If you look at the responses to that editorial, they note that the 11% positivity of igA tests, cited as suggestive of a high level of undetected infection, is actually the false positive rate of the test, among other embarrassing errors.
The first citation on your linked editorial is Ioannidis, which is the same Stanford researcher in the OP article here, and the same group responsible for the utterly flawed Santa Clara study. It seems like it's literally this one group, and a few other weirdos and contrarians, singlehandedly raising spurious doubts about the science, and then being amplified beyond all reason.
The cited Ioannidis study also sucks, he takes a number of seroprevalence studies, including some very flawed or underpowered ones, and then takes the unweighted median for some reason? More details here, including an illustration of how the high-quality, randomly-selected samples do not vary so much https://twitter.com/GidMK/status/1283232023402868737
You're missing a big part of the picture. It is well established that some patients infected with SARS-CoV-2 either never produce detectible levels of antibodies, or have them fade away quickly. In order to measure actual infection numbers we need to look beyond antibody seroprevalence, and factor in CD4+ and CD8+ T cell assays as well.
90% of PCR-positive subjects in the ENE-COVID test, for example, did also have a positive antibody test. So actual infection numbers might be ~10% higher than the raw numbers from the antibody tests (and I believe that this is factored into the results reported by such studies). It doesn't change our big-picture understanding of the disease.
The first citation on your linked editorial is Ioannidis, which is the same Stanford researcher in the OP article here, and the same group responsible for the utterly flawed Santa Clara study. It seems like it's literally this one group, and a few other weirdos and contrarians, singlehandedly raising spurious doubts about the science, and then being amplified beyond all reason.
The cited Ioannidis study also sucks, he takes a number of seroprevalence studies, including some very flawed or underpowered ones, and then takes the unweighted median for some reason? More details here, including an illustration of how the high-quality, randomly-selected samples do not vary so much https://twitter.com/GidMK/status/1283232023402868737