ISI Foundation, 10126 Turin, Italy.
Departamento de Matemáticas, Universidad Carlos III de Madrid, 28911 Leganés, Spain.
Proc Natl Acad Sci U S A. 2022 Jun 28;119(26):e2112182119. doi: 10.1073/pnas.2112182119. Epub 2022 Jun 13.
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.
详细描述严重急性呼吸综合征冠状病毒 2（SARS-CoV-2）在不同环境中的传播情况，可以帮助设计出干扰性更小的干预措施。我们使用了纽约市和西雅图的实时、增强隐私的移动数据，构建了一个详细的基于代理的 SARS-CoV-2 感染模型，以估计大流行第一波期间传播事件的地点、时间和规模。我们估计只有 18%的个体产生了大部分感染（80%），其中约 10%的事件可被视为超级传播事件（SSEs）。尽管大型集会是 SSEs 的一个重要风险，但我们估计大部分传播发生在工作场所、杂货店或食品场所等较小的事件中。在大流行期间，传播最重要的地方发生了变化，并且在不同城市之间也有所不同，这表明它们背后存在着很大的潜在行为因素。我们的模型补充了病例研究和流行病学数据，并表明实时跟踪传播事件可以帮助评估和定义有针对性的缓解政策。
