ArticleJournal of the Royal Society, Interface2018
Detection of HIV transmission clusters from phylogenetic trees using a multi-state birth-death model.
Article in Journal of the Royal Society, Interface, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- New insights on angiosperm crown age based on Bayesian node dating and skyline fossilized birth-death approaches.Nature communications · 2025Article
- DeepDynaForecast: Phylogenetic-informed graph deep learning for epidemic transmission dynamic prediction.PLoS computational biology · 2024Article
- Phylogenetic-informed graph deep learning to classify dynamic transmission clusters in infectious disease epidemics.Bioinformatics advances · 2024Article
- Epidemiological cluster identification using multiple data sources: an approach using logistic regression.Microbial genomics · 2023Article
- Characterization of HIV-1 Transmission Clusters Inferred from the Brazilian Nationwide Genotyping Service Database.Viruses · 2022Article
- Optimized phylogenetic clustering of HIV-1 sequence data for public health applications.PLoS computational biology · 2022Article
- The impact of sampling bias on viral phylogeographic reconstruction.PLOS global public health · 2022Article
- Unifying Phylogenetic Birth-Death Models in Epidemiology and Macroevolution.Systematic biology · 2021Article
- Concordance of HIV transmission risk factors elucidated using viral diversification rate and phylogenetic clustering.Evolution, medicine, and public health · 2021Article
- Decomposing the sources of SARS-CoV-2 fitness variation in the United States.Virus evolution · 2021Article
- Inferring transmission heterogeneity using virus genealogies: Estimation and targeted prevention.PLoS computational biology · 2020Article
- Coupling adaptive molecular evolution to phylodynamics using fitness-dependent birth-death models.eLife · 2019Article
- Inferring putative transmission clusters with Phydelity.Virus evolution · 2019Article
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Authors and funding
3 authors.
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Abstract
HIV patients form clusters in HIV transmission networks. Accurate identification of these transmission clusters is essential to effectively target public health interventions. One reason for clustering is that the underlying contact network contains many local communities. We present a new maximum-likelihood method for identifying transmission clusters caused by community structure, based on phylogenetic trees. The method employs a multi-state birth-death (MSBD) model which detects changes in transmission rate, which are interpreted as the introduction of the epidemic into a new susceptible community, i.e. the formation of a new cluster. We show that the MSBD method is able to reliably infer the clusters and the transmission parameters from a pathogen phylogeny based on our simulations. In contrast to existing cutpoint-based methods for cluster identification, our method does not require that clusters be monophyletic nor is it dependent on the selection of a difficult-to-interpret cutpoint parameter. We present an application of our method to data from the Swiss HIV Cohort Study. The method is available as an easy-to-use R package.
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