Augmented data and neural networks for robust epidemic forecasting: application to COVID-19 in Italy
This work addresses the challenge of robust epidemic forecasting for public health decision-makers, though it represents an incremental improvement combining existing methods.
The authors tackled the problem of improving epidemic forecasting accuracy by developing a data augmentation strategy that generates synthetic data using compartmental models with uncertainty, then calibrates and integrates them with neural networks. Their approach significantly improved predictive performance, with NAR models achieving accurate short-term forecasts and PINNs capturing qualitative long-term trends in COVID-19 simulations for Lombardy, Italy.
In this work, we propose a data augmentation strategy aimed at improving the training phase of neural networks and, consequently, the accuracy of their predictions. Our approach relies on generating synthetic data through a suitable compartmental model combined with the incorporation of uncertainty. The available data are then used to calibrate the model, which is further integrated with deep learning techniques to produce additional synthetic data for training. The results show that neural networks trained on these augmented datasets exhibit significantly improved predictive performance. We focus in particular on two different neural network architectures: Physics-Informed Neural Networks (PINNs) and Nonlinear Autoregressive (NAR) models. The NAR approach proves especially effective for short-term forecasting, providing accurate quantitative estimates by directly learning the dynamics from data and avoiding the additional computational cost of embedding physical constraints into the training. In contrast, PINNs yield less accurate quantitative predictions but capture the qualitative long-term behavior of the system, making them more suitable for exploring broader dynamical trends. Numerical simulations of the second phase of the COVID-19 pandemic in the Lombardy region (Italy) validate the effectiveness of the proposed approach.