4 ms·
There's no one way to estimate it, so any such tool is just going to turn into a reflection of the assumptions used to create it. Setting aside the probability
by timr 6y ago
There's no one way to estimate it, so any such tool is just going to turn into a reflection of the assumptions used to create it.
Setting aside the probability that someone could be asymptomatic and contagious after a negative test (which actually seems to be fairly low), you'd need to know the false-negative characteristics of the different tests that were used. Unfortunately, people seem to be far more concerned with the former risk than the latter -- they assume that their tests are infallible (wrong), but that the risk of asymptomatic transmission is relatively high (also wrong).
- eminence32 6y ago> you'd need to know the false-negative characteristics of the different tests that were used Good point about this. Do we have enough data to compute this? my layman's guess is that if someone is asymptomatic and receives a negative test, they're not likely to follow it up with another test (which would be one way to learn the first test was a false-negative). But maybe there are other clever ways at getting at the false-negative rate for each type of test.
- pbronez 6y agoMy organization has put together some data on this: https://covidtestdb.com/performance/ https://covidtestdb.com/performance/ We pulled together performance data for dozens of COVID-19 diagnostics, which you can download. We also created a stats tool to help you understand the Predictive Value of each test based on how widespread the disease is in the tested population. The key metrics we look at are: Positive predictive value (PPV), the fraction of positive tests where the person actually had the disease. Negative predictive value (NPV), the fraction of negative tests where the person actually did not have the disease.