Evidence map›Paper›PMID 34896438›Full record

ArticleTheoretical population biology2022

Markov genealogy processes.

Aaron A King, Qianying Lin, Edward L Ionides

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Theoretical population biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
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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

3 authors.

Aaron A KingDepartment of Ecology & Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109, USA; Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI 48109, USA; Center for Computational Medicine & Biology, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI 48109, USA. Electronic address: kingaa@umich.edu.
Qianying LinMichigan Institute for Data Science, University of Michigan, Ann Arbor, MI 48109, USA.
Edward L IonidesDepartment of Statistics, University of Michigan, Ann Arbor, MI 48109, USA; Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI 48109, USA.

Funding

Understanding Transmission with Integrated Genetic and Epidemiologic InferenceU54GM111274 · NIGMS · FRED HUTCHINSON CANCER RESEARCH CENTER · PI BEDFORD, TREVOR BC · 2014 to 2018
$12.5M
Dynamical modeling of hospital transmission and antibiotic resistance evolution in a multidrug resistant nosocomial pathogenR01AI143852 · NIAID · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI WOODS, ROBERT J · 2019 to 2023
$2.9M
Integrating immunology, epidemiology, and evolution to understand and control perR01AI101155 · NIAID · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI KING, AARON A., ROHANI, PEJMAN · 2012 to 2016
$1.7M
NIAID NIH HHS R01 AI101155NIAID NIH HHS R01 AI143852NIGMS NIH HHS U54 GM111274
6 · The paper itself

Abstract

We construct a family of genealogy-valued Markov processes that are induced by a continuous-time Markov population process. We derive exact expressions for the likelihood of a given genealogy conditional on the history of the underlying population process. These lead to a nonlinear filtering equation which can be used to design efficient Monte Carlo inference algorithms. We demonstrate these calculations with several examples. Existing full-information approaches for phylodynamic inference are special cases of the theory.

Indexed as

AlgorithmsBayes TheoremMarkov ChainsMonte Carlo MethodHidden Markov modelMolecular epidemiologyPartially observed Markov processPhylodynamicsPhylogenyStatistical inference

Identifiers

PMID34896438
PMCPMC8846264

What OpenQuestion holds

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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.