Please use this identifier to cite or link to this item: https://repositorio.ufrn.br/handle/123456789/30121
Title: Data-driven study of the COVID-19 pandemic via age-structured modelling and prediction of the health system failure in Brazil amid diverse intervention strategies
Authors: Silva, Askery Alexandre Canabarro Barbosa da
Tenório, Elayne
Martins, Renato
Martins, Laís
Brito, Samuraí Gomes de Aguiar
Araújo, Rafael Chaves Souto
Keywords: COVID-19;Pandemic
Issue Date: 30-Jul-2020
Publisher: Public Library of Science
Citation: CANABARRO, Askery; TENÓRIO, Elayne; MARTINS, Renato; MARTINS, Laís; BRITO, Samuraí; CHAVES, Rafael. Data-driven study of the COVID-19 pandemic via age-structured modelling and prediction of the health system failure in Brazil amid diverse intervention strategies. Plos One, [s.l.], v. 15, n. 7, p. 0236310, 30 jul. 2020. Disponível em: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0236310. Acesso em: 04 set. 2020. http://dx.doi.org/10.1371/journal.pone.0236310. http://dx.doi.org/10.1371/journal.pone.0236310.
Portuguese Abstract: In this work we propose a data-driven age-structured census-based SIRD-like epidemiological model capable of forecasting the spread of COVID-19 in Brazil. We model the current scenario of closed schools and universities, social distancing of people above sixty years old and voluntary home quarantine to show that it is still not enough to protect the health system by explicitly computing the demand for hospital intensive care units. We also show that an urgent intense quarantine might be the only solution to avoid the collapse of the health system and, consequently, to minimize the quantity of deaths. On the other hand, we demonstrate that the relaxation of the already imposed control measures in the next days would be catastrophic
URI: https://repositorio.ufrn.br/jspui/handle/123456789/30121
ISSN: 1932-6203
Appears in Collections:COVID 19 - Artigos publicados em periódicos
ECT - Artigos publicados em periódicos
IIF - Artigos publicados em periódicos

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