The preprint on #SARSCoV2 seroprevalence in Santa Clara County continues to make headlines. They estimate 2-4% of the population had #COVID19 by April 4 implying an infection fatality rate (IFR) of 0.1 to 0.2%.

But there are many reasons to be very skeptical. Thread...
What would these numbers imply for areas like New York, Madrid or similar?

There have been ~9k #COVIDー19 deaths in NYC. IFR 0.1 to 0.2% implies 50-100% of 8.4M New Yorkers were already infected even if there were no more deaths!

All of NCY -- not just heavily affected areas!
Numbers for Madrid and other places with high #COVID19 prevalence are similar.

To me, this suggests something is odd with the results from Santa Clara, at least in the way they are interpreted. Let& #39;s look at the data....
They tested n=3300 with k=50 positive tests. The test validation by the manufacturer and the study yielded

TP = 78 + 25
TN = 369 + 30
FP = 2 + 0
FN = 7 + 12

How do we turn this into an estimate of prevalence not knowing the true specificity s and sensitivity r?
Following @NimwegenLab& #39;s analysis, we can turn the results for test validation and testing in Santa Clara into a posterior probability for prevalence f by integrating over the unknown r and s. (Z being the normalization)
This results in a posterior distribution for the prevalence f which is peaked somewhere around 1% but compatible with any value below 2%. Hence the data are pretty uninformative.
I have not considered the demographic corrections they have done in the study as they seem tangential to the main point made here:

All existing evidence points to a high IFR>0.5% and the Santa Clara data does not change this.
This is Erik& #39;s tweet that inspired this thread: https://twitter.com/NimwegenLab/status/1251261262366900232">https://twitter.com/NimwegenL...
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