Evidence map›Paper›PMID 33044955›Full record

ArticlePLoS computational biology2020

Estimating effective population size changes from preferentially sampled genetic sequences.

Michael D Karcher, Luiz Max Carvalho, Marc A Suchard, Gytis Dudas, Vladimir N Minin

Abstract read
In one paragraph

Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

  1. Article
  2. Article
  3. Tracing Dengue's Evolutionary and Phylogenetic Pathways.Current topics in microbiology and immunology · 2026
    Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Statistical Challenges in Tracking the Evolution of SARS-CoV-2.Statistical science : a review journal of the Institute of Mathematical Statistics · 2022
    Article
  14. 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 · 2022
    Article
  15. Distance metrics for ranked evolutionary trees.Proceedings of the National Academy of Sciences of the United States of America · 2020
    Article
  16. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Michael D KarcherDepartment of Statistics, University of Washington, Seattle, Washington, U.S.A.
Luiz Max CarvalhoNational School of Public Health, Oswaldo Cruz Foundation, Brazil.
Marc A SuchardDepartment of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, California, U.S.A.
Gytis DudasVaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center.ORCID 0000-0002-0227-4158
Vladimir N MininDepartment of Statistics, University of California, Irvine, California, U.S.A.ORCID 0000-0002-1917-9288

Funding

Understanding Transmission with Integrated Genetic and Epidemiologic InferenceU54GM111274 · NIGMS · FRED HUTCHINSON CANCER RESEARCH CENTER · PI BEDFORD, TREVOR BC · 2014 to 2018
$12.5M
BAYESIAN MODELING AND DATA INTEGRATION IN INFECTIOUS DISEASE PHYLODYNAMICSR01AI107034 · NIAID · UNIVERSITY OF WASHINGTON · PI MININ, VOLODYMYR · 2013 to 2017
$1.7M
Merging Viral Genetics with Climate and Population Data for Zoonotic SurveillanceR01LM012080 · NLM · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI SCOTCH, MATTHEW · 2015 to 2017
$1.1M
NIAID NIH HHS R01 AI107034NIGMS NIH HHS U54 GM111274NLM NIH HHS R01 LM012080
6 · The paper itself

Abstract

Coalescent theory combined with statistical modeling allows us to estimate effective population size fluctuations from molecular sequences of individuals sampled from a population of interest. When sequences are sampled serially through time and the distribution of the sampling times depends on the effective population size, explicit statistical modeling of sampling times improves population size estimation. Previous work assumed that the genealogy relating sampled sequences is known and modeled sampling times as an inhomogeneous Poisson process with log-intensity equal to a linear function of the log-transformed effective population size. We improve this approach in two ways. First, we extend the method to allow for joint Bayesian estimation of the genealogy, effective population size trajectory, and other model parameters. Next, we improve the sampling time model by incorporating additional sources of information in the form of time-varying covariates. We validate our new modeling framework using a simulation study and apply our new methodology to analyses of population dynamics of seasonal influenza and to the recent Ebola virus outbreak in West Africa.

Indexed as

Models, StatisticalPopulation DensityBayes TheoremComputational BiologyEbolavirusGenetics, PopulationGenome, ViralHemorrhagic Fever, EbolaHumansInfluenza, HumanOrthomyxoviridaePopulation Dynamics

Identifiers

PMID33044955
PMCPMC7580988

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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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.