificial intelligence. The Institute engages the world’s leading scientists to conduct research and train practitioners in the new Science of Digital Transformation, which operates at the intersection of artificial intelligence, machine learning, cloud computing, internet of things, big data analytics, organizational behavior, public policy, and ethics. The ten C3.ai Digital Transformation Institute consortium member universities and laboratories are: University of Illinois at Urbana-Champaign; University of California, Berkeley; Carnegie Mellon University; KTH Royal Institute of Technology; Lawrence Berkeley National Laboratory; Massachusetts Institute of Technology; National Center for Supercomputing Applications at University of Illinois at Urbana-Champaign; Princeton University; Stanford University; and University of Chicago. Learn more at C3DTI.ai.
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