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

fivethirtyeight.com

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

#31

Post by vrinstan »

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.

We did use naive models and turned off half the planet. We should learn not to do that anymore. And beside that, we should hold our politicians accountable for these decisions.

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

#32

Post by paganel »

lonelappde wrote: post #2

The article is a long listing of ways to say GIGO. It's hard to get good clean accurate data.

Just this evening some Italian scientist announced on his twitter account that the number of new positive cases does not correspond to the number of new tests carried out and announced for that day, because those tests could have actually been made 2 or 3 days before or something like that.

Which, presumably, instantly invalidated all the charts and data-modelling based on the "number of new positive cases" / "total number of tests made" (with a lower value being seen as better).

But the Region of Sicily (or its official twitter account, anyway) replied that in their case the number of new positive cases and the total number of tests made are indeed correlated, which of course means that everything is a mess in terms of data coming in and its significance.

Later edit: For those who know Italian this is the tweet [1] I was writing about, and it looks like I was remembering wrong, the guy is not a scientist per se, more like a "data scientist", he seems to be working at a company very similar to fivethirtyeight (but presumably focused on the Italian market).

[1] https://twitter.com/lorepregliasco/status/124827958933764505...

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

#33

Post by moate »

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.

In the 9 days since we still have models that are all over the place. Nate Silver covers the difficulty in predictive modeling of pandemics in his book "The Signal and the Noise" and in the years since it was written not much has changes to they're basically rehashing the points he made then.

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

#34

Post by dfsegoat »

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.

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...

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

#36

Post by simonsarris »

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?

Charitably: To express the tentative nature of the information or model. "Here's our working theory."

(I think it makes it somewhat hard to read in this case)

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

#37

Post by jmoss20 »

KaiserPro wrote: post #24

Its pretty simple: there isn't any decent data. The data we have is strongly biased to older and sicker people. There is no systematic surveillance of a geographic area, only panic testing of those who are showing symptoms. Until there is sampling of a a borough, city or town, from start to finish, we will have wildly wrong models. The only thing that we can plot reasonably accurately is the exponent of the fatalitie…

There have been a few of those samples.

https://www.cebm.net/covid-19/covid-19-what-proportion-are-a...

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

#38

Post by 3fe9a03ccd14ca5 »

jeromegv wrote: post #29
3fe9a03ccd14ca5 wrote: post #4

What about our models for global warming?

The thing with climate is that you don't need to believe in the forecast. You could just look at the PAST results. Let's say just the last 10 years. And compare that to the last 200 years. May be that will teach you that something is going wrong and not going in the right direction.

The problem with that is how far back so you look for things to be “normal”? After all, most of North America was covered with ice at some point.

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

#39

Post by mikorym »

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 stop outbreaks of measles.

The article does mention most of the parameters; but we don't know what values to assign to those parameters. The last scientific writing that I was reading speculated about the R_0, but I believe we are still not sure about that. In any case, the point of the parameters is to find R_0, so we cannot expect to have an accurate R_0 without them.

We also don't know mortality rates in a general population (look at Italy vs. China, when specifying to age groups). But anyway, all I wanted to say is that our models are simplistic, but surprisingly useful.

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

#40

Post by CodeWriter23 »

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.

Well, politically things are moving quickly too. Bottom line, all the “science” failed to engage The Cautionary Principle when they should have known that what they did know was likely outweighed by what they didn’t.
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