用时间演化模型推断未测序病例间的遗传距离，提升病原体时空模型精度。
A Probabilistic Framework for Imputing Genetic Distances in Spatiotemporal Pathogen Models
- 基于采样时间和已知突变距离，建模遗传差异的时序规律。
- 无需序列比对或已知传播链，即可合理填补缺失遗传数据。
- 适用于野生鸟类禽流感数据，支持带不确定性的基因组数据扩展。
病原体基因组数据为空间模型提供了宝贵结构，但因测序覆盖不全而受限。本文提出一种概率框架，通过时间感知的演化距离建模，推断未测序病例与已知序列间在指定传播链中的遗传距离。该方法基于采集日期和观测到的遗传距离估计成对分歧，实现生物学上合理的数据填补，无需序列比对或已知传播路径。应用于美国野鸟中高致病性禽流感A/H5病例，该方法支持可扩展、带不确定性的基因组数据增强，提升进化信息在时空建模流程中的整合能力。
原文摘要 · Abstract (English)
Pathogen genome data offers valuable structure for spatial models, but its utility is limited by incomplete sequencing coverage. We propose a probabilistic framework for inferring genetic distances between unsequenced cases and known sequences within defined transmission chains, using time-aware evolutionary distance modeling. The method estimates pairwise divergence from collection dates and observed genetic distances, enabling biologically plausible imputation grounded in observed divergence patterns, without requiring sequence alignment or known transmission chains. Applied to highly pathogenic avian influenza A/H5 cases in wild birds in the United States, this approach supports scalable, uncertainty-aware augmentation of genomic datasets and enhances the integration of evolutionary information into spatiotemporal modeling workflows.
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