I agree with you completely, but the flip side of the coin is that experts can be blinded by assumptions that the field has. Sometimes outsiders aren't aware of these basic assumptions and so are less biased.

There's also the simple issue that sometimes expertise comes from places you least expect for reasons you might not anticipate.

For me there's as many problems in this pandemic related to appeals to authority (at least in the US) -- problems with testing related to FDA regulation and the CDC, problems with lack of healthcare providers due to long-term rent-seeking monopolies in licensing and practice scope, problems related to academic fraudulence and incentives (see: Didier Raoult) -- that I think it's dangerous to raise appeal to authority as anything but a bias.

For me there's multiple levels of problems to this, the first of which is the conspiracy and anti-science culture surrounding the pandemic. Above that is an appeal to medical and scientific expertise and authority that has sometimes been helpful but sometimes harmful. Above that still is an appeal to rigorous thinking and risk management, which transcends expertise boundaries.

Likewise now with hydroxychloroquine--if you listen to the epidemiologists all you'd hear is how it's an UNPROVEN drug. What we need instead is coverage of sample sizes, p values, bayesian predictions of effectiveness (in the absence of controlled studies) and serious modeling of the number of ICU beds and ventilators required with and without various levels of treatment, from emergency care to prophylactic use.

The epidemiologists have their head in the sand and think we can just wait 6 months for a proper set of randomized trials. It's the less attached data modelers you need to turn to get predictions that are useful for effective policy choices.

https://thehill.com/changing-america/well-being/prevention-c...

Which model? Your article mostly dwells on the Oxford model in the FT which is as mainstream as it gets. The Daily Mail article is about a prediction by an Imperial academic.

There doesn't appear to be any "mainstream" in epidemiology. This kind of false-consensus argument is one I've seen before and frankly it undermines people's respect in both academic and journalism. How exactly is the "mainstream" established, in your mind? And don't talk about vague, easily manipulated non-metrics like reputation. Mainstream models should come from a single thing: success. Your insistence on rigid academic segmentation is the very thing that makes academia brittle and riven with non-replicable papers.

You know which kind of modellers I respect the most? Actuaries. They have skin in the game. They don't try to predict pandemics or sell insurance products against them, implying they think pandemics can't be modelled. So far based on what I've read about the history of epidemiology, I can't disagree with them.

https://www.newyorker.com/news/q-and-a/the-contrarian-corona...

You're requiring cooperation from multiple different parties here (scientists, journalists, policy makers, readers, etc.) and any of these parties can warp the results in any number of ways regardless of the cooperation of other parties.

Climate science still hasn't solved this problem despite trying to implement what you're talking about.

It's an age-old problem, if a priori you're looking for something hard enough you're bound to find it.

The number of people who agree with something tells you, as a rational person, practically nothing - it reminds me of the absurd compilation argument “One Hundred Authors Against Einstein”: https://archive.org/details/HundertAutorenGegenEinstein/mode...

Einstein’s famous reply: “If I were wrong, then one would have been enough!”

I prefer though to look at problems through the lens of incentive structures (keeping in mind humans generally heavily time discount incentives and what incentivizes people is not always obvious! Death isn't always much of a disincentive beyond a rather short time horizon). And here I'm having a hard time seeing easy ways to tweak the incentive structure.

Which is fine. It'd be far better if people weren't trying to model COVID-19 at all. It's clearly far beyond our societies capabilities today. We can't even get useful data let alone feed it into a simulation of society.

I think in fact it would be better to go even further: not speak from a position of superiority (even if one knows more), but try to acknowledge the audience and persuade effectively. Here's a recent article on the topic: https://undark.org/2020/03/19/coronavirus-myths/ and here's one of my favourites (in a different field of science) from three years ago: https://deansforimpact.org/why-mythbusting-fails-a-guide-to-...

If that scenario was “completely wrong” too, it seems like it would serve as a perfect example of the consequences of this kind of (still hypothetical) misinformation.

As we should expect, because data scientists and quants are EXACTLY the people with the set of skills necessary to make such predictive models. At best, it's an ancillary skill for epidemiologist, and we've seen many cases in this pandemic where they have wielded these tools incorrectly.

Given that "people with background in infectious diseases" have largely failed, as a group, to warn us in January-February about this pandemic (see this Twitter thread for a slew of concrete examples https://twitter.com/RokoMijicUK/status/1246509433145917443), my conclusion is that unless someone has a background in hard quantitative field (regardless of what that field is), that person should not be let anywhere near quantitative models.