May Michael R, Rannala Bruce
Department of Evolution and Ecology, University of California Davis, Davis, CA USA.
medRxiv. 2023 Aug 5:2023.07.28.23293332. doi: 10.1101/2023.07.28.23293332.
As demonstrated by the SARS-CoV-2 pandemic, the emergence of novel viral strains with increased transmission rates poses a significant threat to global health. Viral genome sequences, combined with statistical models of sequence evolution, may provide a critical tool for early detection of these strains. Using a novel statistical model that links transmission rates to the entire viral genome sequence, we study the power of phylogenetic methods-using a phylogenetic tree relating viral samples-and count-based methods-using case-counts of variants over time-to detect increased transmission rates, and to identify causative mutations. We find that phylogenies in particular can detect novel variants very soon after their origin, and may facilitate the development of early detection systems for outbreak surveillance.
正如新冠疫情所表明的那样，传播率增加的新型病毒株的出现对全球健康构成了重大威胁。病毒基因组序列与序列进化的统计模型相结合，可能为这些病毒株的早期检测提供关键工具。我们使用一种将传播率与整个病毒基因组序列联系起来的新型统计模型，研究了系统发育方法（利用病毒样本之间的系统发育树）和基于计数的方法（利用随时间变化的变体病例数）检测传播率增加以及识别致病突变的能力。我们发现，系统发育尤其能够在新型变体出现后不久就检测到它们，并可能有助于开发用于疫情监测的早期检测系统。
