Title: Epidemiology-Aware Neural ODE with Continuous Disease Transmission Graph Show Chinese title

Title: 流行病学感知的神经ODE与连续疾病传播图

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. Show Chinese abstract

Abstract: 有效疫情预测对于公共卫生策略和高效的医疗资源分配至关重要，尤其是在面对迅速传播的传染病时。然而，现有的深度学习方法常常忽视疫情的动态特性，并未能考虑疾病传播的具体机制。为应对这些挑战，本文引入了一种创新的端到端框架，称为流行病学感知神经ODE与连续疾病传播图（EARTH）。为了学习连续和区域性的疾病传播模式，我们首先提出了EANO，它无缝地将神经ODE方法与流行病机制相结合，考虑了流行病演变过程中的复杂空间传播过程。此外，我们引入GLTG来建模全球感染趋势，并利用这些信号动态地指导局部传播。为了适应疫情趋势的全局一致性和疫情传播模式的局部细节，我们构建了一个交叉注意力方法，以融合最有意义的信息进行预测。通过两个组件的平滑协同作用，EARTH提供了一种更强大和灵活的方法来理解和预测传染病的传播。大量实验表明，EARTH在预测现实世界疫情方面优于最先进的方法。代码将在https://github.com/Emory-Melody/EpiLearn上提供。