TY - JOUR
AU - Awais, Muhammad
AU - Ali, Abu Safyan
AU - Dimarco, Giacomo
AU - Ferrarese, Federica
AU - Pareschi, Lorenzo
PY - 2025
DA - June
TI - A data augmentation strategy for deep neural networks with application to epidemic modelling
JO - Bollettino dell'Unione Matematica Italiana
DO - https://doi.org/10.1007/s40574-025-00486-3
AB - In this work, we integrate the predictive capabilities of compartmental disease dynamics models with machine learning’s ability to analyze complex, high-dimensional data and uncover patterns that conventional models may overlook. Specifically, we present a proof of concept demonstrating the application of data-driven methods and deep neural networks to a recently introduced Susceptible-Infected-Recovered type model with social features, including a saturated incidence rate, to improve epidemic prediction and forecasting. Our results show that a robust data augmentation strategy trough suitable data-driven models can improve the reliability of Feed-Forward Neural Networks and Nonlinear Autoregressive Networks, providing a complementary strategy to Physics-Informed Neural Networks, particularly in settings where data augmentation from mechanistic models can enhance learning. This approach enhances the ability to handle nonlinear dynamics and offers scalable, data-driven solutions for epidemic forecasting, prioritizing predictive accuracy over the constraints of physics-based models. Numerical simulations of the lockdown and post-lockdown phase of the COVID-19 epidemic in Italy and Spain validate our methodology.
PB - Springer Nature
UR - https://publichealthscotland.scot/id/208890
KW - Coronavirus (COVID-19)
ER