Department of Biochemistry and Molecular Genetics, University of Louisville School of Medicine, Louisville, KY 40202, United States of America.
Bioinformatics Core, Interdisciplinary Center for Biotechnology Research, University of Florida, Gainesville, FL 32610, United States of America.
Infect Genet Evol. 2024 Oct;124:105661. doi: 10.1016/j.meegid.2024.105661. Epub 2024 Aug 24.
Molecular data analysis is invaluable in understanding the overall behavior of a rapidly spreading virus population when epidemiological surveillance is problematic. It is also particularly beneficial in describing subgroups within the population, often identified as clades within a phylogenetic tree that represent individuals connected via direct transmission or transmission via differing risk factors in viral spread. However, transmission patterns or viral dynamics within these smaller groups should not be expected to exhibit homogeneous behavior over time. As such, standard phylogenetic approaches that identify clusters based on summary statistics would not be expected to capture dynamic clusters of transmission. We, therefore, sought to evaluate the performance of existing and adapted phylogeny-based cluster identification tools on simulated transmission clusters exhibiting dynamic transmission behavior over time. Despite the complementarity of the tools, we provide strong evidence that novel cluster identification methods are needed for reliable detection of epidemiologically linked individuals, particularly those exhibiting changing transmission dynamics during dynamic outbreak scenarios.
分子数据分析在理解快速传播病毒群体的整体行为时非常有价值，尤其是在流行病学监测存在问题时。它在描述群体中的亚组方面也特别有益，这些亚组通常被确定为系统发育树中的分支，代表通过直接传播或通过病毒传播中不同风险因素传播的个体。然而，这些较小群体中的传播模式或病毒动态不应期望随着时间的推移表现出均匀的行为。因此，基于汇总统计数据识别聚类的标准系统发育方法预计无法捕获动态传播聚类。因此，我们试图评估现有的和适应的基于系统发育的聚类识别工具在表现出随时间变化的动态传播行为的模拟传播聚类上的性能。尽管这些工具具有互补性，但我们提供了强有力的证据表明，需要新的聚类识别方法来可靠地检测具有流行病学关联的个体，特别是那些在动态爆发场景中表现出传播动态变化的个体。
