EARTH: Epidemiology-Aware Neural ODE with Continuous Disease Transmission Graph

Guancheng Wan, Zewen Liu, Xiaojun Shan, Max Sy Lau, B. Aditya Prakash, Wei Jin
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:61929-61941, 2025.

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 is available at https://github.com/GuanchengWan/EARTH.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-wan25b, title = {{EARTH}: Epidemiology-Aware Neural {ODE} with Continuous Disease Transmission Graph}, author = {Wan, Guancheng and Liu, Zewen and Shan, Xiaojun and Lau, Max Sy and Prakash, B. Aditya and Jin, Wei}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {61929--61941}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/wan25b/wan25b.pdf}, url = {https://proceedings.mlr.press/v267/wan25b.html}, 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 is available at https://github.com/GuanchengWan/EARTH.} }
Endnote
%0 Conference Paper %T EARTH: Epidemiology-Aware Neural ODE with Continuous Disease Transmission Graph %A Guancheng Wan %A Zewen Liu %A Xiaojun Shan %A Max Sy Lau %A B. Aditya Prakash %A Wei Jin %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-wan25b %I PMLR %P 61929--61941 %U https://proceedings.mlr.press/v267/wan25b.html %V 267 %X 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 is available at https://github.com/GuanchengWan/EARTH.
APA
Wan, G., Liu, Z., Shan, X., Lau, M.S., Prakash, B.A. & Jin, W.. (2025). EARTH: Epidemiology-Aware Neural ODE with Continuous Disease Transmission Graph. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:61929-61941 Available from https://proceedings.mlr.press/v267/wan25b.html.

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