Saw this as a comment here.
Bayesian conditional probability is being completely ignored. Let’s say a COVID-19 test is 90% accurate and 1 out of 100 people have COVID-19. You test positive for COVID-19, what’s the chance the test is correct? 100-1=99 people don’t have COVID-19 but 99×10%= ~10 will falsely test positive. The probability that you actually have COVID-19 is only 1/(1+10)= ~9%. ~90% of positive cases/deaths are FALSE!
Bayesian probability is, of course, the only way to validate such tests accurately. BTW read the whole article: