Quantifying the importance and location of SARS-CoV-2 transmission events in large metropolitan areas
Name
pnas.2112182119.pdf
Description
Published version
Size
7.29 MB
Format
Adobe PDF
Checksum (MD5)
c9519acf7e837d3d8b172d2e29a6536d
Author(s)
Aleta, Alberto
Martín-Corral, David
Bakker, Michiel A
Pastore y Piontti, Ana
Ajelli, Marco
Litvinova, Maria
Chinazzi, Matteo
Dean, Natalie E
Halloran, M Elizabeth
Longini, Ira M
Date Issued
2022
Journal
Proceedings of the National Academy of Sciences of the United States of America
Publisher
Proceedings of the National Academy of Sciences
Citation
Aleta, Alberto, Martín-Corral, David, Bakker, Michiel A, Pastore y Piontti, Ana, Ajelli, Marco et al. 2022. "Quantifying the importance and location of SARS-CoV-2 transmission events in large metropolitan areas." Proceedings of the National Academy of Sciences of the United States of America, 119 (26).
Version
Final published version
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.
MIT Department
MIT Connection Science (Research institute)
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1073/PNAS.2112182119
