Evidence map›Paper›PMID 36035966›Full record

ArticleJournal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America2022

Adaptive Preferential Sampling in Phylodynamics With an Application to SARS-CoV-2.

Lorenzo Cappello, Julia A Palacios

Abstract read
In one paragraph

Article in Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Statistical Challenges in Tracking the Evolution of SARS-CoV-2.Statistical science : a review journal of the Institute of Mathematical Statistics · 2022
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Lorenzo CappelloDepartment of Statistics, Stanford University, Stanford, CA.
Julia A PalaciosDepartment of Statistics, Stanford University, Stanford, CA.

Funding

Scalable Coalescent Inference for Large Data SetsR01GM131404 · NIGMS · STANFORD UNIVERSITY · PI PALACIOS, JULIA · 2018 to 2021
$1.2M
NIGMS NIH HHS R01 GM131404
6 · The paper itself

Abstract

Longitudinal molecular data of rapidly evolving viruses and pathogens provide information about disease spread and complement traditional surveillance approaches based on case count data. The coalescent is used to model the genealogy that represents the sample ancestral relationships. The basic assumption is that coalescent events occur at a rate inversely proportional to the effective population size

Indexed as

Coalescent processMarkov random fieldsPoisson processesPopulation size

Identifiers

PMID36035966
PMCPMC9409340

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.