检测COVID超级传播者的系统发育分析

John R. Jungck, Hajae Ko
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引用次数: 0

摘要

目的:通过对收集时间间隔较短的个体群体的核苷酸序列进行系统发育分析,检测超传播事件。 研究设计:检索核酸序列,构建多个序列比对,建立系统发育网络,以确定感染源。 研究地点和时间:本研究于2022年6月至8月在特拉华大学特拉华生物技术研究所进行。使用的数据来自GIS AID数据库。 方法:用于分析的序列从GISAID倡议的开放获取SARS-CoV-2基因组数据库中取样。我们选择了特拉华州实验室在2021年3月18日至4月14日期间提交的高质量核苷酸序列,这4周是Alpha变体在特拉华州人群中迅速传播的重要时期。 结果:4个来源占401个序列中的215个。换句话说,54%的案例只来源于五个来源。 结论:因此,超级传播似乎对感染COVID的人群中个体的比例产生了重大影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Phylogenetic Analysis to Detect COVID Superspreaders
Aims: Detection of superspreading events by phylogenetic analysis of nucleotide sequences from a population of individuals collected from a narrow time interval. Study Design: Retrieve nucleic acid sequences, construct multiple sequence alignments, and build phylogenetic networks to determine sources of infection. Place and Duration of Study: This study was performed at the Delaware Biotechnology Institute of the University of Delaware over the period: June-August, 2022. The data used were from the GIS AID database. Methodology: Sequences for analysis were sampled from the GISAID initiative’s open-access SARS-CoV-2 genome database. We selected high-quality nucleotide sequences submitted by Delaware labs between March 18 and April 14, 2021, an important period of 4 weeks which saw the Alpha variant spread rapidly in the Delaware population. Results: Four sources accounted for 215 of the 401 sequences. In other words, 54% of all cases were rooted in just five sources. Conclusion: Thus, superspreading seems to have a major impact on the proportion of individuals in a population affected with COVID.
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