ANÁLISE DA DISTRIBUIÇÃO ESPACIAL DO NÚMERO DE CASOS DE COVID-19 NO MUNICÍPIO DE TOLEDO - PARANÁ/Analysis of the spatial distribution of the number of COVID-19 cases in the municipality of Toledo-Paraná

Detalhes bibliográficos
Autor(a) principal: Garcia da Silva, Amilton Luciano
Data de Publicação: 2024
Outros Autores: Dalposso, Gustavo Henrique, Uribe-Opazo, Miguel Angel, Cima, Elizabeth Giron
Tipo de documento: Artigo
Idioma: por
Título da fonte: Informe Gepec (Online)
Texto Completo: https://e-revista.unioeste.br/index.php/gepec/article/view/31804
Resumo: Spatial statistics plays an important role in epidemiology, providing methodologies that enable the identification of spatial patterns (clusters) and the determination of regions with similar characteristics. These techniques allow for appropriate allocation of resources for localized treatments, reducing the impacts caused by diseases. In this study, spatial statistical area methodologies were used to investigate the growth rate of Covid-19 cases in the municipality of Toledo, in the state of Paraná-Brazil, from June to October 2020. Maps were generated that identified regions where autocorrelation showed statistical significance. It was concluded that neighborhoods with low Covid-19 contamination rates were incorporated into adjacent neighborhoods with high contamination rates. This finding highlights the relevance of spatial statistics, as with proper attention from municipal health authorities, it is possible to prevent similar situations from occurring in the future, avoiding the transformation of the entire region into an extensive cluster characterized by high contamination rates.
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spelling ANÁLISE DA DISTRIBUIÇÃO ESPACIAL DO NÚMERO DE CASOS DE COVID-19 NO MUNICÍPIO DE TOLEDO - PARANÁ/Analysis of the spatial distribution of the number of COVID-19 cases in the municipality of Toledo-ParanáautocorrelaçãoAEDE.Análise de clusters.Economia EspacialÍndice de MoranEstatística EspacialSpatial autocorrelation. Moran’s index. Local spatial autocorrelation index. LISA cluster map. Spatial statistic of area data.Autocorrelación espacial. Índice de Moran. Índice de autocorrelación espacial local. LISA cluster map. Estadística espacial de dados de áreas.Spatial statistics plays an important role in epidemiology, providing methodologies that enable the identification of spatial patterns (clusters) and the determination of regions with similar characteristics. These techniques allow for appropriate allocation of resources for localized treatments, reducing the impacts caused by diseases. In this study, spatial statistical area methodologies were used to investigate the growth rate of Covid-19 cases in the municipality of Toledo, in the state of Paraná-Brazil, from June to October 2020. Maps were generated that identified regions where autocorrelation showed statistical significance. It was concluded that neighborhoods with low Covid-19 contamination rates were incorporated into adjacent neighborhoods with high contamination rates. This finding highlights the relevance of spatial statistics, as with proper attention from municipal health authorities, it is possible to prevent similar situations from occurring in the future, avoiding the transformation of the entire region into an extensive cluster characterized by high contamination rates.La estadística espacial desempeña un papel importante en epidemiología, proporcionando metodologías que permiten la identificación de patrones espaciales (clusters) y la determinación de regiones con características similares. Estas técnicas permiten una adecuada asignación de recursos para tratamientos localizados, reduciendo los impactos causados por las enfermedades. En el presente estudio, se utilizaron las metodologías de estadística espacial de áreas para investigar la tasa de crecimiento de casos de Covid-19 en el municipio de Toledo, en el estado de Paraná-Brasil, en los períodos de junio a octubre del año 2020. Se generaron mapas que identifican las regiones en las que la autocorrelación presentó significancia estadística. Se concluyó que los barrios que presentaban baja tasa de contaminación por Covid-19 fueron incorporados por los barrios adyacentes, que tenían alta tasa de contaminación. Esta constatación resalta la relevancia de la estadística espacial de áreas, ya que mediante la debida atención de las autoridades de salud municipales, es posible prevenir la ocurrencia de situaciones