Space-time clusters of severe acute respiratory syndrome and COVID-19 and hierarchical urban network in the state of Mato Grosso, Brazil, 2020-2021

Detalhes bibliográficos
Autor(a) principal: Alves, Mario Ribeiro
Data de Publicação: 2022
Outros Autores: Souza-Santos, Reinaldo, Almeida, Andréa Sobral de
Tipo de documento: Artigo
Idioma: por
Título da fonte: Espaço para a Saúde (Online)
Texto Completo: https://espacoparasaude.fpp.edu.br/index.php/espacosaude/article/view/836
Resumo: Objective: to analyze the space-time distribution of COVID-19 in the state of Mato Grosso, Brazil. Methods: Weekly case records of Severe Acute Respiratory Syndrome were obtained from the Ministry of Health’s Database related to this syndrome, including data from COVID-19. Temporal and spatiotemporal analysis using scanning statistics to identify clusters of severe acute respiratory syndrome cases were performed with the software SaTScan. Results: A totalof 27,093 cases was observed, with an incidence of 768.33/100,000 inhabitants. The spatial distribution considering the period of study evidenced the heterogeneity of values in the state. The highest incidence rates were observed in more populous municipalities. Conclusion: We highlight priority areas for interventions, aiming at controlling the transmission of the disease and reducing transmission risks to more remote areas of the state of Mato Grosso.
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spelling Space-time clusters of severe acute respiratory syndrome and COVID-19 and hierarchical urban network in the state of Mato Grosso, Brazil, 2020-2021SARS-CoV-2COVID-19Cluster SamplingSARS-CoV-2COVID-19Cluster AnalysisObjective: to analyze the space-time distribution of COVID-19 in the state of Mato Grosso, Brazil. Methods: Weekly case records of Severe Acute Respiratory Syndrome were obtained from the Ministry of Health’s Database related to this syndrome, including data from COVID-19. Temporal and spatiotemporal analysis using scanning statistics to identify clusters of severe acute respiratory syndrome cases were performed with the software SaTScan. Results: A totalof 27,093 cases was observed, with an incidence of 768.33/100,000 inhabitants. The spatial distribution considering the period of study evidenced the heterogeneity of values in the state. The highest incidence rates were observed in more populous municipalities. Conclusion: We highlight priority areas for interventions, aiming at controlling the transmission of the disease and reducing transmission risks to more remote areas of the state of Mato Grosso.Faculdades Pequeno Príncipe2022-04-26info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://espacoparasaude.fpp.edu.br/index.php/espacosaude/article/view/83610.22421/1517-7130/es.2022v23.e836Espaço para a Saúde; Vol. 23 (2022)Espaço para a Saúde; v. 23 (2022)1517-71300103-3832reponame:Espaço para a Saúde (Online)instname:Faculdades Pequeno Príncipe (FPP)instacron:FPPporhttps://espacoparasaude.fpp.edu.br/index.php/espacosaude/article/view/836/657Copyright (c) 1969 Mario Ribeiro Alves, Reinaldo Souza-Santos, Andréa Sobral de Almeida, Marina Atanakahttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessAlves, Mario RibeiroSouza-Santos, ReinaldoAlmeida, Andréa Sobral de2022-12-08T19:13:13Zoai:ojs.168.194.69.20:article/836Revistahttps://espacoparasaude.fpp.edu.br/index.php/espacosaude/PRIhttps://espacoparasaude.fpp.edu.br/index.php/espacosaude/oaiespaco-saude@fpp.edu.br || elaine.rossi@fpp.edu.br1517-71300103-3832opendoar:2022-12-08T19:13:13Espaço para a Saúde (Online) - Faculdades Pequeno Príncipe (FPP)false
dc.title.none.fl_str_mv Space-time clusters of severe acute respiratory syndrome and COVID-19 and hierarchical urban network in the state of Mato Grosso, Brazil, 2020-2021
