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Why it’s so hard to make a good Covid-19 model
11–20 of 96 posts
Re: Why it’s so hard to make a good Covid-19 model
#12What 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...
Re: Why it’s so hard to make a good Covid-19 model
#13Who thought it would be easy. One person can decide to break quarantine and infect other people.
Re: Why it’s so hard to make a good Covid-19 model
#14What about our models for global warming?
Compared to a spherical cow model we could make as soon as we realized that CO2 the oceans absorbed didn't stay absorbed we've only done a little better. It's a complicated system, we don't know all the detail, and the calibration of the older models we've been able to robustly test at least just wasn't there.
Compared to constant model where the temperature stays the same both the modern sophisticated model and the spherical cow model do much, much better. For simple things like "how much infrared light and in what bands does CO2 absorb" we can measure that in a laboratory and have it nailed down.
Re: Why it’s so hard to make a good Covid-19 model
#15What 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…
It's like trying to calculate the Butterfly Effect during winter at the Reserva de la Biosfera Mariposa Monarca (Monarch Butterfly Biosphere Reserve).
https://en.wikipedia.org/wiki/Butterfly_effect
https://en.wikipedia.org/wiki/Monarch_Butterfly_Biosphere_Re...
Re: Why it’s so hard to make a good Covid-19 model
#16What 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…
>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 schools and workplaces. Maybe you know better?
Re: Why it’s so hard to make a good Covid-19 model
#17What 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…
It's like trying to calculate the Butterfly Effect during winter at the Reserva de la Biosfera Mariposa Monarca (Monarch Butterfly Biosphere Reserve). https://en.wikipedia.org/wiki/Butterfly_effect https://en.wikipedia.org/wiki/Monarch_Butterfly_Biosphere_Re...
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.
Re: Why it’s so hard to make a good Covid-19 model
#18What 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…
>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.
Re: Why it’s so hard to make a good Covid-19 model
#19Very odd little diagrams to show model components in this article - why don't they use digraphs like everyone who knows how to do stats?
oh...
Re: Why it’s so hard to make a good Covid-19 model
#20What about our models for global warming?
Those two things are unrelated. Unlike a virus, the laws of physics don’t appear to mutate. Also, you can’t measure the quality of your model of virus progression with earth observation satellites or hundreds of thousands of years worth of ice core data. Also, rather unfortunately in this case, the lack of meaningful action by world governments means that none of the climate models have to account for feedback caused…
It’s going to be fun to watch this flood of nonsense convince precisely nobody for the next few years.