融合隐式与显式先验知识，提升传染病时空预测精度
Prior Knowledge-enhanced Spatio-temporal Epidemic Forecasting
- 动态调整区域依赖关系，捕捉历史感染模式
- 增强弱信号检测能力，使预测误差降低11.1%
- 适合公共卫生部门用于疫情预警与决策支持
时空传染病预测对公共健康管理至关重要，但现有方法常因对弱疫情信号不敏感、空间关系过于简化及参数估计不稳定而受限。为此，我们提出一种新型混合框架STOEP，融合隐式时空先验与显式专家先验。其包含三个核心组件：(1) 基于病例的邻接学习（CAL），利用历史感染模式动态调整基于移动性的区域依赖；(2) 空间引导的参数估计（SPE），通过可学习的空间先验放大弱疫情信号；(3) 基于滤波的机制化预测（FMF），采用专家指导的自适应阈值策略正则化疫情参数。在真实世界新冠与流感数据集上的大量实验表明，STOEP相比最佳基线在RMSE上提升11.1%。该系统已部署于中国某省级疾控中心，支持下游应用。
原文摘要 · Abstract (English)
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.
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