Integrating Artificial Neural Networks into SEIR Models for Adaptive Epidemic Forecasting

In this paper, we present an extended version of the classical SEIR epidemic model in which the key parameters are no longer fixed but adapt dynamically through an artificial neural network (ANN). By feeding the ANN with external signals such as policy interventions, population mobility, seasonal factors, and vaccination coverage the model is able to reflect how real outbreaks respond to changing social and environmental conditions. Our theoretical analysis confirms that the ANN-SEIR system remains mathematically consistent: solutions stay positive and bounded, and the time-dependent reproduction number R ₀ ( t ) continues to determine whether a disease dies out or persists. Numerical experiments further demonstrate that adaptive behavior and timely interventions can significantly change the trajectory of an epidemic. Overall, the proposed approach brings together the strengths of traditional epidemiological modeling and modern machine learning. It provides a flexible, data-informed tool for understanding epidemic dynamics and for exploring possible outcomes under different policy or behavioral scenarios.