Speaker
Description
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 would not be expected to capture dynamic clusters of transmission. We, therefore, sought to evaluate the performance of existing phylogeny-based cluster identification tools on simulated transmission clusters exhibiting dynamic transmission behavior over time. Based on the performance of these tools, we developed DYNAMITE (DYNAMic Identification of Transmission Epicenters), a cluster identification algorithm that utilizes a dynamic branchwise, rather than traditional cladewise, search for cluster-defining criteria. DYNAMITE was demonstrated to complement existing tools, improving recognition of declining transmission among risk groups. Despite the complementarity of the methods described, we provide strong evidence that novel cluster identification methods are needed for reliable detection of epidemiologically linked individuals, particularly those exhibiting changing transmission dynamics during dynamic outbreak scenarios.
