A data augmentation strategy for deep neural networks with application to epidemic modelling
This work addresses epidemic forecasting for public health by providing a scalable, data-driven approach, though it appears incremental as it builds on existing methods like Physics-Informed Neural Networks.
The paper tackled the problem of improving epidemic prediction by integrating compartmental disease models with deep neural networks, showing that a data augmentation strategy can enhance the reliability of neural networks in forecasting COVID-19 dynamics in Italy and Spain.
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 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.