Zhang Renquan, Tai Jilei, Yao Qing, Yang Wan, Ruggeri Kai, Shaman Jeffrey, Pei Sen
School of Mathematical Sciences, Dalian University of Technology, Dalian, China.
Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America.
PLoS Comput Biol. 2025 Apr 29;21(4):e1012979. doi: 10.1371/journal.pcbi.1012979. eCollection 2025 Apr.
The COVID-19 pandemic in New York City (NYC) was characterized by marked disparities in disease burdens across neighborhoods. Accurate neighborhood-level forecasts are critical for planning more equitable resource allocation to reduce health inequalities; however, such spatially high-resolution forecasts remain scarce in operational use. In this study, we analyze aggregated foot traffic data derived from mobile devices to measure the connectivity among 42 NYC neighborhoods driven by various human activities such as dining, shopping, and entertainment. Using real-world time-varying contact patterns in different place categories, we develop a parsimonious behavior-driven epidemic model that incorporates population mixing, indoor crowdedness, dwell time, and seasonality of virus transmissibility. We fit this model to neighborhood-level COVID-19 case data in NYC and further couple this model with a data assimilation algorithm to generate short-term forecasts of neighborhood-level COVID-19 cases in 2020. We find differential contact patterns and connectivity between neighborhoods driven by different human activities. The behavior-driven model supports accurate modeling of neighborhood-level SARS-CoV-2 transmission throughout 2020. In the best-fitting model, we estimate that the force of infection (FOI) in indoor settings increases sublinearly with crowdedness and dwell time. Retrospective forecasting demonstrates that this behavior-driven model generates improved short-term forecasts in NYC neighborhoods compared to several baseline models. Our findings indicate that aggregated foot-traffic data for routine human activities can support neighborhood-level COVID-19 forecasts in NYC. This behavior-driven model may be adapted for use with other respiratory pathogens sharing similar transmission routes.
纽约市的新冠疫情呈现出各社区疾病负担存在显著差异的特点。准确的社区层面预测对于规划更公平的资源分配以减少健康不平等至关重要；然而，这种高空间分辨率的预测在实际应用中仍然很少见。在本研究中，我们分析了来自移动设备的汇总行人流量数据，以衡量纽约市42个社区之间由餐饮、购物和娱乐等各种人类活动驱动的连通性。利用不同场所类别中随时间变化的真实接触模式，我们开发了一个简约的行为驱动疫情模型，该模型纳入了人群混合、室内拥挤程度、停留时间和病毒传播的季节性。我们将此模型与纽约市社区层面的新冠病例数据进行拟合，并进一步将该模型与数据同化算法相结合，以生成2020年社区层面新冠病例的短期预测。我们发现不同人类活动驱动的社区之间存在不同的接触模式和连通性。行为驱动模型支持对2020年全年社区层面严重急性呼吸综合征冠状病毒2（SARS-CoV-2）传播进行准确建模。在最佳拟合模型中，我们估计室内环境中的感染强度（FOI）随拥挤程度和停留时间呈亚线性增加。回顾性预测表明，与几个基线模型相比，这种行为驱动模型在纽约市社区生成了更好的短期预测。我们的研究结果表明，日常人类活动的汇总行人流量数据可以支持纽约市社区层面的新冠预测。这种行为驱动模型可能适用于与具有相似传播途径的其他呼吸道病原体。
