Forecasting the rate of cumulative cases of COVID-19 infection in Northeast Brazil: a Boltzmann function-based modeling study

Detalhes bibliográficos
Autor(a) principal: Melo,Géssyca Cavalcante de
Data de Publicação: 2020
Outros Autores: Araújo Neto,Renato Américo de, Araújo,Karina Conceição Gomes Machado de
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Cadernos de Saúde Pública
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0102-311X2020000604002
Resumo: Abstract: The COVID-19 death rate in Northeast Brazil is much higher when compared to the national average, demanding a study into the prognosis of the region for planning control measures and preventing the collapse of the health care system. We estimated the potential total cumulative cases of COVID-19 in the region for the next three months. Our study included all confirmed cases, from March 8 until April 28, 2020, collected from the official website that reports the situation of COVID-19 infections in Brazil. The Boltzmann function was applied to a data simulation for each set of data regarding different states. The model data were well fitted, with R2 values close to 0.999. Up to April 28, 20,665 cases were confirmed in the region. The state of Ceará has the highest rate of accumulated cases per 100,000 inhabitants (75.75), followed by Pernambuco. We estimated that the states of Ceará, Sergipe and Paraíba will experience a dramatic increase in the rate of cumulative cases until July 31. Maranhão, Pernambuco, Rio Grande do Norte and Piauí showed a more discreet increase in the model. For Bahia and Alagoas, a 4.7 and 6.6-fold increase in the rate was estimated, respectively. We estimate a substantial increase in the rate of cumulative cases per 100,000 inhabitants in the region within three months, especially for Ceará, Sergipe and Paraíba. The Boltzmann function proved to be a simple tool for epidemiological forecasting that can help planning the measures to contain COVID-19.
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spelling Forecasting the rate of cumulative cases of COVID-19 infection in Northeast Brazil: a Boltzmann function-based modeling studyCOVID-19EpidemiologyMathematical ModelsPandemicAbstract: The COVID-19 death rate in Northeast Brazil is much higher when compared to the national average, demanding a study into the prognosis of the region for planning control measures and preventing the collapse of the health care system. We estimated the potential total cumulative cases of COVID-19 in the region for the next three months. Our study included all confirmed cases, from March 8 until April 28, 2020, collected from the official website that reports the situation of COVID-19 infections in Brazil. The Boltzmann function was applied to a data simulation for each set of data regarding different states. The model data were well fitted, with R2 values close to 0.999. Up to April 28, 20,665 cases were confirmed in the region. The state of Ceará has the highest rate of accumulated cases per 100,000 inhabitants (75.75), followed by Pernambuco. We estimated that the states of Ceará, Sergipe and Paraíba will experience a dramatic increase in the rate of cumulative cases until July 31. Maranhão, Pernambuco, Rio Grande do Norte and Piauí showed a more discreet increase in the model. For Bahia and Alagoas, a 4.7 and 6.6-fold increase in the rate was estimated, respectively. We estimate a substantial increase in the rate of cumulative cases per 100,000 inhabitants in the region within three months, especially for Ceará, Sergipe and Paraíba. The Boltzmann function proved to be a simple tool for epidemiological forecasting that can help planning the measures to contain COVID-19.Escola Nacional de Saúde Pública Sergio Arouca, Fundação Oswaldo Cruz2020-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0102-311X2020000604002Cadernos de Saúde Pública v.36 n.6 2020reponame:Cadernos de Saúde Públicainstname:Fundação Oswaldo Cruz (FIOCRUZ)instacron:FIOCRUZ10.1590/0102-311x00105720info:eu-repo/semantics/openAccessMelo,Géssyca Cavalcante deAraújo Neto,Renato Américo deAraújo,Karina Conceição Gomes Machado deeng2020-07-20T00:00:00Zoai:scielo:S0102-311X2020000604002Revistahttp://cadernos.ensp.fiocruz.br/csp/https://old.scielo.br/oai/scielo-oai.phpcadernos@ensp.fiocruz.br||cadernos@ensp.fiocruz.br1678-44640102-311Xopendoar:2020-07-20T00:00Cadernos de Saúde Pública - Fundação Oswaldo Cruz (FIOCRUZ)false
