DM-PhyClus: A Bayesian phylogenetic algorithm for infectious disease transmission cluster inference

TL;DR

DM-PhyClus is a Bayesian phylogenetic clustering algorithm that accurately identifies transmission clusters in infectious disease data without arbitrary cutpoints, aiding public health efforts.

Contribution

It introduces a novel Bayesian clustering method that improves cluster recovery and interpretability over traditional approaches without using ad hoc thresholds.

Findings

Outperforms conventional methods in simulations

Produces clusters consistent with previous analyses on HIV-1 data

Eliminates need for arbitrary cutpoints in clustering

Abstract

Background. Conventional phylogenetic clustering approaches rely on arbitrary cutpoints applied a posteriori to phylogenetic estimates. Although in practice, Bayesian and bootstrap-based clustering tend to lead to similar estimates, they often produce conflicting measures of confidence in clusters. The current study proposes a new Bayesian phylogenetic clustering algorithm, which we refer to as DM-PhyClus, that identifies sets of sequences resulting from quick transmission chains, thus yielding easily-interpretable clusters, without using any ad hoc distance or confidence requirement. Results. Simulations reveal that DM-PhyClus can outperform conventional clustering methods, as well as the Gap procedure, a pure distance-based algorithm, in terms of mean cluster recovery. We apply DM-PhyClus to a sample of real HIV-1 sequences, producing a set of clusters whose inference is in line with the conclusions of a previous thorough analysis. Conclusions. DM-PhyClus, by eliminating the need for cutpoints and producing sensible inference for cluster configurations, can facilitate transmission cluster detection. Future efforts to reduce incidence of infectious diseases, like HIV-1, will need reliable estimates of transmission clusters. It follows that algorithms like DM-PhyClus could serve to better inform public health strategies.