Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/90552
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dc.contributorSchool of Nursingen_US
dc.creatorTyrovolas, Sen_US
dc.creatorGiné-Vázquez, Ien_US
dc.creatorFernández, Den_US
dc.creatorMorena, Men_US
dc.creatorKoyanagi, Aen_US
dc.creatorJanko, Men_US
dc.creatorHaro, JMen_US
dc.creatorYang, Len_US
dc.creatorLee, Pen_US
dc.creatorPan, Wen_US
dc.creatorPanagiotakos, Den_US
dc.creatorMolassiotis, Aen_US
dc.date.accessioned2021-07-22T05:35:22Z-
dc.date.available2021-07-22T05:35:22Z-
dc.identifier.issn1439-4456en_US
dc.identifier.urihttp://hdl.handle.net/10397/90552-
dc.language.isoenen_US
dc.publisherJMIR Publications, Inc.en_US
dc.rights© Stefanos Tyrovolas, Iago Giné-Vázquez, Daniel Fernández, Marianthi Morena, Ai Koyanagi, Mark Janko, Josep Maria Haro, Yang Lin, Paul Lee, William Pan, Demosthenes Panagiotakos, Alex Molassiotis. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 14.06.2021. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.en_US
dc.rightsThe following publication Tyrovolas S, Giné-Vázquez I, Fernández D, Morena M, Koyanagi A, Janko M, Haro JM, Lin Y, Lee P, Pan W, Panagiotakos D, Molassiotis A Estimating the COVID-19 Spread Through Real-time Population Mobility Patterns: Surveillance in Low- and Middle-Income Countries J Med Internet Res 2021;23(6): e22999 is available at https://doi.org/10.2196/22999en_US
dc.subjectCOVID-19en_US
dc.subjectDatabaseen_US
dc.subjectDigital public healthen_US
dc.subjectEmerging countriesen_US
dc.subjectEstimateen_US
dc.subjectLow and middle-income countriesen_US
dc.subjectMobile dataen_US
dc.subjectPatternen_US
dc.subjectPolicyen_US
dc.subjectReal-timeen_US
dc.subjectSocial distancingen_US
dc.subjectSurveillanceen_US
dc.subjectSurveillanceen_US
dc.subjectTransmissionen_US
dc.titleEstimating the COVID-19 spread through real-time population mobility patterns : surveillance in low- and middle-income countriesen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume23en_US
dc.identifier.issue6en_US
dc.identifier.doi10.2196/22999en_US
dcterms.abstractBackground: On January 21, 2020, the World Health Organization reported the first case of severe acute respiratory syndrome coronavirus 2, which rapidly evolved to the COVID-19 pandemic. Since then, the virus has also rapidly spread among Latin American, Caribbean, and African countries.en_US
dcterms.abstractObjective: The first aim of this study is to identify new emerging COVID-19 clusters over time and space (from January 21 to mid-May 2020) in Latin American, Caribbean, and African regions, using a prospective space–time scan measurement approach. The second aim is to assess the impact of real-time population mobility patterns between January 21 and May 18, 2020, under the implemented government interventions, measurements, and policy restrictions on COVID-19 spread among those regions and worldwide.en_US
dcterms.abstractMethods: We created a global COVID-19 database, of 218 countries and territories, merging the World Health Organization daily case reports with other measures such as population density and country income levels for January 21 to May 18, 2020. A score of government policy interventions was created for low, intermediate, high, and very high interventions. The population’s mobility patterns at the country level were obtained from Google community mobility reports. The prospective space–time scan statistic method was applied in five time periods between January and May 2020, and a regression mixed model analysis was used.en_US
dcterms.abstractResults: We found that COVID-19 emerging clusters within these five periods of time increased from 7 emerging clusters to 28 by mid-May 2020. We also detected various increasing and decreasing relative risk estimates of COVID-19 spread among Latin American, Caribbean, and African countries within the period of analysis. Globally, population mobility to parks and similar leisure areas during at least a minimum of implemented intermediate-level control policies (when compared to low-level control policies) was related to accelerated COVID-19 spread. Results were almost consistent when regional stratified analysis was applied. In addition, worldwide population mobility due to working during high implemented control policies and very high implemented control policies, when compared to low-level control policies, was related to positive COVID-19 spread.en_US
dcterms.abstractConclusions: The prospective space–time scan is an approach that low-income and middle-income countries could use to detect emerging clusters in a timely manner and implement specific control policies and interventions to slow down COVID-19 transmission. In addition, real-time population mobility obtained from crowdsourced digital data could be useful for current and future targeted public health and mitigation policies at a global and regional level.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationJournal of medical Internet research, 14 June 2021, v. 23, no. 6, e22999en_US
dcterms.isPartOfJournal of medical Internet researchen_US
dcterms.issued2021-06-
dc.identifier.scopus2-s2.0-85108304250-
dc.identifier.pmid33950850-
dc.identifier.eissn1438-8871en_US
dc.identifier.artne22999en_US
dc.description.validate202107 bcvcen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumbera0982-n05-
dc.identifier.SubFormID2264-
dc.description.fundingSourceSelf-fundeden_US
dc.description.pubStatusPublisheden_US
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