Spatiotemporal Dynamics of COVID-19 Infections in Mainland Portugal

Detalhes bibliográficos
Autor(a) principal: Silva, Melissa
Data de Publicação: 2022
Outros Autores: Betco, Iuria, Capinha, César, Roquette, Rita, Viana, Cláudia M., Rocha, Jorge
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10451/54254
Resumo: The World Health Organization declared COVID-19 as a pandemic disease on 12 March 2020. Currently, this disease caused by the SARS-CoV-2 virus remains one of the biggest public health problems in the world. Thus, it is essential to apply methods that enable a better understanding of the virus diffusion processes, not only at the spatial level but also at the spatiotemporal one. To that end, we tried to understand the spatial distribution of COVID-19 pathology in continental Portugal at the municipal level and to comprehend how mobility influences transmission. We used autocorrelation indices such as Getis-Ord (with Euclidian distance and commuting values), Local Moran, and a new hybrid approach. Likewise, aiming to identify the spatiotemporal patterns of the virus propagation by using Man–Kendall statistics, we found that most hotspots of infected individuals occur in the municipalities of metropolitan areas. The spatiotemporal analysis identified most of the municipalities as oscillating hotspots.
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spelling Spatiotemporal Dynamics of COVID-19 Infections in Mainland PortugalSARS-CoV-2Spatiotemporal analysisHybrid approachMobilityMainland PortugalThe World Health Organization declared COVID-19 as a pandemic disease on 12 March 2020. Currently, this disease caused by the SARS-CoV-2 virus remains one of the biggest public health problems in the world. Thus, it is essential to apply methods that enable a better understanding of the virus diffusion processes, not only at the spatial level but also at the spatiotemporal one. To that end, we tried to understand the spatial distribution of COVID-19 pathology in continental Portugal at the municipal level and to comprehend how mobility influences transmission. We used autocorrelation indices such as Getis-Ord (with Euclidian distance and commuting values), Local Moran, and a new hybrid approach. Likewise, aiming to identify the spatiotemporal patterns of the virus propagation by using Man–Kendall statistics, we found that most hotspots of infected individuals occur in the municipalities of metropolitan areas. The spatiotemporal analysis identified most of the municipalities as oscillating hotspots.MDPIRepositório da Universidade de LisboaSilva, MelissaBetco, IuriaCapinha, CésarRoquette, RitaViana, Cláudia M.Rocha, Jorge2022-08-31T14:13:40Z20222022-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10451/54254engSilva, M., Betco, I., Capinha, C., Roquette, R., Viana, C. M. & Rocha, J. (2022). Spatiotemporal Dynamics of COVID-19 Infections in Mainland Portugal. Sustainability,14(16), 10370. http://dx.doi.org/10.3390/su14161037010.3390/su1416103702071-1050info: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:10451/54254Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:05:10.114237Repositório 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 Spatiotemporal Dynamics of COVID-19 Infections in Mainland Portugal
title Spatiotemporal Dynamics of COVID-19 Infections in Mainland Portugal
spellingShingle Spatiotemporal Dynamics of COVID-19 Infections in Mainland Portugal
Silva, Melissa
SARS-CoV-2
Spatiotemporal analysis
Hybrid approach
Mobility
Mainland Portugal
title_short Spatiotemporal Dynamics of COVID-19 Infections in Mainland Portugal
title_full Spatiotemporal Dynamics of COVID-19 Infections in Mainland Portugal
title_fullStr Spatiotemporal Dynamics of COVID-19 Infections in Mainland Portugal
title_full_unstemmed Spatiotemporal Dynamics of COVID-19 Infections in Mainland Portugal
title_sort Spatiotemporal Dynamics of COVID-19 Infections in Mainland Portugal
author Silva, Melissa
author_facet Silva, Melissa
Betco, Iuria
Capinha, César
Roquette, Rita
Viana, Cláudia M.
Rocha, Jorge
author_role author
author2 Betco, Iuria
Capinha, César
Roquette, Rita
Viana, Cláudia M.
Rocha, Jorge
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Repositório da Universidade de Lisboa
dc.contributor.author.fl_str_mv Silva, Melissa
Betco, Iuria
Capinha, César
Roquette, Rita
Viana, Cláudia M.
Rocha, Jorge
dc.subject.por.fl_str_mv SARS-CoV-2
Spatiotemporal analysis
Hybrid approach
Mobility
Mainland Portugal
topic SARS-CoV-2
Spatiotemporal analysis
Hybrid approach
Mobility
Mainland Portugal
description The World Health Organization declared COVID-19 as a pandemic disease on 12 March 2020. Currently, this disease caused by the SARS-CoV-2 virus remains one of the biggest public health problems in the world. Thus, it is essential to apply methods that enable a better understanding of the virus diffusion processes, not only at the spatial level but also at the spatiotemporal one. To that end, we tried to understand the spatial distribution of COVID-19 pathology in continental Portugal at the municipal level and to comprehend how mobility influences transmission. We used autocorrelation indices such as Getis-Ord (with Euclidian distance and commuting values), Local Moran, and a new hybrid approach. Likewise, aiming to identify the spatiotemporal patterns of the virus propagation by using Man–Kendall statistics, we found that most hotspots of infected individuals occur in the municipalities of metropolitan areas. The spatiotemporal analysis identified most of the municipalities as oscillating hotspots.
publishDate 2022
dc.date.none.fl_str_mv 2022-08-31T14:13:40Z
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/10451/54254
url http://hdl.handle.net/10451/54254
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Silva, M., Betco, I., Capinha, C., Roquette, R., Viana, C. M. & Rocha, J. (2022). Spatiotemporal Dynamics of COVID-19 Infections in Mainland Portugal. Sustainability,14(16), 10370. http://dx.doi.org/10.3390/su141610370
10.3390/su141610370
2071-1050
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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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
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