Prediction of cumulative rate of COVID-19 deaths in Brazil: a modeling study
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Revista brasileira de epidemiologia (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-790X2020000100205 |
Resumo: | ABSTRACT: Objective: Estimating the potential number of COVID-19 deaths in Brazil for the coming months. Methods: The study included all confirmed cases of COVID-19 deaths, from the first confirmed death on March 17th to May 15th, 2020. These data were collected from an official Brazilian website of the Ministry of Health. The Boltzmann function was applied to a data simulation for each set of data regarding all states of the country. Results: The model data were well-fitted, with R2 values close to 0.999. Up to May 15th, 14,817 COVID-19 deaths have been confirmed in the country. Amazonas has the highest rate of accumulated cases per 1,000,000 inhabitants (321.14), followed by Ceará (161.63). Rio de Janeiro, Roraima, Amazonas, Pará, and Pernambuco are estimated to experience a substantial increase in the rate of cumulative cases until July 15th. Mato Grosso do Sul, Paraná, Minas Gerais, Rio Grande do Sul, and Santa Catarina will show lower rates per 1,000,000 inhabitants. Conclusion: We estimate a substantial increase in the rate of cumulative cases in Brazil over the next months. The Boltzmann function proved to be a simple tool for epidemiological forecasting that can assist in the planning of measures to contain COVID-19. |
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Prediction of cumulative rate of COVID-19 deaths in Brazil: a modeling studyCoronavirus infectionsEpidemiologyMathematical modelingPandemicsBrazilABSTRACT: Objective: Estimating the potential number of COVID-19 deaths in Brazil for the coming months. Methods: The study included all confirmed cases of COVID-19 deaths, from the first confirmed death on March 17th to May 15th, 2020. These data were collected from an official Brazilian website of the Ministry of Health. The Boltzmann function was applied to a data simulation for each set of data regarding all states of the country. Results: The model data were well-fitted, with R2 values close to 0.999. Up to May 15th, 14,817 COVID-19 deaths have been confirmed in the country. Amazonas has the highest rate of accumulated cases per 1,000,000 inhabitants (321.14), followed by Ceará (161.63). Rio de Janeiro, Roraima, Amazonas, Pará, and Pernambuco are estimated to experience a substantial increase in the rate of cumulative cases until July 15th. Mato Grosso do Sul, Paraná, Minas Gerais, Rio Grande do Sul, and Santa Catarina will show lower rates per 1,000,000 inhabitants. Conclusion: We estimate a substantial increase in the rate of cumulative cases in Brazil over the next months. The Boltzmann function proved to be a simple tool for epidemiological forecasting that can assist in the planning of measures to contain COVID-19.Associação Brasileira de Saúde Coletiva2020-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-790X2020000100205Revista Brasileira de Epidemiologia v.23 2020reponame:Revista brasileira de epidemiologia (Online)instname:Associação Brasileira de Saúde Coletiva (ABRASCO)instacron:ABRASCO10.1590/1980-549720200081info:eu-repo/semantics/openAccessMelo,Géssyca Cavalcante deDuprat,Irena PenhaAraújo,Karina Conceição Gomes Machado deFischer,Frida MarinaAraújo Neto,Renato Américo deeng2020-07-24T00:00:00Zoai:scielo:S1415-790X2020000100205Revistahttp://www.scielo.br/rbepidhttps://old.scielo.br/oai/scielo-oai.php||revbrepi@usp.br1980-54971415-790Xopendoar:2020-07-24T00:00Revista brasileira de epidemiologia (Online) - Associação Brasileira de Saúde Coletiva (ABRASCO)false |
dc.title.none.fl_str_mv |
Prediction of cumulative rate of COVID-19 deaths in Brazil: a modeling study |
title |
Prediction of cumulative rate of COVID-19 deaths in Brazil: a modeling study |
spellingShingle |
Prediction of cumulative rate of COVID-19 deaths in Brazil: a modeling study Melo,Géssyca Cavalcante de Coronavirus infections Epidemiology Mathematical modeling Pandemics Brazil |
title_short |
Prediction of cumulative rate of COVID-19 deaths in Brazil: a modeling study |
title_full |
Prediction of cumulative rate of COVID-19 deaths in Brazil: a modeling study |
title_fullStr |
Prediction of cumulative rate of COVID-19 deaths in Brazil: a modeling study |
title_full_unstemmed |
Prediction of cumulative rate of COVID-19 deaths in Brazil: a modeling study |
title_sort |
Prediction of cumulative rate of COVID-19 deaths in Brazil: a modeling study |
author |
Melo,Géssyca Cavalcante de |
author_facet |
Melo,Géssyca Cavalcante de Duprat,Irena Penha Araújo,Karina Conceição Gomes Machado de Fischer,Frida Marina Araújo Neto,Renato Américo de |
author_role |
author |
author2 |
Duprat,Irena Penha Araújo,Karina Conceição Gomes Machado de Fischer,Frida Marina Araújo Neto,Renato Américo de |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Melo,Géssyca Cavalcante de Duprat,Irena Penha Araújo,Karina Conceição Gomes Machado de Fischer,Frida Marina Araújo Neto,Renato Américo de |
dc.subject.por.fl_str_mv |
Coronavirus infections Epidemiology Mathematical modeling Pandemics Brazil |
topic |
Coronavirus infections Epidemiology Mathematical modeling Pandemics Brazil |
description |
ABSTRACT: Objective: Estimating the potential number of COVID-19 deaths in Brazil for the coming months. Methods: The study included all confirmed cases of COVID-19 deaths, from the first confirmed death on March 17th to May 15th, 2020. These data were collected from an official Brazilian website of the Ministry of Health. The Boltzmann function was applied to a data simulation for each set of data regarding all states of the country. Results: The model data were well-fitted, with R2 values close to 0.999. Up to May 15th, 14,817 COVID-19 deaths have been confirmed in the country. Amazonas has the highest rate of accumulated cases per 1,000,000 inhabitants (321.14), followed by Ceará (161.63). Rio de Janeiro, Roraima, Amazonas, Pará, and Pernambuco are estimated to experience a substantial increase in the rate of cumulative cases until July 15th. Mato Grosso do Sul, Paraná, Minas Gerais, Rio Grande do Sul, and Santa Catarina will show lower rates per 1,000,000 inhabitants. Conclusion: We estimate a substantial increase in the rate of cumulative cases in Brazil over the next months. The Boltzmann function proved to be a simple tool for epidemiological forecasting that can assist in the planning of 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=S1415-790X2020000100205 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1415-790X2020000100205 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1980-549720200081 |
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 |
Associação Brasileira de Saúde Coletiva |
publisher.none.fl_str_mv |
Associação Brasileira de Saúde Coletiva |
dc.source.none.fl_str_mv |
Revista Brasileira de Epidemiologia v.23 2020 reponame:Revista brasileira de epidemiologia (Online) instname:Associação Brasileira de Saúde Coletiva (ABRASCO) instacron:ABRASCO |
instname_str |
Associação Brasileira de Saúde Coletiva (ABRASCO) |
instacron_str |
ABRASCO |
institution |
ABRASCO |
reponame_str |
Revista brasileira de epidemiologia (Online) |
collection |
Revista brasileira de epidemiologia (Online) |
repository.name.fl_str_mv |
Revista brasileira de epidemiologia (Online) - Associação Brasileira de Saúde Coletiva (ABRASCO) |
repository.mail.fl_str_mv |
||revbrepi@usp.br |
_version_ |
1754212956249260032 |