ArticleProceedings. Biological sciences2015
How well can the exponential-growth coalescent approximate constant-rate birth-death population dynamics?
Article in Proceedings. Biological sciences, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Optimizing genomic sampling for demographic and epidemiological inference with Markov decision processes.Genetics · 2026Article
- Phylogenetics and molecular evolution to understand and curb the HIV pandemic.Nature reviews. Microbiology · 2026Review
- A Pangenomic Approach to Improve Population Genetics Analysis and Reference Bias in Underrepresented Middle Eastern and Horn of Africa Populations.Biomolecules · 2025Article
- How does date-rounding affect phylodynamic inference for public health?PLoS computational biology · 2025Article
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- Assessing the effect of model specification and prior sensitivity on Bayesian tests of temporal signal.PLoS computational biology · 2024Article
- Deterministic epidemic models overestimate the basic reproduction number of observed outbreaks.Infectious Disease Modelling · 2024Article
- cloneRate: fast estimation of single-cell clonal dynamics using coalescent theory.Bioinformatics (Oxford, England) · 2023Article
- Epidemiological inference from pathogen genomes: A review of phylodynamic models and applications.Virus evolution · 2022Review
- Unifying Phylogenetic Birth-Death Models in Epidemiology and Macroevolution.Systematic biology · 2021Article
- Factors influencing estimates of HIV-1 infection timing using BEAST.PLoS computational biology · 2021Article
- The Impacts of Low Diversity Sequence Data on Phylodynamic Inference during an Emerging Epidemic.Viruses · 2021Article
- Measuring single cell divisions in human tissues from multi-region sequencing data.Nature communications · 2020Article
- Distinct rates and patterns of spread of the major HIV-1 subtypes in Central and East Africa.PLoS pathogens · 2019Article
- Estimating Epidemic Incidence and Prevalence from Genomic Data.Molecular biology and evolution · 2019Article
- An accurate approximation for the expected site frequency spectrum in a Galton-Watson process under an infinite sites mutation model.Theoretical population biology · 2019Article
- History is written by the victors: The effect of the push of the past on the fossil record.Evolution; international journal of organic evolution · 2018Article
- The relationship between transmission time and clustering methods in Mycobacterium tuberculosis epidemiology.EBioMedicine · 2018Article
- Quantifying the fitness cost of HIV-1 drug resistance mutations through phylodynamics.PLoS pathogens · 2018Observational
- Phylodynamics with Migration: A Computational Framework to Quantify Population Structure from Genomic Data.Molecular biology and evolution · 2016Article
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7 authors.
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Abstract
One of the central objectives in the field of phylodynamics is the quantification of population dynamic processes using genetic sequence data or in some cases phenotypic data. Phylodynamics has been successfully applied to many different processes, such as the spread of infectious diseases, within-host evolution of a pathogen, macroevolution and even language evolution. Phylodynamic analysis requires a probability distribution on phylogenetic trees spanned by the genetic data. Because such a probability distribution is not available for many common stochastic population dynamic processes, coalescent-based approximations assuming deterministic population size changes are widely employed. Key to many population dynamic models, in particular epidemiological models, is a period of exponential population growth during the initial phase. Here, we show that the coalescent does not well approximate stochastic exponential population growth, which is typically modelled by a birth-death process. We demonstrate that introducing demographic stochasticity into the population size function of the coalescent improves the approximation for values of R0 close to 1, but substantial differences remain for large R0. In addition, the computational advantage of using an approximation over exact models vanishes when introducing such demographic stochasticity. These results highlight that we need to increase efforts to develop phylodynamic tools that correctly account for the stochasticity of population dynamic models for inference.
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