Public transit mobility as a leading indicator of COVID-19 transmission in 40 cities during the first wave of the pandemic
The rapid global emergence of the COVID-19 pandemic in early 2020 created urgent demand for leading indicators to track the spread of the virus and assess the consequences of public health measures designed to limit transmission. Public transit mobility, which has been shown to be responsive to prev...
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| Vydáno v: | PeerJ (San Francisco, CA) Ročník 12; s. e17455 |
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| Abstract | The rapid global emergence of the COVID-19 pandemic in early 2020 created urgent demand for leading indicators to track the spread of the virus and assess the consequences of public health measures designed to limit transmission. Public transit mobility, which has been shown to be responsive to previous societal disruptions such as disease outbreaks and terrorist attacks, emerged as an early candidate.
We conducted a longitudinal ecological study of the association between public transit mobility reductions and COVID-19 transmission using publicly available data from a public transit app in 40 global cities from March 16 to April 12, 2020. Multilevel linear regression models were used to estimate the association between COVID-19 transmission and the value of the mobility index 2 weeks prior using two different outcome measures: weekly case ratio and effective reproduction number.
Over the course of March 2020, median public transit mobility, measured by the volume of trips planned in the app, dropped from 100% (first quartile (Q
)-third quartile (Q
) = 94-108%) of typical usage to 10% (Q
-Q
= 6-15%). Mobility was strongly associated with COVID-19 transmission 2 weeks later: a 10% decline in mobility was associated with a 12.3% decrease in the weekly case ratio (exp(
) = 0.877; 95% confidence interval (CI): [0.859-0.896]) and a decrease in the effective reproduction number (
= -0.058; 95% CI: [-0.068 to -0.048]). The mobility-only models explained nearly 60% of variance in the data for both outcomes. The adjustment for epidemic timing attenuated the associations between mobility and subsequent COVID-19 transmission but only slightly increased the variance explained by the models.
Our analysis demonstrated the value of public transit mobility as a leading indicator of COVID-19 transmission during the first wave of the pandemic in 40 global cities, at a time when few such indicators were available. Factors such as persistently depressed demand for public transit since the onset of the pandemic limit the ongoing utility of a mobility index based on public transit usage. This study illustrates an innovative use of "big data" from industry to inform the response to a global pandemic, providing support for future collaborations aimed at important public health challenges. |
|---|---|
| AbstractList | Background The rapid global emergence of the COVID-19 pandemic in early 2020 created urgent demand for leading indicators to track the spread of the virus and assess the consequences of public health measures designed to limit transmission. Public transit mobility, which has been shown to be responsive to previous societal disruptions such as disease outbreaks and terrorist attacks, emerged as an early candidate. Methods We conducted a longitudinal ecological study of the association between public transit mobility reductions and COVID-19 transmission using publicly available data from a public transit app in 40 global cities from March 16 to April 12, 2020. Multilevel linear regression models were used to estimate the association between COVID-19 transmission and the value of the mobility index 2 weeks prior using two different outcome measures: weekly case ratio and effective reproduction number. Results Over the course of March 2020, median public transit mobility, measured by the volume of trips planned in the app, dropped from 100% (first quartile (Q1)–third quartile (Q3) = 94–108%) of typical usage to 10% (Q1–Q3 = 6–15%). Mobility was strongly associated with COVID-19 transmission 2 weeks later: a 10% decline in mobility was associated with a 12.3% decrease in the weekly case ratio (exp(β) = 0.877; 95% confidence interval (CI): [0.859–0.896]) and a decrease in the effective reproduction number (β = −0.058; 95% CI: [−0.068 to −0.048]). The mobility-only models explained nearly 60% of variance in the data for both outcomes. The adjustment for epidemic timing attenuated the associations between mobility and subsequent COVID-19 transmission but only slightly increased the variance explained by the models. Discussion Our analysis demonstrated the value of public transit mobility as a leading indicator of COVID-19 transmission during the first wave of the pandemic in 40 global cities, at a time when few such indicators were available. Factors such as persistently depressed demand for public transit since the onset of the pandemic limit