The author of that study has been downplaying COVID since the start. Here's him last March:
https://www.statnews.com/2020/03/17/a-fiasco-in-the-making-a...
> If we assume that case fatality rate among individuals infected by SARS-CoV-2 is 0.3% in the general population — a mid-range guess from my Diamond Princess analysis — and that 1% of the U.S. population gets infected (about 3.3 million people), this would translate to about 10,000 deaths.
(We're at well over 500k now.)
His estimates last April from Santa Clara, California were wildly inaccurate as well.
https://www.wired.com/story/new-covid-19-antibody-study-resu...
> Skeptics have noted that the conclusions seem at odds with some basic math. In New York City, where more than 10,000 people, or about 0.1 percent of the population, have already died from Covid-19, this estimated fatality rate would mean nearly everyone in the city has already been infected.
And this:
https://www.buzzfeednews.com/article/stephaniemlee/ioannidis...
> And in May, Ioannidis presented a picture of the virus’s deadliness based on antibody study data from around the world, including his Stanford study. This preprint was roundly criticized for including groups of patients who don’t reflect the general public and, in earlier versions, omitting data from large groups with higher fatality rates, among other errors.
https://www.imperial.ac.uk/mrc-global-infectious-disease-ana...
> Using these age-specific estimates, we estimate the overall IFR in a typical low-income country, with a population structure skewed towards younger individuals, to be 0.23% (0.14-0.42 95% prediction interval range). In contrast, in a typical high income country, with a greater concentration of elderly individuals, we estimate the overall IFR to be 1.15% (0.78-1.79 95% prediction interval range).
Confounding matters: any currently published estimate is likely to lack data from the current large spike in India.
https://pubmed.ncbi.nlm.nih.gov/33716331/