Back

COVID-19: Rapid Antigen detection for SARS-CoV-2 by lateral flow assay: a national systematic evaluation for mass-testing

UK COVID-19 Lateral Flow Oversight Team, ; Peto, T.

2021-01-15 infectious diseases
10.1101/2021.01.13.21249563 medRxiv
Show abstract

Lateral flow device (LFD) viral antigen immunoassays have been developed around the world as diagnostic tests for SARS-CoV-2 infection. They have been proposed to deliver an infrastructure-light, cost-economical solution giving results within half an hour. Here we report on standardised laboratory evaluations of LFDs, and for those that met the published criteria, field testing in the Falcon-C19 research study and UK pilots (UK COVID-19 testing centres, hospital, schools, armed forces). 4/64 LFDs so far have desirable performance characteristics (Orient Gene, Deepblue, Abbott and Innova SARS-CoV-2 Antigen Rapid Qualitative Test). All these LFDs have a viral antigen detection of >90% at 100,000 RNA copies/ml. 8951 Innova LFD tests were performed with a kit failure rate of 5.6% (502/8951, 95% CI: 5.1-6.1), false positive rate of 0.32% (22/6954, 95% CI: 0.20-0.48). Viral antigen detection/sensitivity across the sampling cohort when performed by laboratory scientists (156/198, 95% CI 72.4-84.3) was 78.8%. Our results suggest LFDs have promising performance characteristics for mass population testing and can be used to identify infectious positive individuals. The Innova LFD shows good viral antigen detection/sensitivity with excellent specificity, although kit failure rates and the impact of training are potential issues. These results support the expanded evaluation of LFDs, and assessment of greater access to testing on COVID-19 transmission. FundingDepartment of Health and Social Care. University of Oxford. Public Health England Porton Down, Manchester University NHS Foundation Trust, National Institute of Health Research.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.