Quantifying the importance and location of SARS-CoV-2 transmission events in large metropolitan areas
Year: 2022
Type: article
Abstract: Detailed characterization of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission across different settings can help design less disruptive interventions. We used real-time, privacy-enhanced mobility data in the New York City, NY and Seattle, WA metropolitan areas to build a detailed agent-based model of SARS-CoV-2 infection to estimate the where, when, and magnitude of transmission events during the pandemic's first wave. We estimate that only 18% of individuals produce most infections (80%), with about 10% of events that can be considered superspreading events (SSEs). Although mass gatherings present an important risk for SSEs, we estimate that the bulk of transmission occurred in smaller events in settings like workplaces, grocery stores, or food venues. The places most important for transmission change during the pandemic and are different across cities, signaling the large underlying behavioral component underneath them. Our modeling complements case studies and epidemiological data and indicates that real-time tracking of transmission events could help evaluate and define targeted mitigation policies.
Institutions: Institute for Scientific Interchange, Fundación para el Fomento de la Innovación Industrial, Universidad Carlos III de Madrid, Massachusetts Institute of Technology, Northeastern University, Indiana University Bloomington, Indiana University, University of Florida Health, University of Florida, University of Washington, Fred Hutch Cancer Center, Universidad de Zaragoza, Instituto de Biocomputación y Física de Sistemas Complejos
Language: English
Subfield: Modeling and Simulation
Field: Mathematics
Domain: Physical Sciences
Open Access status: hybrid
Funders: Massachusetts Institute of Technology, University of Washington, Banco Santander, Intesa Sanpaolo Innovation Center, Institute for Scientific Interchange, Ministerio de Economía y Competitividad, Universidad Carlos III de Madrid, Universidad de Zaragoza, Gobierno de Aragón, Seqirus, National Institutes of Health, Centers for Disease Control and Prevention, Office of the Assistant Secretary for Health, Agencia Estatal de Investigación, National Institute of General Medical Sciences, National Institute of Allergy and Infectious Diseases, Office of Disease Prevention and Health Promotion
Awards: G1175115578, G1321047484, G136730649, G1911185652, G1942787488, G1958462422, G2262748287, G2402940403, G2437629510, G2825053243, G300979063, G3020948319, G3156314389, G34346518, G3480869486, G3501374035, G3845300180, G425668148, Física Estadística y No lineal (FENOL), G5080475149, G5637127297, G5666352710, G5929674780, G7084143925, Mathematical and Statistical Methods for the Control of Global Infectious Disease Threats, FLUMOD - CENTER FOR THE MULTISCALE MODELING OF PANDEMIC AND SEASONAL FLU PREVENTION AND CONTROL, G8684520763, G8753523660, G8767619643, G8787585507
OpenAlex ID: W4282564340