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Title: Forecasting Covid-19 dynamics in Brazil : a data driven approach
Authors: Pereira, Igor Gadelha
Guerin, Joris Michel
Silva Júnior, Andouglas Gonçalves
Garcia, Gabriel Santos
Piscitelli, Prisco
Miani, Alessandro
Distante, Cosimo
Gonçalves, Luiz Marcos Garcia
metadata.dc.identifier.orcid: https://orcid.org/0000-0001-7539-4663
https://orcid.org/0000-0002-8048-8960
https://orcid.org/0000-0003-0579-8464
https://orcid.org/0000-0002-8014-8659
https://orcid.org/0000-0003-4556-6182
https://orcid.org/0000-0003-3534-1553
https://orcid.org/0000-0002-1073-2390
https://orcid.org/0000-0002-7735-5630
Assunto:: Análise de séries temporais
Covid-19
Issue Date: 2020
Publisher: MDPI
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.
Abstract: 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.
Licença:: © 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/).
DOI: https://doi.org/10.3390/ijerph17145115
Appears in Collections:ECL - Artigos publicados em periódicos
UnB - Covid-19

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