Testing: specificity, sensitivity, false positives and negatives, Bayes
If you know your Bayes
WP, you already know that the numbers of false positives and false negatives, in a covid-19 test, have important implications for conclusions you draw from the results.
In medical tests, I learned recently that there are two metrics regarding the "falses", specificity (roughly, does this test detect covid-19 and only covid-19?), and sensitivity (roughly, how completely does this test detect covid-19).
This fairly recent STAT article explains some testing-related challenges (I think STAT is a reliable source): "
Covid-19 testing: overcoming challenges in the next phase of the epidemic" (
link).
A covid-19 test that is both 100% specific and 100% sensitive may exist in some alternative universe; in ours, not so much.
When you're desparately short on tests - still true throughout the US, sadly - it likely doesn't merit wasting brain cycles thinking about rates of "falses", for this or that covid-19 test, done here or there.
However, if you want to understand the reported test results - from Vò, Diamond Princess, Theodore Roosevelt, Iceland, or even Telluride - you do. Apparently even whoever is in charge of China's borders does too: 14 day mandatory quarantine for all international arrivals, no exceptions (a similar rule may apply in Australia too, especially since the Ruby Princess fiasco).
If the US CDC - and equivalents elsewhere - are on the ball, one criterion for giving approval to a covid-19 test would be reliable data on that test's specificity and sensitivity, and ensuring that that data is made public.