Title:	Modeling and forecasting the early evolution of the Covid‑19 pandemic in Brazil
Authors:	Bastos, Saulo B. Cajueiro, Daniel Oliveira
Assunto::	Covid-19 - Brasil Modelos matemáticos
Issue Date:	10-Nov-2020
Publisher:	Springer Nature
Citation:	BASTOS, Saulo B.; CAJUEIRO, Daniel O. Modeling and forecasting the early evolution of the Covid-19 pandemic in Brazil. Scientific Reports, v. 10, 19457, 2020. DOI: https://doi.org/10.1038/s41598-020-76257-1. Disponível em: https://www.nature.com/articles/s41598-020-76257-1. Acesso em: 23 dez. 2020.
Abstract:	We model and forecast the early evolution of the COVID-19 pandemic in Brazil using Brazilian recent data from February 25, 2020 to March 30, 2020. This early period accounts for unawareness of the epidemiological characteristics of the disease in a new territory, sub-notifcation of the real numbers of infected people and the timely introduction of social distancing policies to fatten the spread of the disease. We use two variations of the SIR model and we include a parameter that comprises the efects of social distancing measures. Short and long term forecasts show that the social distancing policy imposed by the government is able to fatten the pattern of infection of the COVID-19. However, our results also show that if this policy does not last enough time, it is only able to shift the peak of infection into the future keeping the value of the peak in almost the same value. Furthermore, our long term simulations forecast the optimal date to end the policy. Finally, we show that the proportion of asymptomatic individuals afects the amplitude of the peak of symptomatic infected, suggesting that it is important to test the population.
metadata.dc.description.unidade:	Faculdade de Economia, Administração, Contabilidade e Gestão de Políticas Públicas (FACE) Departamento de Economia (FACE ECO)
Licença::	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/. © The Author(s) 2020
DOI:	https://doi.org/10.1038/s41598-020-76257-1
Appears in Collections:	Artigos publicados em periódicos e afins UnB - Covid-19

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