Abstract
This paper presents a modified SEIR epidemiological model integrated with machine learning techniques to improve COVID-19 transmission dynamics forecasting. The proposed approach extends the classical framework with time-dependent parameters capturing the effects of social distancing, vaccination coverage, and population mobility. The Grid Auto-Search methodology enables automatic parameter optimization and adaptive trainingperiod selection. XGBoost and LSTM algorithms are applied to forecast the time-varying transmission coefficient β(t). The dataset comprises 1,143 daily observations for France from three open sources (Johns Hopkins University, Our World in Data, Google Mobility). The model achieves R² up to 0.96 across 30–90-day horizons; crossvalidation confirms the absence of significant overfitting. The results support the practical applicability of this hybrid approach for healthcare decision-support systems. The research results are confirmed by two Qazpatent certificates of copyright registration.