Predicting epidemic evolution is essential for informed decision-making and guiding the implementation of necessary countermeasures. Computational models are vital tools that provide insights into illness progression and enable early detection, proactive intervention, and targeted preventive measures. This paper introduces Sybil, a framework that integrates machine learning and variant-aware compartmental models, leveraging a fusion of data-centric and analytic methodologies. To validate and evaluate Sybil's forecasts, we employed COVID-19 data from two European countries. The dataset included the number of new and recovered cases, fatalities, and variant presence over time. We evaluate the forecasting precision of Sybil in periods in which there is a change in the trend of the pandemic evolution or a new variant appears. Results demonstrate that Sybil outperforms a conventional data-centric approach, being able to forecast accurately the changes in the trend, the magnitude of these changes, and the future prevalence of new variants.

The authors have declared no competing interest.

This work was supported by grants from ″Ripresa delle attività socio-economiche e delle scuole: modelli per la progettazione e supporto di linee guida per la convivenza con il Covid-19″ (Cod. ROL 73459, 2020, PI Matteo Sereno), project funded by CRT foundation.

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