Author: Emily R Adams; Rekha Anand; Monique I Andersson; Kathryn Auckland; J Kenneth Baillie; Eleanor Barnes; John Bell; Tamsin Berry; Sagida Bibi; Miles Carroll; Senthil Chinnakannan; Elizabeth Clutterbuck; Richard J Cornall; Derrick W Crook; Thushan De Silva; Wanwisa Dejnirattisai; Kate E Dingle; Christina Dold; David W Eyre; Helen Farmer; Sarah J Hoosdally; Alistair Hunter; Katie Jeffrey; Paul Klenerman; Julian Knight; Clarice Knowles; Andrew J Kwok; Ullrich Leuschner; Chang Liu; Cesar Lopez-Camacho; Philippa C Matthews; Hannah McGivern; Alexander J Mentzer; Jonathan Milton; Juthathip Mongkolsapaya; Shona C Moore; Marta S Oliveira; Fiona Pereira; Timothy Peto; Rutger J Ploeg; Andrew Pollard; Tessa Prince; David J Roberts; Justine K Rudkin; Gavin R Screaton; Malcolm G Semple; Donal T Skelly; Elliot Nathan Smith; Julie Staves; David Stuart; Piyada Supasa; Tomas Surik; Pat Tsang; Lance Turtle; A Sarah Walker; Beibei Wang; Charlotte Washington; Nicholas Watkins; James Whitehouse; Sally Beer; Robert Levin; Alexis Espinosa; Dominique Georgiou; Jose Carlos Martinez Garrido; Hannah Thraves; Elena Perez Lopez; Maria del Rocio Fernandez Mendoza; Alberto Jose Sobrino Diaz; Veronica Sanchez
Title: Evaluation of antibody testing for SARS-Cov-2 using ELISA and lateral flow immunoassays Document date: 2020_4_20
ID: 5trox1i5_34
Snippet: Appraisal of test performance should also consider the influence of population prevalence, acknowledging that this changes over time, geography and within different population groups (e.g. healthcare workers, teachers). The potential risk of a test providing false reassurance and release from lock-down of non-immune individuals can be considered as the proportion of all positive tests that are wrong, as well as the number of incorrect positive te.....
Document: Appraisal of test performance should also consider the influence of population prevalence, acknowledging that this changes over time, geography and within different population groups (e.g. healthcare workers, teachers). The potential risk of a test providing false reassurance and release from lock-down of non-immune individuals can be considered as the proportion of all positive tests that are wrong, as well as the number of incorrect positive tests per 1000 people tested. Based on the working 'best case' scenario of a LFIA test with 70% sensitivity and 98% specificity, the proportion of positive tests that are wrong is 35% at 5% population seroprevalence (19 false-positives/1000 tested), 13% at 20% seroprevalence (16 falsepositives/1000) and 3% at 50% seroprevalence (10 false-positives/1000) (Figure 4 ). However, more data are needed to investigate antibody-positivity as a correlate of protective immunity.
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