用预训练时间先验提升疫情传播重建准确率，发现标签不确定性影响干预决策。
A Transferable Learned Temporal Prior for Transmission Reconstruction and Decision-Relevant Uncertainty in Real Outbreak Labels
- 用11类疾病数据训练固定时间先验，直接应用于29个任务不微调。
- 在安第斯病毒任务中MRR达0.571，比最优基线高两倍以上（p<0.0002）。
- 发现54.67%的传播关系基因组证据不足，保留不确定连接会改变优先干预对象。
疫情传播重建通常将流行病学时间与传播标签视为确定性真实值，但未被系统评估。我们在11种疾病家族上训练了一个逻辑回归时间先验，锁定所有参数后，未进行任何微调即应用于严格的安第斯病毒（ANDV）父代排序基准，涵盖29项任务。该固定先验在平均倒数排名（MRR）上达到0.571，显著优于最佳源模型基线（0.274），在Top-1准确率上也从13.8%提升至37.9%（置换检验p≤0.0002；需7-8次颠倒才失去显著性）。对75对纽约市猴痘人际传播样本的系统性基因一致性审计（独立于先验验证）显示，54.67%（95%置信区间：42.75%-66.21%）的传播关系无法通过基因组证据支持或分辨。在ANDV与广东德尔塔图中保留不确定边，使前五名源病例优先级集的雅各布相似度在0.429至0.667之间变化。研究证实传播标签不确定性可量化，并在所考察的疫情证据模块中影响干预优先级判定。
原文摘要 · Abstract (English)
Outbreak transmission reconstruction treats epidemiological timing and transmission labels as deterministic ground truth; neither has been systematically evaluated. We trained a logistic regression temporal prior on eleven disease families, locked all parameters before accessing any target outbreak data, and applied it without refitting to a strict Andes virus (ANDV) parent-ranking benchmark of 29 tasks. The locked prior achieved mean reciprocal rank (MRR) 0.571 versus 0.274 and Top-1 accuracy 37.9% versus 13.8% against the best source-trained parametric baseline (permutation p <= 0.0002; 7-8 reversals to lose MRR significance). A phylogenetic concordance audit of 75 NYC mpox inter-host pairs - independent label-reliability evidence rather than a prior validation - found that 54.67% (exact 95% CI: 42.75-66.21%) were genomically unresolved or unsupported. Retaining uncertain edges in ANDV and Guangdong Delta graphs shifted top-5 source-priority sets (Jaccard 0.429-0.667). Transmission-label uncertainty was measurable in the outbreak evidence modules examined, and retaining uncertain links changed which source cases were prioritized for intervention.
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