dc.title.none.fl_str_mv Forecasting the rate of cumulative cases of COVID-19 infection in Northeast Brazil: a Boltzmann function-based modeling study
title Forecasting the rate of cumulative cases of COVID-19 infection in Northeast Brazil: a Boltzmann function-based modeling study
spellingShingle Forecasting the rate of cumulative cases of COVID-19 infection in Northeast Brazil: a Boltzmann function-based modeling study
Melo,Géssyca Cavalcante de
COVID-19
Epidemiology
Mathematical Models
Pandemic
title_short Forecasting the rate of cumulative cases of COVID-19 infection in Northeast Brazil: a Boltzmann function-based modeling study
title_full Forecasting the rate of cumulative cases of COVID-19 infection in Northeast Brazil: a Boltzmann function-based modeling study
title_fullStr Forecasting the rate of cumulative cases of COVID-19 infection in Northeast Brazil: a Boltzmann function-based modeling study
title_full_unstemmed Forecasting the rate of cumulative cases of COVID-19 infection in Northeast Brazil: a Boltzmann function-based modeling study
title_sort Forecasting the rate of cumulative cases of COVID-19 infection in Northeast Brazil: a Boltzmann function-based modeling study
author Melo,Géssyca Cavalcante de
author_facet Melo,Géssyca Cavalcante de
Araújo Neto,Renato Américo de
Araújo,Karina Conceição Gomes Machado de
author_role author
author2 Araújo Neto,Renato Américo de
Araújo,Karina Conceição Gomes Machado de
author2_role author
author
dc.contributor.author.fl_str_mv Melo,Géssyca Cavalcante de
Araújo Neto,Renato Américo de
Araújo,Karina Conceição Gomes Machado de
dc.subject.por.fl_str_mv COVID-19
Epidemiology
Mathematical Models
Pandemic
topic COVID-19
Epidemiology
Mathematical Models
Pandemic
description Abstract: The COVID-19 death rate in Northeast Brazil is much higher when compared to the national average, demanding a study into the prognosis of the region for planning control measures and preventing the collapse of the health care system. We estimated the potential total cumulative cases of COVID-19 in the region for the next three months. Our study included all confirmed cases, from March 8 until April 28, 2020, collected from the official website that reports the situation of COVID-19 infections in Brazil. The Boltzmann function was applied to a data simulation for each set of data regarding different states. The model data were well fitted, with R2 values close to 0.999. Up to April 28, 20,665 cases were confirmed in the region. The state of Ceará has the highest rate of accumulated cases per 100,000 inhabitants (75.75), followed by Pernambuco. We estimated that the states of Ceará, Sergipe and Paraíba will experience a dramatic increase in the rate of cumulative cases until July 31. Maranhão, Pernambuco, Rio Grande do Norte and Piauí showed a more discreet increase in the model. For Bahia and Alagoas, a 4.7 and 6.6-fold increase in the rate was estimated, respectively. We estimate a substantial increase in the rate of cumulative cases per 100,000 inhabitants in the region within three months, especially for Ceará, Sergipe and Paraíba. The Boltzmann function proved to be a simple tool for epidemiological forecasting that can help planning the measures to contain COVID-19.
publishDate 2020
dc.date.none.fl_str_mv 2020-01-01
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=S0102-311X2020000604002
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0102-311X2020000604002
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/0102-311x00105720
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 Escola Nacional de Saúde Pública Sergio Arouca, Fundação Oswaldo Cruz
publisher.none.fl_str_mv Escola Nacional de Saúde Pública Sergio Arouca, Fundação Oswaldo Cruz
dc.source.none.fl_str_mv Cadernos de Saúde Pública v.36 n.6 2020
reponame:Cadernos de Saúde Pública
instname:Fundação Oswaldo Cruz (FIOCRUZ)
instacron:FIOCRUZ
instname_str Fundação Oswaldo Cruz (FIOCRUZ)
instacron_str FIOCRUZ
institution FIOCRUZ
reponame_str Cadernos de Saúde Pública
collection Cadernos de Saúde Pública
repository.name.fl_str_mv Cadernos de Saúde Pública - Fundação Oswaldo Cruz (FIOCRUZ)
repository.mail.fl_str_mv cadernos@ensp.fiocruz.br||cadernos@ensp.fiocruz.br
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