They've always leaned far more conservative than almost any other academic institution I can think of. Probably it's to do with the pervading influence of big business there. The Hoover Institution is one of the oldest conservative think tanks in the US.
What was that "very very wrong" number? I found [1] from April in which Bhattacharya estimates that the IFR is probably one tenth of the CFR of 3-4%, which would mean an IFR of 0.3-0.4%. That doesn't seem very contrarian or wrong. The CDC in May estimated 0.26% IFR [2]. The WHO just the other day [3] said that 10% of the world's population may have been infected with the virus. They also estimate ~1,000,000 deaths globally. That would imply an IFR much lower than 0.3%.
[1] https://padailypost.com/2020/04/06/stanford-experts-say-covi...
[2] https://www.usatoday.com/story/news/factcheck/2020/06/05/fac...
[3] https://apnews.com/article/virus-outbreak-archive-united-nat...
[4] https://www.who.int/docs/default-source/coronaviruse/situati...
The WHO estimates a fatality rate of 0.6%. If you are simply dividing deaths by (an estimate of) current cases then you will get an inaccurate number because many of the current cases will eventually die of the disease.
> The WHO estimates a fatality rate of 0.6%.
I don't see how that makes an estimate 0.3% to 0.4% "very very very wrong", especially considering that we haven't heard the final word on this.
> If you are simply dividing deaths by (an estimate of) current cases then you will get an inaccurate number because many of the current cases will eventually die of the disease.
I didn't do that, but if you look at the numbers you'll see that it wouldn't make a meaningful difference anyway. WHO numbers show over a million deaths since the start of the epidemic, and around 5000 deaths per day over the past month. Whether you use 0.13% or 0.14% for the implied IFR, the WHO's comments the other day are out of whack with the 0.6% fatality rate estimate.
Bhattacharya's estimate is being criticized because, even though he was taking a contrarian view on the virus and getting results that were out of wack with other lines of evidence, he rushed out a highly flawed study (the Santa Clara seroprevalence study). This came out when we had little data about antibody prevalence, his study was one of the first ones, and it had the big name of Stanford behind it, so it was reported very widely and misled an awful lot of people about how dangerous this virus is. In his position he should have been bending over backwards to make sure he was on sure footing and not misleading the public, and instead he let this paper full of basic mistakes go to press.