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Using discrete wavelet transform for optimizing COVID-19 new cases and deaths prediction worldwide with deep neural
Erick Giovani Sperandio Nascimento1,2, Júnia Ortiz1, Adhvan Novais Furtado1
1Manufacturing and Technology Integrated Campus-SENAI CIMATEC, Salvador, Bahia, Brazil.
Plos One
|April 6, 2023
Summary
Deep learning models, including Long-Short Term Memory (LSTM) and Convolutional Neural Network (CNN) with Discrete Wavelet Transform (DWT), accurately predict COVID-19 cases and deaths. CNN+LSTM models showed superior performance in forecasting epidemic trends.
Area of Science:
- Epidemiology
- Computational Biology
- Data Science
Background:
- Accurate forecasting of COVID-19 cases and deaths is crucial for public health response.
- Deep learning models offer potential for time-series prediction of epidemic dynamics.
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