Time series analysis and mechanistic modelling of heterogeneity and sero-reversion in antibody responses to mild SARS‑CoV-2 infection

  1. Charlotte Manisty
  2. Thomas Alexander Treibel
  3. Melanie Jensen
  4. Amanda Semper
  5. George Joy
  6. Rishi K Gupta
  7. Teresa Cutino-Moguel
  8. Mervyn Andiapen
  9. Jessica Jones
  10. Stephen Taylor
  11. Ashley Otter
  12. Corrina Pade
  13. Joseph Gibbons
  14. Jason Lee
  15. Joanna Bacon
  16. Steve Thomas
  17. Chris Moon
  18. Meleri Jones
  19. Dylan Williams
  20. Jonathan Lambourne
  21. Marianna Fontana
  22. Daniel M Altmann
  23. Rosemary Boyton
  24. Mala Maini
  25. Aine McKnight
  26. Benjamin Chain
  27. Mahdad Noursadeghi
  28. James C Moon

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Abstract

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  1. Version published to 10.1016/j.ebiom.2021.103259
  2. ScreenIT

    SciScore for 10.1101/2020.11.04.20225920: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementIRB: Study design and participants: The study was approved by a UK Research Ethics Committee (South Central - Oxford A Research Ethics Committee, reference 20/SC/0149).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    These were the Euroimmun anti-SARS-CoV-2 enzyme-linked immunosorbent assay (ELISA) (IgG) targeting IgG specific for the SARS-CoV-2 S1 antigen, and the Roche Elecsys Anti-SARS-CoV-2 electrochemiluminescence immunoassay (ECLIA) that detects antibodies (including IgG) directed against the SARS-CoV-2 nucleocapsid protein (NP).
    anti-SARS-CoV-2 enzyme-linked immunosorbent assay (ELISA) (IgG
    suggested: None
    SARS-CoV-2 S1 antigen,
    suggested: None
    SARS-CoV-2 nucleocapsid protein (NP
    suggested: None
    Univariable and multivariable associations of characteristics (including age, sex, ethnicity and case-defining symptom status) with peak antibody levels were also quantified using linear regression for anti-N IgG/IgM and anti-S1 IgG.
    anti-N IgG/IgM
    suggested: None
    anti-S1 IgG
    suggested: None
    The levels of anti-S1 or anti-NP antibody in blood were compared to the model, over a range of the parameters (AbPr1, AbPr2 as a proportion of AbPr1, r and t_stop) by calculating the root mean square distance between data and model output, and the parameter set with the minimum distance was selected.
    anti-S1
    suggested: None
    In our primary analysis, we restricted mathematical modelling to seropositive participants with ≥8 antibody data points (N=92 for anti-S1, N=86 for anti-NP, Supplementary Figure 3).
    anti-NP
    suggested: None

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our study has important limitations. Our time series analysis was limited to individual semiquantitative assays for each antigenic target. Direct comparison of antibody levels was not possible due to differences in dynamic range of these assays and their co-linearity. At present, results from any single assay are not generalisable. Moreover, these assays did not provide any differential assessment of antibody subclasses, which may exhibit differential kinetics. The significant correlation between near-contemporary Euroimmun anti-S1 measurements and functional pseudovirus nAb titres increased confidence in our assessment of anti-S1 levels, and is in line with data using live virus micro-neutralisation.37 Nonetheless, the correlation coefficient was modest suggesting that Euroimmun anti-S1 measurements do not explain all humoral neutralising activity. Moreover, emerging data on T cell reactivity to SARS-CoV-2 highlights the potential role of cellular immunity.16,29,38 Therefore, the Euroimmun anti-S1 measurements are not likely to provide a comprehensive measure of protective immunity following natural infection. In addition, our study population is not generalisable to all. Instead, it is representative of a workforce with likely high exposure, and low risk of severe COVID-19. Cohort 1 may have underestimated rates of infection because we were not able to recruit those who were in self-isolation at the peak of transmission, whilst cohort 2 may have overestimated rates of infec...

    Results from TrialIdentifier: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    NCT04318314RecruitingCOVID-19: Healthcare Worker Bioresource: Immune Protection a…

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.11.04.20225920v1 on medRxiv
  4. Version published to 10.1101/2020.11.04.20225920v2 on medRxiv