SARS-CoV-2 serology across scales: a framework for unbiased seroprevalence estimation incorporating antibody kinetics and epidemic recency

  1. Saki Takahashi
  2. Michael J. Peluso
  3. Jill Hakim
  4. Keirstinne Turcios
  5. Owen Janson
  6. Isobel Routledge
  7. Michael P. Busch
  8. Rebecca Hoh
  9. Viva Tai
  10. J. Daniel Kelly
  11. Jeffrey N. Martin
  12. Steven G. Deeks
  13. Timothy J. Henrich
  14. Bryan Greenhouse
  15. Isabel Rodríguez-Barraquer

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Abstract

Serosurveys are a key resource for measuring SARS-CoV-2 cumulative incidence. A growing body of evidence suggests that asymptomatic and mild infections (together making up over 95% of all infections) are associated with lower antibody titers than severe infections. Antibody levels also peak a few weeks after infection and decay gradually. We developed a statistical approach to produce adjusted estimates of seroprevalence from raw serosurvey results that account for these sources of spectrum bias. We incorporate data on antibody responses on multiple assays from a post-infection longitudinal cohort, along with epidemic time series to account for the timing of a serosurvey relative to how recently individuals may have been infected. We applied this method to produce adjusted seroprevalence estimates from five large-scale SARS-CoV-2 serosurveys across different settings and study designs. We identify substantial differences between reported and adjusted estimates of over two-fold in the results of some surveys, and provide a tool for practitioners to generate adjusted estimates with pre-set or custom parameter values. While unprecedented efforts have been launched to generate SARS-CoV-2 seroprevalence estimates over this past year, interpretation of results from these studies requires properly accounting for both population-level epidemiologic context and individual-level immune dynamics.

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    SciScore for 10.1101/2021.09.09.21263139: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Antibodies
    SentencesResources
    Estimating time-varying, severity-specific assay sensitivities: To estimate time-varying, severity-specific assay sensitivities, we used longitudinal antibody response data collected from a cohort of participants with PCR-confirmed SARS-CoV-2 through the University of California, San Francisco-based Long term Impact of Infection with Novel Coronavirus (LIINC) natural history study (NCT04357821).
    NCT04357821
    suggested: None
    Software and Algorithms
    SentencesResources
    We used the R statistical software (version 3.5.3), EpiNow2 R package (version 1.2.1), and the Stan programming language (versions 2.19.3 and 2.21.2) for all analyses.
    EpiNow2
    suggested: None

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    There are a number of caveats associated with this analysis. The accuracy of this approach hinges on the accuracy of symptom onset curves reconstructed from the selected reported time series. This limitation is not unique to seroprevalence estimation; accurate estimates of downstream metrics such as the time-varying reproduction number similarly rely on the robustness of these data streams over time (32,47). Estimation will also be sensitive to the data type chosen; for example, hospitalizations and deaths are generally more robust to temporal trends in under-ascertainment than cases, but this may be context-specific. A key consideration here is the small sample size for asymptomatically infected individuals, who potentially comprise a majority of all SARS-CoV-2 infections (48,49). While the distinction between asymptomatic versus minimally symptomatic may be difficult to define, it is imperative to better understand the magnitude and kinetics of antibody responses in this group of individuals to better understand the extent of bias in seroprevalence estimates. The decision to model asymptomatic individuals as their own severity group or aggregated with the other non-hospitalized individuals has a major effect on overall adjusted seropositivity. Our framework accounts for differences in assay sensitivity by disease severity and time, but does not explicitly incorporate other potentially important sources of variation, such as age and sex (16,50,51). Lastly, our focus has been...

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

    IdentifierStatusTitle
    NCT04357821RecruitingCombinatorial Therapy to Induce an HIV Remission

    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.

    Results from scite Reference Check: We found no unreliable references.

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  2. Version published to 10.1101/2021.09.09.21263139v1 on medRxiv