A toy example of why test sensitivity and specificity matter in serosurveys.

Imagine population seroprevalence is 4%. Test sensitivity is 80%. Specificity is 99.9%. For a random sample of 3000 participants, you would expect ~100 positives, 3% of which will be false positives. pic.twitter.com/TMyCZGNXkq

— Natalie E. Dean, PhD (@nataliexdean) April 21, 2020

EPI 101 thread on sensitivity and specificity, plus its application to #COVID19 screening. For those who want a refresher!

The way we summarize the accuracy of a test (such as RT-PCR on a #coronavirus throat swab) is with two quantities: sensitivity and specificity. 1/12

— Natalie E. Dean, PhD (@nataliexdean) March 13, 2020

Here is the math, assuming 5% prevalence of antibodies in the population, and a 90% accurate test. Under these assumptions, the test gives the wrong result about 68% of the time among the people who test positive for antibodies. pic.twitter.com/k67i1jkwX5

— Steven Strogatz (@stevenstrogatz) May 16, 2020