Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 Jun 11:5:139.
doi: 10.12688/wellcomeopenres.15927.1. eCollection 2020.

Antibody testing for COVID-19: A report from the National COVID Scientific Advisory Panel

Emily R Adams  1 , Mark Ainsworth  2 , Rekha Anand  3 , Monique I Andersson  2 , Kathryn Auckland  4 , J Kenneth Baillie  5 , Eleanor Barnes  2   4 , Sally Beer  2 , John I Bell  4 , Tamsin Berry  6 , Sagida Bibi  7 , Miles Carroll  4   8 , Senthil K Chinnakannan  4 , Elizabeth Clutterbuck  7 , Richard J Cornall  2   4 , Derrick W Crook  2   4 , Thushan de Silva  9 , Wanwisa Dejnirattisai  4 , Kate E Dingle  4 , Christina Dold  7 , Alexis Espinosa  2 , David W Eyre  2   4 , Helen Farmer  6 , Maria Fernandez Mendoza  2 , Dominique Georgiou  2 , Sarah J Hoosdally  4 , Alastair Hunter  10 , Katie Jefferey  2 , Dominic F Kelly  2   7 , Paul Klenerman  2   4 , Julian Knight  2   4 , Clarice Knowles  6 , Andrew J Kwok  4 , Ullrich Leuschner  11 , Robert Levin  12 , Chang Liu  4 , César López-Camacho  4 , Jose Martinez  2 , Philippa C Matthews  2   4 , Hannah McGivern  13 , Alexander J Mentzer  2   4 , Jonathan Milton  13 , Juthathip Mongkolsapaya  4 , Shona C Moore  14 , Marta S Oliveira  13 , Fiona Pereira  15 , Elena Perez  2 , Timothy Peto  2   4 , Rutger J Ploeg  2   13 , Andrew Pollard  2   7 , Tessa Prince  14 , David J Roberts  11 , Justine K Rudkin  4 , Veronica Sanchez  2 , Gavin R Screaton  4 , Malcolm G Semple  14   16 , Jose Slon-Campos  4 , Donal T Skelly  2   17 , Elliot Nathan Smith  6 , Alberto Sobrinodiaz  2 , Julie Staves  2 , David I Stuart  4   18 , Piyada Supasa  4 , Tomas Surik  13 , Hannah Thraves  2 , Pat Tsang  11 , Lance Turtle  14   19 , A Sarah Walker  4 , Beibei Wang  4 , Charlotte Washington  3 , Nicholas Watkins  20 , James Whitehouse  6 ; National COVID Testing Scientific Advisory Panel
Affiliations

Antibody testing for COVID-19: A report from the National COVID Scientific Advisory Panel

Emily R Adams et al. Wellcome Open Res. .

Abstract

Background: The COVID-19 pandemic caused >1 million infections during January-March 2020. There is an urgent need for reliable antibody detection approaches to support diagnosis, vaccine development, safe release of individuals from quarantine, and population lock-down exit strategies. We set out to evaluate the performance of ELISA and lateral flow immunoassay (LFIA) devices. Methods: We tested plasma for COVID (severe acute respiratory syndrome coronavirus 2; SARS-CoV-2) IgM and IgG antibodies by ELISA and using nine different LFIA devices. We used a panel of plasma samples from individuals who have had confirmed COVID infection based on a PCR result (n=40), and pre-pandemic negative control samples banked in the UK prior to December-2019 (n=142). Results: ELISA detected IgM or IgG in 34/40 individuals with a confirmed history of COVID infection (sensitivity 85%, 95%CI 70-94%), vs. 0/50 pre-pandemic controls (specificity 100% [95%CI 93-100%]). IgG levels were detected in 31/31 COVID-positive individuals tested ≥10 days after symptom onset (sensitivity 100%, 95%CI 89-100%). IgG titres rose during the 3 weeks post symptom onset and began to fall by 8 weeks, but remained above the detection threshold. Point estimates for the sensitivity of LFIA devices ranged from 55-70% versus RT-PCR and 65-85% versus ELISA, with specificity 95-100% and 93-100% respectively. Within the limits of the study size, the performance of most LFIA devices was similar. Conclusions: Currently available commercial LFIA devices do not perform sufficiently well for individual patient applications. However, ELISA can be calibrated to be specific for detecting and quantifying SARS-CoV-2 IgM and IgG and is highly sensitive for IgG from 10 days following first symptoms.

Keywords: COVID-19; ELISA; IgG; IgM; SARS-CoV-2; antibodies; epidemiology; exposure; immunoassay; lateral flow; serology.

