Enhancing epidemic forecasting with a physics-informed spatial identity neural network.

ABSTRACT: Forecasting the future number of confirmed cases in each region is a critical challenge in controlling the spread of infectious diseases. Accurate predictions enable the proactive development of optimal containment strategies. Recently, deep learning-based models have increasingly leveraged graph structures to capture the spatial dynamics of epidemic spread. While intuitive, this approach often increases model complexity, and the resulting performance gains may not justify the added burden. In some cases, it may even lead to overfitting. Moreover, infectious disease data is typically noisy, making it difficult to extract infectious disease-specific dynamics from data without guidance based on epidemiological domain knowledge. To address these issues, we propose a simple yet effective hybri

SUBMITTER: Fujita S 

PROVIDER: S-EPMC12435659 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature