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The research aims to enhance the SIDARTHE model for COVID-19 forecasting using advanced data-driven methodologies.

February 8, 2026

Improving pandemic prediction: integrating Physics-Informed Neural Networks and symbolic regression for COVID-19 modeling

Key Points

The research aims to enhance the SIDARTHE model for COVID-19 forecasting using advanced data-driven methodologies.
Refined the SIDARTHE model specifically for Italy's COVID-19 outbreak.
Employed a two-stage approach comparing Maximum Likelihood Estimation (MLE) and Physics-Informed Neural Networks (PINNs).
Applied Symbolic Regression using gplearn and PySR to optimize model equations.
PINNs demonstrate superior accuracy compared to MLE in parameter estimation.
Symbolic Regression via PySR outperformed gplearn in generating robust equations.
The integrated model notably reduces predictive uncertainty and closely matches observed data.

Abstract

This study refines the SIDARTHE model for Italy's COVID-19 outbreak using a hybrid, data-driven framework. A two-stage approach compares Maximum Likelihood Estimation (MLE) with Physics-Informed Neural Networks (PINNs) for parameter estimation, then applies Symbolic Regression (via gplearn and PySR) to optimize the governing equations. Results show PINNs surpass MLE in accuracy, and PySR outperforms gplearn in deriving robust expressions. The final integrated model-combining PINN estimation with Symbolic Regression-significantly reduces predictive uncertainty and aligns closely with observed data, providing a resilient tool for public health planning.

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Improving pandemic prediction: integrating Physics-Informed Neural Networks and symbolic regression for COVID-19 modeling

Key Points

Abstract

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