the ongoing utility of a mobility index based on public transit usage. This study illustrates an innovative use of “big data” from industry to inform the response to a global pandemic, providing support for future collaborations aimed at important public health challenges. Background The rapid global emergence of the COVID-19 pandemic in early 2020 created urgent demand for leading indicators to track the spread of the virus and assess the consequences of public health measures designed to limit transmission. Public transit mobility, which has been shown to be responsive to previous societal disruptions such as disease outbreaks and terrorist attacks, emerged as an early candidate. Methods We conducted a longitudinal ecological study of the association between public transit mobility reductions and COVID-19 transmission using publicly available data from a public transit app in 40 global cities from March 16 to April 12, 2020. Multilevel linear regression models were used to estimate the association between COVID-19 transmission and the value of the mobility index 2 weeks prior using two different outcome measures: weekly case ratio and effective reproduction number. Results Over the course of March 2020, median public transit mobility, measured by the volume of trips planned in the app, dropped from 100% (first quartile (Q.sub.1 )-third quartile (Q.sub.3 ) = 94-108%) of typical usage to 10% (Q.sub.1 -Q.sub.3 = 6-15%). Mobility was strongly associated with COVID-19 transmission 2 weeks later: a 10% decline in mobility was associated with a 12.3% decrease in the weekly case ratio (exp([beta]) = 0.877; 95% confidence interval (CI): [0.859-0.896]) and a decrease in the effective reproduction number ([beta] = -0.058; 95% CI: [-0.068 to -0.048]). The mobility-only models explained nearly 60% of variance in the data for both outcomes. The adjustment for epidemic timing attenuated the associations between mobility and subsequent COVID-19 transmission but only slightly increased the variance explained by the models. Discussion Our analysis demonstrated the value of public transit mobility as a leading indicator of COVID-19 transmission during the first wave of the pandemic in 40 global cities, at a time when few such indicators were available. Factors such as persistently depressed demand for public transit since the onset of the pandemic limit the ongoing utility of a mobility index based on public transit usage. This study illustrates an innovative use of "big data" from industry to inform the response to a global pandemic, providing support for future collaborations aimed at important public health challenges. The rapid global emergence of the COVID-19 pandemic in early 2020 created urgent demand for leading indicators to track the spread of the virus and assess the consequences of public health measures designed to limit transmission. Public transit mobility, which has been shown to be responsive to previous societal disruptions such as disease outbreaks and terrorist attacks, emerged as an early candidate. We conducted a longitudinal ecological study of the association between public transit mobility reductions and COVID-19 transmission using publicly available data from a public transit app in 40 global cities from March 16 to April 12, 2020. Multilevel linear regression models were used to estimate the association between COVID-19 transmission and the value of the mobility index 2 weeks prior using two different outcome measures: weekly case ratio and effective reproduction number. Over the course of March 2020, median public transit mobility, measured by the volume of trips planned in the app, dropped from 100% (first quartile (Q.sub.1 )-third quartile (Q.sub.3 ) = 94-108%) of typical usage to 10% (Q.sub.1 -Q.sub.3 = 6-15%). Mobility was strongly associated with COVID-19 transmission 2 weeks later: a 10% decline in mobility was associated with a 12.3% decrease in the weekly case ratio (exp([beta]) = 0.877; 95% confidence interval (CI): [0.859-0.896]) and a decrease in the effective reproduction number ([beta] = -0.058; 95% CI: [-0.068 to -0.048]). The mobility-only models explained nearly 60% of variance in the data for both outcomes. The adjustment for epidemic timing attenuated the associations between mobility and subsequent COVID-19 transmission but only slightly increased the variance explained by the models. Our analysis demonstrated the value of public transit mobility as a leading indicator of COVID-19 transmission during the first wave of the pandemic in 40 global cities, at a time when few such indicators were available. Factors such as persistently depressed demand for public transit since the onset of the pandemic limit the ongoing utility of a mobility index based on public transit usage. This study illustrates an innovative use of "big data" from industry to inform the response to a global pandemic, providing support for future collaborations aimed at important public health challenges. The rapid global