Mobility and Dissemination of COVID-19 in Portugal: Correlations and Estimates from Google’s Mobility Data

Detalhes bibliográficos
Autor(a) principal: Mileu, Nelson
Data de Publicação: 2022
Outros Autores: Marques da Costa, Nuno, Marques da Costa, Eduarda, Alves, André
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10451/54260
Resumo: The spread of the coronavirus disease 2019 (COVID-19) has important links with population mobility. Social interaction is a known determinant of human-to-human transmission of infectious diseases and, in turn, population mobility as a proxy of interaction is of paramount importance to analyze COVID-19 diffusion. Using mobility data from Google’s Community Reports, this paper captures the association between changes in mobility patterns through time and the corresponding COVID-19 incidence at a multi-scalar approach applied to mainland Portugal. Results demonstrate a strong relationship between mobility data and COVID-19 incidence, suggesting that more mobility is associated with more COVID-19 cases. Methodological procedures can be summarized in a multiple linear regression with a time moving window. Model validation demonstrate good forecast accuracy, particularly when we consider the cumulative number of cases. Based on this premise, it is possible to estimate and predict future evolution of the number of COVID-19 cases using near real-time information of population mobility
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spelling Mobility and Dissemination of COVID-19 in Portugal: Correlations and Estimates from Google’s Mobility DataCOVID-19MobilityContainment measuresCases estimationPredictive modelThe spread of the coronavirus disease 2019 (COVID-19) has important links with population mobility. Social interaction is a known determinant of human-to-human transmission of infectious diseases and, in turn, population mobility as a proxy of interaction is of paramount importance to analyze COVID-19 diffusion. Using mobility data from Google’s Community Reports, this paper captures the association between changes in mobility patterns through time and the corresponding COVID-19 incidence at a multi-scalar approach applied to mainland Portugal. Results demonstrate a strong relationship between mobility data and COVID-19 incidence, suggesting that more mobility is associated with more COVID-19 cases. Methodological procedures can be summarized in a multiple linear regression with a time moving window. Model validation demonstrate good forecast accuracy, particularly when we consider the cumulative number of cases. Based on this premise, it is possible to estimate and predict future evolution of the number of COVID-19 cases using near real-time information of population mobilityMDPIRepositório da Universidade de LisboaMileu, NelsonMarques da Costa, NunoMarques da Costa, EduardaAlves, André2022-08-31T14:48:57Z20222022-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10451/54260eng10.3390/data70801072306-5729info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-08T17:00:40Zoai:repositorio.ul.pt:10451/54260Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:05:10.205651Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Mobility and Dissemination of COVID-19 in Portugal: Correlations and Estimates from Google’s Mobility Data
title Mobility and Dissemination of COVID-19 in Portugal: Correlations and Estimates from Google’s Mobility Data
spellingShingle Mobility and Dissemination of COVID-19 in Portugal: Correlations and Estimates from Google’s Mobility Data
Mileu, Nelson
COVID-19
Mobility
Containment measures
Cases estimation
Predictive model
title_short Mobility and Dissemination of COVID-19 in Portugal: Correlations and Estimates from Google’s Mobility Data
title_full Mobility and Dissemination of COVID-19 in Portugal: Correlations and Estimates from Google’s Mobility Data
title_fullStr Mobility and Dissemination of COVID-19 in Portugal: Correlations and Estimates from Google’s Mobility Data
title_full_unstemmed Mobility and Dissemination of COVID-19 in Portugal: Correlations and Estimates from Google’s Mobility Data
title_sort Mobility and Dissemination of COVID-19 in Portugal: Correlations and Estimates from Google’s Mobility Data
author Mileu, Nelson
author_facet Mileu, Nelson
Marques da Costa, Nuno
Marques da Costa, Eduarda
Alves, André
author_role author
author2 Marques da Costa, Nuno
Marques da Costa, Eduarda
Alves, André
author2_role author
author
author
dc.contributor.none.fl_str_mv Repositório da Universidade de Lisboa
dc.contributor.author.fl_str_mv Mileu, Nelson
Marques da Costa, Nuno
Marques da Costa, Eduarda
Alves, André
dc.subject.por.fl_str_mv COVID-19
Mobility
Containment measures
Cases estimation
Predictive model
topic COVID-19
Mobility
Containment measures
Cases estimation
Predictive model
description The spread of the coronavirus disease 2019 (COVID-19) has important links with population mobility. Social interaction is a known determinant of human-to-human transmission of infectious diseases and, in turn, population mobility as a proxy of interaction is of paramount importance to analyze COVID-19 diffusion. Using mobility data from Google’s Community Reports, this paper captures the association between changes in mobility patterns through time and the corresponding COVID-19 incidence at a multi-scalar approach applied to mainland Portugal. Results demonstrate a strong relationship between mobility data and COVID-19 incidence, suggesting that more mobility is associated with more COVID-19 cases. Methodological procedures can be summarized in a multiple linear regression with a time moving window. Model validation demonstrate good forecast accuracy, particularly when we consider the cumulative number of cases. Based on this premise, it is possible to estimate and predict future evolution of the number of COVID-19 cases using near real-time information of population mobility
publishDate 2022
dc.date.none.fl_str_mv 2022-08-31T14:48:57Z
2022
2022-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10451/54260
url http://hdl.handle.net/10451/54260
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.3390/data7080107
2306-5729
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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