Why Mandatory Mass Testing for COVID-19 is a Poor Policy
In this note I describe simple logic behind COVID-19 mass testing, which explains why any underlying policy is economically unsubstantiated. The application of basic probability theory shows that unless the testing accuracy is close to a hundred percent, even a small number of false positives introduces significant bias into random tests making them extremely unreliable, which is further aggravated by the presence of false negatives. For example, at 5% false positive rate, for a random person living in the USA without any symptoms or previous contact with infected people, the likelihood of actually having COVID-19 after testing positive is only 32.63%. This probability increases with lower false positive rates and higher infection rates. Still, at 3% false positive rate, a randomly selected person only in 12 states will have a probability higher than 50% (up to 56%) of having COVID-19 after testing positive. Assuming independence of tests, in some states (e.g. Vermont) a person who has no reason to suspect the disease may need to test a dozen times to make sure that he/she is actually sick.