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Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil
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,...
Autores principales: | , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7803757/ https://www.ncbi.nlm.nih.gov/pubmed/33436608 http://dx.doi.org/10.1038/s41467-020-19798-3 |
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author | Oliveira, Juliane F. Jorge, Daniel C. P. Veiga, Rafael V. Rodrigues, Moreno S. Torquato, Matheus F. da Silva, Nivea B. Fiaccone, Rosemeire L. Cardim, Luciana L. Pereira, Felipe A. C. de Castro, Caio P. Paiva, Aureliano S. S. Amad, Alan A. S. Lima, Ernesto A. B. F. Souza, Diego S. Pinho, Suani T. R. Ramos, Pablo Ivan P. Andrade, Roberto F. S. |
author_facet | Oliveira, Juliane F. Jorge, Daniel C. P. Veiga, Rafael V. Rodrigues, Moreno S. Torquato, Matheus F. da Silva, Nivea B. Fiaccone, Rosemeire L. Cardim, Luciana L. Pereira, Felipe A. C. de Castro, Caio P. Paiva, Aureliano S. S. Amad, Alan A. S. Lima, Ernesto A. B. F. Souza, Diego S. Pinho, Suani T. R. Ramos, Pablo Ivan P. Andrade, Roberto F. S. |
author_sort | Oliveira, Juliane F. |
collection | PubMed |
description | 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 COVID-19 in Bahia, a state in northeastern Brazil, considering the influences of asymptomatic/non-detected 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 R(0). Finally, we discuss our results in light of epidemiological data that became available after the initial analyses. |
format | Online Article Text |
id | pubmed-7803757 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-78037572021-01-21 Mathematical modeling of COVID-19 in 14.8 million individuals in Bahia, Brazil Oliveira, Juliane F. Jorge, Daniel C. P. Veiga, Rafael V. Rodrigues, Moreno S. Torquato, Matheus F. da Silva, Nivea B. Fiaccone, Rosemeire L. Cardim, Luciana L. Pereira, Felipe A. C. de Castro, Caio P. Paiva, Aureliano S. S. Amad, Alan A. S. Lima, Ernesto A. B. F. Souza, Diego S. Pinho, Suani T. R. Ramos, Pablo Ivan P. Andrade, Roberto F. S. Nat Commun Article 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 COVID-19 in Bahia, a state in northeastern Brazil, considering the influences of asymptomatic/non-detected 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 R(0). Finally, we discuss our results in light of epidemiological data that became available after the initial analyses. Nature Publishing Group UK 2021-01-12 /pmc/articles/PMC7803757/ /pubmed/33436608 http://dx.doi.org/10.1038/s41467-020-19798-3 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Oliveira, Juliane F. Jorge, Daniel C. P. Veiga, Rafael V. Rodrigues, Moreno S. Torquato, Matheus F. da Silva, Nivea B. Fiaccone, Rosemeire L. Cardim, Luciana L. Pereira, Felipe A. C. de Castro, Caio P. Paiva, Aureliano S. S. Amad, Alan A. S. Lima, Ernesto A. B. F. Souza, Diego S. Pinho, Suani T. R. Ramos, Pablo Ivan P. Andrade, Roberto F. S. 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 |
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_short | 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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7803757/ https://www.ncbi.nlm.nih.gov/pubmed/33436608 http://dx.doi.org/10.1038/s41467-020-19798-3 |
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