Cited by 22SARS-CoV-2 detection and testingCOVID-19 epidemiological studiesSARS-CoV-2 and COVID-19 Research

Diagnostic Strategies for Endemic Coronavirus Disease 2019 (COVID-19)

Open access
Kost, Gerald J.·2022·Archives of Pathology & Laboratory Medicine

Abstract

Context.—: Coronavirus disease 2019 (COVID-19) rapid antigen tests generate intrinsically fast, inherently spatial, and immediately actionable results. They quickly confirm COVID-19, but weakly rule out infection. Test performance depends on prevalence and testing protocol. Both affect predictive values. Objectives.—: To use original mathematics and visual logistics for interpreting COVID-19 rapid antigen test performance patterns, gauge the influence of prevalence, and evaluate repeated testing. Design.—: Mathematica and open access software helped graph relationships, perform recursive computations, and compare performance patterns. PubMed retrieved articles addressing endemic COVID-19 were reviewed. Results.—: Tiered sensitivity/specificity comprise the following: T1 (90%/95%), T2 (95%/97.5%), and T3 (100%/≥99%). Performance of self-tests and home antigen tests with US Food and Drug Administration Emergency Use Authorization peaks in low prevalence. Fall-off in performance appears with increasing prevalence because suboptimal sensitivity creates false negatives. The rate of false omissions limits clinical use because of prevalence boundaries based on tolerance for risk. Mathematical analysis supports testing twice to improve predictive values and extend prevalence boundaries nearly to levels of herd immunity. Conclusions.—: COVID-19 is quickly becoming endemic. Suboptimal sensitivity of rapid antigen tests limits performance in high prevalence. Risk of contagion in packed spaces (eg, airplanes) might be avoided with dual testing 36 hours apart, allowing time for viral load to increase. Awareness of community prevalence and proof of improved performance with repeated testing will help manage COVID-19 risk, while meeting rapid decision-making needs for highly contagious and new variants (eg, Delta). New COVID-19 variants call for high-quality, low cost, readily accessible, fast, user friendly, and ubiquitous point-of-care testing.

Integrity signals

Registry

Crossref · 10.5858/arpa.2021-0386-sa

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Tiered sensitivity/specificity comprise Tier-1 90%/95%, Tier-2 95%/97.5%, Tier-3 100%/≥99%.

    “Tiered sensitivity/speci fi city comprise: Tier-1) 90 %, 95 %; Tier-2) 95 %, 97.5 %; and Tier-3) 100 %, ≥ 99 %, respectively [74 -77].”

    From Point-of-care testing: state-of-the art and perspectives (2025) · cites this paper as [74]

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Kost, G. J.
Affiliations
University of California, Davis (US)
Corresponding author
Kost, Gerald J.
Venue
Archives of Pathology & Laboratory Medicine
Published
2021-09-22
Volume
146
Issue
1
Pages
16-25
ISSN
  • 0003-9985 (ISSN-L)
  • 1543-2165
OpenAlex
W4206791061
Cited by
22
References
1
Citations by year
2025: 2 · 2024: 6 · 2023: 2 · 2022: 7 · 2021: 5
FWCI
1.59
Type
journal article
Language
en
Open access
Locations
  • Archives of Pathology & Laboratory Medicine (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 detection and testing
  • Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
  • Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
Keywords
COVID-19 pandemic, SARS-CoV-2, rapid antigen test, diagnostic performance, prevalence, predictive value, retesting, sensitivity and specificity, false-negative results, self-testing, point-of-care testing, herd immunity, viral load, airborne transmission
MeSH terms
COVID-19, SARS-CoV-2, COVID-19 Testing, Humans, Sensitivity and Specificity

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review