Understanding spatiotemporal patterns of COVID-19 incidence in Portugal

Detalhes bibliográficos
Autor(a) principal: Ribeiro, Manuel
Data de Publicação: 2024
Outros Autores: Azevedo, Leonardo, Santos, André Peralta, Leite, Pedro Pinto, Pereira, Maria João
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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spelling 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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