Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study.

Area of Science:

• Epidemiology
• Data Science
• Computational Biology

Background:

• The COVID-19 pandemic presents significant challenges to healthcare systems globally.
• Accurate short-term forecasting of new and recovered cases is essential for resource optimization and disease containment.
• Deep learning models have shown promise in analyzing time-series data for various applications.

Purpose of the Study:

• To compare the performance of five deep learning methods for forecasting COVID-19 cases.
• To evaluate the effectiveness of Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Units (GRUs), and Variational AutoEncoder (VAE) algorithms.
• To assess the potential of these models in predicting daily new and recovered COVID-19 cases using limited data.

Main Methods:

• Applied five deep learning algorithms: RNN, LSTM, BiLSTM, GRUs, and VAE.
• Utilized daily confirmed and recovered case data from six countries: Italy, Spain, France, China, USA, and Australia.
• Focused on global forecasting of COVID-19 cases with a small dataset.

Main Results:

• Deep learning models demonstrate significant potential for forecasting COVID-19 cases.
• The Variational AutoEncoder (VAE) algorithm exhibited superior performance compared to RNN, LSTM, BiLSTM, and GRUs.
• The study highlights the efficacy of deep learning in handling time-series data for epidemiological forecasting.

Conclusions:

• Deep learning approaches, especially VAE, are effective tools for short-term COVID-19 case forecasting.
• These models can aid in optimizing healthcare resource allocation during pandemics.
• Further research into deep learning for epidemiological prediction is warranted.