A data augmentation strategy for deep neural networks with application to epidemic modelling

TL;DR

This paper proposes a data augmentation approach combining mechanistic epidemic models with deep neural networks to improve forecasting accuracy, validated through COVID-19 data from Italy and Spain.

Contribution

It introduces a novel data augmentation strategy that enhances neural network predictions by integrating disease dynamics models, surpassing traditional physics-based methods.

Findings

Data augmentation improves neural network reliability.

Method outperforms physics-informed neural networks in certain scenarios.

Validated on COVID-19 epidemic data from Italy and Spain.

Abstract

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.