Right, so it's a variable, but if we can independently nail down a false negative/positive rate, then we could try to infer the true rate of infection, but prob

Right, so it's a variable, but if we can independently nail down a false negative/positive rate, then we could try to infer the true rate of infection, but problem is (assuming US numbers): * assume false positive rate of 10%, assume true positive rate of 100% (it's not but let's be generous) * maybe 1/10 of the population (30 million) gets tested * lets say 100,000 people have COVID right now (50x the official number) number of positive tests = 30 million * 0.1 + 100k * 1 = 3,100,000 fraction of postive tests that actually have the disease = 1 / 31 = 3.2% problem is FPR (1) depends on the population tested (i.e. p(covid | positive test) != p(covid|positive test, some symptoms) != ...), (2) we need very accurate measurements of FPR because: lets say we constrain FPR to 10% +/- 1% ==> 10% uncertainty in FPR -- that means our inference of the number of infected people is: n_infected = (n_positive_tests - FPR * n_tested) which is: -200,000 to 400,000 so...not very useful.