emergence of the COVID-19 pandemic in early 2020 created urgent demand for leading indicators to track the spread of the virus and assess the consequences of public health measures designed to limit transmission. Public transit mobility, which has been shown to be responsive to previous societal disruptions such as disease outbreaks and terrorist attacks, emerged as an early candidate. We conducted a longitudinal ecological study of the association between public transit mobility reductions and COVID-19 transmission using publicly available data from a public transit app in 40 global cities from March 16 to April 12, 2020. Multilevel linear regression models were used to estimate the association between COVID-19 transmission and the value of the mobility index 2 weeks prior using two different outcome measures: weekly case ratio and effective reproduction number. Over the course of March 2020, median public transit mobility, measured by the volume of trips planned in the app, dropped from 100% (first quartile (Q )-third quartile (Q ) = 94-108%) of typical usage to 10% (Q -Q = 6-15%). Mobility was strongly associated with COVID-19 transmission 2 weeks later: a 10% decline in mobility was associated with a 12.3% decrease in the weekly case ratio (exp( ) = 0.877; 95% confidence interval (CI): [0.859-0.896]) and a decrease in the effective reproduction number ( = -0.058; 95% CI: [-0.068 to -0.048]). The mobility-only models explained nearly 60% of variance in the data for both outcomes. The adjustment for epidemic timing attenuated the associations between mobility and subsequent COVID-19 transmission but only slightly increased the variance explained by the models. Our analysis demonstrated the value of public transit mobility as a leading indicator of COVID-19 transmission during the first wave of the pandemic in 40 global cities, at a time when few such indicators were available. Factors such as persistently depressed demand for public transit since the onset of the pandemic limit the ongoing utility of a mobility index based on public transit usage. This study illustrates an innovative use of "big data" from industry to inform the response to a global pandemic, providing support for future collaborations aimed at important public health challenges. The rapid global emergence of the COVID-19 pandemic in early 2020 created urgent demand for leading indicators to track the spread of the virus and assess the consequences of public health measures designed to limit transmission. Public transit mobility, which has been shown to be responsive to previous societal disruptions such as disease outbreaks and terrorist attacks, emerged as an early candidate.BackgroundThe rapid global emergence of the COVID-19 pandemic in early 2020 created urgent demand for leading indicators to track the spread of the virus and assess the consequences of public health measures designed to limit transmission. Public transit mobility, which has been shown to be responsive to previous societal disruptions such as disease outbreaks and terrorist attacks, emerged as an early candidate.We conducted a longitudinal ecological study of the association between public transit mobility reductions and COVID-19 transmission using publicly available data from a public transit app in 40 global cities from March 16 to April 12, 2020. Multilevel linear regression models were used to estimate the association between COVID-19 transmission and the value of the mobility index 2 weeks prior using two different outcome measures: weekly case ratio and effective reproduction number.MethodsWe conducted a longitudinal ecological study of the association between public transit mobility reductions and COVID-19 transmission using publicly available data from a public transit app in 40 global cities from March 16 to April 12, 2020. Multilevel linear regression models were used to estimate the association between COVID-19 transmission and the value of the mobility index 2 weeks prior using two different outcome measures: weekly case ratio and effective reproduction number.Over the course of March 2020, median public transit mobility, measured by the volume of trips planned in the app, dropped from 100% (first quartile (Q1)-third quartile (Q3) = 94-108%) of typical usage to 10% (Q1-Q3 = 6-15%). Mobility was strongly associated with COVID-19 transmission 2 weeks later: a 10% decline in mobility was associated with a 12.3% decrease in the weekly case ratio (exp(β) = 0.877; 95% confidence interval (CI): [0.859-0.896]) and a decrease in the effective reproduction number (β = -0.058; 95% CI: [-0.068 to -0.048]). The mobility-only models explained nearly 60% of variance in the data for both outcomes. The adjustment for epidemic timing attenuated the associations between mobility and subsequent COVID-19 transmission but only slightly increased the variance explained by the models.ResultsOver the course of March 2020, median public transit mobility, measured by the volume of trips planned