Stanford's John Ioannidis Tried to Warn Trump Against Lockdowns in March
buzzfeednews.com
buzzfeednews.com
"Don't ever be wrong or we'll punish you" is how you stifle science.
Edit: in light of comments, I'll add that I'm not saying he is wrong, but the culture of don't be wrong is crippling.
Your implication is that he be should be treated like a pure scientist that isn't in the rough and tumble world of politics and should be treated more deferentially. It was the Stanford authors choice to directly criticize the lockdown policy in popular media, and they should not be surprised in the least when people who disagree reply in the same manner and the same forums.
Oh, and Ioannidis became famous and was offered the professorship at Stanford because he wrote a paper called "Why Most Published Research Findings are False.", which is in large part about how researchers misuse statistics to report findings that aren't true. It's like an O Henry story.
By “making his own call”, Ioannidis has thrown that out the window. Instead of presenting the data clearly and getting scientific consensus, he has tried to backdoor the process and go straight to the White House. Even the data he has collected has been widely criticized as skewed. Ioannidis is an influential scientist but he is not a politician or a policy maker, this isn’t his call to make.
It's not up to an "expert's judgement" to say that they advocate an informed decision.
It's up to their experctly use of methodology and experctly analysing and taking account of available information -- which he kind of didn't do.
Being an expert doesn't make your decisions informed. Being an expert makes you know the processes for making and evaluating informed decisions. But then you also need to follow that process.
The primary criticism of the article is obvious and correct: Ioannidis advocated for an unorthodox position in public policy without the evidence that would support his position.
In addition, his correspondence always misrepresents what starting a lockdown is: a lockdown need not be necessarily long, yet Ioannidis constantly portrays it as a "long lockdown". Ideally, lockdowns happen at the early part of a poorly understood epidemic, and as soon as reliable evidence becomes available that the epidemic is not dangerous, they are quickly lifted.
Well, the scientist also, against his better sense and past achievements, gave bad advice, despite months of available hindsight at the time he gave it with the situation in China, Italy, and Spain -- which he cherry-picked or ignored to match his premises...
And that's 10,000 assuming his preferred no-lockdown strategy and no vaccine. This prediction has not panned out.
In that, he says: Projecting the Diamond Princess mortality rate onto the age structure of the U.S. population, the death rate among people infected with Covid-19 would be 0.125%. But since this estimate is based on extremely thin data — there were just seven deaths among the 700 infected passengers and crew — the real death rate could stretch from five times lower (0.025%) to five times higher (0.625%). It is also possible that some of the passengers who were infected might die later, and that tourists may have different frequencies of chronic diseases — a risk factor for worse outcomes with SARS-CoV-2 infection — than the general population. Adding these extra sources of uncertainty, reasonable estimates for the case fatality ratio in the general U.S. population vary from 0.05% to 1%.
This strikes me as the sort of thing a careful statistician might say. He then goes on to say that if the rate is at the lower end of this, a shutdown would be unwarranted: A population-wide case fatality rate of 0.05% is lower than seasonal influenza. If that is the true rate, locking down the world with potentially tremendous social and financial consequences may be totally irrational.
One can definitely disagree with his reasoning, but based on this article I can't see how Buzzfeed would be justified in claiming that he "predicted that roughly 10,000 Americans could die", unless you are interpreting "could" very literally as a lower bound. I'd definitely update my opinion of Ioannidis negatively if you can find evidence that he confidently predicted that such a small number of deaths the most likely outcome. Conversely, I'd hope you might update toward seeing the article as a "hit piece" if you can't find any such evidence.
> 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
That was the only point estimate he made in that piece, so it seems quite fair to hold him to it. It wasn't being presented as a lower bound, but as a reasonable middle ground.
Although I'd argue that neither is an "estimate", he also says this: In the most pessimistic scenario, which I do not espouse, if the new coronavirus infects 60% of the global population and 1% of the infected people die, that will translate into more than 40 million deaths globally, matching the 1918 influenza pandemic.
