Abstract

Longitudinal molecular data of rapidly evolving viruses and pathogens provide information about disease spread and complement traditional surveillance approaches based on case count data. The coalescent is used to model the genealogy that represents the sample ancestral relationships. The basic assumption is that coalescent events occur at a rate inversely proportional to the effective population size $N_e(t)$, a time-varying measure of genetic diversity. When the sampling process (collection of samples over time) depends on $N_e(t)$, the coalescent and the sampling processes can be jointly modeled to improve estimation of $N_e(t)$. Failing to do so can lead to bias due to model misspecification. However, the way that the sampling process depends on the effective population size may vary over time. We introduce an approach where the sampling process is modeled as an inhomogeneous Poisson process with rate equal to the product of $N_e(t)$ and a time-varying coefficient, making minimal assumptions on their functional shapes via Markov random field priors. We provide efficient algorithms for inference, show the model performance vis-a-vis alternative methods in a simulation study, and apply our model to SARS-CoV-2 sequences from Los Angeles and Santa Clara counties. The methodology is implemented and available in the R package adapref. Supplementary files for this article are available online.

 抽象的

快速进化的病毒和病原体的纵向分子数据提供了有关疾病传播的信息，并补充了基于病例计数数据的传统监测方法。合并用于对代表样本祖先关系的家谱进行建模。基本假设是合并事件的发生率与有效种群规模成反比 ${\mathrm{<span\; data-immersive-translate-walked="69b9dfa5-6a72-4f09-9090-bad096b57561">氮</span>}}_{\mathrm{<span\; data-immersive-translate-walked="69b9dfa5-6a72-4f09-9090-bad096b57561">e</span>}}<span\; data-immersive-translate-walked="69b9dfa5-6a72-4f09-9090-bad096b57561">（</span>\mathrm{<span\; data-immersive-translate-walked="69b9dfa5-6a72-4f09-9090-bad096b57561">t</span>}<span\; data-immersive-translate-walked="69b9dfa5-6a72-4f09-9090-bad096b57561">）</span>$ ，遗传多样性的随时间变化的度量。当采样过程（随着时间的推移收集样本）取决于 ${\mathrm{<span\; data-immersive-translate-walked="69b9dfa5-6a72-4f09-9090-bad096b57561">氮</span>}}_{\mathrm{<span\; data-immersive-translate-walked="69b9dfa5-6a72-4f09-9090-bad096b57561">e</span>}}<span\; data-immersive-translate-walked="69b9dfa5-6a72-4f09-9090-bad096b57561">（</span>\mathrm{<span\; data-immersive-translate-walked="69b9dfa5-6a72-4f09-9090-bad096b57561">t</span>}<span\; data-immersive-translate-walked="69b9dfa5-6a72-4f09-9090-bad096b57561">）</span>$ ，可以对合并和采样过程进行联合建模，以改进对 ${\mathrm{<span\; data-immersive-translate-walked="69b9dfa5-6a72-4f09-9090-bad096b57561">氮</span>}}_{\mathrm{<span\; data-immersive-translate-walked="69b9dfa5-6a72-4f09-9090-bad096b57561">e</span>}}<span\; data-immersive-translate-walked="69b9dfa5-6a72-4f09-9090-bad096b57561">（</span>\mathrm{<span\; data-immersive-translate-walked="69b9dfa5-6a72-4f09-9090-bad096b57561">t</span>}<span\; data-immersive-translate-walked="69b9dfa5-6a72-4f09-9090-bad096b57561">）</span>$ 。如果不这样做，可能会因模型指定错误而导致偏差。然而，抽样过程取决于有效总体规模的方式可能会随着时间的推移而变化。 我们引入一种方法，其中采样过程被建模为非齐次泊松过程，其速率等于 ${\mathrm{<span\; data-immersive-translate-walked="69b9dfa5-6a72-4f09-9090-bad096b57561">氮</span>}}_{\mathrm{<span\; data-immersive-translate-walked="69b9dfa5-6a72-4f09-9090-bad096b57561">e</span>}}<span\; data-immersive-translate-walked="69b9dfa5-6a72-4f09-9090-bad096b57561">（</span>\mathrm{<span\; data-immersive-translate-walked="69b9dfa5-6a72-4f09-9090-bad096b57561">t</span>}<span\; data-immersive-translate-walked="69b9dfa5-6a72-4f09-9090-bad096b57561">）</span>$和时变系数，通过马尔可夫随机场先验对其函数形状做出最小假设。我们提供有效的推理算法，在模拟研究中展示模型相对于替代方法的性能，并将我们的模型应用于来自洛杉矶和圣克拉拉县的 SARS-CoV-2 序列。该方法在 R 包 adapref 中实现并可用。本文的补充文件可在线获取。