Estimating the prevalence: Properties of the estimator regarding specificity and sensitivity of the underlying test

We provide a calculation tool to assess the properties of a maximumlikelihood (ML) estimator that extrapolates the true prevalence of an infectious disease from a random sample. The tools allow the researcher to correct for the specificity and sensitivity of the underlying medical test, calculate the standard deviation of the estimator and to plan the needed sample size. This document explains the underlying methods of the calculation tools and provides instructions for their proper use. We apply an adaption of the epidemiological SEIR-model to show that ML-estimators from random sampling tests provide a more realistic rate of infection than common approaches.