Random population sampling provides the basis for unbiased epidemiological estimates of COVID-19 prevalence by avoiding the selection biases inherent in passive testing data. However, robust estimation requires properly adjusting for diagnostic sensitivity, specificity, and complex sample designs.
The claim states that random population sampling provides unbiased estimates of true COVID-19 prevalence. Papers [2] and [11] explicitly support the use of random sampling and population-based surveys for estimating disease prevalence while addressing methodological adjustments for test characteristics and sample design. None of the papers refute the core premise that random sampling achieves unbiased prevalence estimation (when properly adjusted). Therefore, the verdict is SUPPORTED.