Comment by buboard | Hacker News Reader

Comment by buboard | Hacker News Reader

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Ioannidis is willing to take a temporary hit to his personal reputation by making pointed remarks. The data is slowly erring towards his (low) estimates , but this is going to be a tortuous progress. The bigger issue here is that all this science has been politicized, especially in US & UK where leaders appeared alongside seasoned scientists as if they know their shit - they don't (and have proved it).

Plenty of other countries took a cautious approach, admitting that they are venturing into the unknown, and many of them are now cautiously opening up or planning to open. Uniquely in US and UK people are doubling down on any scientific evidence as if it's political, and this becomes a self-fulfilling prophecy. Now even molecules are partisan. What's more bizarre is that the citizens at large are siding with these "displays of leadership" from either side and often scoring politics points sems to take precedence to science. And then, after displaying such despotic devotion to their politics, they are also accusing asian governments (which have been largely successful) for despoticism. We 're probably witnessing a globalization of values in a disappointing way

> The data is slowly erring towards his (low) estimates

I'm not sure what you mean by this. Two months ago he estimated[1] that the total death count in the U.S. would be 10,000, and we're already 10x that.

[1]: https://www.statnews.com/2020/03/17/a-fiasco-in-the-making-a...

that is a thought experiment not an estimate:

> 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.

his own estimate is

> reasonable estimates for the case fatality ratio in the general U.S. population vary from 0.05% to 1%.

The Gamgelt study for example estimates IFR at 0.28%. he has recently published a meta review of those studies https://www.medrxiv.org/content/10.1101/2020.05.13.20101253v...

Though he doesn't come out and say it, it's clearly framed as a reasonable estimate of the ultimate death count: 'This sounds like a huge number, but it is buried within the noise of the estimate of deaths from “influenza-like illness.” If we had not known about a new virus out there, and had not checked individuals with PCR tests, the number of total deaths due to “influenza-like illness” would not seem unusual this year. At most, we might have casually noted that flu this season seems to be a bit worse than average. The media coverage would have been less than for an NBA game between the two most indifferent teams.'

None of that commentary makes sense except under the assumption that the author thinks the U.S. death total will be 10,000ish, and would have been unnoticeable without the availability of PCR testing.

It's probably too late to be noticed, but I feel compelled to say that I think you are wrong here. I've read enough of Ioannidis' work (and that of other statisticians) to know that this is just an example of how the math would work out if those assumptions were true. Later in the piece, he uses the different numbers of 60% infection with a 1% death rate. Neither is a projection, or a claim that the assumptions are true.

His actual belief, which he states several times in the piece is that we don't yet know how many people are going to be infected or what the death rate is, and that our priority should be gathering more information: "The most valuable piece of information for answering those questions would be to know the current prevalence of the infection in a random sample of a population and to repeat this exercise at regular time intervals to estimate the incidence of new infections. Sadly, that’s information we don’t have."

Different people have different styles of argument. From what you say, presumably if you used the example that Ioannidis did, this would imply that you believed this estimate was correct. Others have different styles, and you should not assume that they are making an assumption just because they happen to use numbers for an example.

This is the kind of political goggles that i referred to, which twist every kind of argument. It's pure bad faith argumentation not worthy of follow up.

(which the groupthink here unfortunately encourages)

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