ArticleMolecular biology and evolution2015
Phylodynamic Inference with Kernel ABC and Its Application to HIV Epidemiology.
Article in Molecular biology and evolution, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Phylogenetics and molecular evolution to understand and curb the HIV pandemic.Nature reviews. Microbiology · 2026Review
- Phylogenetic identification of influenza virus candidates for seasonal vaccines.Science advances · 2023Article
- Statistical Challenges in Tracking the Evolution of SARS-CoV-2.Statistical science : a review journal of the Institute of Mathematical Statistics · 2022Article
- Markov genealogy processes.Theoretical population biology · 2022Article
- Network science inspires novel tree shape statistics.PloS one · 2021Article
- On the minimum value of the Colless index and the bifurcating trees that achieve it.Journal of mathematical biology · 2020Article
- Predicting the short-term success of human influenza virus variants with machine learning.Proceedings. Biological sciences · 2020Article
- Phylodynamic Model Adequacy Using Posterior Predictive Simulations.Systematic biology · 2019Article
- Phylogenies from dynamic networks.PLoS computational biology · 2019Article
- Bayesian phylodynamic inference with complex models.PLoS computational biology · 2018Article
- Inferring demographic parameters in bacterial genomic data using Bayesian and hybrid phylogenetic methods.BMC evolutionary biology · 2018Article
- An open-source k-mer based machine learning tool for fast and accurate subtyping of HIV-1 genomes.PloS one · 2018Article
- Infectious Disease Dynamics Inferred from Genetic Data via Sequential Monte Carlo.Molecular biology and evolution · 2017Article
- Phylodynamic Inference across Epidemic Scales.Molecular biology and evolution · 2017Article
- Inferring epidemiological parameters from phylogenies using regression-ABC: A comparative study.PLoS computational biology · 2017Article
- Inference of Transmission Network Structure from HIV Phylogenetic Trees.PLoS computational biology · 2017Article
- Phylogenetic Tools for Generalized HIV-1 Epidemics: Findings from the PANGEA-HIV Methods Comparison.Molecular biology and evolution · 2017Article
- Reconstructing contact network parameters from viral phylogenies.Virus evolution · 2016Article
- Impacts and shortcomings of genetic clustering methods for infectious disease outbreaks.Virus evolution · 2016Article
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Abstract
The shapes of phylogenetic trees relating virus populations are determined by the adaptation of viruses within each host, and by the transmission of viruses among hosts. Phylodynamic inference attempts to reverse this flow of information, estimating parameters of these processes from the shape of a virus phylogeny reconstructed from a sample of genetic sequences from the epidemic. A key challenge to phylodynamic inference is quantifying the similarity between two trees in an efficient and comprehensive way. In this study, I demonstrate that a new distance measure, based on a subset tree kernel function from computational linguistics, confers a significant improvement over previous measures of tree shape for classifying trees generated under different epidemiological scenarios. Next, I incorporate this kernel-based distance measure into an approximate Bayesian computation (ABC) framework for phylodynamic inference. ABC bypasses the need for an analytical solution of model likelihood, as it only requires the ability to simulate data from the model. I validate this "kernel-ABC" method for phylodynamic inference by estimating parameters from data simulated under a simple epidemiological model. Results indicate that kernel-ABC attained greater accuracy for parameters associated with virus transmission than leading software on the same data sets. Finally, I apply the kernel-ABC framework to study a recent outbreak of a recombinant HIV subtype in China. Kernel-ABC provides a versatile framework for phylodynamic inference because it can fit a broader range of models than methods that rely on the computation of exact likelihoods.
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