Spatial analysis of the COVID-19 distribution pattern in São Paulo State, Brazil
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Ciência & Saúde Coletiva (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1413-81232020000903377 |
Resumo: | Abstract At the end of 2019, the outbreak of COVID-19 was reported in Wuhan, China. The outbreak spread quickly to several countries, becoming a public health emergency of international interest. Without a vaccine or antiviral drugs, control measures are necessary to understand the evolution of cases. Here, we report through spatial analysis the spatial pattern of the COVID-19 outbreak. The study site was the State of São Paulo, Brazil, where the first case of the disease was confirmed. We applied the Kernel Density to generate surfaces that indicate where there is higher density of cases and, consequently, greater risk of confirming new cases. The spatial pattern of COVID-19 pandemic could be observed in São Paulo State, in which its metropolitan region standed out with the greatest cases, being classified as a hotspot. In addition, the main highways and airports that connect the capital to the cities with the highest population density were classified as medium density areas by the Kernel Density method.It indicates a gradual expansion from the capital to the interior. Therefore, spatial analyses are fundamental to understand the spread of the virus and its association with other spatial data can be essential to guide control measures. |
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Spatial analysis of the COVID-19 distribution pattern in São Paulo State, BrazilCoronavirusRespiratory diseasePandemicKernel densityAbstract At the end of 2019, the outbreak of COVID-19 was reported in Wuhan, China. The outbreak spread quickly to several countries, becoming a public health emergency of international interest. Without a vaccine or antiviral drugs, control measures are necessary to understand the evolution of cases. Here, we report through spatial analysis the spatial pattern of the COVID-19 outbreak. The study site was the State of São Paulo, Brazil, where the first case of the disease was confirmed. We applied the Kernel Density to generate surfaces that indicate where there is higher density of cases and, consequently, greater risk of confirming new cases. The spatial pattern of COVID-19 pandemic could be observed in São Paulo State, in which its metropolitan region standed out with the greatest cases, being classified as a hotspot. In addition, the main highways and airports that connect the capital to the cities with the highest population density were classified as medium density areas by the Kernel Density method.It indicates a gradual expansion from the capital to the interior. Therefore, spatial analyses are fundamental to understand the spread of the virus and its association with other spatial data can be essential to guide control measures.ABRASCO - Associação Brasileira de Saúde Coletiva2020-09-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1413-81232020000903377Ciência & Saúde Coletiva v.25 n.9 2020reponame:Ciência & Saúde Coletiva (Online)instname:Associação Brasileira de Saúde Coletiva (ABRASCO)instacron:ABRASCO10.1590/1413-81232020259.17082020info:eu-repo/semantics/openAccessRex,Franciel EduardoBorges,Cléber Augusto de SouzaKäfer,Pâmela Suéleneng2020-08-25T00:00:00Zoai:scielo:S1413-81232020000903377Revistahttp://www.cienciaesaudecoletiva.com.brhttps://old.scielo.br/oai/scielo-oai.php||cienciasaudecoletiva@fiocruz.br1678-45611413-8123opendoar:2020-08-25T00:00Ciência & Saúde Coletiva (Online) - Associação Brasileira de Saúde Coletiva (ABRASCO)false |
dc.title.none.fl_str_mv |
Spatial analysis of the COVID-19 distribution pattern in São Paulo State, Brazil |
title |
Spatial analysis of the COVID-19 distribution pattern in São Paulo State, Brazil |
spellingShingle |
Spatial analysis of the COVID-19 distribution pattern in São Paulo State, Brazil Rex,Franciel Eduardo Coronavirus Respiratory disease Pandemic Kernel density |
title_short |
Spatial analysis of the COVID-19 distribution pattern in São Paulo State, Brazil |
title_full |
Spatial analysis of the COVID-19 distribution pattern in São Paulo State, Brazil |
title_fullStr |
Spatial analysis of the COVID-19 distribution pattern in São Paulo State, Brazil |
title_full_unstemmed |
Spatial analysis of the COVID-19 distribution pattern in São Paulo State, Brazil |
title_sort |
Spatial analysis of the COVID-19 distribution pattern in São Paulo State, Brazil |
author |
Rex,Franciel Eduardo |
author_facet |
Rex,Franciel Eduardo Borges,Cléber Augusto de Souza Käfer,Pâmela Suélen |
author_role |
author |
author2 |
Borges,Cléber Augusto de Souza Käfer,Pâmela Suélen |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Rex,Franciel Eduardo Borges,Cléber Augusto de Souza Käfer,Pâmela Suélen |
dc.subject.por.fl_str_mv |
Coronavirus Respiratory disease Pandemic Kernel density |
topic |
Coronavirus Respiratory disease Pandemic Kernel density |
description |
Abstract At the end of 2019, the outbreak of COVID-19 was reported in Wuhan, China. The outbreak spread quickly to several countries, becoming a public health emergency of international interest. Without a vaccine or antiviral drugs, control measures are necessary to understand the evolution of cases. Here, we report through spatial analysis the spatial pattern of the COVID-19 outbreak. The study site was the State of São Paulo, Brazil, where the first case of the disease was confirmed. We applied the Kernel Density to generate surfaces that indicate where there is higher density of cases and, consequently, greater risk of confirming new cases. The spatial pattern of COVID-19 pandemic could be observed in São Paulo State, in which its metropolitan region standed out with the greatest cases, being classified as a hotspot. In addition, the main highways and airports that connect the capital to the cities with the highest population density were classified as medium density areas by the Kernel Density method.It indicates a gradual expansion from the capital to the interior. Therefore, spatial analyses are fundamental to understand the spread of the virus and its association with other spatial data can be essential to guide control measures. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-09-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1413-81232020000903377 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1413-81232020000903377 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1413-81232020259.17082020 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
ABRASCO - Associação Brasileira de Saúde Coletiva |
publisher.none.fl_str_mv |
ABRASCO - Associação Brasileira de Saúde Coletiva |
dc.source.none.fl_str_mv |
Ciência & Saúde Coletiva v.25 n.9 2020 reponame:Ciência & Saúde Coletiva (Online) instname:Associação Brasileira de Saúde Coletiva (ABRASCO) instacron:ABRASCO |
instname_str |
Associação Brasileira de Saúde Coletiva (ABRASCO) |
instacron_str |
ABRASCO |
institution |
ABRASCO |
reponame_str |
Ciência & Saúde Coletiva (Online) |
collection |
Ciência & Saúde Coletiva (Online) |
repository.name.fl_str_mv |
Ciência & Saúde Coletiva (Online) - Associação Brasileira de Saúde Coletiva (ABRASCO) |
repository.mail.fl_str_mv |
||cienciasaudecoletiva@fiocruz.br |
_version_ |
1754213046133194752 |