A simple yet effective approach for predicting disease spread using mathematically-inspired diffusion-informed neural networks.

ABSTRACT: The COVID-19 outbreak has highlighted the importance of mathematical epidemic models like the Susceptible-Infected-Recovered (SIR) model, for understanding disease spread dynamics. However, enhancing their predictive accuracy complicates parameter estimation. To address this, we proposed a novel model that integrates traditional mathematical modeling with deep learning which has shown improved predicted power across diverse fields. The proposed model includes a simple artificial neural network (ANN) for regional disease incidences, and a graph convolutional neural network (GCN) to capture spread to adjacent regions. GCNs are a recent deep learning algorithm designed to learn spatial relationship from graph-structured data. We applied the model to COVID-19 incidences in Spain to evaluate it

SUBMITTER: Jeong B 

PROVIDER: S-EPMC12041234 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature