Why it’s so hard to make a good Covid-19 model • Hacker News Board

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

#41

Post by sgt101 » Thu, Apr 09, 2020, 5:50 PM UTC

guscost wrote: post #18

sgt101 wrote: post #16

Earlier quoted context omitted.

>the models themselves are so simplified that they can't capture much of the important dynamics going on That isn't my (outsider) understanding, could you provide a reference? By reference I mean a recent survey that demonstrates the specific limitations? My understanding of the Imperial College model is that it uses a 30m*30m grid of the world and the expected people in the world and various additions to simulate sc…

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.

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

#42

Post by _Microft » Thu, Apr 09, 2020, 5:51 PM UTC

systemvoltage wrote: post #35

Completely tangent observation: What's the point of using sketch scribbles over the diagrams? They could just make it in powerpoint and simplify. It would be easier to read as well. Decoration for the sake of decoration? Why?

I like the look of it.

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

#43

Post by sgt101 » Thu, Apr 09, 2020, 5:52 PM UTC

deadmetheny wrote: post #26

sgt101 wrote: post #17

Earlier quoted context omitted.

I don't think it is - I think you can write "x is like y" but you have to understand and explain why. Otherwise I can say "a hippo is like a bright blue sky" and then claim that I am right! I don't think that this is like modelling the impact of a butterfly on storms - I think this is more like modelling the diffusion of a bottle of dye in a swimming pool.

Grandparent is somewhat nonsensical, but I think what they were trying to say is that since human society is a highly dynamical system, it's not really possible to have a highly accurate model to represent how COVID-19 is going to spread. The best we can do is use naive models and plan around worst-case scenarios.

But we know really quite accurately how people en-mass move and associate prior to intervention. The proper models are using this information.

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

#44

Post by thereisnospork » Thu, Apr 09, 2020, 5:54 PM UTC

mikorym wrote: post #39

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…

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 eliminate spontaneous spreading given herd immunity %'s' is worth knowing.

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

#45

Post by watwut » Thu, Apr 09, 2020, 5:58 PM UTC

ck2 wrote: post #23

Good luck trying to predict how much of a population is going to refuse to isolate and interact anyway. Also relies on governments and politicians to not fudge testing and death counts, which is never going to be accurate. btw the financial times has maybe the best graph on the stats however flawed: http://com.ft.imagepublish.upp-prod-us.s3.amazonaws.com/2251...

The actual model used by UK imperial college estimated compliance with karantene of sick people 75% and compliance with general stay-at-home 50%.

What I find frustrating is that actual models used by epidemiologists back in february and March incorporated pretty much all "gotchas" non-epidemiologists discovered today. And it does not matter, because non-epidemiologists still assume they are first ones to ever discover them.

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

#46

Post by YetAnotherNick » Thu, Apr 09, 2020, 5:59 PM UTC

I tried to do some exploration with the data to get some idea on the final number. One of the best plot I found that could tell the final number is plotting the percentage of cases in the next week with the cases till now. It is like negative half parabola and the time it meets the x axis will give the final number per country.

This is the final plot: https://i.imgur.com/5p4Xife.png. You could make a good guess from the data how many people it will affect in the lifetime per country by continuing the same pattern till it reaches x axis.

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

#47

Post by m463 » Thu, Apr 09, 2020, 5:59 PM UTC

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…

What you said reminded me of Richard Feynman when he worked on the connection machine:

http://longnow.org/essays/richard-feynman-connection-machine...

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

#48

Post by watwut » Thu, Apr 09, 2020, 6:00 PM UTC

mikorym wrote: post #39

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…

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…

R_0 changes as people change their behavior. It is not one constant common to all places and times with virus.

Edit: people down voting this, please look at definition and those papers. R_0 does changes.

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

#49

Post by watwut » Thu, Apr 09, 2020, 6:02 PM UTC

sgt101 wrote: post #41

guscost wrote: post #18

Earlier quoted context omitted.

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.

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

#50

Post by SpicyLemonZest » Thu, Apr 09, 2020, 6:05 PM UTC

sgt101 wrote: post #41

guscost wrote: post #18

Earlier quoted context omitted.

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.

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.