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Comment by jMyles | Hacker News Reader

This paper was life-changing for me as an undergrad (and I didn't discover the rest of his body of work until I ran into it later here on HN in 2010 or so).

We are blessed as a species that John stuck to his principles - and his thirst for empiricism - during the COVID-19 panic, and supported / encouraged his colleagues to do likewise.

This video is only the first 12 minutes of the talk. The rest is here (though it is possibly semi-paywalled? It let me watch it, even though it said it was going to make me sign up for a trial):

https://iai.tv/video/why-most-published-research-findings-ar...

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My understanding was that Ioannidis hugely underestimated the IFR of COVID and he did it mostly by cherry picking a handful of small-sample-size studies that were friendly to his political views. It was very much a “your heroes will absolutely let you down” moment in my scientific life, and to the extent that non-scientists have forgotten the episode, that’s kind of what I expect.

https://www.dailymail.co.uk/news/article-8843927/amp/Just-0-...

No, the IFR of Covid was hugely overstated, which is why the projected population level impacts were completely wrong, even in places with limited interventions. Attributing cause of death is not as easy as it might seem.

Thanks. This guy (John Ioannidis) is the real deal. You can tell both by the dense, detailed fact sets, presented one after the other in logical order. And hearing the honest, intelligent tone of voice ensures his fidelity.

"The voice never lies." --Blind woman speaking to a friend

And his story about his hearing of Theranos is lowkey hilarious. And topical, because he's a Dunning-Kruger true-expert.

During the early pandemic, he underestimated the rate of fatality from COVID (which, remember, was much higher before vaccines and paxlovid were deployed), and forcefully advocated for policy based on his lower fatality rate estimates. It was a stunning display of hubris: working on very limited information, he was pushing incautious policy responses which could have cost millions of lives.

https://en.m.wikipedia.org/wiki/John_Ioannidis

As someone who felt the policy reaction to COVID was poor (no balanced assessment of the cost of false positives and negatives in decision-making, poor accounting of uncertainty), I concur that he didn’t apply his usual rigor or his critiques to his own work. He also had, IIRC, a conflict of interest and was funded by the airline industry for this research.

It's funny that the wildly overestimated IFRs like 3.4%, which were taken as gospel in the early days of the pandemic and drove the actual policy of sweeping shutdowns of schools, preventive medical care, and the economy at large -- a totally experimental and unprecedented measure with no empirical evidence whatsoever to demonstrate the benefits would outweigh the costs -- are not subjected to this same label of "hubris".

Iaonnidis's estimates of IFR in the 0.1% to 0.2% range were much closer to the mark.

Ionnandis didn't adjust for infection-death lag (which has big impact in the exponential infection increase part of the curve); his California IFR study was very flawed and undisclosed funded by the founder of Jet Blue.

His early influential paper before that was way off and said IFR might be even lower, around the common cold.

Around 0.5%-1.5% IFR without overwhelmed hospitals was the common scientific consensus very early on and was more right. Some treatment methods like proning and demonstrated effectiveness of steroids in a certain schedule helped drop things a good bit a few months in, around 30% if I remember.

Early research always had 1.0% in its confidence intervals, which is most likely the right IFR during the first phases.

The 0.1%-0.2% was just bad science, taking medians over countries with lagging statistics reports.

Where did you find 3.4%? Isn’t that an upper bound?

Who was claiming 3.4%?

Here's a study from March 2020: https://pmc.ncbi.nlm.nih.gov/articles/PMC7118348/

> Adjusting for delay from confirmation to death, we estimated case and infection fatality ratios (CFR, IFR) for coronavirus disease (COVID-19) on the Diamond Princess ship as 2.6% (95% confidence interval (CI): 0.89–6.7) and 1.3% (95% CI: 0.38–3.6), respectively. Comparing deaths on board with expected deaths based on naive CFR estimates from China, we estimated CFR and IFR in China to be 1.2% (95% CI: 0.3–2.7) and 0.6% (95% CI: 0.2–1.3), respectively.

Please provide specific, contemporaneous examples of the 3.4% estimate and evidence of it being "taken as gospel" and "driving the actual policy."

> Who was claiming 3.4%?

https://www.instagram.com/thejrecompanion/reel/DCxS6R9SiZM/

https://www.factcheck.org/2020/03/trump-and-the-coronavirus-...

The fact check page you provided seems clear that as early as March 5, 2020 (when the fact check was published) experts believed and were stating publicly that the IFR was not 3.4%:

> A Feb. 28 editorial in the New England Journal of Medicine, co-authored by Anthony Fauci, director of the National Institute of Allergy and Infectious Diseases, took the position that the mortality rate may well fall dramatically. It said if one assumed that there were several times as many people who had the disease with minimal or no symptoms as the number of reported cases, the mortality rate may be considerably less than 1%. That would suggest “the overall clinical consequences of Covid-19 may ultimately be more akin to those of a severe seasonal influenza,” the editorial said.

There are many similar lines.

That’s not IFR.

The “I can be trusted with scientific comprehension” crowd fails comprehension once again!

The World Health Organization was claiming 3.4%. It turns out they were completely wrong.

https://www.usatoday.com/story/news/politics/2020/03/05/coro...

> Globally, about 3.4% of reported COVID-19 cases have died.

That’s not IFR, and carries appropriate caveats literally in the same press release.

Closer to the mark where? If you aggregate over countries with disproportionately young demographics and questionable reporting, yes! In much of the developed world, during the phase of the pandemic when JI did his reporting, heck no.

> Iaonnidis's estimates of IFR in the 0.1% to 0.2% range were much closer to the mark.

The problem is, that was not his estimate for the IFR. It was his estimate for the CFR.

He was predicting 10k dead in the US, which was off by two orders of magnitude. I don't know that anyone was further from the mark than him.

Jay Battacharya, our incoming head of NIH, was pretty close to as hubristically wrong though!

People need to get over their fertilization of contrarianism. Very often, the consensus view is correct.

HAH, whoops. Fertilization was meant to be fetishization.

>It's funny that the wildly overestimated IFRs like 3.4%

Where. Source it.

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