Using volunteered geographic information to assess mobility in the early phases of the COVID-19 pandemic: a cross-city time series analysis of 41 cities in 22 countries from March 2nd to 26th 2020
Restricting mobility is a central aim for lowering contact rates and preventing COVID-19 transmission. Yet the impact on mobility of different non-pharmaceutical countermeasures in the earlier stages of the pandemic is not well-understood. Trends were evaluated using Citymapper's mobility index...
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| Veröffentlicht in: | Globalization and health 2020-09, Vol.16 (1), p.85-85, Article 85 |
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| creator | Vannoni, Matia McKee, Martin Semenza, Jan C Bonell, Chris Stuckler, David |
| description | Restricting mobility is a central aim for lowering contact rates and preventing COVID-19 transmission. Yet the impact on mobility of different non-pharmaceutical countermeasures in the earlier stages of the pandemic is not well-understood.
Trends were evaluated using Citymapper's mobility index covering 2nd to 26th March 2020, expressed as percentages of typical usage periods from 0% as the lowest and 100% as normal. China and India were not covered. Multivariate fixed effects models were used to estimate the association of policies restricting movement on mobility before and after their introduction. Policy restrictions were assessed using the Oxford COVID-19 Government Response Stringency Index as well as measures coding the timing and degree of school and workplace closures, transport restrictions, and cancellation of mass gatherings.
41 cities worldwide.
Citymapper's mobility index.
Mobility declined in all major cities throughout March. Larger declines were seen in European than Asian cities. The COVID-19 Government Response Stringency Index was strongly associated with declines in mobility (r = - 0.75, p |
| doi_str_mv | 10.1186/s12992-020-00598-9 |
| format | Article |
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Trends were evaluated using Citymapper's mobility index covering 2nd to 26th March 2020, expressed as percentages of typical usage periods from 0% as the lowest and 100% as normal. China and India were not covered. Multivariate fixed effects models were used to estimate the association of policies restricting movement on mobility before and after their introduction. Policy restrictions were assessed using the Oxford COVID-19 Government Response Stringency Index as well as measures coding the timing and degree of school and workplace closures, transport restrictions, and cancellation of mass gatherings.
41 cities worldwide.
Citymapper's mobility index.
Mobility declined in all major cities throughout March. Larger declines were seen in European than Asian cities. The COVID-19 Government Response Stringency Index was strongly associated with declines in mobility (r = - 0.75, p < 0.001). After adjusting for time-trends, we observed that implementing non-pharmaceutical countermeasures was associated with a decline of mobility of 10.0% for school closures (95% CI: 4.36 to 15.7%), 15.0% for workplace closures (95% CI: 10.2 to 19.8%), 7.09% for cancelling public events (95% CI: 1.98 to 12.2%), 18.0% for closing public transport (95% CI: 6.74 to 29.2%), 13.3% for restricting internal movements (95% CI: 8.85 to 17.8%) and 5.30% for international travel controls (95% CI: 1.69 to 8.90). In contrast, as expected, there was no association between population mobility changes and fiscal or monetary measures or emergency healthcare investment.
