Quantifying the importance and location of SARS-CoV-2 transmission events in large metropolitan areas

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 119; no. 26; pp. 1 - 8
• Main Authors: Aleta, Alberto, Martín-Corral, David, Bakker, Michiel A., y Pionttif, Ana Pastore, Ajelli, Marco, Litvinova, Maria, Chinazzi, Matteo, Dean, Natalie E., Halloran, M. Elizabeth, Longini, Ira M., Pentland, Alex, Vespignania, Alessandro, Moreno, Yamir, Moro, Esteban
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 28.06.2022
• Subjects: Case studies; Contact Tracing; Coronaviruses; COVID-19; COVID-19 - transmission; Epidemiology; Humans; Metropolitan areas; New York City - epidemiology; Pandemics; Physical Sciences; Population Dynamics; Real time; SARS-CoV-2; Severe acute respiratory syndrome coronavirus 2; Time Factors; Viral diseases; Washington - epidemiology; Workplaces; New York City; Washington; COVID-19; mobility; location; superspreading event
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.2112182119

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.