A Population Data-Driven Workflow for COVID-19 Modeling and Learning

Authors: Jonathan Ozik, Justin M. Wozniak, Nicholson Collier, and Charles M. Macal (Argonne National Laboratory (ANL)) and Mickael Binois (French Institute for Research in Computer Science and Automation (INRIA))

Abstract: CityCOVID is a detailed agent-based model (ABM) that represents the behaviors and social interactions of 2.7 million residents of Chicago as they move between and colocate in 1.2 million distinct places, including households, schools, workplaces and hospitals, as determined by individual hourly activity schedules and dynamic behaviors such as isolating because of symptom onset. Disease progression dynamics incorporated within each agent track transitions between possible COVID-19 disease states, based on heterogeneous agent attributes, exposure through colocation, and effects of self-protective behaviors on viral transmissibility. Throughout the COVID-19 epidemic, CityCOVID model outputs have been provided to city, county and state stakeholders in response to evolving decision-making priorities, incorporating emerging information on SARS-CoV-2 epidemiology. Here we demonstrate our efforts in integrating our high-performance epidemiological simulation model with large-scale machine learning to develop a generalizable, flexible and performant analytical platform for planning and crisis response.




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