Understanding the effect of public policy on mobility in the early stages is crucial to slowing and reducing COVID-19 transmission. By using Citymapper's mobility index, this work provides the first evidence about trends in mobility and the impacts of different policy interventions, suggesting that closure of public transport, workplaces and schools are particularly impactful.</description><identifier>ISSN: 1744-8603</identifier><identifier>EISSN: 1744-8603</identifier><identifier>DOI: 10.1186/s12992-020-00598-9</identifier><identifier>PMID: 32967691</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>Analysis ; Cellular telephones ; Cities ; Cities - epidemiology ; Closures ; Coronavirus Infections - epidemiology ; Coronavirus Infections - prevention & control ; Coronaviruses ; COVID-19 ; Epidemics ; Geographic Information Systems ; Global Health ; Humans ; Mobility ; Pandemics ; Pandemics - prevention & control ; Pharmaceuticals ; Pneumonia, Viral - epidemiology ; Pneumonia, Viral - prevention & control ; Public Policy ; Public transportation ; Schools ; Time Factors ; Time series ; Travel - legislation & jurisprudence ; Travel - statistics & numerical data ; Trends ; Volunteers ; Workplaces</subject><ispartof>Globalization and health, 2020-09, Vol.16 (1), p.85-85, Article 85</ispartof><rights>COPYRIGHT 2020 BioMed Central Ltd.</rights><rights>2020. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>The Author(s) 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c597t-b4cffae6015d179c9ca8246e47ea58156a5e5f6089e33a58670dfb970d96827d3</citedby><cites>FETCH-LOGICAL-c597t-b4cffae6015d179c9ca8246e47ea58156a5e5f6089e33a58670dfb970d96827d3</cites><orcidid>0000-0002-6253-6498 ; 0000-0002-1288-8401 ; 0000-0002-4625-874X ; 0000-0002-0121-9683 ; 0000-0002-8335-6402</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7509494/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7509494/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32967691$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Vannoni, Matia</creatorcontrib><creatorcontrib>McKee, Martin</creatorcontrib><creatorcontrib>Semenza, Jan C</creatorcontrib><creatorcontrib>Bonell, Chris</creatorcontrib><creatorcontrib>Stuckler, David</creatorcontrib><title>Using volunteered geographic information to assess mobility in the early phases of the COVID-19 pandemic: a cross-city time series analysis of 41 cities in 22 countries from March 2nd to 26th 2020</title><title>Globalization and health</title><addtitle>Global Health</addtitle><description>Restricting mobility is a central aim for lowering contact rates and preventing COVID-19 transmission. Yet the impact on mobility of different non-pharmaceutical countermeasures in the earlier stages of the pandemic is not well-understood.
Trends were evaluated using Citymapper's mobility index covering 2nd to 26th March 2020, expressed as percentages of typical usage periods from 0% as the lowest and 100% as normal. China and India were not covered. Multivariate fixed effects models were used to estimate the association of policies restricting movement on mobility before and after their introduction. Policy restrictions were assessed using the Oxford COVID-19 Government Response Stringency Index as well as measures coding the timing and degree of school and workplace closures, transport restrictions, and cancellation of mass gatherings.
41 cities worldwide.
Citymapper's mobility index.
Mobility declined in all major cities throughout March. Larger declines were seen in European than Asian cities. The COVID-19 Government Response Stringency Index was strongly associated with declines in mobility (r = - 0.75, p < 0.001). After adjusting for time-trends, we observed that implementing non-pharmaceutical countermeasures was associated with a decline of mobility of 10.0% for school closures (95% CI: 4.36 to 15.7%), 15.0% for workplace closures (95% CI: 10.2 to 19.8%), 7.09% for cancelling public events (95% CI: 1.98 to 12.2%), 18.0% for closing public transport (95% CI: 6.74 to 29.2%), 13.3% for restricting internal movements (95% CI: 8.85 to 17.8%) and 5.30% for international travel controls (95% CI: 1.69 to 8.90). In contrast, as expected, there was no association between population mobility changes and fiscal or monetary measures or emergency healthcare investment.
