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