In this work, we integrate the predictive capabilities of compartmental disease dynamics models with machine learning 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 SIR-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 (FNNs) and Nonlinear Autoregressive Networks (NARs), making them viable alternatives to Physics-Informed Neural Networks (PINNs). 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 post-lockdown phase of the COVID-19 epidemic in Italy and Spain validate our methodology.
View on arXiv@article{awais2025_2502.21033,
title={ A data augmentation strategy for deep neural networks with application to epidemic modelling },
author={ Muhammad Awais and Abu Sayfan Ali and Giacomo Dimarco and Federica Ferrarese and Lorenzo Pareschi },
journal={arXiv preprint arXiv:2502.21033},
year={ 2025 }
}