Understanding the effect of public policy on mobility in the early stages is crucial to slowing and reducing COVID-19 transmission. By using Citymapper's mobility index, this work provides the first evidence about trends in mobility and the impacts of different policy interventions, suggesting that closure of public transport, workplaces and schools are particularly impactful.</description><subject>Analysis</subject><subject>Cellular telephones</subject><subject>Cities</subject><subject>Cities - epidemiology</subject><subject>Closures</subject><subject>Coronavirus Infections - epidemiology</subject><subject>Coronavirus Infections - prevention & control</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>Epidemics</subject><subject>Geographic Information Systems</subject><subject>Global Health</subject><subject>Humans</subject><subject>Mobility</subject><subject>Pandemics</subject><subject>Pandemics - prevention & control</subject><subject>Pharmaceuticals</subject><subject>Pneumonia, Viral - epidemiology</subject><subject>Pneumonia, Viral - prevention & control</subject><subject>Public Policy</subject><subject>Public transportation</subject><subject>Schools</subject><subject>Time Factors</subject><subject>Time series</subject><subject>Travel - legislation & jurisprudence</subject><subject>Travel - statistics & numerical data</subject><subject>Trends</subject><subject>Volunteers</subject><subject>Workplaces</subject><issn>1744-8603</issn><issn>1744-8603</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>KPI</sourceid><sourceid>BENPR</sourceid><sourceid>DOA</sourceid><recordid>eNptUk1v1DAQjRCIlsIf4IAscYFDiu04dswBqSpfK4rKgXK1HGey6yqJt3a2Yv8NvwWJ_8UkW0oXISu2NfPei2fmZdlTRo8Zq-SrxLjWPKec5pSWusr1veyQKSHyStLi_p37QfYopUtKBRWFfpgdFFxLJTU7zH5dJD8syXXoNsMIEKEhSwjLaNcr74gf2hB7O_owkDEQmxKkRPpQ-86PW0yTcQUEbOy2ZL2ymCWhnWOn598Wb3OmydoODfTevSb25w8XQ0q5m7ij74EkiB45drDdNvmZLBjB_BRFdc6JC_iwGdXG0JPPNroV4UMzvYfLEe9Y_-PsQWu7BE9uzqPs4v27r6cf87PzD4vTk7PclVqNeS1c21qQlJUNU9ppZysuJAgFtqxYKW0JZStppaEoMCIVbdpa465lxVVTHGWLnW4T7KVZR9_buDXBejMHQlwaG0fvOjCCQq2krZoKp6AabnkhalpW2irdKlGj1pud1npT99A4wDJttye6nxn8yizDtVEl1UILFHhxIxDD1QbSaHqfHHSdHSBskuFCYNWCK4rQ5_9AL8MmYtcnVMk0U5Srv6ilxQKm2eN_3SRqTmQhNcWvQNTxf1C45jGHAVqP8T3Cyz0CYkb4Pi7tJiXz6ctiH8t32NkpEdrbfjBqJtObnekNDt3MpjcaSc_udvKW8sflxW9hXPvo</recordid><startdate>20200923</startdate><enddate>20200923</enddate><creator>Vannoni, Matia</creator><creator>McKee, Martin</creator><creator>Semenza, Jan C</creator><creator>Bonell, Chris</creator><creator>Stuckler, David</creator><general>BioMed Central Ltd</general><general>BioMed Central</general><general>BMC</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>KPI</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8C1</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>COVID</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PATMY</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PYCSY</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-6253-6498</orcidid><orcidid>https://orcid.org/0000-0002-1288-8401</orcidid><orcidid>https://orcid.org/0000-0002-4625-874X</orcidid><orcidid>https://orcid.org/0000-0002-0121-9683</orcidid><orcidid>https://orcid.org/0000-0002-8335-6402</orcidid></search><sort><creationdate>20200923</creationdate><title>Using volunteered geographic information to assess mobility in the early phases of the COVID-19 pandemic: a cross-city time series analysis of 41 cities in 22 countries from March 2nd to 26th 2020</title><author>Vannoni, Matia ; McKee, Martin ; Semenza, Jan C ; Bonell, Chris ; Stuckler, David</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c597t-b4cffae6015d179c9ca8246e47ea58156a5e5f6089e33a58670dfb970d96827d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Analysis</topic><topic>Cellular telephones</topic><topic>Cities</topic><topic>Cities - epidemiology</topic><topic>Closures</topic><topic>Coronavirus Infections - epidemiology</topic><topic>Coronavirus Infections - prevention & control</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>Epidemics</topic><topic>Geographic Information Systems</topic><topic>Global Health</topic><topic>Humans</topic><topic>Mobility</topic><topic>Pandemics</topic><topic>Pandemics - prevention & control</topic><topic>Pharmaceuticals</topic><topic>Pneumonia, Viral - epidemiology</topic><topic>Pneumonia, Viral - prevention & control</topic><topic>Public Policy</topic><topic>Public transportation</topic><topic>Schools</topic><topic>Time Factors</topic><topic>Time series</topic><topic>Travel - legislation & jurisprudence</topic><topic>Travel - statistics & numerical data</topic><topic>Trends</topic><topic>Volunteers</topic><topic>Workplaces</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Vannoni, Matia</creatorcontrib><creatorcontrib>McKee, Martin</creatorcontrib><creatorcontrib>Semenza, Jan C</creatorcontrib><creatorcontrib>Bonell, Chris</creatorcontrib><creatorcontrib>Stuckler, David</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Global Issues</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Public Health Database</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>Coronavirus Research Database</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Environmental Science Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Environmental Science Collection</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Globalization and health</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Vannoni, Matia</au><au>McKee, Martin</au><au>Semenza, Jan C</au><au>Bonell, Chris</au><au>Stuckler, David</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Using volunteered geographic information to assess mobility in the early phases of the COVID-19 pandemic: a cross-city time series analysis of 41 cities in 22 countries from March 2nd to 26th 2020</atitle><jtitle>Globalization and health</jtitle><addtitle>Global Health</addtitle><date>2020-09-23</date><risdate>2020</risdate><volume>16</volume><issue>1</issue><spage>85</spage><epage>85</epage><pages>85-85</pages><artnum>85</artnum><issn>1744-8603</issn><eissn>1744-8603</eissn><abstract>Restricting mobility is a central aim for lowering contact rates and preventing COVID-19 transmission. Yet the impact on mobility of different non-pharmaceutical countermeasures in the earlier stages of the pandemic is not well-understood.
