Yang Wan, Parton Hilary, Li Wenhui, Watts Elizabeth A, Lee Ellen, Yuan Haokun
Department of Epidemiology, Columbia University, New York, NY, USA.
New York City Department of Health and Mental Hygiene, Queens, NY, USA.
Commun Med (Lond). 2025 Apr 7;5(1):102. doi: 10.1038/s43856-025-00826-6.
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been widespread since 2020 and will likely continue to cause substantial recurring epidemics. However, understanding the underlying infection burden and dynamics, particularly since late 2021 when the Omicron variant emerged, is challenging. Here, we leverage extensive surveillance data available in New York City (NYC) and a comprehensive model-inference system to reconstruct SARS-CoV-2 dynamics therein through August 2023.
We fit a metapopulation network SEIRSV (Susceptible-Exposed-Infectious-(re)Susceptible-Vaccination) model to age- and neighborhood-specific data of COVID-19 cases, emergency department visits, and deaths in NYC from the pandemic onset in March 2020 to August 2023. We further validate the model-inference estimates using independent SARS-CoV-2 wastewater viral load data.
The validated model-inference estimates indicate a very high infection burden-the number of infections (i.e., including undetected asymptomatic/mild infections) totaled twice the population size ( > 5 times documented case count) during the first 3.5 years. Estimated virus transmissibility increased around 3-fold, whereas estimated infection-fatality risk (IFR) decreased by >10-fold during this period. The detailed estimates also reveal highly complex variant dynamics and immune landscape, and higher infection risk during winter in NYC over the study period.
This study provides highly detailed epidemiological estimates and identifies key transmission dynamics and drivers of SARS-CoV-2 during its first 3.5 years of circulation in a large urban center (i.e., NYC). These transmission dynamics and drivers may be relevant to other populations and inform future planning to help mitigate the public health burden of SARS-CoV-2.
严重急性呼吸综合征冠状病毒2（SARS-CoV-2）自2020年以来广泛传播，很可能继续引发大规模反复流行。然而，了解潜在的感染负担和动态变化，尤其是自2021年末奥密克戎变异株出现以来，具有挑战性。在此，我们利用纽约市（NYC）现有的广泛监测数据和一个全面的模型推理系统，重建截至2023年8月SARS-CoV-2在该市的动态变化。
我们将一个异质群体网络SEIRSV（易感-暴露-感染-（再）易感-接种）模型拟合到2020年3月大流行开始至2023年8月纽约市按年龄和社区划分的新冠肺炎病例、急诊科就诊和死亡数据。我们还使用独立的SARS-CoV-2污水病毒载量数据验证模型推理估计值。
经过验证的模型推理估计值表明感染负担非常高——在最初的3.5年里，感染数量（即包括未检测到无症状/轻症感染）总计为人口规模的两倍（>记录病例数的5倍）。在此期间，估计的病毒传播性增加了约3倍，而估计的感染致死风险（IFR）下降了10倍以上。详细估计值还揭示了高度复杂的变异株动态变化和免疫格局，以及研究期间纽约市冬季较高的感染风险。
本研究提供了高度详细的流行病学估计值，并确定了SARS-CoV-2在一个大型城市中心（即纽约市）传播的前3.5年中的关键传播动态和驱动因素。这些传播动态和驱动因素可能与其他人群相关，并为未来规划提供信息，以帮助减轻SARS-CoV-2的公共卫生负担。
