30m is quite a lot bigger than the Planck length, and you can’t predict human behavior by playing The Sims.

But the purpose is not to predict individual behaviour. I find it really frustrating that people are completely happy to discount 100 years of epidemiology so casually.

Me too. It is also frustrating that they reject it on assumption on what it might take into account instead of trying to find out what it actually take into account.

Like, people in this forum assume models are done by data analysts with no special additional knowledge about domain.

You can hype up the craft all you want, but none of the models got anywhere close to what we're seeing in reality (look at the estimated number of hospital beds in the IHME model, for example). How can you explain that without admitting that the models were based on poor assumptions and/or data?

The models I have seen made model and then tested how it reacts as assumptions (or variables) changes.

Then there were models that specifically searched best case scenario or worst case scenario. The assumptions were clearly stated too, so you was able to determine whether you agree with then or not.

They made predictions about asymptomatic cases before those were actually measured. They made predictions before those hit the news.

They were also pretty open about unknowns and explicitely said what they are not trying to predict.

IMO the IHME/Murray model communication was really confusing, especially considering it immediately became the gold-standard model for the USA.

CT Bergstrom (UW bio prof in communication with IHME team) lays it out in his rapid peer review here: https://twitter.com/CT_Bergstrom/status/1243837050253496320

The uncertainty intervals are pretty confusing even to educated users trying to understand the results in good faith. The entire model used some version of the Wuhan intervention as a prior, and only the uncertainty in fitting curves to that prior is represented in the intervals.

The problem is that the overwhelming majority of the uncertainty in actual outcome doesn't come from the curve-fitting uncertainty, but rather what prior to fit a curve to.

The IHME model seems OK for what it does, but I'm baffled as to how it become the most-cited tool that we have. It's totally inflexible, and pretty confusing in terms of what it actually represents. Its overestimates are now being used as a bludgeon against people that take the virus seriously, which IMO is a major issue and I think wrong, but difficult to refute given how inflexible and confusing the model is.

I'm not happy to discount 100 years of epidemiology, and don't do so casually. But everything we've seen suggests that it can't produce accurate estimates of how bad a pandemic will be.

I don't mean to imply epidemiologists are bad at their jobs! Some areas of science are just like that. It's similarly hard to predict from first principles how effective a new drug will be or what properties a new material will have. But I've seen a lot of people say "we've gotta do suchandsuch because this model I saw projected it as the best option", and that's not a level of confidence these models can actually provide.

Actually, it seems to me that more or less, that is exactly how some of the network based contact tracing models work:

"Therefore, characteristics of mixing networks—and how these deviate from the random-mixing norm—have become important applied concerns that may enhance the understanding and prediction of epidemic patterns and intervention measures." [1]

And as a follow-on, that is why there is so much discussion about using mobile apps for contact tracing - it builds the network for you, passively.

1 - https://royalsocietypublishing.org/doi/10.1098/rsif.2005.005...

you can predict human behavior with big data and linear models/embeddings with a few billion parameters. This works so unbelievably well, I expect that eventually health modelling folks will do this, provided they have enough high quality data.

Alice gives Bob a number, x, and tells Bob to calculate f(x), where f(x) is defined as the partial function with goedel index x applied to input x. She says that if or when Bob finishes, he should come bring her his answer and they'll get drinks together. Given parameter x, do Bob and Alice ever get drinks together?

Well, shit yus, because Bob has a dominant option to go and get Alice to have a drink if he has an answer or not. If Bob gets the answer Alice will be impressed. If he doesn't Alice like him anyway - so what's the odds? It's undecided what happens next.