similares en el futuro, evitando la transformación de toda la región en un extenso conglomerado caracterizado por tasas elevadas de contaminación.A estatística espacial desempenha um papel importante em diferentes áreas do conhecimento, fornecendo metodologias que possibilitam a identificação de padrões espaciais (clusters) e a determinação de regiões com características semelhantes dos dados em estudo. No presente estudo, foi utilizada a metodologia  chamada de estatística espacial de dados de  áreas para investigar a taxa de crescimento de casos de COVID-19 no município de Toledo, no estado do Paraná-Brasil, nos períodos de junho a outubro do ano de 2020. Foram gerados mapas que identificaram as regiões em que a autocorrelação espacial apresentou significância estatística. Concluiu-se que bairros que apresentavam baixa taxa de contaminação pela COVID-19 foram incorporados pelos bairros adjacentes, que possuíam alta taxa de contaminação. Essa constatação ressalta a relevância da estatística espacial de dados  de áreas, uma vez que, mediante a devida atenção das autoridades de saúde municipais, permitem uma alocação adequada de recursos para tratamentos localizados, reduzindo os impactos causados pelas doenças, possibilitando prevenir a ocorrência de situações semelhantes no futuro e evitando a transformação de toda a região em um extenso aglomerado caracterizado por taxas elevadas de contaminação. Abstract: Spatial statistic play a significant role in various fields of knowledge, providing methodologies that enable the identification of spatial patterns (clusters) and the determination of regions with similar characteristics of the data under study. In the present study, a methodology called spatial statistics of area data was used to investigate the growth rate of COVID-19 cases in the city of Toledo, in the state of Paraná-Brazil, during the periods from june to october 2020. Maps were generated that identified the regions where spatial autocorrelation showed statistical significance. It was concluded that neighborhoods that had a low rate of COVID-19 contamination were incorporated by adjacent neighborhoods, which had a high rate of contamination. This finding underscores the relevance of spatial statistic of area data, as, with due attention from municipal health authorities, they allow for an appropriate allocation of resources for localized treatments, reducing the impacts caused by diseases, enabling the prevention of similar situations in the future, and avoiding the transformation of the entire region into an extensive cluster characterized by high contamination rates.  EdUnioeste2024-02-23info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://e-revista.unioeste.br/index.php/gepec/article/view/3180410.48075/igepec.v28i1.31804Informe GEPEC; v. 28 n. 1 (2024): Revista Informe GEPEC; 128-1431679-415X1676-067010.48075/igepec.v28i1reponame:Informe Gepec (Online)instname:Universidade Estadual do Oeste do Paraná (UNIOESTE)instacron:UNIOESTEporhttps://e-revista.unioeste.br/index.php/gepec/article/view/31804/22962Copyright (c) 2024 Informe GEPEChttps://creativecommons.org/licenses/by-nc-sa/4.0info:eu-repo/semantics/openAccessGarcia da Silva, Amilton LucianoDalposso, Gustavo HenriqueUribe-Opazo, Miguel AngelCima, Elizabeth Giron2024-02-23T20:17:50Zoai:ojs.e-revista.unioeste.br:article/31804Revistahttps://e-revista.unioeste.br/index.php/gepecPUBhttps://e-revista.unioeste.br/index.php/gepec/oairevista.gepec@gmail.com1679-415X1676-0670opendoar:2024-02-23T20:17:50Informe Gepec (Online) - Universidade Estadual do Oeste do Paraná (UNIOESTE)false
dc.title.none.fl_str_mv ANÁLISE DA DISTRIBUIÇÃO ESPACIAL DO NÚMERO DE CASOS DE COVID-19 NO MUNICÍPIO DE TOLEDO - PARANÁ/Analysis of the spatial distribution of the number of COVID-19 cases in the municipality of Toledo-Paraná
title ANÁLISE DA DISTRIBUIÇÃO ESPACIAL DO NÚMERO DE CASOS DE COVID-19 NO MUNICÍPIO DE TOLEDO - PARANÁ/Analysis of the spatial distribution of the number of COVID-19 cases in the municipality of Toledo-Paraná
spellingShingle ANÁLISE DA DISTRIBUIÇÃO ESPACIAL DO NÚMERO DE CASOS DE COVID-19 NO MUNICÍPIO DE TOLEDO - PARANÁ/Analysis of the spatial distribution of the number of COVID-19 cases in the municipality of Toledo-Paraná
Garcia da Silva, Amilton Luciano
autocorrelação
AEDE.
Análise de clusters.
Economia Espacial
Índice de Moran
Estatística Espacial
Spatial autocorrelation. Moran’s index. Local spatial autocorrelation index. LISA cluster map. Spatial statistic of area data.
