Epidemiology-Aware Neural ODE with Continuous Disease Transmission Graph

TL;DR

This paper presents EARTH, a novel neural ODE framework that models continuous disease transmission dynamics by integrating epidemic mechanisms and global trends, significantly improving epidemic forecasting accuracy.

Contribution

It introduces EANO and GLTG components to capture regional transmission patterns and global infection trends, enhancing epidemic prediction models.

Findings

EARTH outperforms existing methods in real-world epidemic forecasting.

The integrated approach improves robustness and flexibility in modeling disease spread.

Code availability facilitates further research and application.

Abstract

Effective epidemic forecasting is critical for public health strategies and efficient medical resource allocation, especially in the face of rapidly spreading infectious diseases. However, existing deep-learning methods often overlook the dynamic nature of epidemics and fail to account for the specific mechanisms of disease transmission. In response to these challenges, we introduce an innovative end-to-end framework called Epidemiology-Aware Neural ODE with Continuous Disease Transmission Graph (EARTH) in this paper. To learn continuous and regional disease transmission patterns, we first propose EANO, which seamlessly integrates the neural ODE approach with the epidemic mechanism, considering the complex spatial spread process during epidemic evolution. Additionally, we introduce GLTG to model global infection trends and leverage these signals to guide local transmission dynamically. To accommodate both the global coherence of epidemic trends and the local nuances of epidemic transmission patterns, we build a cross-attention approach to fuse the most meaningful information for forecasting. Through the smooth synergy of both components, EARTH offers a more robust and flexible approach to understanding and predicting the spread of infectious diseases. Extensive experiments show EARTH superior performance in forecasting real-world epidemics compared to state-of-the-art methods. The code will be available at https://github.com/Emory-Melody/EpiLearn.