Forecasting the rate of cumulative cases of COVID-19 infection in Northeast Brazil: a Boltzmann function-based modeling study
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Cadernos de Saúde Pública |
Texto Completo: | https://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/7495 |
Resumo: | 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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Forecasting the rate of cumulative cases of COVID-19 infection in Northeast Brazil: a Boltzmann function-based modeling studyCOVID-19EpidemiologyMathematical ModelsPandemicThe 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.La región del nordeste brasileño cuenta con una tasa de mortalidad mucho más alta debido a la COVID-19, si se compara con la media nacional, por lo que es necesario un estudio en la prognosis de la región para planificar medidas de control y prevenir el colapso del sistema de salud. Estimamos el potencial total acumulativo de casos de COVID-19 en esta región durante los próximos tres meses. El estudio incluyó todos los casos confirmados de COVID-19, desde el primer caso, confirmado el 8 de marzo, hasta el 28 de abril de 2020, recogido del sitio web oficial que informa la situación de las infecciones por COVID-19 en Brasil. La función de Boltzmann se aplicó a la simulación de datos para cada conjunto de datos, referentes a diferentes estados. El modelo de datos estuvo bien ajustado, con valores R2 cercanos a 0,999. Hasta el 28 de abril, se confirmaron 20.665 casos en la región. Ceará contó con la tasa más alta de incidencia acumulada por 100.000 habitantes (75,75), seguida de Pernambuco. Estimamos que Ceará, Sergipe y Paraíba sufrirán un dramático aumento en la tasa de incidencia acumulada de casos hasta el 31 de julio. Maranhão, Pernambuco, Rio Grande do Norte y Piauí mostraron un incremento más discreto en este modelo. En el caso de Bahía y Alagoas, se estimó un incremento de un 4,7 y 6,6, respectivamente. Estimamos un aumento sustancial en la tasa de incidencia acumulada de casos por 100.000 habitantes dentro de esta región, respecto a los tres próximos meses, especialmente en Ceará, Sergipe y Paraíba. La función de Boltzmann probó ser una herramienta simple para la previsión epidemiológica que puede ser de ayuda en la planificación de medidas para contener a la COVID-19.A Região Nordeste do Brasil tem uma taxa de letalidade muito mais elevada por COVID-19, comparado com a média nacional, o que exige uma investigação do prognóstico da região para o planejamento de medidas de controle e para prevenir o colapso do sistema de saúde. Estimamos o total potencial de casos acumulados de COVID-19 na região nos próximos três meses. O estudo incluiu todos os casos confirmados de COVID-19, desde o primeiro caso confirmado, em 8 de março, até 28 de abril de 2020, coletados no site oficial que relata a situação das infecções por COVID-19 no Brasil. A função de Boltzmann foi aplicada a uma simulação de dados para cada conjunto de dados dos diversos estados do Nordeste. Os dados do modelo mostraram bom ajuste, com valores de R2 próximos a 0,999. Até 28 de abril, haviam sido confirmados 20.665 casos na Região Nordeste. O estado do Ceará apresenta a maior taxa de casos acumulados por 100.000 habitantes (75,75), seguido pelo estado de Pernambuco. Estimamos que Ceará, Sergipe e Paraíba apresentarão um aumento dramático na taxa de casos acumulados até 31 de julho. Maranhão, Pernambuco, Rio Grande do Norte e Piauí mostraram aumentos mais discretos de acordo com o modelo. Para Bahia e Alagoas, foram estimados aumentos de 4,7 e 6,6 vezes nas taxas, respectivamente. Estimamos um aumento substancial na taxa de casos acumulados por 100.000 habitantes na Região Nordeste ao longo dos próximos três meses, especialmente no Ceará, Sergipe e Paraíba. A função de Boltzmann mostrou ser uma ferramenta simples para projeções epidemiológicas, podendo auxiliar no planejamento de medidas para conter a COVID-19.Reports in Public HealthCadernos de Saúde Pública2020-06-26info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlapplication/pdfhttps://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/7495Reports in Public Health; Vol. 36 No. 6 (2020): JuneCadernos de Saúde Pública; v. 36 n. 6 (2020): Junho1678-44640102-311Xreponame:Cadernos de Saúde Públicainstname:Fundação Oswaldo Cruz (FIOCRUZ)instacron:FIOCRUZenghttps://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/7495/16618https://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/7495/16619Géssyca Cavalcante de MeloRenato Américo de Araújo NetoKarina Conceição Gomes Machado de Araújoinfo:eu-repo/semantics/openAccess2024-03-06T15:29:58Zoai:ojs.teste-cadernos.ensp.fiocruz.br:article/7495Revistahttps://cadernos.ensp.fiocruz.br/ojs/index.php/csphttps://cadernos.ensp.fiocruz.br/ojs/index.php/csp/oaicadernos@ensp.fiocruz.br||cadernos@ensp.fiocruz.br1678-44640102-311Xopendoar:2024-03-06T13:08:37.236530Cadernos de Saúde Pública - Fundação Oswaldo Cruz (FIOCRUZ)true |
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 Géssyca Cavalcante de Melo 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 |
Géssyca Cavalcante de Melo |
author_facet |
Géssyca Cavalcante de Melo Renato Américo de Araújo Neto Karina Conceição Gomes Machado de Araújo |
author_role |
author |
author2 |
Renato Américo de Araújo Neto Karina Conceição Gomes Machado de Araújo |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Géssyca Cavalcante de Melo Renato Américo de Araújo Neto Karina Conceição Gomes Machado de Araújo |
dc.subject.por.fl_str_mv |
COVID-19 Epidemiology Mathematical Models Pandemic |
topic |
COVID-19 Epidemiology Mathematical Models Pandemic |
description |
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 |
2020-06-26 |
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://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/7495 |
url |
https://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/7495 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/7495/16618 https://cadernos.ensp.fiocruz.br/ojs/index.php/csp/article/view/7495/16619 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html application/pdf |
dc.publisher.none.fl_str_mv |
Reports in Public Health Cadernos de Saúde Pública |
publisher.none.fl_str_mv |
Reports in Public Health Cadernos de Saúde Pública |
dc.source.none.fl_str_mv |
Reports in Public Health; Vol. 36 No. 6 (2020): June Cadernos de Saúde Pública; v. 36 n. 6 (2020): Junho 1678-4464 0102-311X 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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1816705382115966976 |