@article{Awais_M-2025_208890,
title = {A data augmentation strategy for deep neural networks with application to epidemic modelling},
author = {Awais, M. and Ali, A. and Dimarco, G. and Ferrarese, F. and Pareschi, L.},
month = {jun},
year = {2025},
abstract = {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.},
journal = {Bollettino dell'Unione Matematica Italiana},
publisher = {Springer Nature},
url = {https://doi.org/10.1007/s40574-025-00486-3},
}
