The COVID-19 (SARS-CoV-2) uncertainty tripod in Brazil : assessments on model-based predictions with large under-reporting

Bastos, Saulo B.; Morato, Marcelo M.; Cajueiro, Daniel Oliveira; Normey Rico, Julio E.

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Campo DC	Valor	Idioma
dc.contributor.author	Bastos, Saulo B.	-
dc.contributor.author	Morato, Marcelo M.	-
dc.contributor.author	Cajueiro, Daniel Oliveira	-
dc.contributor.author	Normey Rico, Julio E.	-
dc.date.accessioned	2021-04-09T16:43:31Z	-
dc.date.available	2021-04-09T16:43:31Z	-
dc.date.issued	2021-03-18	-
dc.identifier.citation	BASTOS, Saulo B. The COVID-19 (SARS-CoV-2) uncertainty tripod in Brazil: assessments on model-based predictions with large under-reporting. Alexandria Engineering Journal, v. 60, n. 5, p. 4363-4380, 2021. DOI: https://doi.org/10.1016/j.aej.2021.03.004. Disponível em: https://www.sciencedirect.com/science/article/pii/S1110016821001599. Acesso em: 09 abr. 2021.	pt_BR
dc.identifier.uri	https://repositorio.unb.br/handle/10482/40487	-
dc.language.iso	Inglês	pt_BR
dc.publisher	Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University	pt_BR
dc.rights	Acesso Aberto	pt_BR
dc.title	The COVID-19 (SARS-CoV-2) uncertainty tripod in Brazil : assessments on model-based predictions with large under-reporting	pt_BR
dc.type	Artigo	pt_BR
dc.subject.keyword	Covid-19 - Brasil	pt_BR
dc.subject.keyword	Sub-relatório	pt_BR
dc.subject.keyword	Modelo SIR	pt_BR
dc.subject.keyword	Incerteza	pt_BR
dc.rights.license	© 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University.This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).	pt_BR
dc.identifier.doi	https://doi.org/10.1016/j.aej.2021.03.004	pt_BR
dc.description.abstract1	The COVID-19 pandemic (SARS-CoV-2 virus) is the global crisis of our time. The absence of mass testing and the relevant presence of asymptomatic individuals causes the available data of the COVID-19 pandemic in Brazil to be largely under-reported regarding the number of infected individuals and deaths. We develop an adapted Susceptible-Infected-Recovered (SIR) model, which explicitly incorporates the under-reporting and the response of the population to public health policies (confinement measures, widespread use of masks, etc). Large amounts of uncertainty could provide misleading predictions of the COVID-19 spread. In this paper, we discuss the role of uncertainty in these model-based predictions, which is illustrated regarding three key aspects: (i) Assuming that the number of infected individuals is under-reported, we demonstrate anticipation regarding the infection peak. Furthermore, while a model with a single class of infected individuals yields forecasts with increased peaks, a model that considers both symptomatic and asymptomatic infected individuals suggests a decrease of the peak of symptomatic cases. (ii) Considering that the actual amount of deaths is larger than what is being registered, we demonstrate an increase of the mortality rates. (iii) When we consider generally under-reported data, we demonstrate how the transmission and recovery rate model parameters change qualitatively and quantitatively. We also investigate the “the uncertainty tripod”: under-reporting level in terms of cases, deaths, and the true mortality rate of the disease. We demonstrate that if two of these factors are known, the remainder can be inferred, as long as proportions are kept constant. The proposed approach allows one to determine the margins of uncertainty by assessments on the observed and true mortality rates.	pt_BR
dc.description.unidade	Faculdade de Economia, Administração, Contabilidade e Gestão de Políticas Públicas (FACE)	pt_BR
dc.description.unidade	Departamento de Economia (FACE ECO)	pt_BR
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