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