Characterizing COVID-19 epidemics dissemination and previsions for Curitiba,Brazil using a modified SIR model

Detalhes bibliográficos
Autor(a) principal: Montanheiro, Lecio
Data de Publicação: 2020
Outros Autores: Dartora, Cesar
Tipo de documento: preprint
Idioma: eng
Título da fonte: SciELO Preprints
Texto Completo: https://preprints.scielo.org/index.php/scielo/preprint/view/1094
Resumo: The epidemic outbreak of the new coronavirus has fastly reached a pandemic status. That has awaken interest from the academy on mathematical models that alow for contagion curves previsions. A SIR model with modied solutions has been presented and numerical solutions of R0 and τ for data corrected including probable non-notied cases has been reached. Previsions have been made to support government decision-makers on strategies to ght the pandemic. Main topics discussed were state-wide inward dissemination and probable second waves of dissemination in large urban areas taking Curitiba-PR, Manaus-Am and the state of Parana as study cases. Also, a correlation between susceptibles urban density and dissemination parameters R0 and τ are shown to be precise do a 10% error margin. That was quite instrumental on previewing second wave parameters. Were considered data available up to may, 8, 2020.
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spelling Characterizing COVID-19 epidemics dissemination and previsions for Curitiba,Brazil using a modified SIR modelCaracterização da disseminação da epidemia de COVID-19 e previsões para Curitiba-BR utilizando modelo SIR modificadoSARS-COV2SIRR0tempo de recuperaçãodensidade de susceptíveisCuritibaParanáSIRSARS-CoV-2R0CuritibaParanásusceptible densitycharacterizationprevisionsThe epidemic outbreak of the new coronavirus has fastly reached a pandemic status. That has awaken interest from the academy on mathematical models that alow for contagion curves previsions. A SIR model with modied solutions has been presented and numerical solutions of R0 and τ for data corrected including probable non-notied cases has been reached. Previsions have been made to support government decision-makers on strategies to ght the pandemic. Main topics discussed were state-wide inward dissemination and probable second waves of dissemination in large urban areas taking Curitiba-PR, Manaus-Am and the state of Parana as study cases. Also, a correlation between susceptibles urban density and dissemination parameters R0 and τ are shown to be precise do a 10% error margin. That was quite instrumental on previewing second wave parameters. Were considered data available up to may, 8, 2020.O surto epidêmico do novo coronavírus, que rapidamente alcançou o status de pandemia, despertou o interesse por modelos matemáticos que permitam realizar previsões de desenvolvimento das curvas de contágio. Um modelo SIR com soluções modificadas foi apresentado e soluções numéricas dos parâmetros de disseminação R0 e tempo de recuperação baseadas em dados corrigidos com prováveis não-notificados foram alcançadas. Previsões foram feitas para amparar as autoridades públicas na adoção de estratégias para o combate a pandemia quanto a interiorização da disseminação e análise de prováveis segundas ondas em centro urbanos tomando Curitiba-PR, Manaus-Am e o estado do Paraná como casos de estudo. Uma correlação entre densidade urbana de susceptíveis e os parâmetros de disseminação R0 e tempo de recuperação foram levantadas com margem de erro de 10% o que permitiu ótima previsão dos parâmetros de segundas ondas. Foram considerandos os dados disponíveis para o Brasil até 08 de maio de 2020.SciELO PreprintsSciELO PreprintsSciELO Preprints2020-08-14info:eu-repo/semantics/preprintinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://preprints.scielo.org/index.php/scielo/preprint/view/109410.1590/SciELOPreprints.1094enghttps://preprints.scielo.org/index.php/scielo/article/view/1094/1610Copyright (c) 2020 Lecio Montanheiro, Cesar Dartorahttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessMontanheiro, LecioDartora, Cesarreponame:SciELO Preprintsinstname:SciELOinstacron:SCI2020-08-12T20:30:41Zoai:ops.preprints.scielo.org:preprint/1094Servidor de preprintshttps://preprints.scielo.org/index.php/scieloONGhttps://preprints.scielo.org/index.php/scielo/oaiscielo.submission@scielo.orgopendoar:2020-08-12T20:30:41SciELO Preprints - SciELOfalse
