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

International Journal of Environmental Research and Public Health
Igor G. PereiraLuiz M. G. Gonçalves

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 confirm...Continue Reading

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May 1, 2020·International Journal of Environmental Research and Public Health·Cosimo DistanteAlessandro Miani

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Citations

Aug 7, 2020·International Journal of Environmental Research and Public Health·Mitsuyoshi UrashimaTaisuke Akutsu
Oct 15, 2020·SN Comprehensive Clinical Medicine·Lanlan FangGuixia Pan
Dec 15, 2020·Chaos, Solitons, and Fractals·Mahdi Kalantari
May 1, 2021·International Journal of Environmental Research and Public Health·Jelena MusulinElitza Markova-Car
Jun 3, 2021·JMIRx Med·Eduardo Atem De Carvalho, Rogerio Atem De Carvalho

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