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

arXiv: https://arxiv.org/abs/2502.21033 · HTML: https://mlsanity.com/paper/2502.21033/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 50 references that are in the map.
- First posted on arXiv 28 Feb 2025.

## Builds on (most cited first, 8 of 8)

- [Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning](https://mlsanity.com/paper/2402.10926/) (2024)
- [Asymptotic-Preserving Neural Networks for multiscale hyperbolic models of epidemic spread](https://mlsanity.com/paper/2206.12625/) (2022)
- [Approaching epidemiological dynamics of COVID-19 with physics-informed neural networks](https://mlsanity.com/paper/2302.08796/) (2023)
- [A Statistical-Modelling Approach to Feedforward Neural Network Model Selection](https://mlsanity.com/paper/2207.04248/) (2022)
- [A Recurrent Neural Network and Differential Equation Based Spatiotemporal Infectious Disease Model with Application to COVID-19](https://mlsanity.com/paper/2007.10929/) (2020)
- [Kinetic modelling of epidemic dynamics: social contacts, control with uncertain data, and multiscale spatial dynamics](https://mlsanity.com/paper/2110.00293/) (2021)
- [Estimation of the parameters of an infectious disease model using neural networks](https://mlsanity.com/paper/1503.01847/) (2015)
- [Early Recognition of Parkinson's Disease Through Acoustic Analysis and Machine Learning](https://mlsanity.com/paper/2407.16091/) (2024)

## Built on by (most cited first, 1 of 1)

- [Augmented data and neural networks for robust epidemic forecasting: application to COVID-19 in Italy](https://mlsanity.com/paper/2510.09192/) (2025)

## Abstract (arXiv)

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.