Evidence map›Paper›PMID 33764982›Full record

ArticlePloS one2021

Identifying optimal COVID-19 testing strategies for schools and businesses: Balancing testing frequency, individual test technology, and cost.

Gregory D Lyng, Natalie E Sheils, Caleb J Kennedy, Daniel O Griffin, Ethan M Berke

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 2 pooled it
2.0field-weighted citation impact, top 11% 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

20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 33 citations in OpenAlex.

  1. Pooled it
  2. Measures implemented in the school setting to contain the COVID-19 pandemicThe Cochrane database of systematic reviews · 2022
    Pooled it
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  8. Modeling for COVID-19 college reopening decisions: Cornell, a case study.Proceedings of the National Academy of Sciences of the United States of America · 2022
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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

5 authors at 1 institution in 1 country.

Gregory D LyngOptumLabs, UnitedHealth Group, Minnetonka, MN, United States of America.
Natalie E SheilsOptumLabs, UnitedHealth Group, Minnetonka, MN, United States of America.ORCID 0000-0002-0631-5834
Caleb J KennedyOptumLabs, UnitedHealth Group, Minnetonka, MN, United States of America.
Daniel O GriffinDivision of Infectious Diseases, Department of Medicine, Columbia University, New York, NY, United States of America.ORCID 0000-0001-5853-6906
Ethan M BerkeOptumLabs, UnitedHealth Group, Minnetonka, MN, United States of America.ORCID 0000-0003-0805-8218
Optum (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCOVID-19 test sensitivity and specificity have been widely examined and discussed, yet optimal use of these tests will depend on the goals of testing, the population or setting, and the anticipated underlying disease prevalence. We model various combinations of key variables to identify and compare a range of effective and practical surveillance strategies for schools and businesses.

methodsWe coupled a simulated data set incorporating actual community prevalence and test performance characteristics to a susceptible, infectious, removed (SIR) compartmental model, modeling the impact of base and tunable variables including test sensitivity, testing frequency, results lag, sample pooling, disease prevalence, externally-acquired infections, symptom checking, and test cost on outcomes including case reduction and false positives.

findingsIncreasing testing frequency was associated with a non-linear positive effect on cases averted over 100 days. While precise reductions in cumulative number of infections depended on community disease prevalence, testing every 3 days versus every 14 days (even with a lower sensitivity test) reduces the disease burden substantially. Pooling provided cost savings and made a high-frequency approach practical; one high-performing strategy, testing every 3 days, yielded per person per day costs as low as $1.32.

interpretationA range of practically viable testing strategies emerged for schools and businesses. Key characteristics of these strategies include high frequency testing with a moderate or high sensitivity test and minimal results delay. Sample pooling allowed for operational efficiency and cost savings with minimal loss of model performance.

Indexed as

Cost-Benefit AnalysisCOVID-19COVID-19 TestingDelayed DiagnosisHumansMass ScreeningPrevalenceRNA, ViralSARS-CoV-2SchoolsSensitivity and SpecificityRNA, Viral

Identifiers

PMID33764982
PMCPMC7993807
OpenAlexW3139103154

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.