I formulate three basic biomedical/statistical assumptions that should ideally guide well-designed population prevalence studies of the present or past disease including COVID-19 On the basis of these assumptions alone, I compute several probability distributions required for statistical analysis of testing data collected from a sample of individuals drawn from a heterogeneous population I also construct a consistent asymptotically unbiased estimator of the population prevalence of the disease or infection from the collected data and derive a simple upper bound for its variance All the results are rigorously proved and valid for any test for COVID-19 or other disease provided that the sum of the test&rsquo;s sensitivity and specificity is larger than 1 A few recommendations for the design of COVID-19 prevalence studies informed by the results of this work are formulated The methodology developed in this article may prove applicable to diseases and conditions other than COVID-19 as well as in some non-epidemiological settings
Estimation of Population Prevalence of COVID-19 Using Imperfect Tests