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dc.contributor.authorKissler, Stephen
dc.contributor.authorKishore, Nishant
dc.contributor.authorPrabhu, Malavika
dc.contributor.authorGoffman, Dena
dc.contributor.authorBeilin, Yaakov
dc.contributor.authorLandau, Ruth
dc.contributor.authorGyamfi-Bannerman, Cynthia
dc.contributor.authorBateman, Brian
dc.contributor.authorKatz, Daniel
dc.contributor.authorGal, Jonathan
dc.contributor.authorBianco, Angela
dc.contributor.authorStone, Joanne
dc.contributor.authorLarremore, Daniel
dc.contributor.authorBuckee, Caroline
dc.contributor.authorGrad, Yonatan
dc.date.accessioned2020-05-08T16:39:20Z
dc.date.issued2020
dc.identifier.citationKissler, Stephen M., Nishant Kishore, Malavika Prabhu, Dena Goffman, Yaakov Beilin, et al. Reductions in commuting mobility predict geographic differences in SARS-CoV-2 prevalence in New York City (2020).en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:42665370*
dc.description.abstractImportance: New York City is the epicenter of the SARS-CoV-2 pandemic in the United States. Mortality and hospitalizations have differed substantially between different neighborhoods. Mitigation efforts in the coming months will require knowing the extent of geographic variation in SARS-CoV-2 prevalence and understanding the drivers of these differences. Objective: To estimate the prevalence of SARS-CoV-2 infection by New York City borough between March 22nd and May 3rd, 2020, and to associate variation in prevalence with antecedent reductions in mobility, defined as aggregated daily physical movements into and out of each borough. Design: Observational study of universal SARS-CoV-2 test results obtained from women hospitalized for delivery. Setting: Four New York-Presbyterian hospital campuses and two Mount Sinai hospital campuses in New York City. Participants: 1,746 women with New York City ZIP codes hospitalized for delivery. Exposures: Infection with SARS-CoV-2. Main outcomes: Population prevalence of SARS-CoV-2 by borough and correlation with the reduction in daily commuting-style movements into and out of each borough. Results: The estimated population prevalence of SARS-CoV-2 ranged from 11.3% (95% credible interval 8.9%, 13.9%) in Manhattan to 26.0% (95% credible interval 15.3%, 38.9%) in South Queens, with an estimated city-wide prevalence of 15.6% (95% credible interval 13.9%, 17.4%). The peak city-wide prevalence was during the week of March 30th, though temporal trends in prevalence varied substantially between boroughs. Population revalence was lowest in boroughs with the greatest reductions in morning commutes out of and evening commutes into the borough (Pearson R = –0.88, 95% credible interval –0.52, –0.99). Conclusions and relevance: Reductions in between-borough mobility predict geographic differences in the prevalence of SARS-CoV-2 infection in New York City. Large parts of the city may remain at risk for substantial SARS-CoV-2 outbreaks. Widespread testing should be conducted to identify geographic disparities in prevalence and assess the risk of future outbreaks.en_US
dc.language.isoen_USen_US
dash.licenseLAA
dc.titleReductions in commuting mobility predict geographic differences in SARS-CoV-2 prevalence in New York Cityen_US
dc.typeJournal Articleen_US
dc.description.versionAuthor's Originalen_US
dash.depositing.authorGrad, Yonatan
dc.date.available2020-05-08T16:39:20Z
dash.contributor.affiliatedBateman, Brian
dash.contributor.affiliatedKishore, Nishant
dash.contributor.affiliatedKissler, Stephen
dash.contributor.affiliatedBuckee, Caroline
dash.contributor.affiliatedGrad, Yonatan


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