An Integrated Framework for Infectious Disease Control Using Mathematical Modeling and Deep Learning.

Area of Science:

• Epidemiology
• Computational Biology
• Public Health

Background:

• Infectious diseases pose a significant global health challenge.
• Accurate modeling is crucial for effective disease control strategies.
• Data imbalance, noise, and intensity inhomogeneity complicate disease detection.

Purpose of the Study:

• To propose a novel infectious disease pattern prediction system.
• To integrate deterministic, stochastic, and deep learning models for improved prediction.
• To investigate the influence of time delays on infection and vaccination rates.

Main Methods:

• Analysis of global stability at disease-free equilibrium using Routh-Haurwitz criteria and Lyapunov method.
• Analysis of endemic equilibrium using non-linear Volterra integral equations.
• Application of a deep learning model to predict vaccination's influence on infection rates, utilizing long-term dependencies.

Main Results:

• The integrated model demonstrates improved prediction performance.
• The study quantifies the impact of time delays on infection and vaccination dynamics.
• The deep learning component accurately predicts vaccination's effect on infection rates.

Conclusions:

• The proposed framework offers a robust method for infectious disease modeling and prediction.
• The model effectively analyzes stability considering vaccination and migration rates.
• The integration of deep learning enhances predictive accuracy for disease control interventions.