Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil
| Autor(a) principal: | |
|---|---|
| Data de Publicação: | 2021 |
| Outros Autores: | , , , , , , , , , , , , , , , |
| Tipo de documento: | Artigo |
| Idioma: | eng |
| Título da fonte: | Repositório Institucional da FIOCRUZ (ARCA) |
| Texto Completo: | https://www.arca.fiocruz.br/handle/icict/46240 |
Resumo: | Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brazil (CAPES)—Finance Code 001 |
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Oliveira, Juliane Fonseca deJorge, Daniel Cardoso PereiraVeiga, Rafael ValenteRodrigues, Moreno Magalhães de SouzaTorquato, Matheus FernandesSilva, Nívea Bispo daFiaccone, Rosemeire LeovigildoCardim, Luciana LobatoPereira, Felipe Augusto CardosoCastro, Caio Porto dePaiva, Aureliano Sancho SouzaAmad, Alan Alves SantanaLima, Ernesto Augusto Bueno da FonsecaSouza, Diego S.Pinho, Suani Tavares Rubim deRamos, Pablo Ivan PereiraAndrade, Roberto F. S.2021-03-03T13:30:47Z2021-03-03T13:30:47Z2021OLIVEIRA, Juliane Fonseca de et al. Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil. Nature Communications, v. 12, n. 333, p. 1-13, 12 Jan. 2021.2041-1723https://www.arca.fiocruz.br/handle/icict/4624010.1038/s41467-020-19798-32041-1723Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brazil (CAPES)—Finance Code 001International Cooperation grant (process number INT0002/2016) from BahiaResearch Foundation (FAPESB)National Institute of Science and Technology—Complex Systems from CNPq, Brazil.Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimentos para Saúde. Salvador, BA, Brasil / Universidade do Porto. Faculdade de Ciências. Centro de Matemática. Departamento de Matemática. Porto, Portugal.Universidade Federal da Bahia. Instituto de Física. Salvador, BA, Brasil.Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimentos para Saúde. Salvador, BA, Brasil.Fundação Oswaldo Cruz. Fiocruz Rondônia. Porto Velho, RO, Brasil.Swansea University. College Of Engineering. Swansea, United Kingdom.Universidade Federal da Bahia. Instituto de Matemática e Estatística. Salvador, BA, Brasil.Universidade Federal da Bahia. Instituto de Matemática e Estatística. Salvador, BA, Brasil.Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimentos para Saúde. Salvador, BA, Brasil.Universidade de São Paulo. Instituto de Física. São Paulo, SP, Brasil.Universidade Federal da Bahia. Instituto de Física. Salvador, BA, Brasil.Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimentos para Saúde. Salvador, BA, Brasil.Swansea University. College Of Engineering. Swansea, United Kingdom.The University of Texas at Austin. Oden Institute for Computational Engineering and Sciences. Austin, TX, United States.Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimentos para Saúde. Salvador, BA, Brasil.Universidade Federal da Bahia. Instituto de Física. Salvador, BA, Brasil.Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimentos para Saúde. Salvador, BA, Brasil.Fundação Oswaldo Cruz. Instituto Gonçalo Moniz. Centro de Integração de Dados e Conhecimentos para Saúde. Salvador, BA, Brasil / Universidade Federal da Bahia. Instituto de Física. Salvador, BA, Brasil.COVID-19 is affecting healthcare resources worldwide, with lower and middle-income countries being particularly disadvantaged to mitigate the challenges imposed by the disease, including the availability of a sufficient number of infirmary/ICU hospital beds, ventilators, and medical supplies. Here, we use mathematical modelling to study the dynamics of COVID19 in Bahia, a state in northeastern Brazil, considering the influences of asymptomatic/nondetected cases, hospitalizations, and mortality. The impacts of policies on the transmission rate were also examined. Our results underscore the difficulties in maintaining a fully operational health infrastructure amidst the pandemic. Lowering the transmission rate is paramount to this objective, but current local efforts, leading to a 36% decrease, remain insufficient to prevent systemic collapse at peak demand, which could be accomplished using periodic interventions. Non-detected cases contribute to a ∽55% increase in R0. Finally, we discuss our results in light of epidemiological data that became available after the initial analyses.engNature ResearchCOVID-19PandemiasRecursos em saúdeMortalidadeTransmissãoBrasilCOVID-19PandemicsHealthcare resourcesMortalityTransmissionBrazilMathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazilinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da FIOCRUZ (ARCA)instname:Fundação Oswaldo Cruz (FIOCRUZ)instacron:FIOCRUZLICENSElicense.txtlicense.txttext/plain; charset=utf-83097https://www.arca.fiocruz.br/bitstream/icict/46240/1/license.txt36b51ef91c52b5338d9d29ba0cc807bcMD51ORIGINALOliveira, Juliane F Mathematical....pdfOliveira, Juliane F Mathematical....pdfapplication/pdf1877738https://www.arca.fiocruz.br/bitstream/icict/46240/2/Oliveira%2c%20Juliane%20F%20Mathematical....pdf4e404c6da22570123ffb982f1e860685MD52TEXTOliveira, Juliane F 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| dc.title.en.fl_str_mv |
Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil |
| title |
Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil |
| spellingShingle |
Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil Oliveira, Juliane Fonseca de COVID-19 Pandemias Recursos em saúde Mortalidade Transmissão Brasil COVID-19 Pandemics Healthcare resources Mortality Transmission Brazil |
| title_short |
Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil |
| title_full |
Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil |
| title_fullStr |
Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil |
| title_full_unstemmed |
Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil |
| title_sort |
Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil |
| author |
Oliveira, Juliane Fonseca de |
| author_facet |
Oliveira, Juliane Fonseca de Jorge, Daniel Cardoso Pereira Veiga, Rafael Valente Rodrigues, Moreno Magalhães de Souza Torquato, Matheus Fernandes Silva, Nívea Bispo da Fiaccone, Rosemeire Leovigildo Cardim, Luciana Lobato Pereira, Felipe Augusto Cardoso Castro, Caio Porto de Paiva, Aureliano Sancho Souza Amad, Alan Alves Santana Lima, Ernesto Augusto Bueno da Fonseca Souza, Diego S. Pinho, Suani Tavares Rubim de Ramos, Pablo Ivan Pereira Andrade, Roberto F. S. |
| author_role |
author |
| author2 |
Jorge, Daniel Cardoso Pereira Veiga, Rafael Valente Rodrigues, Moreno Magalhães de Souza Torquato, Matheus Fernandes Silva, Nívea Bispo da Fiaccone, Rosemeire Leovigildo Cardim, Luciana Lobato Pereira, Felipe Augusto Cardoso Castro, Caio Porto de Paiva, Aureliano Sancho Souza Amad, Alan Alves Santana Lima, Ernesto Augusto Bueno da Fonseca Souza, Diego S. Pinho, Suani Tavares Rubim de Ramos, Pablo Ivan Pereira Andrade, Roberto F. S. |
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author author author author author author author author author author author author author author author author |
| dc.contributor.author.fl_str_mv |
Oliveira, Juliane Fonseca de Jorge, Daniel Cardoso Pereira Veiga, Rafael Valente Rodrigues, Moreno Magalhães de Souza Torquato, Matheus Fernandes Silva, Nívea Bispo da Fiaccone, Rosemeire Leovigildo Cardim, Luciana Lobato Pereira, Felipe Augusto Cardoso Castro, Caio Porto de Paiva, Aureliano Sancho Souza Amad, Alan Alves Santana Lima, Ernesto Augusto Bueno da Fonseca Souza, Diego S. Pinho, Suani Tavares Rubim de Ramos, Pablo Ivan Pereira Andrade, Roberto F. S. |
| dc.subject.other.pt_BR.fl_str_mv |
COVID-19 Pandemias Recursos em saúde Mortalidade Transmissão Brasil |
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COVID-19 Pandemias Recursos em saúde Mortalidade Transmissão Brasil COVID-19 Pandemics Healthcare resources Mortality Transmission Brazil |
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COVID-19 Pandemics Healthcare resources Mortality Transmission Brazil |
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brazil (CAPES)—Finance Code 001 |
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2021 |
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2021-03-03T13:30:47Z |
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2021-03-03T13:30:47Z |
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2021 |
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OLIVEIRA, Juliane Fonseca de et al. Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil. Nature Communications, v. 12, n. 333, p. 1-13, 12 Jan. 2021. |
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https://www.arca.fiocruz.br/handle/icict/46240 |
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2041-1723 |
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10.1038/s41467-020-19798-3 |
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2041-1723 |
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OLIVEIRA, Juliane Fonseca de et al. Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil. Nature Communications, v. 12, n. 333, p. 1-13, 12 Jan. 2021. 2041-1723 10.1038/s41467-020-19798-3 |
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