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Using discrete wavelet transform for optimizing COVID-19 new cases and deaths prediction worldwide with deep neural networks

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  • Erick Giovani Sperandio Nascimento
  • Júnia Ortiz
  • Adhvan Novais Furtado
  • Diego Frias

Abstract

This work aims to compare deep learning models designed to predict daily number of cases and deaths caused by COVID-19 for 183 countries, using a daily basis time series, in addition to a feature augmentation strategy based on Discrete Wavelet Transform (DWT). The following deep learning architectures were compared using two different feature sets with and without DWT: (1) a homogeneous architecture containing multiple LSTM (Long-Short Term Memory) layers and (2) a hybrid architecture combining multiple CNN (Convolutional Neural Network) layers and multiple LSTM layers. Therefore, four deep learning models were evaluated: (1) LSTM, (2) CNN + LSTM, (3) DWT + LSTM and (4) DWT + CNN + LSTM. Their performances were quantitatively assessed using the metrics: Mean Absolute Error (MAE), Normalized Mean Squared Error (NMSE), Pearson R, and Factor of 2. The models were designed to predict the daily evolution of the two main epidemic variables up to 30 days ahead. After a fine-tuning procedure for hyperparameters optimization of each model, the results show a statistically significant difference between the models’ performances both for the prediction of deaths and confirmed cases (p-value

Suggested Citation

  • Erick Giovani Sperandio Nascimento & Júnia Ortiz & Adhvan Novais Furtado & Diego Frias, 2023. "Using discrete wavelet transform for optimizing COVID-19 new cases and deaths prediction worldwide with deep neural networks," PLOS ONE, Public Library of Science, vol. 18(4), pages 1-34, April.
    • Handle: RePEc:plo:pone00:0282621
    DOI: 10.1371/journal.pone.0282621
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    References listed on IDEAS

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    1. Alakus, Talha Burak & Turkoglu, Ibrahim, 2020. "Comparison of deep learning approaches to predict COVID-19 infection," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    2. Zeroual, Abdelhafid & Harrou, Fouzi & Dairi, Abdelkader & Sun, Ying, 2020. "Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    3. Chakraborty, Tanujit & Ghosh, Indrajit, 2020. "Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    4. Shastri, Sourabh & Singh, Kuljeet & Kumar, Sachin & Kour, Paramjit & Mansotra, Vibhakar, 2020. "Time series forecasting of Covid-19 using deep learning models: India-USA comparative case study," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    5. Claudia Barría-Sandoval & Guillermo Ferreira & Katherine Benz-Parra & Pablo López-Flores, 2021. "Prediction of confirmed cases of and deaths caused by COVID-19 in Chile through time series techniques: A comparative study," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-16, April.
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