Identifying SARS-CoV-2 regional introductions and transmission clusters in real time

Abstract

The unprecedented SARS-CoV-2 global sequencing effort has suffered from an analytical bottleneck. Many existing methods for phylogenetic analysis are designed for sparse, static datasets and are too computationally expensive to apply to densely sampled, rapidly expanding datasets when results are needed immediately to inform public health action. For example, public health is often concerned with identifying clusters of closely related samples, but the sheer scale of the data prevents manual inspection and the current computational models are often too expensive in time and resources. Even when results are available, intuitive data exploration tools are of critical importance to effective public health interpretation and action. To help address this need, we present a phylogenetic summary statistic which quickly and efficiently identifies newly introduced strains in a region, resulting clusters of infected individuals, and their putative geographic origins. We show that this approach performs well on simulated data and is congruent with a more sophisticated analysis performed during the pandemic. We also introduce Cluster Tracker (https://clustertracker.gi.ucsc.edu/), a novel interactive web-based tool to facilitate effective and intuitive SARS-CoV-2 geographic data exploration and visualization. Cluster-Tracker is updated daily and automatically identifies and highlights groups of closely related SARS-CoV-2 infections resulting from inter-regional transmission across the United States, streamlining public health tracking of local viral diversity and emerging infection clusters. The combination of these open-source tools will empower detailed investigations of the geographic origins and spread of SARS-CoV-2 and other densely-sampled pathogens.

matOptimize: a parallel tree optimization method enables online phylogenetics for SARS-CoV-2

“…This tree is being used widely by health officials and researchers worldwide through UShER’s command line ( https://github.com/yatisht/usher ) and web interface ( https://genome.ucsc.edu/cgi-bin/hgPhyloPlace ) to perform genomic surveillance ( Abe and Arita, 2021 ; Dudas et al , 2021 ; Foster et al , 2022 ; Garushyants et al , 2021 ) and contact tracing ( Lam-Hine, 2021 ) of the COVID-19 pathogen. This tree has also been applied for SARS-CoV-2 lineage assignment in the optional mode of Pangolin ( https://github.com/cov-lineages/pangolin/ ), the most widely used scientific nomenclature system for SARS-CoV-2 lineages ( Rambaut et al , 2020 ), as well as in several recently developed phylogenetic toolkits ( Chen et al , 2021 ; McBroome et al , 2022 ; Sanderson, 2021 ). GISAID’s Audacity tree ( Shu and McCauley, 2017 ) is also based on the UShER package.…”

“…for identifying and naming its new variants ( Rambaut et al , 2020 ) and for tracking the different SARS-CoV-2 variants circulating in a given geographic region ( da Silva Filipe et al , 2021 ; Deng et al , 2020 ). Phylogenetic trees have also helped in establishing transmission links between infections ( Lam-Hine, 2021 ), in disambiguating community transmission from outside introductions ( Komissarov et al , 2021 ; McBroome et al , 2022 ), in identifying the mutations that might have conferred increased transmissibility to the virus ( Korber et al , 2020 ; Richard et al , 2021 ), and for estimating the reproduction number (R0) of the virus and its variants ( Lai et al , 2020 ; Volz et al , 2021 ).…”

Maximum likelihood pandemic-scale phylogenetics

“…Genomic data play a crucial role in epidemiology, as exemplified during the SARS-CoV-2 pandemic. Genomic data can be used to track and reconstruct the spread of disease within communities and within and between countries [17, 33, 36, 58], understand the dynamics of transmission [36, 41, 52], estimate the efficacy of containment measures [8, 18, 26, 31], predict future epidemiological dynamics, [57, 58], and for the tracking of pathogen evolution, as showcased by the identification of new SARS-CoV-2 variants and mutations of concern [30, 40].…”

Variational phylodynamic inference using pandemic-scale data

“…In particular, there has been significant recent progress in estimating and/or placing novel sequences onto very large phylogenies (Minh et al, 2020; Turakhia et al, 2021a; Aksamentov et al, 2021; Ye et al, 2022a,b). Accurate estimation of the underlying phylogeny has numerous downstream applications, including contact tracing (e.g., Lam-Hine et al, 2021; McBroome et al, 2022), surveillance (e.g., Abe and Arita, 2021; Klink et al, 2021), and improved understanding of pathogen biology (e.g. Majumdar and Sarkar, 2021; Turakhia et al, 2021b).…”