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: | Repositório Institucional da UFLA |
Texto Completo: | http://repositorio.ufla.br/jspui/handle/1/42190 |
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 studyProjeção da taxa de casos acumulados de COVID-19 no Nordeste brasileiro: um estudo de modelagem com base na função de BoltzmannPrevisión de la tasa de incidencia acumulada por infección de COVID-19 en la región nordeste de Brasil: un estudio de modelado basado en funciones de BoltzmannCOVID-19EpidemiologyMathematical modelsPandemicEpidemiologiaModelos teóricosPandemiasEpidemiologíaThe 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.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.Escola Nacional de Saúde Pública Sergio Arouca, Fundação Oswaldo Cruz2020-08-04T12:00:47Z2020-08-04T12:00:47Z2020info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfMELO, G. C. de; ARAÚJO NETO, R. A. de; ARAÚJO, K. C. G. M. de. Forecasting the rate of cumulative cases of COVID-19 infection in Northeast Brazil: a Boltzmann function-based modeling study. Cadernos de Saúde Pública, Rio de Janeiro, v. 36, n. 6, 2020.http://repositorio.ufla.br/jspui/handle/1/42190Cadernos de Saúde Públicareponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessMelo, Géssyca Cavalcante deAraújo Neto, Renato Américo deAraújo, Karina Conceição Gomes Machado deeng2020-08-04T12:00:47Zoai:localhost:1/42190Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2020-08-04T12:00:47Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)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 Projeção da taxa de casos acumulados de COVID-19 no Nordeste brasileiro: um estudo de modelagem com base na função de Boltzmann Previsión de la tasa de incidencia acumulada por infección de COVID-19 en la región nordeste de Brasil: un estudio de modelado basado en funciones de Boltzmann |
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 Epidemiologia Modelos teóricos Pandemias Epidemiología |
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 Epidemiologia Modelos teóricos Pandemias Epidemiología |
topic |
COVID-19 Epidemiology Mathematical models Pandemic Epidemiologia Modelos teóricos Pandemias Epidemiología |
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 |
2020-08-04T12:00:47Z 2020-08-04T12:00:47Z 2020 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
MELO, G. C. de; ARAÚJO NETO, R. A. de; ARAÚJO, K. C. G. M. de. Forecasting the rate of cumulative cases of COVID-19 infection in Northeast Brazil: a Boltzmann function-based modeling study. Cadernos de Saúde Pública, Rio de Janeiro, v. 36, n. 6, 2020. http://repositorio.ufla.br/jspui/handle/1/42190 |
identifier_str_mv |
MELO, G. C. de; ARAÚJO NETO, R. A. de; ARAÚJO, K. C. G. M. de. Forecasting the rate of cumulative cases of COVID-19 infection in Northeast Brazil: a Boltzmann function-based modeling study. Cadernos de Saúde Pública, Rio de Janeiro, v. 36, n. 6, 2020. |
url |
http://repositorio.ufla.br/jspui/handle/1/42190 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
http://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 |
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 reponame:Repositório Institucional da UFLA instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
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UFLA |
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UFLA |
reponame_str |
Repositório Institucional da UFLA |
collection |
Repositório Institucional da UFLA |
repository.name.fl_str_mv |
Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
nivaldo@ufla.br || repositorio.biblioteca@ufla.br |
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1815439265815855104 |