ArticleJournal of the Royal Society, Interface2014
Sampling through time and phylodynamic inference with coalescent and birth-death models.
Article in Journal of the Royal Society, Interface, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed.
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- Advances in Contact Tracing: A Bayesian Framework to Improve Network and Transmission Models.Journal of molecular evolution · 2026Review
- Quantifying the real-world impact of antibiotic use and genetic determinants of resistance on gonococcal dynamics.Nature microbiology · 2026Article
- Inference of Multiple Mergers while Dating a Pathogen Phylogeny.Systematic biology · 2025Article
- Tracking Seasonal Influenza Trends in South Tyrol During 2022/2023 Using Genomic Surveillance Data.Influenza and other respiratory viruses · 2025Article
- How does date-rounding affect phylodynamic inference for public health?PLoS computational biology · 2025Article
- Identifying impacts of contact tracing on HIV epidemiological inference from phylogenetic data.Virus evolution · 2025Article
- Evolutionary and epidemic dynamics of COVID-19 in Germany exemplified by three Bayesian phylodynamic case studies.Bioinformatics and biology insights · 2025Article
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- Assessing the effect of model specification and prior sensitivity on Bayesian tests of temporal signal.PLoS computational biology · 2024Article
- Estimating the fitness cost and benefit of antimicrobial resistance from pathogen genomic data.Journal of the Royal Society, Interface · 2023Article
- Decoding the Fundamental Drivers of Phylodynamic Inference.Molecular biology and evolution · 2023Article
- Epidemiological inference for emerging viruses using segregating sites.Nature communications · 2023Article
- Comparing the transmission potential from sequence and surveillance data of 2009 North American influenza pandemic waves.Infectious Disease Modelling · 2023Article
- adaPop: Bayesian inference of dependent population dynamics in coalescent models.PLoS computational biology · 2023Article
- Reconstructing relative transmission rates in Bayesian phylodynamics: Two-fold transmission advantage of Omicron in Berlin, Germany during December 2021.Virus evolution · 2023Article
- Bayesian Inference of Clonal Expansions in a Dated Phylogeny.Systematic biology · 2022Article
- Article
- Statistical Challenges in Tracking the Evolution of SARS-CoV-2.Statistical science : a review journal of the Institute of Mathematical Statistics · 2022Article
- Adaptive Preferential Sampling in Phylodynamics With an Application to SARS-CoV-2.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2022Article
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2 authors.
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Abstract
Many population genetic models have been developed for the purpose of inferring population size and growth rates from random samples of genetic data. We examine two popular approaches to this problem, the coalescent and the birth–death-sampling model (BDM), in the context of estimating population size and birth rates in a population growing exponentially according to the birth–death branching process. For sequences sampled at a single time, we found the coalescent and the BDM gave virtually indistinguishable results in terms of the growth rates and fraction of the population sampled, even when sampling from a small population. For sequences sampled at multiple time points, we find that the birth–death model estimators are subject to large bias if the sampling process is misspecified. Since BDMs incorporate a model of the sampling process, we show how much of the statistical power of BDMs arises from the sequence of sample times and not from the genealogical tree. This motivates the development of a new coalescent estimator, which is augmented with a model of the known sampling process and is potentially more precise than the coalescent that does not use sample time information.
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