Hacker News Reader

Comment by epistasis | Hacker News Reader

It's weird because I had never heard that Jay Bhattacharya was super political. But early on he designed a study with unethical participant recruitment, juked and terrible stats, to push a bad pre-print for a contrarian number on the death rate that has proven to be very very very wrong. And he went out into media and pushed his bad study, at a crucial time. That the Mercury News is publishing his words is a huge black eye both for Stanford and for the Mercury News. It's clear that his COVID work is not driven by science, but rather strange political beliefs that can not be justified by data. Shameful.

View on HN

> ... a contrarian number on the death rate that has proven to be very very very wrong.

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 CDC estimate was from before we had any good studies using antibody prevalence. It was too low.

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.

If CDC's mis-estimate from May can be forgiven by virtue of there not being enough good studies at the time, then why is Bhattacharya's even earlier (and more accurate) estimate being characterized as unconscionable?

> 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.

I don't quite know what to make of the WHO estimate. I can't find further details about it. I don't think it makes sense to take this estimate and try to extract an IFR from it. They could be suggesting that the IFR of the virus is much lower than expected, but they could also be suggesting that deaths have been badly undercounted (e.g. in areas with poor health infrastructure), or that it has spread more widely among younger populations in places like Africa, where IFR would be expected to be much lower.

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.

Reply on news.ycombinator.com