Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil

Low-resource settings can face additional challenges in managing the COVID-19 pandemic. Here, the authors use mathematical modelling to investigate transmission in the state of Bahia, Brazil, and quantify control measures needed to prevent the hospital system becoming overwhelmed.

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Detalles Bibliográficos
Autores principales:	Juliane F. Oliveira, Daniel C. P. Jorge, Rafael V. Veiga, Moreno S. Rodrigues, Matheus F. Torquato, Nivea B. da Silva, Rosemeire L. Fiaccone, Luciana L. Cardim, Felipe A. C. Pereira, Caio P. de Castro, Aureliano S. S. Paiva, Alan A. S. Amad, Ernesto A. B. F. Lima, Diego S. Souza, Suani T. R. Pinho, Pablo Ivan P. Ramos, Roberto F. S. Andrade
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/7ac0b52971ed429ead95ba31036d770e
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spelling	oai:doaj.org-article:7ac0b52971ed429ead95ba31036d770e2021-12-02T14:11:45ZMathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil10.1038/s41467-020-19798-32041-1723https://doaj.org/article/7ac0b52971ed429ead95ba31036d770e2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-19798-3https://doaj.org/toc/2041-1723Low-resource settings can face additional challenges in managing the COVID-19 pandemic. Here, the authors use mathematical modelling to investigate transmission in the state of Bahia, Brazil, and quantify control measures needed to prevent the hospital system becoming overwhelmed.Juliane F. OliveiraDaniel C. P. JorgeRafael V. VeigaMoreno S. RodriguesMatheus F. TorquatoNivea B. da SilvaRosemeire L. FiacconeLuciana L. CardimFelipe A. C. PereiraCaio P. de CastroAureliano S. S. PaivaAlan A. S. AmadErnesto A. B. F. LimaDiego S. SouzaSuani T. R. PinhoPablo Ivan P. RamosRoberto F. S. AndradeNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Juliane F. Oliveira Daniel C. P. Jorge Rafael V. Veiga Moreno S. Rodrigues Matheus F. Torquato Nivea B. da Silva Rosemeire L. Fiaccone Luciana L. Cardim Felipe A. C. Pereira Caio P. de Castro Aureliano S. S. Paiva Alan A. S. Amad Ernesto A. B. F. Lima Diego S. Souza Suani T. R. Pinho Pablo Ivan P. Ramos Roberto F. S. Andrade Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil
description	Low-resource settings can face additional challenges in managing the COVID-19 pandemic. Here, the authors use mathematical modelling to investigate transmission in the state of Bahia, Brazil, and quantify control measures needed to prevent the hospital system becoming overwhelmed.
format	article
author	Juliane F. Oliveira Daniel C. P. Jorge Rafael V. Veiga Moreno S. Rodrigues Matheus F. Torquato Nivea B. da Silva Rosemeire L. Fiaccone Luciana L. Cardim Felipe A. C. Pereira Caio P. de Castro Aureliano S. S. Paiva Alan A. S. Amad Ernesto A. B. F. Lima Diego S. Souza Suani T. R. Pinho Pablo Ivan P. Ramos Roberto F. S. Andrade
author_facet	Juliane F. Oliveira Daniel C. P. Jorge Rafael V. Veiga Moreno S. Rodrigues Matheus F. Torquato Nivea B. da Silva Rosemeire L. Fiaccone Luciana L. Cardim Felipe A. C. Pereira Caio P. de Castro Aureliano S. S. Paiva Alan A. S. Amad Ernesto A. B. F. Lima Diego S. Souza Suani T. R. Pinho Pablo Ivan P. Ramos Roberto F. S. Andrade
author_sort	Juliane F. Oliveira
title	Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, 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
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7ac0b52971ed429ead95ba31036d770e
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Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil

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