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A simple mathematical model for the evaluation of the long first wave of the COVID-19 pandemic in Brazil
We propose herein a mathematical model to predict the COVID-19 evolution and evaluate the impact of governmental decisions on this evolution, attempting to explain the long duration of the pandemic in the 26 Brazilian states and their capitals well as in the Federative Unit. The prediction was perfo...
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/PMC8361144/ https://www.ncbi.nlm.nih.gov/pubmed/34385538 http://dx.doi.org/10.1038/s41598-021-95815-9 |
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author | Tang, Yuanji Serdan, Tamires D. A. Alecrim, Amanda L. Souza, Diego R. Nacano, Bruno R. M. Silva, Flaviano L. R. Silva, Eliane B. Poma, Sarah O. Gennari-Felipe, Matheus Iser-Bem, Patrícia N. Masi, Laureane N. Tang, Sherry Levada-Pires, Adriana C. Hatanaka, Elaine Cury-Boaventura, Maria F. Borges, Fernanda T. Pithon-Curi, Tania C. Curpertino, Marli C. Fiamoncini, Jarlei Leandro, Carol Gois Gorjao, Renata Curi, Rui Hirabara, Sandro Massao |
author_facet | Tang, Yuanji Serdan, Tamires D. A. Alecrim, Amanda L. Souza, Diego R. Nacano, Bruno R. M. Silva, Flaviano L. R. Silva, Eliane B. Poma, Sarah O. Gennari-Felipe, Matheus Iser-Bem, Patrícia N. Masi, Laureane N. Tang, Sherry Levada-Pires, Adriana C. Hatanaka, Elaine Cury-Boaventura, Maria F. Borges, Fernanda T. Pithon-Curi, Tania C. Curpertino, Marli C. Fiamoncini, Jarlei Leandro, Carol Gois Gorjao, Renata Curi, Rui Hirabara, Sandro Massao |
author_sort | Tang, Yuanji |
collection | PubMed |
description | We propose herein a mathematical model to predict the COVID-19 evolution and evaluate the impact of governmental decisions on this evolution, attempting to explain the long duration of the pandemic in the 26 Brazilian states and their capitals well as in the Federative Unit. The prediction was performed based on the growth rate of new cases in a stable period, and the graphics plotted with the significant governmental decisions to evaluate the impact on the epidemic curve in each Brazilian state and city. Analysis of the predicted new cases was correlated with the total number of hospitalizations and deaths related to COVID-19. Because Brazil is a vast country, with high heterogeneity and complexity of the regional/local characteristics and governmental authorities among Brazilian states and cities, we individually predicted the epidemic curve based on a specific stable period with reduced or minimal interference on the growth rate of new cases. We found good accuracy, mainly in a short period (weeks). The most critical governmental decisions had a significant temporal impact on pandemic curve growth. A good relationship was found between the predicted number of new cases and the total number of inpatients and deaths related to COVID-19. In summary, we demonstrated that interventional and preventive measures directly and significantly impact the COVID-19 pandemic using a simple mathematical model. This model can easily be applied, helping, and directing health and governmental authorities to make further decisions to combat the pandemic. |
format | Online Article Text |
id | pubmed-8361144 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-83611442021-08-17 A simple mathematical model for the evaluation of the long first wave of the COVID-19 pandemic in Brazil Tang, Yuanji Serdan, Tamires D. A. Alecrim, Amanda L. Souza, Diego R. Nacano, Bruno R. M. Silva, Flaviano L. R. Silva, Eliane B. Poma, Sarah O. Gennari-Felipe, Matheus Iser-Bem, Patrícia N. Masi, Laureane N. Tang, Sherry Levada-Pires, Adriana C. Hatanaka, Elaine Cury-Boaventura, Maria F. Borges, Fernanda T. Pithon-Curi, Tania C. Curpertino, Marli C. Fiamoncini, Jarlei Leandro, Carol Gois Gorjao, Renata Curi, Rui Hirabara, Sandro Massao Sci Rep Article We propose herein a mathematical model to predict the COVID-19 evolution and evaluate the impact of governmental decisions on this evolution, attempting to explain the long duration of the pandemic in the 26 Brazilian states and their capitals well as in the Federative Unit. The prediction was performed based on the growth rate of new cases in a stable period, and the graphics plotted with the significant governmental decisions to evaluate the impact on the epidemic curve in each Brazilian state and city. Analysis of the predicted new cases was correlated with the total number of hospitalizations and deaths related to COVID-19. Because Brazil is a vast country, with high heterogeneity and complexity of the regional/local characteristics and governmental authorities among Brazilian states and cities, we individually predicted the epidemic curve based on a specific stable period with reduced or minimal interference on the growth rate of new cases. We found good accuracy, mainly in a short period (weeks). The most critical governmental decisions had a significant temporal impact on pandemic curve growth. A good relationship was found between the predicted number of new cases and the total number of inpatients and deaths related to COVID-19. In summary, we demonstrated that interventional and preventive measures directly and significantly impact the COVID-19 pandemic using a simple mathematical model. This model can easily be applied, helping, and directing health and governmental authorities to make further decisions to combat the pandemic. Nature Publishing Group UK 2021-08-12 /pmc/articles/PMC8361144/ /pubmed/34385538 http://dx.doi.org/10.1038/s41598-021-95815-9 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Tang, Yuanji Serdan, Tamires D. A. Alecrim, Amanda L. Souza, Diego R. Nacano, Bruno R. M. Silva, Flaviano L. R. Silva, Eliane B. Poma, Sarah O. Gennari-Felipe, Matheus Iser-Bem, Patrícia N. Masi, Laureane N. Tang, Sherry Levada-Pires, Adriana C. Hatanaka, Elaine Cury-Boaventura, Maria F. Borges, Fernanda T. Pithon-Curi, Tania C. Curpertino, Marli C. Fiamoncini, Jarlei Leandro, Carol Gois Gorjao, Renata Curi, Rui Hirabara, Sandro Massao A simple mathematical model for the evaluation of the long first wave of the COVID-19 pandemic in Brazil |
title | A simple mathematical model for the evaluation of the long first wave of the COVID-19 pandemic in Brazil |
title_full | A simple mathematical model for the evaluation of the long first wave of the COVID-19 pandemic in Brazil |
title_fullStr | A simple mathematical model for the evaluation of the long first wave of the COVID-19 pandemic in Brazil |
title_full_unstemmed | A simple mathematical model for the evaluation of the long first wave of the COVID-19 pandemic in Brazil |
title_short | A simple mathematical model for the evaluation of the long first wave of the COVID-19 pandemic in Brazil |
title_sort | simple mathematical model for the evaluation of the long first wave of the covid-19 pandemic in brazil |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8361144/ https://www.ncbi.nlm.nih.gov/pubmed/34385538 http://dx.doi.org/10.1038/s41598-021-95815-9 |
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