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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-aware Adjacency Learning (CAL), which dynamically adjusts mobility-based regional dependencies using historical infection patterns; (2) Space-informed Parameter Estimating (SPE), which employs learnable spatial priors to amplify weak epidemic signals; and (3) Filter-based Mechanistic Forecasting (FMF), which uses an expert-guided adaptive thresholding strategy to regularize epidemic parameters. Extensive experiments on real-world COVID-19 and influenza datasets demonstrate that STOEP outperforms the best baseline by 11.1% in RMSE. The system has been deployed at a provincial CDC in China to facilitate downstream applications.
| Comments: | 12 pages, 10 figures, accepted to IJCAI 2026 |
| Subjects: | Machine Learning (cs.LG); Populations and Evolution (q-bio.PE) |
| Cite as: | arXiv:2602.22270 [cs.LG] |
| (or arXiv:2602.22270v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2602.22270 arXiv-issued DOI via DataCite |
From: Jinyu Li [view email]
[v1]
Wed, 25 Feb 2026 07:52:11 UTC (1,709 KB)
[v2]
Thu, 21 May 2026 14:59:20 UTC (1,681 KB)
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