dc.title.none.fl_str_mv Characterizing COVID-19 epidemics dissemination and previsions for Curitiba,Brazil using a modified SIR model
Caracterização da disseminação da epidemia de COVID-19 e previsões para Curitiba-BR utilizando modelo SIR modificado
title Characterizing COVID-19 epidemics dissemination and previsions for Curitiba,Brazil using a modified SIR model
spellingShingle Characterizing COVID-19 epidemics dissemination and previsions for Curitiba,Brazil using a modified SIR model
Montanheiro, Lecio
SARS-COV2
SIR
R0
tempo de recuperação
densidade de susceptíveis
Curitiba
Paraná
SIR
SARS-CoV-2
R0
Curitiba
Paraná
susceptible density
characterization
previsions
title_short Characterizing COVID-19 epidemics dissemination and previsions for Curitiba,Brazil using a modified SIR model
title_full Characterizing COVID-19 epidemics dissemination and previsions for Curitiba,Brazil using a modified SIR model
title_fullStr Characterizing COVID-19 epidemics dissemination and previsions for Curitiba,Brazil using a modified SIR model
title_full_unstemmed Characterizing COVID-19 epidemics dissemination and previsions for Curitiba,Brazil using a modified SIR model
title_sort Characterizing COVID-19 epidemics dissemination and previsions for Curitiba,Brazil using a modified SIR model
author Montanheiro, Lecio
author_facet Montanheiro, Lecio
Dartora, Cesar
author_role author
author2 Dartora, Cesar
author2_role author
dc.contributor.author.fl_str_mv Montanheiro, Lecio
Dartora, Cesar
dc.subject.por.fl_str_mv SARS-COV2
SIR
R0
tempo de recuperação
densidade de susceptíveis
Curitiba
Paraná
SIR
SARS-CoV-2
R0
Curitiba
Paraná
susceptible density
characterization
previsions
topic SARS-COV2
SIR
R0
tempo de recuperação
densidade de susceptíveis
Curitiba
Paraná
SIR
SARS-CoV-2
R0
Curitiba
Paraná
susceptible density
characterization
previsions
description The epidemic outbreak of the new coronavirus has fastly reached a pandemic status. That has awaken interest from the academy on mathematical models that alow for contagion curves previsions. A SIR model with modied solutions has been presented and numerical solutions of R0 and τ for data corrected including probable non-notied cases has been reached. Previsions have been made to support government decision-makers on strategies to ght the pandemic. Main topics discussed were state-wide inward dissemination and probable second waves of dissemination in large urban areas taking Curitiba-PR, Manaus-Am and the state of Parana as study cases. Also, a correlation between susceptibles urban density and dissemination parameters R0 and τ are shown to be precise do a 10% error margin. That was quite instrumental on previewing second wave parameters. Were considered data available up to may, 8, 2020.
publishDate 2020
dc.date.none.fl_str_mv 2020-08-14
dc.type.driver.fl_str_mv info:eu-repo/semantics/preprint
info:eu-repo/semantics/publishedVersion
format preprint
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://preprints.scielo.org/index.php/scielo/preprint/view/1094
10.1590/SciELOPreprints.1094
url https://preprints.scielo.org/index.php/scielo/preprint/view/1094
identifier_str_mv 10.1590/SciELOPreprints.1094
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://preprints.scielo.org/index.php/scielo/article/view/1094/1610
dc.rights.driver.fl_str_mv Copyright (c) 2020 Lecio Montanheiro, Cesar Dartora
https://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2020 Lecio Montanheiro, Cesar Dartora
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 SciELO Preprints
SciELO Preprints
SciELO Preprints
publisher.none.fl_str_mv SciELO Preprints
SciELO Preprints
SciELO Preprints
dc.source.none.fl_str_mv reponame:SciELO Preprints
instname:SciELO
instacron:SCI
instname_str SciELO
instacron_str SCI
institution SCI
reponame_str SciELO Preprints
collection SciELO Preprints
repository.name.fl_str_mv SciELO Preprints - SciELO
repository.mail.fl_str_mv scielo.submission@scielo.org
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