Cluster-Tracker

Cluster-Tracker identifies clusters of closely related SARS-CoV-2 genomes within regions using a phylogenetic heuristic to enable rapid detection of introductions and inference of geographic origins from large-scale sequencing datasets.

Key Features:

• Phylogenetic heuristic: employs a heuristic algorithm to rapidly identify newly introduced SARS-CoV-2 strains within a region and to infer clusters and putative geographic origins.
• Automatic cluster detection: automatically identifies groups of closely related SARS-CoV-2 infections and tracks local viral diversity and emerging clusters, including introductions related to inter-regional travel, with daily updates.
• Scalability for densely sampled data: processes large-scale sequencing datasets that are computationally challenging for traditional phylogenetic methods.
• Configurable regional analysis: supports analysis focused on specified geographic regions, including the USA and other regions.

Scientific Applications:

• Public health surveillance: enables rapid identification of infection clusters and probable geographic sources to inform public health responses.
• Transmission inference: facilitates detection of introductions and inter-regional transmission patterns from genomic data.
• Model comparison: generates results largely congruent with Bayesian phylogeographic models, enabling comparison with more computationally intensive approaches.

Methodology:

Uses a phylogenetic heuristic that processes large-scale SARS-CoV-2 genomic data to identify clusters of closely related samples while avoiding the computational expense of traditional phylogenetic analyses.