Spatiotemporal Dynamics of COVID-19 Infections in Mainland Portugal
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , |
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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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:RCAAP2024-11-20T18:16:25Zoai:repositorio.ul.pt:10451/54254Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-11-20T18:16:25Repositório 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 |
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 |
repository.mail.fl_str_mv |
mluisa.alvim@gmail.com |
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1817549201999921152 |