Evidence map›Paper›PMID 32814072›Full record

ArticleJournal of theoretical biology2020

Rapid, large-scale, and effective detection of COVID-19 via non-adaptive testing.

Matthias Täufer

Open access · greenAbstract read
In one paragraph

Article in Journal of theoretical biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
1.0field-weighted citation impact, top 14% of its field
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

18 citing papers in PubMed, 40 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Constructions and Comparisons of Pooling Matrices for Pooled Testing of COVID-19.IEEE transactions on network science and engineering · 2022
    Article
  5. Review
  6. Pooled Testing Strategies for SARS-CoV-2 diagnosis: A comprehensive review.Diagnostic microbiology and infectious disease · 2021
    Review
  7. Article
  8. Article
  9. Article
  10. Considerations for Pooled Testing of Employees for SARS-CoV-2.Journal of occupational and environmental medicine · 2021
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. 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

1 author at 1 institution in 1 country.

Matthias TäuferSchool of Mathematical Sciences, Queen Mary University of London, United Kingdom.
Queen Mary University of London · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pooling of samples can increase lab capacity when using Polymerase chain reaction (PCR) to detect diseases such as COVID-19. However, pool testing is typically performed via an adaptive testing strategy which requires a feedback loop in the lab and at least two PCR runs to confirm positive results. This can cost precious time. We discuss a non-adaptive testing method where each sample is distributed in a prescribed manner over several pools, and which yields reliable results after one round of testing. More precisely, assuming knowledge about the overall incidence rate, we calculate explicit error bounds on the number of false positives which scale favourably with pool size and sample multiplicity. This allows for hugely streamlined PCR testing and cuts in detection times for a large-scale testing scenario. A viable consequence of this method could be real-time screening of entire communities, frontline healthcare workers and international flight passengers, for example, using the PCR machines currently in operation.

Indexed as

BetacoronavirusClinical Laboratory TechniquesCoronavirus InfectionsCOVID-19COVID-19 TestingFalse Positive ReactionsHumansMass ScreeningPandemicsPneumonia, ViralReal-Time Polymerase Chain ReactionSARS-CoV-2Time FactorsCOVID-19Non-adaptive testingPolymerase chain reactionPool testingSARS-CoV-2

Identifiers

PMID32814072
PMCPMC7428746
OpenAlexW3048889434

What OpenQuestion holds

Textmetadata
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.