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Laboratory evaluation of 185 commercial assays for detecting SARS-CoV-2: the UK response for mass testing

Abstract:

Background Public Health England and Oxford University were commissioned in August 2020, together with NHS Test and Trace, to establish a rapid programme for evaluating antigen lateral flow devices (LFDs) for SARS-CoV-2 mass community testing.

Methods The programme used three stages: desktop assessment of candidate kits, laboratory testing using SARS-CoV-2 and PCR-negative samples, and larger-scale laboratory testing using positive and negative clinical samples. The methodology was subsequently adapted as variants of concern emerged and laboratory workflows developed.

Findings The programme screened 1017 kits at phase 1, tested 185 at phase 2 and 91 at phase 3. Some devices failed because of inadequate performance or variant detection. The overall laboratory-evaluation pass rate was 35%, and five kits were ultimately procured for the UK national COVID-19 testing programme.

Interpretation The complete dataset had previously remained unpublished partly because of time-sensitive commercial considerations. More than 2.5 billion self-test kits were deployed following purchasing decisions informed by the programme. The evaluation process may provide a model for rapidly assessing diagnostic devices during future pandemics.

Funding UK Department of Health and Social Care; UK Health Security Agency (formerly Public Health England and NHS Test and Trace); and the University of Oxford NIHR Biomedical Research Centre.

Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1016/j.eclinm.2025.103416

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Institution:
University of Oxford
Division:
MSD
Department:
NDM
Role:
Author

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Role:
Contributor


Publisher:
Elsevier
Journal:
EClinicalMedicine More from this journal
Volume:
87
Article number:
103416
Publication date:
2025-08-27
Acceptance date:
2025-07-23
DOI:
EISSN:
2589-5370


Language:
English
Keywords:
Pubs id:
2285191
Local pid:
pubs:2285191
Deposit date:
2025-09-01
ARK identifier:

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