Estimating SARS-CoV-2 seroprevalence and epidemiological parameters with uncertainty from serological surveys

  1. Daniel B Larremore  Is a corresponding author
  2. Bailey K Fosdick
  3. Kate M Bubar
  4. Sam Zhang
  5. Stephen M Kissler
  6. C Jessica E Metcalf
  7. Caroline Buckee
  8. Yonatan H Grad  Is a corresponding author
  1. University of Colorado Boulder, United States
  2. Colorado State University, United States
  3. Harvard TH Chan School of Public Health, United States
  4. Princeton University, United States
  5. Harvard T H Chan School of Public Health, United States

Abstract

Establishing how many people have been infected by SARS-CoV-2 remains an urgent priority for controlling the COVID-19 pandemic. Serological tests that identify past infection can be used to estimate cumulative incidence, but the relative accuracy and robustness of various sampling strategies has been unclear. We developed a flexible framework that integrates uncertainty from test characteristics, sample size, and heterogeneity in seroprevalence across subpopulations to compare estimates from sampling schemes. Using the same framework and making the assumption that seropositivity indicates immune protection, we propagated estimates and uncertainty through dynamical models to assess uncertainty in the epidemiological parameters needed to evaluate public health interventions, and found that sampling schemes informed by demographics and contact networks outperform uniform sampling. The framework can be adapted to optimize serosurvey design given test characteristics and capacity, population demography, sampling strategy, and modeling approach, and can be tailored to support decision-making around introducing or removing interventions.

Data availability

Reproduction code is open source and provided by the authors at github.com/LarremoreLab/covid_serological_sampling

Article and author information

Author details

  1. Daniel B Larremore

    Department of Computer Science & BioFrontiers Institute, University of Colorado Boulder, Boulder, United States
    For correspondence
    daniel.larremore@colorado.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5273-5234
  2. Bailey K Fosdick

    Statistics, Colorado State University, Fort Collins, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Kate M Bubar

    Applied Mathematics, University of Colorado Boulder, Boulder, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Sam Zhang

    Applied Mathematics, University of Colorado Boulder, Boulder, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Stephen M Kissler

    Department of Immunology and Infectious Diseases, Harvard TH Chan School of Public Health, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3062-7800
  6. C Jessica E Metcalf

    Department of Ecology and Evolutionary Biology, Princeton University, Princeton, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3166-7521
  7. Caroline Buckee

    Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8386-5899
  8. Yonatan H Grad

    Department of Immunology and Infectious Diseases, Harvard TH Chan School of Public Health, Boston, United States
    For correspondence
    ygrad@hsph.harvard.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5646-1314

Funding

Morris-Singer Fund for the Center for Communicable Disease Dynamics

  • Stephen M Kissler
  • Caroline Buckee
  • Yonatan H Grad

National Cancer Institute (1U01CA261277-01)

  • Daniel B Larremore
  • Yonatan H Grad

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

Reviewing Editor

  1. Isabel Rodriguez-Barraquer, University of California, San Francisco, United States

Publication history

  1. Received: October 21, 2020
  2. Accepted: March 4, 2021
  3. Accepted Manuscript published: March 5, 2021 (version 1)
  4. Version of Record published: March 19, 2021 (version 2)
  5. Version of Record updated: April 8, 2022 (version 3)

Copyright

© 2021, Larremore 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. Daniel B Larremore
  2. Bailey K Fosdick
  3. Kate M Bubar
  4. Sam Zhang
  5. Stephen M Kissler
  6. C Jessica E Metcalf
  7. Caroline Buckee
  8. Yonatan H Grad
(2021)
Estimating SARS-CoV-2 seroprevalence and epidemiological parameters with uncertainty from serological surveys
eLife 10:e64206.
https://doi.org/10.7554/eLife.64206
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