Understanding spatiotemporal patterns of COVID-19 incidence in Portugal
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Data de Publicação: | 2024 |
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/10362/169070 |
Resumo: | Publisher Copyright: © 2024 Ribeiro et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
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Understanding spatiotemporal patterns of COVID-19 incidence in Portugala functional data analysis from August 2020 to March 2022GeneralSDG 3 - Good Health and Well-beingPublisher Copyright: © 2024 Ribeiro et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.During the SARS-CoV-2 pandemic, governments and public health authorities collected massive amounts of data on daily confirmed positive cases and incidence rates. These data sets provide relevant information to develop a scientific understanding of the pandemic's spatiotemporal dynamics. At the same time, there is a lack of comprehensive approaches to describe and classify patterns underlying the dynamics of COVID-19 incidence across regions over time. This seriously constrains the potential benefits for public health authorities to understand spatiotemporal patterns of disease incidence that would allow for better risk communication strategies and improved assessment of mitigation policies efficacy. Within this context, we propose an exploratory statistical tool that combines functional data analysis with unsupervised learning algorithms to extract meaningful information about the main spatiotemporal patterns underlying COVID-19 incidence on mainland Portugal. We focus on the timeframe spanning from August 2020 to March 2022, considering data at the municipality level. First, we describe the temporal evolution of confirmed daily COVID-19 cases by municipality as a function of time, and outline the main temporal patterns of variability using a functional principal component analysis. Then, municipalities are classified according to their spatiotemporal similarities through hierarchical clustering adapted to spatially correlated functional data. Our findings reveal disparities in disease dynamics between northern and coastal municipalities versus those in the southern and hinterland. We also distinguish effects occurring during the 2020-2021 period from those in the 2021-2022 autumn-winter seasons. The results provide proof-of-concept that the proposed approach can be used to detect the main spatiotemporal patterns of disease incidence. The novel approach expands and enhances existing exploratory tools for spatiotemporal analysis of public health data.Escola Nacional de Saúde Pública (ENSP)Comprehensive Health Research Centre (CHRC) - Pólo ENSPCentro de Investigação em Saúde Pública (CISP/PHRC)RUNRibeiro, ManuelAzevedo, LeonardoSantos, André PeraltaLeite, Pedro PintoPereira, Maria João2024-06-25T22:26:35Z2024-022024-02-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/169070eng1932-6203PURE: 93929943https://doi.org/10.1371/journal.pone.0297772info: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:RCAAP2024-07-08T01:33:53Zoai:run.unl.pt:10362/169070Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-07-08T01:33:53Repositório 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 |
Understanding spatiotemporal patterns of COVID-19 incidence in Portugal a functional data analysis from August 2020 to March 2022 |
title |
Understanding spatiotemporal patterns of COVID-19 incidence in Portugal |
spellingShingle |
Understanding spatiotemporal patterns of COVID-19 incidence in Portugal Ribeiro, Manuel General SDG 3 - Good Health and Well-being |
title_short |
Understanding spatiotemporal patterns of COVID-19 incidence in Portugal |
title_full |
Understanding spatiotemporal patterns of COVID-19 incidence in Portugal |
title_fullStr |
Understanding spatiotemporal patterns of COVID-19 incidence in Portugal |
title_full_unstemmed |
Understanding spatiotemporal patterns of COVID-19 incidence in Portugal |
title_sort |
Understanding spatiotemporal patterns of COVID-19 incidence in Portugal |
author |
Ribeiro, Manuel |
author_facet |
Ribeiro, Manuel Azevedo, Leonardo Santos, André Peralta Leite, Pedro Pinto Pereira, Maria João |
author_role |
author |
author2 |
Azevedo, Leonardo Santos, André Peralta Leite, Pedro Pinto Pereira, Maria João |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Escola Nacional de Saúde Pública (ENSP) Comprehensive Health Research Centre (CHRC) - Pólo ENSP Centro de Investigação em Saúde Pública (CISP/PHRC) RUN |
dc.contributor.author.fl_str_mv |
Ribeiro, Manuel Azevedo, Leonardo Santos, André Peralta Leite, Pedro Pinto Pereira, Maria João |
dc.subject.por.fl_str_mv |
General SDG 3 - Good Health and Well-being |
topic |
General SDG 3 - Good Health and Well-being |
description |
Publisher Copyright: © 2024 Ribeiro et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
publishDate |
2024 |
dc.date.none.fl_str_mv |
2024-06-25T22:26:35Z 2024-02 2024-02-01T00:00:00Z |
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/10362/169070 |
url |
http://hdl.handle.net/10362/169070 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1932-6203 PURE: 93929943 https://doi.org/10.1371/journal.pone.0297772 |
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.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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1817546228372602880 |