A population data-driven workflow for COVID-19 modeling and learning

Ozik, Jonathan; Wozniak, Justin M.; Collier, Nicholson; Macal, Charles M.; Binois, Mickaël

doi:10.1177/10943420211035164

Title: A population data-driven workflow for COVID-19 modeling and learning

Journal Article · Fri Sep 10 00:00:00 EDT 2021 · International Journal of High Performance Computing Applications

DOI:https://doi.org/10.1177/10943420211035164· OSTI ID:1819798

^[1]; Wozniak, Justin M. ^[1]; Collier, Nicholson ^[1]; Macal, Charles M. ^[1]; Binois, Mickaël ^[2]

Argonne National Laboratory, Lemont, IL, USA
Inria Sophia Antipolis - Méditerranée, France

CityCOVID is a detailed agent-based model 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 protective behaviors of individuals 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, while 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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Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER); National Institute of Allergy and Infectious Diseases (NIAID)

Grant/Contract Number:: AC02-06CH11357; R01AI136056

OSTI ID:: 1819798

Alternate ID(s):: OSTI ID: 1823470

Journal Information:: International Journal of High Performance Computing Applications, Journal Name: International Journal of High Performance Computing Applications Vol. 35 Journal Issue: 5; ISSN 1094-3420

Publisher:: SAGE PublicationsCopyright Statement

Country of Publication:: United States

Language:: English

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97 MATHEMATICS AND COMPUTING
agent-based modeling
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Title: A population data-driven workflow for COVID-19 modeling and learning

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