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Obtaining Prevalence Estimates of Coronavirus Disease 2019: A Model to Inform Decision-Making

Ida Sahlu, Alexander B Whittaker

    American Journal of Epidemiology
    |April 8, 2021
    PubMed

    Insights

    Accurate COVID-19 prevalence estimation requires random sampling and testing, with crucial adjustments for misclassification error. This method ensures true prevalence is captured, regardless of disease levels, improving public health data reliability.

    Area of Science:

    • Epidemiology
    • Biostatistics
    • Public Health

    Background:

    • Accurate estimation of coronavirus disease 2019 (COVID-19) prevalence is vital for effective public health response.
    • Diagnostic testing for COVID-19 is subject to misclassification error (false positives and false negatives), potentially biasing prevalence estimates.
    • Publicly reported case data may not fully reflect the true burden of disease due to testing limitations and errors.

    Purpose of the Study:

    • To evaluate the effectiveness of random sampling and testing with misclassification error adjustment in capturing true COVID-19 prevalence.
    • To quantify the impact of misclassification error bias on COVID-19 case data in Maryland.
    • To determine if adjustment for misclassification error improves the accuracy of COVID-19 prevalence estimates across different prevalence levels.

    Main Methods:

    • Utilized a simulated Maryland population with stratified random sampling of 50,000 individuals.
    • Employed Bayesian models, incorporating published diagnostic test validity estimates, to adjust for misclassification error.
    • Examined prevalence estimation under low (0.07%-2%), medium (2%-5%), and high (6%-10%) true prevalence scenarios.

    Main Results:

    • Adjustment for misclassification error successfully captured the true COVID-19 prevalence 100% of the time, irrespective of the prevalence level.
    • Prevalence estimates without adjustment varied significantly based on true prevalence and test type, generally worsening as prevalence increased.
    • Applying adjustment to Maryland's reported data resulted in a minimal, non-significant increase in estimated average daily cases.

    Conclusions:

    • Random sampling and testing for COVID-19, coupled with adjustment for misclassification error, are essential for accurate prevalence estimation.
    • Failure to account for misclassification error can lead to substantial biases in reported COVID-19 case data, particularly at higher prevalence levels.
    • The study underscores the importance of robust statistical methods to correct for diagnostic errors in infectious disease surveillance.

    Keywords:
    prevalenceCOVID-19misclassification errorrandom testing

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