Critical Role of the Subways in the Initial Spread of SARS-CoV-2 in New York City
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Abstract
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.
Article activity feed
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Yunya Xiao
Review 1: "Critical Role of the Subways in the Initial Spread of SARS-CoV-2 in New York City"
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Strength of evidence
Reviewers: Y Xiao (Indiana University ) | 📗📗📗📗◻️
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SciScore for 10.1101/2021.07.03.21259973: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources First, we used Stata mapping software to verify that most CBGs were uniquely contained in a given ZCTA (Supplement Fig. Stata mappingsuggested: NoneData on Smartphone Device Movements: Our data on smartphone device movements come from the Social Distancing database maintained by SafeGraph. SafeGraphsuggested: NoneResults from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:The …
SciScore for 10.1101/2021.07.03.21259973: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources First, we used Stata mapping software to verify that most CBGs were uniquely contained in a given ZCTA (Supplement Fig. Stata mappingsuggested: NoneData on Smartphone Device Movements: Our data on smartphone device movements come from the Social Distancing database maintained by SafeGraph. SafeGraphsuggested: NoneResults from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:The evidence presented here also highlights the methodological limitations of alternative approaches to studying the role of the subways in the propagation of SARS-CoV-2. The test conducted in Figs. 2b–2e demonstrates the importance of studying changes in subway volume during the course of the COVID-19 outbreak. Less informative would be a study relating COVID-19 rates to static survey data on the proportion of individuals in each ZCTA regularly riding public transit prior to the epidemic. Our results also point to the importance of conducting tests of causation when baseline subway volume and COVID-19 incidence are high. A finding that coronavirus cases no longer relate to subway volume once subway use has plummeted to below 10 percent of baseline reveals little if anything about what happened back in March. The map of the Flushing Local line in Fig. 2b further highlights the pitfalls of studies that assign the entire volume of turnstile entries into a subway station to its enclosing ZCTA.7,8 Such a procedure, which effectively assumes that only people who live in the same ZCTA take the local subway, would erroneously discard the high-incidence ZCTAs 11369 and 11370, which have no subway within their boundaries. If we are to successfully control future pandemic threats – and, for that matter, future outbreaks of COVID-19 – we need to understand in exhaustive detail how SARS-CoV-2 first took hold and then established hot spots in major urban epicenters throughout the world. C...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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