用贝叶斯方法动态决定何时仅靠污水数据预警，何时需追加临床数据。
Bayesian Selective Latent Inference for Wastewater-First Influenza Monitoring
- 基于贝叶斯推断，动态评估污水数据是否足够反映流感传播
- 在5933次预测中提升成本效益，3102次模糊源情况下保守不决策
- 适合公共卫生监测系统，可减少误报与资源浪费
污水流感监测可在临床报告前揭示社区传播情况，但污水数据本身无法完全代表人群感染负担。现有模型假设证据集固定，通用数据获取方法又将官方监测视为可互换的高成本特征。本文将污水优先的流感监测问题建模为选择性决策：从强制性的污水证据出发，系统需判断污水数据是否足够、应查询哪个延迟的官方数据流、何时应放弃决策以应对源头模糊。我们提出贝叶斯选择性隐变量推断（BSLI），一种基于贝叶斯原理的方法，通过后验分布维护对隐含感染负担和可识别性的认知，以明确的科学门槛认证可回答性，并利用精确校准成本的贝尔曼策略优化查询停止决策。证明了关键的变分性、可回答性、贝尔曼最优性及一维成本校准性质。在包含5,933次预测和3,102次源模糊情况的公开基准上，BSLI在相同预算下提升了成本-性能边界，同时在源模糊时保持保守弃权。
原文摘要 · Abstract (English)
Wastewater influenza surveillance can reveal community circulation before clinical reporting, but wastewater alone is not a fully identifiable proxy for human burden. Existing wastewater models assume a fixed evidence set, while generic evidence-acquisition methods treat official surveillance streams as interchangeable costly features. We cast wastewater-first influenza monitoring as a selective decision problem: starting from mandatory wastewater evidence, the system must decide whether wastewater is sufficient, which delayed official stream to query next, and when abstention is the only scientifically defensible action under source ambiguity. We propose Bayesian Selective Latent Inference (BSLI), a principled Bayesian method that maintains a posterior over latent burden and identifiability, certifies answerability through explicit scientific gates, and optimizes query-stop decisions with an exact cost-calibrated Bellman policy. We prove the key variational, answerability, Bellman-optimality, and one-dimensional cost-calibration properties. On a fixed public-data benchmark with 5,933 forecasting episodes and 3,102 source-ambiguity episodes, BSLI improves the matched-budget cost-performance frontier while preserving conservative abstention under source ambiguity.
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