Cappello Lorenzo, Kim Jaehee, Liu Sifan, Palacios Julia A
Departments of Economics and Business, Universitat Pompeu Fabra, 08005, Spain.
Department of Computational Biology, Cornell University, Ithaca, New York 14853, USA\.
Stat Sci. 2022 May;37(2):162-182. doi: 10.1214/22-sts853. Epub 2022 May 16.
Genomic surveillance of SARS-CoV-2 has been instrumental in tracking the spread and evolution of the virus during the pandemic. The availability of SARS-CoV-2 molecular sequences isolated from infected individuals, coupled with phylodynamic methods, have provided insights into the origin of the virus, its evolutionary rate, the timing of introductions, the patterns of transmission, and the rise of novel variants that have spread through populations. Despite enormous global efforts of governments, laboratories, and researchers to collect and sequence molecular data, many challenges remain in analyzing and interpreting the data collected. Here, we describe the models and methods currently used to monitor the spread of SARS-CoV-2, discuss long-standing and new statistical challenges, and propose a method for tracking the rise of novel variants during the epidemic.
对严重急性呼吸综合征冠状病毒2（SARS-CoV-2）的基因组监测在追踪该病毒在大流行期间的传播和进化方面发挥了重要作用。从感染个体中分离出的SARS-CoV-2分子序列，再结合系统发育动力学方法，为了解该病毒的起源、进化速率、引入时间、传播模式以及在人群中传播的新变种的出现提供了线索。尽管各国政府、实验室和研究人员在全球范围内做出了巨大努力来收集和测序分子数据，但在分析和解释所收集的数据方面仍存在许多挑战。在此，我们描述了目前用于监测SARS-CoV-2传播的模型和方法，讨论了长期存在的和新出现的统计挑战，并提出了一种在疫情期间追踪新变种出现的方法。
