The quality of the inferences we make from pathogen sequence data is determined by the number and composition of pathogen sequences that make up the sample used to drive that inference. However, there remains limited guidance on how to best structure and power studies when the end goal is phylogenetic inference. One question that we can attempt to answer with molecular data is whether some people are more likely to transmit a pathogen than others. Here we present an estimator to quantify differential transmission, as measured by the ratio of reproductive numbers between people with different characteristics, using transmission pairs linked by molecular data, along with a sample size calculation for this estimator. We also provide extensions to our method to correct for imperfect identification of transmission linked pairs, overdispersion in the transmission process, and group imbalance. We validate this method via simulation and provide tools to implement it in an R package, phylosamp.

翻译：从病原体序列数据中推断结论的质量取决于构成该推断样本的病原体序列数量与组成。然而，当最终目标为系统发育推断时，关于如何最佳构建研究方案并计算统计效能仍缺乏明确指导。分子数据可尝试解答的一个问题是：某些个体是否比其他人更易传播病原体？本文提出一种量化差异性传播的估计量——通过分子数据关联的传播对来测量具有不同特征的个体间基本再生数（R0）比率，并给出该估计量的样本量计算方法。我们进一步扩展方法以校正传播关联对的不完美识别、传播过程过度离散及组间不平衡问题。通过模拟验证方法有效性，并在R包phylosamp中提供实现工具。