Enhancing Covid-19 Forecasting Precision Through The Integration Of Compartmental Models, Machine Learning And Variants

This article discusses the importance of computational models in predicting epidemic evolution and introduces Sybil, a framework that integrates machine learning and compartmental models to accurately forecast changes in the trend and prevalence of new variants. The framework was validated using COVID-19 data and outperformed conventional data-centric approaches.