ArticleMolecular biology and evolution2020
Jointly Inferring the Dynamics of Population Size and Sampling Intensity from Molecular Sequences.
Article in Molecular biology and evolution, 2020. 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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20 citing papers in PubMed, 38 citations in OpenAlex.
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- Scalable gradients enable Hamiltonian Monte Carlo sampling for phylodynamic inference under episodic birth-death-sampling models.PLoS computational biology · 2024Article
- Scalable gradients enable Hamiltonian Monte Carlo sampling for phylodynamic inference under episodic birth-death-sampling models.bioRxiv : the preprint server for biology · 2023Article
- adaPop: Bayesian inference of dependent population dynamics in coalescent models.PLoS computational biology · 2023Article
- Self-assembled multiepitope nanovaccine based on NoV P particles induces effective and lasting protection against H3N2 influenza virus.Nano research · 2023Article
- The Occurrence Birth-Death Process for Combined-Evidence Analysis in Macroevolution and Epidemiology.Systematic biology · 2022Article
- An Efficient Coalescent Epoch Model for Bayesian Phylogenetic Inference.Systematic biology · 2022Article
- Using multiple sampling strategies to estimate SARS-CoV-2 epidemiological parameters from genomic sequencing data.Nature communications · 2022Article
- Phylogenetic and phylodynamic approaches to understanding and combating the early SARS-CoV-2 pandemic.Nature reviews. Genetics · 2022Review
- Statistical Challenges in Tracking the Evolution of SARS-CoV-2.Statistical science : a review journal of the Institute of Mathematical Statistics · 2022Article
- A computationally tractable birth-death model that combines phylogenetic and epidemiological data.PLoS computational biology · 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
- Epidemiological inference from pathogen genomes: A review of phylodynamic models and applications.Virus evolution · 2022Review
- Accounting for spatial sampling patterns in Bayesian phylogeography.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Are Skyline Plot-Based Demographic Estimates Overly Dependent on Smoothing Prior Assumptions?Systematic biology · 2021Article
- Distance metrics for ranked evolutionary trees.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- An exact method for quantifying the reliability of end-of-epidemic declarations in real time.PLoS computational biology · 2020Article
- Adaptive Estimation for Epidemic Renewal and Phylogenetic Skyline Models.Systematic biology · 2020Article
- The Recovery, Interpretation and Use of Ancient Pathogen Genomes.Current biology : CB · 2020Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Estimating past population dynamics from molecular sequences that have been sampled longitudinally through time is an important problem in infectious disease epidemiology, molecular ecology, and macroevolution. Popular solutions, such as the skyline and skygrid methods, infer past effective population sizes from the coalescent event times of phylogenies reconstructed from sampled sequences but assume that sequence sampling times are uninformative about population size changes. Recent work has started to question this assumption by exploring how sampling time information can aid coalescent inference. Here, we develop, investigate, and implement a new skyline method, termed the epoch sampling skyline plot (ESP), to jointly estimate the dynamics of population size and sampling rate through time. The ESP is inspired by real-world data collection practices and comprises a flexible model in which the sequence sampling rate is proportional to the population size within an epoch but can change discontinuously between epochs. We show that the ESP is accurate under several realistic sampling protocols and we prove analytically that it can at least double the best precision achievable by standard approaches. We generalize the ESP to incorporate phylogenetic uncertainty in a new Bayesian package (BESP) in BEAST2. We re-examine two well-studied empirical data sets from virus epidemiology and molecular evolution and find that the BESP improves upon previous coalescent estimators and generates new, biologically useful insights into the sampling protocols underpinning these data sets. Sequence sampling times provide a rich source of information for coalescent inference that will become increasingly important as sequence collection intensifies and becomes more formalized.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.