Is the difference for you that in the first case he only says "if" and in the second he also appends "which I do not espouse"? All I can conclude is we must use language differently. I would never interpret a statistician as making an estimate if they give a single worked example from a range of numbers, much less consider this a prediction.
I would consider it a reasonable and serious criticism to point out that his range was wrong, though! Was it? Or do the current numbers fall somewhere within his range? My guess (although I don't know the current US numbers well enough to verify) his range is biased very low, but the upper end is still possibly correct.
It turns out that he was almost certainly wrong not to take it more seriously: that information, incomplete as it was and is, was nevertheless pointing in the right direction.
When Ioannidis published that 10k number, the US death toll was at 120 people. Three weeks later, it was at 20k people. Even at that point Ioannidis was still predicting (and this time there can be no ambiguity about it being a prediction):
> If I were to make an informed estimate based on the limited testing data we have, I would say that covid-19 will result in fewer than 40,000 deaths this season in the USA.
It was an absolutely nonsensical number, there was no possible justification for it. Given the time delays involved, you would have gotten to over 40k deaths even if all further Covid transmission were to stop on dime the moment he made the prediction. This number was exceeded 10 days later.
If his "informed estimate" in this situation was 40k, it's quite implausible that the 10k number in much better conditions was just intended as some kind of illustration of what the low end of the range was. Certainly calling him out on the 10k estimate does not make the posted article a hitpiece, like you claimed.
> Or do the current numbers fall somewhere within his range?
So even if we're charitable and assume he consistently meant IFR when writing CFR, the low end of his range was already known to be impossible at the time of writing. The reality will probably intersect with his range, but that's just because it's so absurdly wide and if we're only concerned with the IFR. But the problem is that what uncertanties remained at the time had an inverse correlation between R0 and IFR. Ioannidis was assuming both to be low.
If this article had been all there was to it, one could maybe give Ioannidis the benefit of the doubt. Maybe he really was trying to highlight the amount of uncertainty, and was too naive to realize how the general public would understand the 10k number that he just put in as a meaningless bon mot. But this article wasn't the end of it. He's continued to consistently cherry-pick and misrepresent the data, always in the direction that tries to make Covid appear less serious.
There was absolutely no reason (much less a "careful statistician's reason) to write ("A population-wide case fatality rate of 0.05% is lower than seasonal influenza.") except to imply that's a plausible scenario. Especially since he closes with that nugget...
And even ignoring that a "careful statistician" (who knows biology) would also not discard long term health effects and just focus on deaths, as if once you've not died from covid it's all a-ok...
• causes cancer patients to miss cancer treatment, with an increase in deaths • caused people to defer medical treatment, including vital “elective” surgery as well as diagnosis of treatable conditions • caused harm to mental health and increase in domestic violence, which has led to suicides, and will lead to more suicides • caused students to get a worse education and abnormal social development; with unknown effects on the rest of their life • caused significant damage to the economy, limiting the economic potential of the future and the kids that are growing up now
When we tally up all the damage, and compare it versus Covid deaths of a no-lockdown but ban large events, isolate seniors approach, which one will win?
And I might get some flak for saying this, but I don’t believe all lives to be equal. A teenager who kills themselves has lost 70 years of life. A senior in their 90s who died has quite possibility lost 5 or less years of life.
If you’re interested in researching this further, I’d encourage you to check out LockdownSkepticsm on reddit. We aren’t against public health measures like universal mask wearing, but we are for evidence-based assessment of what has lower TOTAL damage.
And yes, the initial data supporting lockdowns did turn out to be incredibly wrong. We know because we went from estimating 2-3% CFR to 0.4% CFR (CDC, May 2020). And today, we know how effective MASKS are, which public health authorities discouraged during the start...
That doesn't sound like predominately affecting people in their 70s and above to me.
The long term effects of contracting COVID-19 aren’t fully known and fully defined yet. That will take time to figure out. Meanwhile, fighting against every public safety measure (like some people are wont to do) is very problematic.
> but we are for evidence-based assessment
Uh-huh. So is it that this "evidence-based" community doesn't understand the difference between IFR and CFR, or that you're intentionally using the two interchangably if it helps your argument?