Department of Economics, Massachusetts Institute of Technology, Cambridge, MA, United States.
Eisner Health, Los Angeles, CA, United States.
Front Public Health. 2021 Dec 23;9:754767. doi: 10.3389/fpubh.2021.754767. eCollection 2021.
We studied the possible role of the subways in the spread of SARS-CoV-2 in New York City during late February and March 2020. Data on cases and hospitalizations, along with phylogenetic analyses of viral isolates, demonstrate rapid community transmission throughout all five boroughs within days. The near collapse of subway ridership during the second week of March was followed within 1-2 weeks by the flattening of COVID-19 incidence curve. We observed persistently high entry into stations located along the subway line serving a principal hotspot of infection in Queens. We used smartphone tracking data to estimate the volume of subway visits originating from each zip code tabulation area (ZCTA). Across ZCTAs, the estimated volume of subway visits on March 16 was strongly predictive of subsequent COVID-19 incidence during April 1-8. In a spatial analysis, we distinguished between the conventional notion of geographic contiguity and a novel notion of contiguity along subway lines. We found that the March 16 subway-visit volume in subway-contiguous ZCTAs had an increasing effect on COVID-19 incidence during April 1-8 as we enlarged the radius of influence up to 5 connected subway stops. By contrast, the March 31 cumulative incidence of COVID-19 in geographically-contiguous ZCTAs had an increasing effect on subsequent COVID-19 incidence as we expanded the radius up to three connected ZCTAs. The combined evidence points to the initial citywide dissemination of SARS-CoV-2 via a subway-based network, followed by percolation of new infections within local hotspots.
我们研究了地铁在 2020 年 2 月下旬和 3 月期间在纽约市 SARS-CoV-2 传播中的可能作用。病例和住院数据，以及病毒分离株的系统发育分析表明，在几天内，所有五个行政区都迅速发生了社区传播。3 月第二周地铁客流量几乎崩溃，随后在 1-2 周内 COVID-19 发病率曲线趋于平稳。我们观察到，沿着服务皇后区主要感染热点的地铁线路沿线的车站，始终有大量人员进站。我们使用智能手机跟踪数据来估算每个邮政编码区（ZCTA）的地铁出行量。在各个 ZCTA 中，3 月 16 日的地铁出行量预计与 4 月 1 日至 8 日的后续 COVID-19 发病率密切相关。在空间分析中，我们区分了传统的地理连续性概念和地铁沿线的新连续性概念。我们发现，随着影响半径扩大到 5 个相连的地铁站，3 月 16 日地铁出行量对 4 月 1 日至 8 日期间 COVID-19 发病率的影响呈递增趋势。相比之下，随着影响半径扩大到 3 个相连的 ZCTA，3 月 31 日地理上连续的 ZCTA 中的 COVID-19 累积发病率对随后的 COVID-19 发病率的影响呈递增趋势。综合证据表明，SARS-CoV-2 最初通过基于地铁的网络在全市范围内传播，随后在当地热点地区渗透新的感染。
