Forecasting Covid-19 dynamics in Brazil : a data driven approach

Pereira, Igor Gadelha; Guerin, Joris Michel; Silva Júnior, Andouglas Gonçalves; Garcia, Gabriel Santos; Piscitelli, Prisco; Miani, Alessandro; Distante, Cosimo; Gonçalves, Luiz Marcos Garcia

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Campo DC	Valor	Idioma
dc.contributor.author	Pereira, Igor Gadelha	-
dc.contributor.author	Guerin, Joris Michel	-
dc.contributor.author	Silva Júnior, Andouglas Gonçalves	-
dc.contributor.author	Garcia, Gabriel Santos	-
dc.contributor.author	Piscitelli, Prisco	-
dc.contributor.author	Miani, Alessandro	-
dc.contributor.author	Distante, Cosimo	-
dc.contributor.author	Gonçalves, Luiz Marcos Garcia	-
dc.date.accessioned	2020-11-18T11:12:35Z	-
dc.date.available	2020-11-18T11:12:35Z	-
dc.date.issued	2020	-
dc.identifier.citation	PEREIRA, Igor Gadelha et al. Forecasting Covid-19 dynamics in Brazil: a data driven approach. International Journal of Environmental Research and Public Health, v. 17, n. 14, 5115, 2020. DOI: https://doi.org/10.3390/ijerph17145115. Disponível em: https://www.mdpi.com/1660-4601/17/14/5115. Acesso em: 18 nov. 2020.	pt_BR
dc.identifier.uri	https://repositorio.unb.br/handle/10482/39639	-
dc.language.iso	Inglês	pt_BR
dc.publisher	MDPI	pt_BR
dc.rights	Acesso Aberto	pt_BR
dc.title	Forecasting Covid-19 dynamics in Brazil : a data driven approach	pt_BR
dc.type	Artigo	pt_BR
dc.subject.keyword	Análise de séries temporais	pt_BR
dc.subject.keyword	Covid-19	pt_BR
dc.rights.license	© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).	pt_BR
dc.identifier.doi	https://doi.org/10.3390/ijerph17145115	pt_BR
dc.description.abstract1	The contribution of this paper is twofold. First, a new data driven approach for predicting the Covid-19 pandemic dynamics is introduced. The second contribution consists in reporting and discussing the results that were obtained with this approach for the Brazilian states, with predictions starting as of 4 May 2020. As a preliminary study, we first used an Long Short Term Memory for Data Training-SAE (LSTM-SAE) network model. Although this first approach led to somewhat disappointing results, it served as a good baseline for testing other ANN types. Subsequently, in order to identify relevant countries and regions to be used for training ANN models, we conduct a clustering of the world’s regions where the pandemic is at an advanced stage. This clustering is based on manually engineered features representing a country’s response to the early spread of the pandemic, and the different clusters obtained are used to select the relevant countries for training the models. The final models retained are Modified Auto-Encoder networks, that are trained on these clusters and learn to predict future data for Brazilian states. These predictions are used to estimate important statistics about the disease, such as peaks and number of confirmed cases. Finally, curve fitting is carried out to find the distribution that best fits the outputs of the MAE, and to refine the estimates of the peaks of the pandemic. Predicted numbers reach a total of more than one million infected Brazilians, distributed among the different states, with São Paulo leading with about 150 thousand confirmed cases predicted. The results indicate that the pandemic is still growing in Brazil, with most states peaks of infection estimated in the second half of May 2020. The estimated end of the pandemics (97% of cases reaching an outcome) spread between June and the end of August 2020, depending on the states.	pt_BR
dc.identifier.orcid	https://orcid.org/0000-0001-7539-4663	pt_BR
dc.identifier.orcid	https://orcid.org/0000-0002-8048-8960	pt_BR
dc.identifier.orcid	https://orcid.org/0000-0003-0579-8464	pt_BR
dc.identifier.orcid	https://orcid.org/0000-0002-8014-8659	pt_BR
dc.identifier.orcid	https://orcid.org/0000-0003-4556-6182	pt_BR
dc.identifier.orcid	https://orcid.org/0000-0003-3534-1553	pt_BR
dc.identifier.orcid	https://orcid.org/0000-0002-1073-2390	pt_BR
dc.identifier.orcid	https://orcid.org/0000-0002-7735-5630	pt_BR
Aparece nas coleções:	Artigos publicados em periódicos e afins UnB - Covid-19