Applying Prospective Tree-Temporal Scan Statistics to Genomic Surveillance Data to Detect Emerging SARS-CoV-2 Variants and Salmonellosis Clusters in New York City

Sharon K. Greene, Julia Latash, Eric R. Peterson, Alison Levin-Rector, Elizabeth Luoma, Jade C. Wang, Kevin Bernard, Aaron Olsen, Lan Li, HaeNa Waechter, Aria Mattias, Rebecca Rohrer, Martin Kulldorff
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Abstract

Genomic surveillance data are used to detect communicable disease clusters, typically by applying rule-based signaling criteria, which can be arbitrary. We applied the prospective tree-temporal scan statistic (TreeScan) to genomic data with a hierarchical nomenclature to search for recent case increases at any granularity, from large phylogenetic branches to small groups of indistinguishable isolates. Using COVID-19 and salmonellosis cases diagnosed among New York City (NYC) residents and reported to the NYC Health Department, we conducted weekly analyses to detect emerging SARS-CoV-2 variants based on Pango lineages and clusters of Salmonella isolates based on allele codes. The SARS-CoV-2 Omicron subvariant EG.5.1 first signaled as locally emerging on June 22, 2023, seven weeks before the World Health Organization designated it as a variant of interest. During one year of salmonellosis analyses, TreeScan detected fifteen credible clusters worth investigating for common exposures and two data quality issues for correction. A challenge was maintaining timely and specific lineage assignments, and a limitation was that genetic distances between tree nodes were not considered. By automatically sifting through genomic data and generating ranked shortlists of nodes with statistically unusual recent case increases, TreeScan assisted in detecting emerging communicable disease clusters and in prioritizing them for investigation.
将前瞻性树状时空扫描统计应用于基因组监测数据,以检测纽约市新出现的 SARS-CoV-2 变异体和沙门氏菌病簇群
基因组监测数据用于检测传染病群,通常采用基于规则的信号标准,而这些标准可以是任意的。我们将前瞻性树状时空扫描统计量(TreeScan)应用于具有层次命名法的基因组数据,以搜索任何粒度的近期病例增加情况,从大型系统发育分支到难以区分的分离株小群。利用 COVID-19 和纽约市(NYC)居民中确诊并向纽约市卫生局报告的沙门氏菌病病例,我们每周进行一次分析,根据 Pango 系谱检测新出现的 SARS-CoV-2 变体,并根据等位基因代码检测沙门氏菌分离物群。2023 年 6 月 22 日,SARS-CoV-2 Omicron 亚变异体 EG.5.1 首次发出本地出现的信号,比世界卫生组织将其指定为相关变异体早了七周。在一年的沙门氏菌病分析中,TreeScan 发现了 15 个值得调查共同暴露的可信群集和两个需要纠正的数据质量问题。其中一个挑战是如何保持及时和具体的世系分配,而限制因素则是没有考虑树节点之间的遗传距离。TreeScan 通过自动筛选基因组数据并生成近期病例异常增加的节点排序短名单,有助于发现新出现的传染病集群并将其列为优先调查对象。
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