Why it’s so hard to make a good Covid-19 model

Post by jacobr1 » Thu, Apr 09, 2020, 10:28 PM UTC

thereisnospork wrote: post #44

mikorym wrote: post #39

Earlier quoted context omitted.

There is another point too, which also to some extend goes against your point—that we model epidemiology in standard ways, and that in order to use those standard ways we need the parameters in the set of differential equations. And these do make predictions based on the equilibria. I am not saying they solve everything, but they are routinely used to for example calculate how many people need to be vaccinated to sto…

The point of the parent is you can't actually find R_0 because R_0 depends on the behaviors of millions of people which cumulatively are based on billions of factors. R_0 isn't a constant, it's an equation. Mathematically, unless you get really lucky or have a very idealized system, the standard models underfit the actual system. The models can still be useful though, e.g. 'we must reduce r0 below r_critical to elimi…

Post by sgt101 » Thu, Apr 09, 2020, 10:50 PM UTC

used_noise wrote: post #64

dekhn wrote: post #27

Earlier quoted context omitted.

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?

Post by sharken » Fri, Apr 10, 2020, 1:00 AM UTC

tunesmith wrote: post #60

jefftk wrote: post #9

Note that this article is from March 31st, and while that wouldn't normally be very long ago things are moving extremely quickly.

The main shift I've noticed (as a layman) is that people are thinking the illness is more contagious and less fatal than first thought. That explains the downward revisions in future deaths, but it also means that physical distancing is more effective and more important than first thought. Because a carrier that stays home is infecting ~5 less people rather than ~2 less people. The thing I'm worried about today is po…

Post by gammarator » Fri, Apr 10, 2020, 2:33 AM UTC

m0zg wrote: post #72

macintux wrote: post #70

Earlier quoted context omitted.

I don’t know that anyone expected (nearly) every state to impose a stay-at-home order. Crashing our economy into a brick wall is not an easy decision. I frankly expected things to get much worse before state governments took action. Obviously we could have done much better, much sooner, but I certainly can believe why early estimates were much more pessimistic. And that’s all disregarding the fact that we still don’t…

> I don’t know that anyone expected (nearly) every state to impose a stay-at-home order. IHME model did. And it still overestimated (and continues to overestimate) by a lot. What it predicted initially isn't even observed in countries where there's no lockdown at all.

Post by sesuximo » Fri, Apr 10, 2020, 6:25 AM UTC

jhayward wrote: post #12

Thrymr wrote: post #10

What this article misses is that simple models of complex systems in science are most useful for understanding the dynamics of phenomena, not for making accurate quantitative predictions. This is not a model of a mass accelerating in a vacuum where Newton's laws are sufficient to a high degree of accuracy, or even a numerical model of the aerodynamics of an airplane, where the physics are well understood but there ar…

Agree. I think a better exposition on COVID-19 models is Zenep Tufecki's article in The Atlantic [1]. TLDR: They're more directional roadmaps, showing you which possible outcome paths you need to prune off the tree of outcomes, not exact numerical predictions. [1] https://www.theatlantic.com/technology/archive/2020/04/coron...