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- W4300539063 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." @default.
- W4300539063 created "2022-10-03" @default.
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- W4300539063 date "2017-08-08" @default.
- W4300539063 modified "2023-10-18" @default.
- W4300539063 title "DM-PhyClus: A Bayesian phylogenetic algorithm for infectious disease transmission cluster inference" @default.
- W4300539063 doi "https://doi.org/10.48550/arxiv.1708.02648" @default.
- W4300539063 hasPublicationYear "2017" @default.
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