Authors:Sijie Ruan (1), Jinyu Li (1), Jia Wei (1), Zenghao Xu (2), Jie Bao (3), Junshi Xu (4), Junyang Qiu (5), Shuliang Wang (1), Xiaoxiao Wang (2), Hanning Yuan (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)
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)
此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。