This offers a useful summary of the doubts about the recent serology studies in Santa Clara and Los Angeles, and then raises a concern based on New York City data:
[T]he current serology tests have relatively high percentages of false positives, ones which may be higher than the prevalence of COVID19 in the population itself. Here’s how to understand this. If you have a test that produces 5% false positives and your study finds an infection rate of 3% in the population it’s possible all your positives are false positives. You really don’t know what your results are telling you. There are also questions about how representative the samples were (a difficult task in these initial studies.) But the accuracy of the tests is the key issue, especially in populations where on a tiny fraction has been exposed....
[F]or something in the range of [the serology study's author's] IFR to be accurate [they estimate .1 to .2%], literally the entire population of New York City would have to have been infected already.
The numbers are straightforward: as of two days ago, there were 9,101 lab confirmed cases and 4,582 presumptive diagnosed cases for a total of 13,683 fatalities In New York City. The population of New York City is 8,398,748. That comes to either .11% or .16% depending on which death toll number is used.
I do not think anyone thinks 100% exposure is at all possible. Even if we assume what I think most experts would consider the highly unlikely possibility that 50% of New Yorkers have been infected with COVID19 that would mean a .33% IFR. To be generous, let’s say a third of the population of New York City had already been infected with COVID19 – very high but not inconceivable. That would mean a IFR of .49%.
Thoughts from readers on this analysis? (Thanks to Ned Block for the pointer.)
ADDENDUM: And for additional doubts, see this piece. (Thanks to Dr. David Ozonoff for the pointer.)
