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Epidemiological behavior of the COVID-19 contamination curve in Brazil: Time-series analysis

Brazil is experiencing the greatest episode of sanitary collapse ever known in the country’s history. Therefore, the relevance of this study is highlighted for the scientific advance of the epidemiological behavior of the virus in Brazil, enabling the development of analyses and discussions on the f...

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Autores principales: de Morais, Thiffany Nayara Bento, Costa, Ketyllem Tayanne da Silva, Capistrano, Gustavo Nepomuceno, de Andrade, Fábia Barbosa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498950/
https://www.ncbi.nlm.nih.gov/pubmed/36136969
http://dx.doi.org/10.1371/journal.pone.0268169
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author de Morais, Thiffany Nayara Bento
Costa, Ketyllem Tayanne da Silva
Capistrano, Gustavo Nepomuceno
de Andrade, Fábia Barbosa
author_facet de Morais, Thiffany Nayara Bento
Costa, Ketyllem Tayanne da Silva
Capistrano, Gustavo Nepomuceno
de Andrade, Fábia Barbosa
author_sort de Morais, Thiffany Nayara Bento
collection PubMed
description Brazil is experiencing the greatest episode of sanitary collapse ever known in the country’s history. Therefore, the relevance of this study is highlighted for the scientific advance of the epidemiological behavior of the virus in Brazil, enabling the development of analyses and discussions on the factors that influenced the high rates of contamination by SARS-CoV-2 in the country. Given the above, this study aims to analyze the epidemiological behavior of the COVID-19 contamination curve by epidemiological weeks (EW), in the years 2020–2021, in Brazil. This is an ecological study of time series, prepared using information collected through secondary means. The country of origin of the study is Brazil, and its main theme is the number of people infected during the COVID-19 pandemic, this being the dependent variable of the study. The data has been analyzed from February 23, 2020, when the first case was confirmed in Brazil, to January 1, 2022. In 2021, the country’s graph shows an exponential growth, reaching a peak of approximately 250 new cases per 100,000 inhabitants in the 12th EW. This data represents the highest rate of the pandemic in Brazil, and did not vary significantly for the next twelve weeks. Thus, it was identified that Brazil was severely impacted by the new coronavirus, considering the high rates of confirmed cases of the virus in the country, the low adherence of the population to preventive measures, the late start of mass vaccination in the Brazilian population, and the lack of structure in the health system, which was not appropriately prepared for the high demand generated by COVID-19.
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spelling pubmed-94989502022-09-23 Epidemiological behavior of the COVID-19 contamination curve in Brazil: Time-series analysis de Morais, Thiffany Nayara Bento Costa, Ketyllem Tayanne da Silva Capistrano, Gustavo Nepomuceno de Andrade, Fábia Barbosa PLoS One Research Article Brazil is experiencing the greatest episode of sanitary collapse ever known in the country’s history. Therefore, the relevance of this study is highlighted for the scientific advance of the epidemiological behavior of the virus in Brazil, enabling the development of analyses and discussions on the factors that influenced the high rates of contamination by SARS-CoV-2 in the country. Given the above, this study aims to analyze the epidemiological behavior of the COVID-19 contamination curve by epidemiological weeks (EW), in the years 2020–2021, in Brazil. This is an ecological study of time series, prepared using information collected through secondary means. The country of origin of the study is Brazil, and its main theme is the number of people infected during the COVID-19 pandemic, this being the dependent variable of the study. The data has been analyzed from February 23, 2020, when the first case was confirmed in Brazil, to January 1, 2022. In 2021, the country’s graph shows an exponential growth, reaching a peak of approximately 250 new cases per 100,000 inhabitants in the 12th EW. This data represents the highest rate of the pandemic in Brazil, and did not vary significantly for the next twelve weeks. Thus, it was identified that Brazil was severely impacted by the new coronavirus, considering the high rates of confirmed cases of the virus in the country, the low adherence of the population to preventive measures, the late start of mass vaccination in the Brazilian population, and the lack of structure in the health system, which was not appropriately prepared for the high demand generated by COVID-19. Public Library of Science 2022-09-22 /pmc/articles/PMC9498950/ /pubmed/36136969 http://dx.doi.org/10.1371/journal.pone.0268169 Text en © 2022 de Morais et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
de Morais, Thiffany Nayara Bento
Costa, Ketyllem Tayanne da Silva
Capistrano, Gustavo Nepomuceno
de Andrade, Fábia Barbosa
Epidemiological behavior of the COVID-19 contamination curve in Brazil: Time-series analysis
title Epidemiological behavior of the COVID-19 contamination curve in Brazil: Time-series analysis
title_full Epidemiological behavior of the COVID-19 contamination curve in Brazil: Time-series analysis
title_fullStr Epidemiological behavior of the COVID-19 contamination curve in Brazil: Time-series analysis
title_full_unstemmed Epidemiological behavior of the COVID-19 contamination curve in Brazil: Time-series analysis
title_short Epidemiological behavior of the COVID-19 contamination curve in Brazil: Time-series analysis
title_sort epidemiological behavior of the covid-19 contamination curve in brazil: time-series analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498950/
https://www.ncbi.nlm.nih.gov/pubmed/36136969
http://dx.doi.org/10.1371/journal.pone.0268169
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