The Silent Pandemic COVID-19 in the Asymptomatic Population

  1. Robert L. Stout
  2. Steven J. Rigatti

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Abstract

As the COVID-19 pandemic continues to ravage the world there is a great need to understand the dynamics of spread. Currently the seroprevalence of asymptomatic COVID-19 doubles every 3 months, this silent epidemic of new infections may be the main driving force behind the rapid increase in SARS-CoV-2 cases.

Public health official quickly recognized that clinical cases were just the tip of the iceberg. In fact a great deal of the spread was being driven by the asymptomatically infected who continued to go out, socialize and go to work. While seropositivity is an insensitive marker for acute infection it does tell us about the prevalence COVID-19 in the population.

Objective

Describe the seroprevalence of SARS-CoV-2 infection in the United States over time.

Methodology

Repeated convenience samples from a commercial laboratory dedicated to the assessment of life insurance applicants were tested for the presence of antibodies to SARS-CoV-2, in several time periods between May and December of 2020. US census data were used to estimate the population prevalence of seropositivity.

Results

The raw seroprevalence in the May-June, September, and December timeframes were 3.0%, 6.6% and 10.4%, respectively. Higher rates were noted in younger vs. older age groups. Total estimated seroprevalence in the US is estimated at 25.7 million cases.

Conclusions

The seroprevalence of SARS-CoV-2 demonstrates a significantly larger pool of individuals who have contract COVID-19 and recovered, implying a lower case rate of hospitalizations and deaths than have been reported so far.

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  1. ScreenIT

    SciScore for 10.1101/2020.12.29.20248985: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: This study design was approved by the Western Institutional Review Board and determined to be exempt under the Common Rule and applicable guidance and determined it is exempt under 45 CFR § 46.104(d)(4) using de-identified study samples for epidemiologic investigation, WIRB Work Order #1-1324846-1.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    The antibody tests were performed using the Roche Elecsys Anti-SARS-CoV-2 kit on the Roche 602 analyzer, with a stated sensitivity of 100% and specificity of 99.8%, utilizing an electrochemiluminescence immunoassay.
    Anti-SARS-CoV-2
    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:
    CDC has a guidance paper on reducing the risk for infection including advice for people with health impairments13 Weaknesses of the study include the imbalanced representation of the US states, as well as the lack of samples from those under age 20 or over age 80. The age distribution is also more heavily weighted to the young adult years, which is not representative of the US population. Although the sample size was large, it was not large enough to stratify by both age and geography when estimating population seroprevalence. Finally, the life insurance-buying population tends to be both healthier and wealthier than average, and this could also bias the results in an indeterminate direction.

    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.

  2. Version published to 10.1101/2020.12.29.20248985v1 on medRxiv