Forecasting COVID-19 new cases using deep learning methods.

Area of Science:

• Epidemiology
• Computational Biology
• Public Health

Background:

• The COVID-19 pandemic, caused by the SARS-CoV-2 virus, continues to pose a significant global health threat due to viral mutations.
• Accurate prediction of virus transmission patterns is crucial for effective pandemic preparedness and control strategies.
• Machine learning, particularly deep learning, has emerged as a powerful tool for forecasting infectious disease trends.

Purpose of the Study:

• To develop and evaluate deep learning models for predicting COVID-19 case numbers.
• To compare the performance of Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and CNN-LSTM models.
• To assess the potential of these models for application in Brazil, India, and Russia, and potentially other countries.

Main Methods:

• Implementation of three deep learning architectures: CNN, LSTM, and CNN-LSTM.
• Training and validation of models using COVID-19 case data from Brazil, India, and Russia.
• Comparative analysis of model performance against existing deep learning approaches.

Main Results:

• All developed deep learning models showed significant improvements in prediction performance compared to previous methods.
• The LSTM model exhibited the highest forecasting accuracy among the evaluated models.
• The models demonstrated effectiveness in forecasting COVID-19 cases for the selected countries.

Conclusions:

• Deep learning models, especially LSTM, offer a promising approach for accurate COVID-19 case forecasting.
• The developed models can be adapted for use in other countries, supporting global pandemic management.
• Accurate forecasting aids in resource allocation and public health interventions to combat the pandemic.