Early Release Estimates for SARS-CoV-2 Prevalence and Antibody Response Interim Weighting for Probability-Based Sample Surveys

This article has been Reviewed by the following groups

Read the full article See related articles

Abstract

Many months into the SARS-CoV-2 pandemic, basic epidemiologic parameters describing burden of disease are lacking. To reduce selection bias in current burden of disease estimates derived from diagnostic testing data or serologic testing in convenience samples, we are conducting a national probability-based sample SARS-CoV-2 serosurvey. Sampling from a national address-based frame and using mailed recruitment materials and test kits will allow us to estimate national prevalence of SARS-CoV-2 infection and antibodies, overall and by demographic, behavioral, and clinical characteristics. Data will be weighted for unequal selection probabilities and non-response and will be adjusted to population benchmarks. Due to the urgent need for these estimates, expedited interim weighting of serosurvey responses will be undertaken to produce early release estimates, which will be published on the study website, COVIDVu.org. Here, we describe a process for computing interim survey weights and guidelines for release of interim estimates.

Article activity feed

  1. SciScore for 10.1101/2020.09.15.20195099: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.


    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.


    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.