Spatial correlates of COVID-19 first wave across continental 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/10451/53588 |
Resumo: | The first case of COVID-19 in continental Portugal was documented on the 2nd of March 2020 and about seven months later more than 75 thousand infections had been reported. Although several factors correlate significantly with the spatial incidence of COVID-19 worldwide, the drivers of spatial incidence of this virus remain poorly known and need further exploration. In this study, we analyse the spatiotemporal patterns of COVID-19 incidence in the at the municipality level and test for significant relationships between these patterns and environmental, socioeconomic, demographic and human mobility factors to identify the mains drivers of COVID-19 incidence across time and space. We used a generalized liner mixed model, which accounts for zero inflated cases and spatial autocorrelation to identify significant relationships between the spatiotemporal incidence and the considered set of driving factors. Some of these relationships were particularly consistent across time, including the ‘percentage of employment in services’; ‘average time of commuting using individual transportation’; ‘percentage of employment in the agricultural sector’; and ‘average family size’. Comparing the preventive measures in Portugal (e.g., restrictions on mobility and crowd around) with the model results clearly show that COVID-19 incidence fluctuates as those measures are imposed or relieved. This shows that our model can be a useful tool to help decision-makers in defining prevention and/or mitigation policies. |
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Spatial correlates of COVID-19 first wave across continental PortugalCOVID-19Drivers of transmissionSocio-economic conditionsSpatial incidencePortugalThe first case of COVID-19 in continental Portugal was documented on the 2nd of March 2020 and about seven months later more than 75 thousand infections had been reported. Although several factors correlate significantly with the spatial incidence of COVID-19 worldwide, the drivers of spatial incidence of this virus remain poorly known and need further exploration. In this study, we analyse the spatiotemporal patterns of COVID-19 incidence in the at the municipality level and test for significant relationships between these patterns and environmental, socioeconomic, demographic and human mobility factors to identify the mains drivers of COVID-19 incidence across time and space. We used a generalized liner mixed model, which accounts for zero inflated cases and spatial autocorrelation to identify significant relationships between the spatiotemporal incidence and the considered set of driving factors. Some of these relationships were particularly consistent across time, including the ‘percentage of employment in services’; ‘average time of commuting using individual transportation’; ‘percentage of employment in the agricultural sector’; and ‘average family size’. Comparing the preventive measures in Portugal (e.g., restrictions on mobility and crowd around) with the model results clearly show that COVID-19 incidence fluctuates as those measures are imposed or relieved. This shows that our model can be a useful tool to help decision-makers in defining prevention and/or mitigation policies.PAGEPressRepositório da Universidade de LisboaBarbosa, BrunoSilva, MelissaCapinha, CésarGarcia, RicardoRocha, Jorge2022-07-01T09:10:10Z20222022-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10451/53588engBarbosa, B., Silva, M., Capinha, C., Garcia, R. A., & Rocha, J. (2022). Spatial correlates of COVID-19 first wave across continental Portugal. Geospatial Health, 17(s1):1073. https://doi.org/10.4081/gh.2022.10731827-19871970-7096info: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:RCAAP2023-11-08T16:59:31Zoai:repositorio.ul.pt:10451/53588Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:04:32.692093Repositório 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 |
Spatial correlates of COVID-19 first wave across continental Portugal |
title |
Spatial correlates of COVID-19 first wave across continental Portugal |
spellingShingle |
Spatial correlates of COVID-19 first wave across continental Portugal Barbosa, Bruno COVID-19 Drivers of transmission Socio-economic conditions Spatial incidence Portugal |
title_short |
Spatial correlates of COVID-19 first wave across continental Portugal |
title_full |
Spatial correlates of COVID-19 first wave across continental Portugal |
title_fullStr |
Spatial correlates of COVID-19 first wave across continental Portugal |
title_full_unstemmed |
Spatial correlates of COVID-19 first wave across continental Portugal |
title_sort |
Spatial correlates of COVID-19 first wave across continental Portugal |
author |
Barbosa, Bruno |
author_facet |
Barbosa, Bruno Silva, Melissa Capinha, César Garcia, Ricardo Rocha, Jorge |
author_role |
author |
author2 |
Silva, Melissa Capinha, César Garcia, Ricardo Rocha, Jorge |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Repositório da Universidade de Lisboa |
dc.contributor.author.fl_str_mv |
Barbosa, Bruno Silva, Melissa Capinha, César Garcia, Ricardo Rocha, Jorge |
dc.subject.por.fl_str_mv |
COVID-19 Drivers of transmission Socio-economic conditions Spatial incidence Portugal |
topic |
COVID-19 Drivers of transmission Socio-economic conditions Spatial incidence Portugal |
description |
The first case of COVID-19 in continental Portugal was documented on the 2nd of March 2020 and about seven months later more than 75 thousand infections had been reported. Although several factors correlate significantly with the spatial incidence of COVID-19 worldwide, the drivers of spatial incidence of this virus remain poorly known and need further exploration. In this study, we analyse the spatiotemporal patterns of COVID-19 incidence in the at the municipality level and test for significant relationships between these patterns and environmental, socioeconomic, demographic and human mobility factors to identify the mains drivers of COVID-19 incidence across time and space. We used a generalized liner mixed model, which accounts for zero inflated cases and spatial autocorrelation to identify significant relationships between the spatiotemporal incidence and the considered set of driving factors. Some of these relationships were particularly consistent across time, including the ‘percentage of employment in services’; ‘average time of commuting using individual transportation’; ‘percentage of employment in the agricultural sector’; and ‘average family size’. Comparing the preventive measures in Portugal (e.g., restrictions on mobility and crowd around) with the model results clearly show that COVID-19 incidence fluctuates as those measures are imposed or relieved. This shows that our model can be a useful tool to help decision-makers in defining prevention and/or mitigation policies. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-07-01T09:10:10Z 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/10451/53588 |
url |
http://hdl.handle.net/10451/53588 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Barbosa, B., Silva, M., Capinha, C., Garcia, R. A., & Rocha, J. (2022). Spatial correlates of COVID-19 first wave across continental Portugal. Geospatial Health, 17(s1):1073. https://doi.org/10.4081/gh.2022.1073 1827-1987 1970-7096 |
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 |
PAGEPress |
publisher.none.fl_str_mv |
PAGEPress |
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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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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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 |
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1799134597035851776 |