ArticleSystematic biology2019
Phylodynamic Model Adequacy Using Posterior Predictive Simulations.
Article in Systematic biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Limits of the Constant-rate Birth-Death Prior for Phylogenetic Tree Topology Inference.Systematic biology · 2024Pooled it
- The Phylodynamic Threshold of Measurably Evolving Populations.Molecular biology and evolution · 2026Article
- When Complex Models Fit the Wrong Mechanistic Complexity in Phylogenomic Analysis.Journal of molecular evolution · 2026Article
- Phylodynamic analysis ofMicrobial genomics · 2026Article
- A Bayesian phylodynamic inference framework for single-cell CRISPR/Cas9 lineage tracing barcode data with dependent target sites.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Article
- Comparison of Bayesian Coalescent Skyline Plot Models for Inferring Demographic Histories.Molecular biology and evolution · 2024Article
- ReMASTER: improved phylodynamic simulation for BEAST 2.7.Bioinformatics (Oxford, England) · 2024Article
- The build-up of the present-day tropical diversity of tetrapods.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Comparing the transmission potential from sequence and surveillance data of 2009 North American influenza pandemic waves.Infectious Disease Modelling · 2023Article
- Practical guidelines for Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC).Open research Europe · 2023Article
- Model design for nonparametric phylodynamic inference and applications to pathogen surveillance.Virus evolution · 2023Article
- TiDeTree: a Bayesian phylogenetic framework to estimate single-cell trees and population dynamic parameters from genetic lineage tracing data.Proceedings. Biological sciences · 2022Article
- A simple hierarchical model for heterogeneity in the evolutionary correlation on a phylogenetic tree.PeerJ · 2022Article
- Epidemiological inference from pathogen genomes: A review of phylodynamic models and applications.Virus evolution · 2022Review
- Assessing model adequacy for Bayesian Skyline plots using posterior predictive simulation.PloS one · 2022Article
- The Impacts of Low Diversity Sequence Data on Phylodynamic Inference during an Emerging Epidemic.Viruses · 2021Article
- The mitogenome of Phytophthora agathidicida: Evidence for a not so recent arrival of the "kauri killing" Phytophthora in New Zealand.PloS one · 2021Article
- On the minimum value of the Colless index and the bifurcating trees that achieve it.Journal of mathematical biology · 2020Article
- On Sackin's original proposal: the variance of the leaves' depths as a phylogenetic balance index.BMC bioinformatics · 2020Article
- Testing adequacy for DNA substitution models.BMC bioinformatics · 2019Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rapidly evolving pathogens, such as viruses and bacteria, accumulate genetic change at a similar timescale over which their epidemiological processes occur, such that, it is possible to make inferences about their infectious spread using phylogenetic time-trees. For this purpose it is necessary to choose a phylodynamic model. However, the resulting inferences are contingent on whether the model adequately describes key features of the data. Model adequacy methods allow formal rejection of a model if it cannot generate the main features of the data. We present TreeModelAdequacy, a package for the popular BEAST2 software that allows assessing the adequacy of phylodynamic models. We illustrate its utility by analyzing phylogenetic trees from two viral outbreaks of Ebola and H1N1 influenza. The main features of the Ebola data were adequately described by the coalescent exponential-growth model, whereas the H1N1 influenza data were best described by the birth-death susceptible-infected-recovered model.
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