Evidence map›Paper›PMID 34707835›Full record

ArticleEcology and evolution2021

Estimating viral prevalence with data fusion for adaptive two-phase pooled sampling.

Andrew Hoegh, Alison J Peel, Wyatt Madden, Manuel Ruiz Aravena, Aaron Morris, Alex Washburne, Raina K Plowright

Abstract read
In one paragraph

Article in Ecology and evolution, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

7 authors.

Andrew HoeghDepartment of Mathematical Sciences Montana State University Bozeman MT USA.ORCID https://orcid.org/0000-0003-1176-4965
Alison J PeelCentre for Planetary Health and Food Security Griffith University Nathan QLD Australia.
Wyatt MaddenDepartment of Microbiology and Immunology Montana State University Bozeman MT USA.
Manuel Ruiz AravenaDepartment of Microbiology and Immunology Montana State University Bozeman MT USA.
Aaron MorrisDepartment of Veterinary Medicine University of Cambridge Cambridge UK.
Alex WashburneSelva Analytics LLC Bozeman MT USA.
Raina K PlowrightDepartment of Microbiology and Immunology Montana State University Bozeman MT USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic has highlighted the importance of efficient sampling strategies and statistical methods for monitoring infection prevalence, both in humans and in reservoir hosts. Pooled testing can be an efficient tool for learning pathogen prevalence in a population. Typically, pooled testing requires a second-phase retesting procedure to identify infected individuals, but when the goal is solely to learn prevalence in a population, such as a reservoir host, there are more efficient methods for allocating the second-phase samples.To estimate pathogen prevalence in a population, this manuscript presents an approach for data fusion with two-phased testing of pooled samples that allows more efficient estimation of prevalence with less samples than traditional methods. The first phase uses pooled samples to estimate the population prevalence and inform efficient strategies for the second phase. To combine information from both phases, we introduce a Bayesian data fusion procedure that combines pooled samples with individual samples for joint inferences about the population prevalence.Data fusion procedures result in more efficient estimation of prevalence than traditional procedures that only use individual samples or a single phase of pooled sampling.The manuscript presents guidance on implementing the first-phase and second-phase sampling plans using data fusion. Such methods can be used to assess the risk of pathogen spillover from reservoir hosts to humans, or to track pathogens such as SARS-CoV-2 in populations.

Indexed as

adaptive samplingBayesian statisticsgroup testing

Identifiers

PMID34707835
PMCPMC8525136

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.