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

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.