DYNAMITE: a phylogenetic tool for identification of dynamic transmission epicenters

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Authors Brittany Rife Magalis, Simone Marini, Marco Salemi, Mattia Prosperi
Journal/Conference Name Methods in Ecology and Evolution
Paper Category , ,
Paper Abstract Molecular data analysis is invaluable in understanding the overall behavior of a rapidly spreading virus population when epidemiological surveillance is problematic. It is also particularly beneficial in describing subgroups within the population, often identified as clades within a phylogenetic tree, that represent individuals connected via direct transmission or transmission via differing risk factors in viral spread. However, transmission patterns or viral dynamics within these smaller groups should not be expected to exhibit homogeneous behavior over time. As such, standard phylogenetic approaches that identify clusters based on summary statistics (e.g., median genetic distance over the clade) would not be expected to capture dynamic clusters of transmission. For this purpose, we have developed DYNAMITE (DYNAMic Identification of Transmission Epicenters), a cluster identification algorithm based on a branch-wise (rather than traditional clade-wise) search for cluster criteria, allowing partial clades to be recognized as clusters. Using simulated viral outbreaks with varying cluster types and dynamics, we show that DYNAMITE is consistently more sensitive than existing tools in detecting both static and dynamic transmission clusters. DYNAMITE has been implemented in R and released as open source at github.com/ProsperiLab/DYNAMITE.
Date of publication 2021
Code Programming Language R
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