Trends were evaluated using Citymapper's mobility index covering 2nd to 26th March 2020, expressed as percentages of typical usage periods from 0% as the lowest and 100% as normal. China and India were not covered. Multivariate fixed effects models were used to estimate the association of policies restricting movement on mobility before and after their introduction. Policy restrictions were assessed using the Oxford COVID-19 Government Response Stringency Index as well as measures coding the timing and degree of school and workplace closures, transport restrictions, and cancellation of mass gatherings.
41 cities worldwide.
Citymapper's mobility index.
Mobility declined in all major cities throughout March. Larger declines were seen in European than Asian cities. The COVID-19 Government Response Stringency Index was strongly associated with declines in mobility (r = - 0.75, p < 0.001). After adjusting for time-trends, we observed that implementing non-pharmaceutical countermeasures was associated with a decline of mobility of 10.0% for school closures (95% CI: 4.36 to 15.7%), 15.0% for workplace closures (95% CI: 10.2 to 19.8%), 7.09% for cancelling public events (95% CI: 1.98 to 12.2%), 18.0% for closing public transport (95% CI: 6.74 to 29.2%), 13.3% for restricting internal movements (95% CI: 8.85 to 17.8%) and 5.30% for international travel controls (95% CI: 1.69 to 8.90). In contrast, as expected, there was no association between population mobility changes and fiscal or monetary measures or emergency healthcare investment.
Understanding the effect of public policy on mobility in the early stages is crucial to slowing and reducing COVID-19 transmission. By using Citymapper's mobility index, this work provides the first evidence about trends in mobility and the impacts of different policy interventions, suggesting that closure of public transport, workplaces and schools are particularly impactful.</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>32967691</pmid><doi>10.1186/s12992-020-00598-9</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0002-6253-6498</orcidid><orcidid>https://orcid.org/0000-0002-1288-8401</orcidid><orcidid>https://orcid.org/0000-0002-4625-874X</orcidid><orcidid>https://orcid.org/0000-0002-0121-9683</orcidid><orcidid>https://orcid.org/0000-0002-8335-6402</orcidid><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; Springer Nature - Complete Springer Journals; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; PubMed Central Open Access; Springer Nature OA Free Journals |
| subjects | Analysis Cellular telephones Cities Cities - epidemiology Closures Coronavirus Infections - epidemiology Coronavirus Infections - prevention & control Coronaviruses COVID-19 Epidemics Geographic Information Systems Global Health Humans Mobility Pandemics Pandemics - prevention & control Pharmaceuticals Pneumonia, Viral - epidemiology Pneumonia, Viral - prevention & control Public Policy Public transportation Schools Time Factors Time series Travel - legislation & jurisprudence Travel - statistics & numerical data Trends Volunteers Workplaces |
| title | Using volunteered geographic information to assess mobility in the early phases of the COVID-19 pandemic: a cross-city time series analysis of 41 cities in 22 countries from March 2nd to 26th 2020 |
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