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[2602.22270] Prior Knowledge-enhanced Spatio-temporal Epidemic Forecasting
Summary
The paper presents a novel framework, STOEP, for spatio-temporal epidemic forecasting, addressing challenges in existing methods by integrating expert knowledge and historical data.
Why It Matters
Accurate epidemic forecasting is crucial for public health management, especially in the context of COVID-19. This research enhances predictive capabilities, potentially improving response strategies and resource allocation during outbreaks.
Key Takeaways
- STOEP integrates implicit and explicit priors for better epidemic predictions.
- Key components include Case-aware Adjacency Learning and Space-informed Parameter Estimating.
- The framework outperforms existing methods by 11.1% in RMSE on real-world datasets.
- STOEP has been deployed in a provincial CDC in China for practical applications.
- The research highlights the importance of expert knowledge in enhancing model accuracy.
Computer Science > Machine Learning arXiv:2602.22270 (cs) COVID-19 e-print Important: e-prints posted on arXiv are not peer-reviewed by arXiv; they should not be relied upon without context to guide clinical practice or health-related behavior and should not be reported in news media as established information without consulting multiple experts in the field. [Submitted on 25 Feb 2026] Title:Prior Knowledge-enhanced Spatio-temporal Epidemic Forecasting Authors:Sijie Ruan (1), Jinyu Li (1), Jia Wei (1), Zenghao Xu (2), Jie Bao (3), Junshi Xu (4), Junyang Qiu (5), Hanning Yuan (1), Xiaoxiao Wang (2), Shuliang Wang (1) ((1) Beijing Institute of Technology, China, (2) Zhejiang Center for Disease Control and Prevention, China, (3) JD Technology, China, (4) The University of Hong Kong, Hong Kong SAR, China, (5) China Mobile Internet, China) View a PDF of the paper titled Prior Knowledge-enhanced Spatio-temporal Epidemic Forecasting, by Sijie Ruan (1) and 19 other authors View PDF HTML (experimental) Abstract:Spatio-temporal epidemic forecasting is critical for public health management, yet existing methods often struggle with insensitivity to weak epidemic signals, over-simplified spatial relations, and unstable parameter estimation. To address these challenges, we propose the Spatio-Temporal priOr-aware Epidemic Predictor (STOEP), a novel hybrid framework that integrates implicit spatio-temporal priors and explicit expert priors. STOEP consists of three key components: (1) Case...
