Ct threshold values, a proxy for viral load in community SARS-CoV-2 cases, demonstrate wide variation across populations and over time

  1. A Sarah Walker  Is a corresponding author
  2. Emma Pritchard
  3. Thomas House
  4. Julie V Robotham
  5. Paul J Birrell
  6. Iain Bell
  7. John Bell
  8. John Newton
  9. Jeremy Farrar
  10. Ian Diamond
  11. Ruth Studley
  12. Jodie Hay
  13. Karina-Doris Vihta
  14. Timothy EA Peto
  15. Nicole Stoesser
  16. Philippa C Matthews
  17. David W Eyre
  18. Koen Pouwels
  19. COVID-19 Infection Survey team
  1. University of Oxford, United Kingdom
  2. University of Manchester, United Kingdom
  3. Public Health England, United Kingdom
  4. Office for National Statistics, United Kingdom
  5. The Wellcome Trust, United Kingdom
  6. University of Glasgow, United Kingdom

Abstract

Background: Information on SARS-CoV-2 in representative community surveillance is limited, particularly cycle threshold (Ct) values (a proxy for viral load).

Methods: We included all positive nose and throat swabs 26-April-2020 to 13-March-2021 from the UK's national COVID-19 Infection Survey, tested by RT-PCR for the N, S and ORF1ab genes. We investigated predictors of median Ct value using quantile regression.

Results: Of 3,312,159 nose and throat swabs, 27,902(0.83%) were RT-PCR-positive, 10,317(37%), 11,012(40%) and 6,550(23%) for 3, 2 or 1 of the N, S and ORF1ab genes respectively, with median Ct=29.2 (~215 copies/ml; IQR Ct=21.9-32.8, 14-56,400 copies/ml). Independent predictors of lower Cts (i.e. higher viral load) included self-reported symptoms and more genes detected, with at most small effects of sex, ethnicity and age. Single-gene positives almost invariably had Ct>30, but Cts varied widely in triple-gene positives, including without symptoms. Population-level Cts changed over time, with declining Ct preceding increasing SARS-CoV-2 positivity.Of 6,189 participants with IgG S-antibody tests post-first RT-PCR-positive, 4,808(78%) were ever antibody-positive; Cts were significantly higher in those remaining antibody-negative.

Conclusions: Marked variation in community SARS-CoV-2 Ct values suggest that they could be a useful epidemiological early-warning indicator.

Funding: Department of Health and Social Care, National Institutes of Health Research, Huo Family Foundation, Medical Research Council UK; Wellcome Trust.

Data availability

De-identified study data are available for access by accredited researchers in the ONS Secure Research Service (SRS) for accredited research purposes under part 5, chapter 5 of the Digital Economy Act 2017. Individuals can apply to be an accredited researcher using the short form on https://researchaccreditationservice.ons.gov.uk/ons/ONS_registration.ofml. Accreditation requires completion of a short free course on accessing the SRS. To request access to data in the SRS, researchers must submit a research project application for accreditation in the Research Accreditation Service (RAS). Research project applications are considered by the project team and the Research Accreditation Panel (RAP) established by the UK Statistics Authority. Project application example guidance and an exemplar of a research project application are available. A complete record of accredited researchers and their projects is published on the UK Statistics Authority website to ensure transparency of access to research data. For further information about accreditation, contact Research.Support@ons.gov.uk or visit the SRS website.Data points underlying Figures are provided in Supplementary File 4 and Stata code in Supplementary File 3.

Article and author information

Author details

  1. A Sarah Walker

    Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    For correspondence
    sarah.walker@ndm.ox.ac.uk
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0412-8509
  2. Emma Pritchard

    Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.
  3. Thomas House

    University of Manchester, Manchester, United Kingdom
    Competing interests
    No competing interests declared.
  4. Julie V Robotham

    Modelling and Economics Unit, Public Health England, London, United Kingdom
    Competing interests
    No competing interests declared.
  5. Paul J Birrell

    Modelling and Economics Unit, Public Health England, London, United Kingdom
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8131-4893
  6. Iain Bell

    Office for National Statistics, Office for National Statistics, London, United Kingdom
    Competing interests
    No competing interests declared.
  7. John Bell

    Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.
  8. John Newton

    Public Health England, Public Health England, London, United Kingdom
    Competing interests
    No competing interests declared.
  9. Jeremy Farrar

    The Wellcome Trust, London, United Kingdom
    Competing interests
    No competing interests declared.
  10. Ian Diamond

    Office for National Statistics, Office for National Statistics, London, United Kingdom
    Competing interests
    No competing interests declared.
  11. Ruth Studley

