Recurrent Neural Network and Reinforcement Learning Model for COVID-19 Prediction

Area of Science:

• Artificial Intelligence in Medicine
• Epidemiological Forecasting
• Deep Learning Applications

Background:

• The COVID-19 pandemic presented significant challenges for accurate forecasting.
• Traditional epidemiological models struggle with the dynamic nature of pandemics.
• AI offers novel opportunities for predicting pandemic parameters and outcomes.

Purpose of the Study:

• To develop and evaluate an AI-driven model for predicting COVID-19 new cases, deaths, and recoveries.
• To compare the performance of a Modified Long Short-Term Memory (MLSTM) model against established methods.
• To explore the optimization of predictive outcomes using deep learning reinforcement based on symptoms.

Main Methods:

• Development of a Modified Long Short-Term Memory (MLSTM) model, a type of Recurrent Neural Network (RNN).
• Integration of deep learning and reinforcement learning for enhanced predictive accuracy.
• Validation of the model using real-world COVID-19 data.

Main Results:

• The MLSTM model demonstrated strong performance in forecasting COVID-19 trajectories.
• The proposed AI approach significantly outperformed standard Long Short-Term Memory (LSTM) and Logistic Regression (LR) models.
• The model achieved a lower error rate in predicting new infections, fatalities, and recoveries.

Conclusions:

• The developed MLSTM model offers a promising tool for prognosticating COVID-19 outcomes.
• AI-driven methods, particularly deep learning, are effective in addressing pandemic prediction challenges.
• The study highlights the potential of advanced ML techniques for public health surveillance.