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We develop a statistical model for the testing of disease prevalence in apopulation. The model assumes a binary test result, positive or negative, butallows for biases in sample selection and both type I (false positive) and typeII (false negative) testing errors. Our model also incorporates multiple testtypes and is able to distinguish between retesting and exclusion after testing.Our quantitative framework allows us to directly interpret testing results as afunction of errors and biases. By applying our testing model to COVID-19testing data and actual case data from specific jurisdictions, we are able toestimate and provide uncertainty quantification of indices that are crucial ina pandemic, such as disease prevalence and fatality ratios.

philosophical transactions - royal society. mathematical, physical and engineering sciences/philosophical transactions - royal society. mathematical, physical and engineering sciences

A statistical model of COVID-19 testing in populations: effects of sampling bias and testing errors