Just a note on the "3% prevalence of antibodies": we need to consider the false positive rate and selection bias for general population antibody tests as well.

Just a note on the "3% prevalence of antibodies": we need to consider the false positive rate and selection bias for general population antibody tests as well. A biologist I follow was skeptical about a study showing that 1.5% of Santa Clara had antibodies for SARS-CoV-2. I don't know anything about the study in the Netherlands, but the 20x or 100x undercount ratio is going to be very sensitive to the antibody test specificity. https://twitter.com/CT_Bergstrom/status/1251344851984986118 https://twitter.com/CT_Bergstrom/status/1251344851984986118 https://twitter.com/CT_Bergstrom/status/1251346572656304128 https://twitter.com/CT_Bergstrom/status/1251346572656304128 > In the supplement they say 2 out of 371 + 35 known negative samples tested positive. This means that the 95% confidence interval for the false positive rate is [0.06%, 1.77%]. In their samples from Santa Clara County they had 50 / 3,349 = 1.5% test positive.