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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 |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Data-drivenStudyCOVID-19_ARAUJO_2020.pdf | Artigo | 957,25 kB | Adobe PDF |  View/Open |
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