title Space-time clusters of severe acute respiratory syndrome and COVID-19 and hierarchical urban network in the state of Mato Grosso, Brazil, 2020-2021
spellingShingle Space-time clusters of severe acute respiratory syndrome and COVID-19 and hierarchical urban network in the state of Mato Grosso, Brazil, 2020-2021
Alves, Mario Ribeiro
SARS-CoV-2
COVID-19
Cluster Sampling
SARS-CoV-2
COVID-19
Cluster Analysis
title_short Space-time clusters of severe acute respiratory syndrome and COVID-19 and hierarchical urban network in the state of Mato Grosso, Brazil, 2020-2021
title_full Space-time clusters of severe acute respiratory syndrome and COVID-19 and hierarchical urban network in the state of Mato Grosso, Brazil, 2020-2021
title_fullStr Space-time clusters of severe acute respiratory syndrome and COVID-19 and hierarchical urban network in the state of Mato Grosso, Brazil, 2020-2021
title_full_unstemmed Space-time clusters of severe acute respiratory syndrome and COVID-19 and hierarchical urban network in the state of Mato Grosso, Brazil, 2020-2021
title_sort Space-time clusters of severe acute respiratory syndrome and COVID-19 and hierarchical urban network in the state of Mato Grosso, Brazil, 2020-2021
author Alves, Mario Ribeiro
author_facet Alves, Mario Ribeiro
Souza-Santos, Reinaldo
Almeida, Andréa Sobral de
author_role author
author2 Souza-Santos, Reinaldo
Almeida, Andréa Sobral de
author2_role author
author
dc.contributor.author.fl_str_mv Alves, Mario Ribeiro
Souza-Santos, Reinaldo
Almeida, Andréa Sobral de
dc.subject.por.fl_str_mv SARS-CoV-2
COVID-19
Cluster Sampling
SARS-CoV-2
COVID-19
Cluster Analysis
topic SARS-CoV-2
COVID-19
Cluster Sampling
SARS-CoV-2
COVID-19
Cluster Analysis
description Objective: to analyze the space-time distribution of COVID-19 in the state of Mato Grosso, Brazil. Methods: Weekly case records of Severe Acute Respiratory Syndrome were obtained from the Ministry of Health’s Database related to this syndrome, including data from COVID-19. Temporal and spatiotemporal analysis using scanning statistics to identify clusters of severe acute respiratory syndrome cases were performed with the software SaTScan. Results: A totalof 27,093 cases was observed, with an incidence of 768.33/100,000 inhabitants. The spatial distribution considering the period of study evidenced the heterogeneity of values in the state. The highest incidence rates were observed in more populous municipalities. Conclusion: We highlight priority areas for interventions, aiming at controlling the transmission of the disease and reducing transmission risks to more remote areas of the state of Mato Grosso.
publishDate 2022
dc.date.none.fl_str_mv 2022-04-26
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://espacoparasaude.fpp.edu.br/index.php/espacosaude/article/view/836
10.22421/1517-7130/es.2022v23.e836
url https://espacoparasaude.fpp.edu.br/index.php/espacosaude/article/view/836
identifier_str_mv 10.22421/1517-7130/es.2022v23.e836
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://espacoparasaude.fpp.edu.br/index.php/espacosaude/article/view/836/657
dc.rights.driver.fl_str_mv https://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Faculdades Pequeno Príncipe
publisher.none.fl_str_mv Faculdades Pequeno Príncipe
dc.source.none.fl_str_mv Espaço para a Saúde; Vol. 23 (2022)
Espaço para a Saúde; v. 23 (2022)
1517-7130
0103-3832
reponame:Espaço para a Saúde (Online)
instname:Faculdades Pequeno Príncipe (FPP)
instacron:FPP
instname_str Faculdades Pequeno Príncipe (FPP)
instacron_str FPP
institution FPP
reponame_str Espaço para a Saúde (Online)
collection Espaço para a Saúde (Online)
repository.name.fl_str_mv Espaço para a Saúde (Online) - Faculdades Pequeno Príncipe (FPP)
repository.mail.fl_str_mv espaco-saude@fpp.edu.br || elaine.rossi@fpp.edu.br
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