Forecasting COVID-19 cases: A comparative analysis between Recurrent and Convolutional Neural Networks.

Area of Science:

• Epidemiology
• Data Science
• Computational Biology

Background:

• COVID-19 surges present challenges due to pandemic fatigue and public apathy.
• Accurate forecasting of future COVID-19 dynamics is essential for strategic preparedness.
• Understanding outbreak trends is critical for effective public health interventions.

Approach:

• The study utilized four deep learning models: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and Multivariate Convolutional Neural Network (MCNN).
• These models were employed to forecast probable future COVID-19 outbreak scenarios in Brazil, Russia, and the United Kingdom.
• Model performance was evaluated based on validation accuracy and forecasting consistency.

Key Points:

• Convolutional Neural Networks (CNN) demonstrated superior performance compared to LSTM, GRU, and MCNN in forecasting COVID-19 outbreaks.
• CNNs provide robust long-term forecasting in time series analysis due to effective feature learning, distortion invariance, and temporal dependence learning.
• Recurrent neural networks like LSTM showed poor prediction accuracy, likely because they attempt to identify absent seasonality and periodic intervals in the data.

Conclusions:

• Convolutional Neural Networks (CNNs) are validated as a promising tool for COVID-19 outbreak forecasting, particularly with limited historical data and few features.
• CNNs offer a more reliable approach than recurrent neural networks for time series forecasting in scenarios lacking clear seasonality.
• The findings support the strategic use of CNNs for enhanced pandemic preparedness and response planning.