The equation below has never been more important. It's Bayes' theorem; I teach it to all my students. I'm not hearing much discussion about how to use this equation to diagnose #COVID19. As a result, I'm concerned we are missing cases. In a nutshell, here's how it works... (1/5) 
When clinicians see a patient w/ possible #COVID19, they should look at the whole picture (all symptoms, lab data, imaging, exposures, etc) & estimate a "pre-test probability" of disease. If it's high enough, they should test. Test comes back either +ive or negative. Next...(2/5)
...the clinician revises the probability, either up (if +ive) or down (if -ive). But just because a test is neg does not fully exclude C-19, & same in reverse. I've heard from colleagues around the world of cases where a doc had very high pre-test probability of C-19, but...(3/5)
...the respiratory swab came back negative. Then C-19 was summarily ruled out. But if the pre-test likelihood is very high, then a neg test, particularly one that is <100% accurate, cannot fully rule-out disease. Even a second neg swab cannot definitively r/o C-19 if the... (4/5)
...clinician remains very concerned. In that case, should check different test (like C-19 in stool) or just assume patient is +ive and act accordingly. There are apps to help docs apply the math, like: apps.apple.com/us/app/bayes-p…. We should all think like Bayesian docs right now(5/5)
