Because many are curious about antibody tests and seroprevalence, I figured I'd cover the http://covidtestingproject.org  results. This project systematically compared SARSCoV2 (COVID19) antibody kits. If we are going to do antibody tests, then we should use good ones...(1/n)

The covidtestingproject has done a great service by comparing 10 tests using the same samples. To me the important metric is specificity: how many times negative sera are measured as negative by the test. For COVID19 samples from 2018 should be reliable negative standards. (2/n)

The project asked how many false positives each test found in 108 negative sera. 3 tests were clearly better than the rest: Sure-Bio (0 false positives), Premier (3), & UCP (2). Below, dark circles are the false positives; just look at the 3rd column which combines IgG+IgM. (3/n)

Interestingly Premier was the test used by the Stanford and USC studies (the Stanford group shared the data regarding test performance). They had found 2 false positives out of 371. So the covidtestingproject is giving a false pos rate of 3/108=2.8% vs Stanford 2/371=0.5%. (4/n)

You may recall 1 of the criticisms of the Stanford study was that the measured positive rate of 1.5% in the population was not significantly different from the false pos rate of 0.5% for the Premier test measured by the Stanford group themselves. (5/n)

And now we have a second measurement of the false positive rate for the Premier test as 2.8%, which suggests again suggests that the actual seroprevalence in the Stanford study could have been as low as 0% and all 1.5% measured positives could have been false positives. (6/n)

One thing to consider is if the negative sera, which in both studies came from a pre-COVID time, might have been affected by long-term storage. Freezing, storage, and thawing may have denatured some proteins. Both increases or decreases in false pos rates are conceivable. (7/n)

The one disappointment in the http://covidtestingproject.org  work is that they could only get 108 negative sera. You'd think between UCSF and MGH they could scrounge up more than 108 negative samples. In fact there are almost as many authors (71) on the study as negative samples (8/n)

It would be nice if the http://covidtestingproject.org  could get ≥1000 neg sera to measure specificity with better confidence. Take-home lesson is, to measure the rate of something occurring n% of the time, you need a test with a false pos rate an order of magnitude lower. (9/n)

Finally, I think the attention paid to antibody tests is premature. I'm not sure why this is such a hot topic; maybe its' from the press and academics trying to get a jump on the next big thing. Until we are at 50% infection and 1M deaths, it won't help us get back to work. (n/n)

And now U Miami is stepping on the same low-specificity landmineThey just claimed 6% of Miami-Dade county has COVID19 antibodies ( https://www.miamiherald.com/news/coronavirus/article242260406.html). Sorry to break this to you but the Biomedomics test you used ( https://cbsloc.al/3aO40gl#.XqPNveHnyfw.twitter) has 13% (!!!) false-positives.