Here’s an overview of #COVID19 testing by @LKucirka, @justinlessler, & co on the relationship between the false negative rate (RT-PCR) & when you were tested. This is a *crucial* graph showing how your *pretest* probability of infection is updated by the test result over time

Let’s focus on one of the lines: the dashed line indicating that the pretest probability is 11%

Context: A recent large study of household contacts estimated the *pretest* probability of infection given someone in your house tested positive is 11.2%

Looking at this graph, if

someone in your household tests positive
you are tested on the 1st day of exposure
you test NEGATIVE (phew!)
your probability of being infected *even though you tested negative* is still 11%

WHY? The test is bad at detecting early results

Waiting a few days can help, but it doesn’t bring the probability of a false negative to 0.

Here’s the overall relationship between the probability of a false negative result & days since exposure - best case scenario, you’re tested on day 8 (~3 days after symptom onset)

Even in this best case scenario, the probability of a false negative test result is twenty percent! Yikes!

So what does this mean?

If you test negative but your pretest probability of infection is *high* to keep yourself & your community safe operate as if you tested positive

What makes your pretest probability high? Here are a few things:

did someone you are in close contact with test positive?
were you recently in an environment that has a high likelihood of exposure?
do you have symptoms consistent with infection? https://twitter.com/lucystats/status/1260898968512364544?s=21

Here’s a link to the paper:

https://www.acpjournals.org/doi/10.7326/M20-1495

AND (I love this ) a link to the researchers’ github with all their code & analysis ( thank you!)

https://github.com/HopkinsIDD/covidRTPCR