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Forecasting Covid-19 Dynamics in Brazil: A Data Driven Approach

Author

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  • Igor Gadelha Pereira

    (Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil
    These authors contributed equally to this work.)

  • Joris Michel Guerin

    (Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil
    These authors contributed equally to this work.)

  • Andouglas Gonçalves Silva Júnior

    (Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil
    Department of Computer Science, Federal Institute of Rio Grande do Norte, Mossoro 59628-330, RN, Brazil
    These authors contributed equally to this work.)

  • Gabriel Santos Garcia

    (Institute of Biological Sciences, University of Brasilia, Distrito Federal 70910-900, Brazil
    These authors contributed equally to this work.)

  • Prisco Piscitelli

    (Euro Mediterranean Scientific Biomedical Institute (ISBEM), 1040 Bruxelles, Belgium)

  • Alessandro Miani

    (Department of Environmental Sciences and Policy, University of Milan, 20133 Milan, Italy)

  • Cosimo Distante

    (Institute of Applied Sciences and Intelligent Systems, 73100 Lecce, Italy)

  • Luiz Marcos Garcia Gonçalves

    (Department of Computer Engineering and Automation, Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil)

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.

Suggested Citation

  • Igor Gadelha Pereira & Joris Michel Guerin & Andouglas Gonçalves Silva Júnior & Gabriel Santos Garcia & Prisco Piscitelli & Alessandro Miani & Cosimo Distante & Luiz Marcos Garcia Gonçalves, 2020. "Forecasting Covid-19 Dynamics in Brazil: A Data Driven Approach," IJERPH, MDPI, vol. 17(14), pages 1-26, July.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:14:p:5115-:d:384964
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    References listed on IDEAS

    as
    1. Fanelli, Duccio & Piazza, Francesco, 2020. "Analysis and forecast of COVID-19 spreading in China, Italy and France," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
    2. Michael te Vrugt & Jens Bickmann & Raphael Wittkowski, 2020. "Effects of social distancing and isolation on epidemic spreading modeled via dynamical density functional theory," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    3. Cosimo Distante & Prisco Piscitelli & Alessandro Miani, 2020. "Covid-19 Outbreak Progression in Italian Regions: Approaching the Peak by the End of March in Northern Italy and First Week of April in Southern Italy," IJERPH, MDPI, vol. 17(9), pages 1-9, April.
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    Cited by:

    1. Mitsuyoshi Urashima & Katharina Otani & Yasutaka Hasegawa & Taisuke Akutsu, 2020. "BCG Vaccination and Mortality of COVID-19 across 173 Countries: An Ecological Study," IJERPH, MDPI, vol. 17(15), pages 1-20, August.
    2. Jelena Musulin & Sandi Baressi Šegota & Daniel Štifanić & Ivan Lorencin & Nikola Anđelić & Tijana Šušteršič & Anđela Blagojević & Nenad Filipović & Tomislav Ćabov & Elitza Markova-Car, 2021. "Application of Artificial Intelligence-Based Regression Methods in the Problem of COVID-19 Spread Prediction: A Systematic Review," IJERPH, MDPI, vol. 18(8), pages 1-39, April.
    3. Emerson Vilar de Oliveira & Dunfrey Pires Aragão & Luiz Marcos Garcia Gonçalves, 2024. "A New Auto-Regressive Multi-Variable Modified Auto-Encoder for Multivariate Time-Series Prediction: A Case Study with Application to COVID-19 Pandemics," IJERPH, MDPI, vol. 21(4), pages 1-19, April.

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