PubMed Disclaimer

Conflict of interest statement

Competing interests: RC reports personal fees and other from MIROBIO Ltd, outside the submitted work. DWE reports personal fees from Gilead, outside the submitted work. SH reports grants from NIHR, during the conduct of the study. AJP reports grants from NIHR Oxford Biomedical Research Centre, outside the submitted work; and AJP is Chair of UK Dept. Health and Social Care’s (DHSC) Joint Committee on Vaccination & Immunisation (JCVI) and is a member of the WHO’s SAGE. The views expressed in this article do not necessarily represent the views of DHSC, JCVI, NIHR or WHO. GRS reports personal fees from GSK Vaccines SAB. MGS reports grants from National Institute of Health Research, grants from Medical Research Council UK, grants from Health Protection Research Unit in Emerging & Zoonotic Infections, University of Liverpool, during the conduct of the study; other from Integrum Scientific LLC, Greensboro, NC, USA, outside the submitted work. ASW reports grants from NIHR, during the conduct of the study. No other author has a conflict of interest to declare.

Figures

Figure 1.
Figure 1.. Cartoon to illustrate the generation of IgM and IgG antibodies to SARS nCoV-2 and detection of antibodies by a lateral flow device.
( A) In vivo generation of antibodies to the trimeric SARS-CoV-2 spike protein. ( B) Projected change in titres of specific IgM and IgG over time following infection, with arrows indicating typical time frames for collection of acute and convalescent samples. ( C) Ex vivo detection of IgG and/or IgM using a lateral flow immunoassay (LFIA): S= sample well, T=test antibody; C=control. Diagram shows a positive sample on the left, with positive lines at both C and T, and a negative test on the right with a line present only at C. Any other combination of lines renders the test invalid. Some devices have two test lines, for separate detection of anti-SARS-CoV-2-IgG and -IgM. Assays variably suggest use of plasma, serum and/or whole blood. ( D) Outcomes of testing negative and positive samples using LFIA. ( E) Calculation of sensitivity, specificity, positive and negative predictive value of a test. Image created with BioRender.com; exported under a paid subscription.
Figure 2.
Figure 2.. Results of testing 90 plasma samples for SARS-CoV-2 IgM and IgG by Enzyme linked Immunosorbent Assay (ELISA).
( A) IgM readings for SARS-CoV-2 pre-pandemic plasma (designated negatives, shown in blue, n=50), and RT-PCR confirmed cases of SARS-CoV-2 infection (designated positives, shown in orange, n=40; divided into acute cases, n=22, and convalescent cases, n=18. Threshold of OD = 0.07 discriminates accurately between negative controls and convalescent sera. ( B) IgG data shown for the same subgroups described for panel ( A). A threshold of OD = 0.4 discriminates between designated negatives and positives. ( C) IgM OD values plotted against the time post symptoms at which plasma was obtained. The line shows the mean OD value expected from a spline-based linear regression model, the ribbon indicates the pointwise 95% confidence interval. ( D) IgG OD values plotted against the time post symptoms at which plasma was obtained. Coloured dots in panels C and D indicate disease severity. OD = optical density.
Figure 3.
Figure 3.. Comparison between ELISA and LFIA for SARS-CoV-2 designated negative and positive plasma.
( A) Quantitative optical density (OD) readout from ELISA for IgG for designated negative plasma (n=50) and from individuals with RT-PCR confirmed infection (n=40, divided into acute and convalescent plasma). IgM results are shown in Extended data, Figure S2 . ( B) Results from LFIA produced by nine manufacturers. Any positive test for IgG, IgM, both or total antibody is shown as positive; see Extended data, Figure S2 for more detailed breakdown . Grey blocks indicate missing data as a result of insufficient devices to test all samples and one assay on one device with an invalid result. Samples in both panels are ranked from left to right by quantitation of IgG, as indicated in panel ( A).
Figure 4.
Figure 4.. Influence of population prevalence of seropositivity on assay performance.
Scenarios with population prevalence of 5%, 20% and 50% are shown within each panel. ( A) The proportion of all positive tests that are wrong (1-positive predictive value), which would lead to false release from lock-down of non-immune individuals, for varying test sensitivity (x-axis) and 1-specificity (line colour). ( B) The proportion of negative tests that are wrong. ( C) The absolute number of false positive tests per 1000 tests. ( D) The absolute number of false negative tests per 1000 tests.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Zhu N, Zhang D, Wang W, et al. : A Novel Coronavirus from Patients with Pneumonia in China, 2019. N Engl J Med. 2020;382(8):727–733. 10.1056/NEJMoa2001017 - DOI - PMC - PubMed
    1. World Health Organisation: Coronavirus disease (COVID-19) Situation Dashboard.2020. [Cited 2020 Mar 31]. Reference Source
    1. Sjödin H, Wilder-Smith A, Osman S, et al. : Only strict quarantine measures can curb the coronavirus disease (COVID-19) outbreak in Italy, 2020. Euro Surveill. 2020;25(13). 10.2807/1560-7917.ES.2020.25.13.2000280 - DOI - PMC - PubMed
    1. Prem K, Liu Y, Russell TW, et al. : The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study. Lancet Public Health. 2020; pii: S2468-2667(20)30073-6. 10.1016/S2468-2667(20)30073-6 - DOI - PMC - PubMed
    1. Public Health England: Guidance and standard operating procedure COVID-19 virus testing in NHS laboratories.2020. [Cited 2020, April 10]. Reference Source