in the app, dropped from 100% (first quartile (Q1)-third quartile (Q3) = 94-108%) of typical usage to 10% (Q1-Q3 = 6-15%). Mobility was strongly associated with COVID-19 transmission 2 weeks later: a 10% decline in mobility was associated with a 12.3% decrease in the weekly case ratio (exp(β) = 0.877; 95% confidence interval (CI): [0.859-0.896]) and a decrease in the effective reproduction number (β = -0.058; 95% CI: [-0.068 to -0.048]). The mobility-only models explained nearly 60% of variance in the data for both outcomes. The adjustment for epidemic timing attenuated the associations between mobility and subsequent COVID-19 transmission but only slightly increased the variance explained by the models.Our analysis demonstrated the value of public transit mobility as a leading indicator of COVID-19 transmission during the first wave of the pandemic in 40 global cities, at a time when few such indicators were available. Factors such as persistently depressed demand for public transit since the onset of the pandemic limit the ongoing utility of a mobility index based on public transit usage. This study illustrates an innovative use of "big data" from industry to inform the response to a global pandemic, providing support for future collaborations aimed at important public health challenges.DiscussionOur analysis demonstrated the value of public transit mobility as a leading indicator of COVID-19 transmission during the first wave of the pandemic in 40 global cities, at a time when few such indicators were available. Factors such as persistently depressed demand for public transit since the onset of the pandemic limit the ongoing utility of a mobility index based on public transit usage. This study illustrates an innovative use of "big data" from industry to inform the response to a global pandemic, providing support for future collaborations aimed at important public health challenges. |
| ArticleNumber | e17455 |
| Audience | Academic |
| Author | Berry, Isha Westwood, Duncan J. Daneman, Nick Sturrock, Shelby L. MacFadden, Derek R. Brown, Kevin A. Soucy, Jean-Paul R. Fisman, David |
| Author_xml | – sequence: 1 givenname: Jean-Paul R. orcidid: 0000-0002-8422-2326 surname: Soucy fullname: Soucy, Jean-Paul R. organization: Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada – sequence: 2 givenname: Shelby L. orcidid: 0000-0003-4795-3258 surname: Sturrock fullname: Sturrock, Shelby L. organization: Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada – sequence: 3 givenname: Isha surname: Berry fullname: Berry, Isha organization: Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada – sequence: 4 givenname: Duncan J. orcidid: 0000-0001-6320-9053 surname: Westwood fullname: Westwood, Duncan J. organization: Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada – sequence: 5 givenname: Nick surname: Daneman fullname: Daneman, Nick organization: Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada, Department of Medicine, University of Toronto, Toronto, Ontario, Canada, ICES, Toronto, Ontario, Canada – sequence: 6 givenname: David surname: Fisman fullname: Fisman, David organization: Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada – sequence: 7 givenname: Derek R. surname: MacFadden fullname: MacFadden, Derek R. organization: Ottawa Hospital Research Institute, Ottawa, Ontario, Canada – sequence: 8 givenname: Kevin A. orcidid: 0000-0002-1483-2188 surname: Brown fullname: Brown, Kevin A. organization: Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada, ICES, Toronto, Ontario, Canada, Public Health Ontario, Toronto, Ontario, Canada |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38832041$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1186/s12992-021-00672-w 10.1126/science.abb8021 10.1371/journal.pone.0000758 10.1126/science.abb4218 10.1001/jamanetworkopen.2021.23139 10.1007/s00168-022-01193-z 10.1503/cmaj.210132 10.1038/s41598-021-92892-8 10.1186/s12916-020-01615-9 10.1038/s42949-021-00030-0 10.1002/bimj.202000112 10.36501/0197-9191/21-026 10.32614/RJ-2017-066 10.2139/ssrn.3569909 10.1001/jama.2020.6602 10.3201/eid2606.200357 10.1038/s41467-021-21776-2 10.1016/j.ijregi.2022.06.002 10.1007/s10900-021-00971-8 10.1038/s42254-021-00407-1 10.1186/s12879-021-05950-x 10.1093/aje/kwt133 10.1038/s41467-021-21358-2 10.1016/S1473-3099(20)30120-1 10.1371/journal.pone.0246772 10.1038/s41598-020-77316-3 10.1038/s41562-021-01079-8 10.1038/s41598-021-81442-x 10.1177/0020852321997552 10.1016/j.ijtst.2021.11.003 |
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| Keywords | COVID-19 SARS-CoV-2 Urban mobility Public transit Social distancing Infectious disease surveillance Physical distancing Human mobility Non-pharmaceutical interventions |
| Language | English |
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| Title | Public transit mobility as a leading indicator of COVID-19 transmission in 40 cities during the first wave of the pandemic |
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