    Office for National Statistics, Office for National Statistics, London, United Kingdom
    Competing interests
    No competing interests declared.
  12. Jodie Hay

    Virology, University of Glasgow, Glasgow, United Kingdom
    Competing interests
    No competing interests declared.
  13. Karina-Doris Vihta

    Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.
  14. Timothy EA Peto

    Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.
  15. Nicole Stoesser

    Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4508-7969
  16. Philippa C Matthews

    Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4036-4269
  17. David W Eyre

    Big Data Institute, University of Oxford, Oxford, United Kingdom
    Competing interests
    David W Eyre, declares lecture fees from Gilead, outside the submitted work..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5095-6367
  18. Koen Pouwels

    Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7097-8950
  19. COVID-19 Infection Survey team

Funding

Department of Health and Social Care (-)

  • A Sarah Walker
  • Emma Pritchard
  • Thomas House
  • Iain Bell
  • Ian Diamond
  • Ruth Studley
  • Jodie Hay
  • Karina-Doris Vihta
  • Koen Pouwels

National Institutes of Health Research (NIHR200915)

  • A Sarah Walker
  • Emma Pritchard
  • Julie V Robotham
  • Karina-Doris Vihta
  • Timothy EA Peto
  • Nicole Stoesser
  • David W Eyre
  • Koen Pouwels

Huo Family Foundation

  • Emma Pritchard
  • Koen Pouwels

Medical Research Council (MC_UU_12023/22)

  • A Sarah Walker

Wellcome Trust (110110/Z/15/Z)

  • Philippa C Matthews

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. M Dawn Teare, Newcastle University, United Kingdom

Ethics

Human subjects: Written informed consent was obtained from participants aged 16 years and older, and from parents/carers for those aged 2-15 years; those aged 10-15 years provided written assent. The study received ethical approval from the South Central Berkshire B Research Ethics Committee (20/SC/0195).

Version history

  1. Received: November 6, 2020
  2. Accepted: July 6, 2021
  3. Accepted Manuscript published: July 12, 2021 (version 1)
  4. Version of Record published: July 15, 2021 (version 2)
  5. Version of Record updated: November 15, 2021 (version 3)

Copyright

© 2021, Walker et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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  1. A Sarah Walker
  2. Emma Pritchard
  3. Thomas House
  4. Julie V Robotham
  5. Paul J Birrell
  6. Iain Bell
  7. John Bell
  8. John Newton
  9. Jeremy Farrar
  10. Ian Diamond
  11. Ruth Studley
  12. Jodie Hay
  13. Karina-Doris Vihta
  14. Timothy EA Peto
  15. Nicole Stoesser
  16. Philippa C Matthews
  17. David W Eyre
  18. Koen Pouwels
  19. COVID-19 Infection Survey team
(2021)
Ct threshold values, a proxy for viral load in community SARS-CoV-2 cases, demonstrate wide variation across populations and over time
eLife 10:e64683.
https://doi.org/10.7554/eLife.64683

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https://doi.org/10.7554/eLife.64683

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    Background:

    Viremia is a critical factor in understanding the pathogenesis of dengue infection, but limited data exist on viremia kinetics. This study aimed to investigate the kinetics of viremia and its effects on subsequent platelet count, severe dengue, and plasma leakage.

    Methods:

    We pooled data from three studies conducted in Vietnam between 2000 and 2016, involving 2340 dengue patients with daily viremia measurements and platelet counts after symptom onset. Viremia kinetics were assessed using a random effects model that accounted for left-censored data. The effects of viremia on subsequent platelet count and clinical outcomes were examined using a landmark approach with a random effects model and logistic regression model with generalized estimating equations, respectively. The rate of viremia decline was derived from the model of viremia kinetics. Its effect on the clinical outcomes was assessed by logistic regression models.

    Results:

    Viremia levels rapidly decreased following symptom onset, with variations observed depending on the infecting serotype. DENV-1 exhibited the highest mean viremia levels during the first 5–6 days, while DENV-4 demonstrated the shortest clearance time. Higher viremia levels were associated with decreased subsequent platelet counts from day 6 onwards. Elevated viremia levels on each illness day increased the risk of developing severe dengue and plasma leakage. However, the effect size decreased with later illness days. A more rapid decline in viremia is associated with a reduced risk of the clinical outcomes.

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    This study provides comprehensive insights into viremia kinetics and its effect on subsequent platelet count and clinical outcomes in dengue patients. Our findings underscore the importance of measuring viremia levels during the early febrile phase for dengue studies and support the use of viremia kinetics as outcome for phase-2 dengue therapeutic trials.

    Funding:

    Wellcome Trust and European Union Seventh Framework Programme.