Autocorrelación espacial. Índice de Moran. Índice de autocorrelación espacial local. LISA cluster map. Estadística espacial de dados de áreas.
title_short ANÁLISE DA DISTRIBUIÇÃO ESPACIAL DO NÚMERO DE CASOS DE COVID-19 NO MUNICÍPIO DE TOLEDO - PARANÁ/Analysis of the spatial distribution of the number of COVID-19 cases in the municipality of Toledo-Paraná
title_full ANÁLISE DA DISTRIBUIÇÃO ESPACIAL DO NÚMERO DE CASOS DE COVID-19 NO MUNICÍPIO DE TOLEDO - PARANÁ/Analysis of the spatial distribution of the number of COVID-19 cases in the municipality of Toledo-Paraná
title_fullStr ANÁLISE DA DISTRIBUIÇÃO ESPACIAL DO NÚMERO DE CASOS DE COVID-19 NO MUNICÍPIO DE TOLEDO - PARANÁ/Analysis of the spatial distribution of the number of COVID-19 cases in the municipality of Toledo-Paraná
title_full_unstemmed ANÁLISE DA DISTRIBUIÇÃO ESPACIAL DO NÚMERO DE CASOS DE COVID-19 NO MUNICÍPIO DE TOLEDO - PARANÁ/Analysis of the spatial distribution of the number of COVID-19 cases in the municipality of Toledo-Paraná
title_sort ANÁLISE DA DISTRIBUIÇÃO ESPACIAL DO NÚMERO DE CASOS DE COVID-19 NO MUNICÍPIO DE TOLEDO - PARANÁ/Analysis of the spatial distribution of the number of COVID-19 cases in the municipality of Toledo-Paraná
author Garcia da Silva, Amilton Luciano
author_facet Garcia da Silva, Amilton Luciano
Dalposso, Gustavo Henrique
Uribe-Opazo, Miguel Angel
Cima, Elizabeth Giron
author_role author
author2 Dalposso, Gustavo Henrique
Uribe-Opazo, Miguel Angel
Cima, Elizabeth Giron
author2_role author
author
author
dc.contributor.author.fl_str_mv Garcia da Silva, Amilton Luciano
Dalposso, Gustavo Henrique
Uribe-Opazo, Miguel Angel
Cima, Elizabeth Giron
dc.subject.por.fl_str_mv autocorrelação
AEDE.
Análise de clusters.
Economia Espacial
Índice de Moran
Estatística Espacial
Spatial autocorrelation. Moran’s index. Local spatial autocorrelation index. LISA cluster map. Spatial statistic of area data.
Autocorrelación espacial. Índice de Moran. Índice de autocorrelación espacial local. LISA cluster map. Estadística espacial de dados de áreas.
topic autocorrelação
AEDE.
Análise de clusters.
Economia Espacial
Índice de Moran
Estatística Espacial
Spatial autocorrelation. Moran’s index. Local spatial autocorrelation index. LISA cluster map. Spatial statistic of area data.
Autocorrelación espacial. Índice de Moran. Índice de autocorrelación espacial local. LISA cluster map. Estadística espacial de dados de áreas.
description Spatial statistics plays an important role in epidemiology, providing methodologies that enable the identification of spatial patterns (clusters) and the determination of regions with similar characteristics. These techniques allow for appropriate allocation of resources for localized treatments, reducing the impacts caused by diseases. In this study, spatial statistical area methodologies were used to investigate the growth rate of Covid-19 cases in the municipality of Toledo, in the state of Paraná-Brazil, from June to October 2020. Maps were generated that identified regions where autocorrelation showed statistical significance. It was concluded that neighborhoods with low Covid-19 contamination rates were incorporated into adjacent neighborhoods with high contamination rates. This finding highlights the relevance of spatial statistics, as with proper attention from municipal health authorities, it is possible to prevent similar situations from occurring in the future, avoiding the transformation of the entire region into an extensive cluster characterized by high contamination rates.
publishDate 2024
dc.date.none.fl_str_mv 2024-02-23
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://e-revista.unioeste.br/index.php/gepec/article/view/31804
10.48075/igepec.v28i1.31804
url https://e-revista.unioeste.br/index.php/gepec/article/view/31804
identifier_str_mv 10.48075/igepec.v28i1.31804
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://e-revista.unioeste.br/index.php/gepec/article/view/31804/22962
dc.rights.driver.fl_str_mv Copyright (c) 2024 Informe GEPEC
https://creativecommons.org/licenses/by-nc-sa/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2024 Informe GEPEC
https://creativecommons.org/licenses/by-nc-sa/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv EdUnioeste
publisher.none.fl_str_mv EdUnioeste
dc.source.none.fl_str_mv Informe GEPEC; v. 28 n. 1 (2024): Revista Informe GEPEC; 128-143
1679-415X
1676-0670
10.48075/igepec.v28i1
reponame:Informe Gepec (Online)
instname:Universidade Estadual do Oeste do Paraná (UNIOESTE)
instacron:UNIOESTE
instname_str Universidade Estadual do Oeste do Paraná (UNIOESTE)
instacron_str UNIOESTE
institution UNIOESTE
reponame_str Informe Gepec (Online)
collection Informe Gepec (Online)
repository.name.fl_str_mv Informe Gepec (Online) - Universidade Estadual do Oeste do Paraná (UNIOESTE)
repository.mail.fl_str_mv revista.gepec@gmail.com
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