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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

#51

Post by guscost »

watwut wrote: post #49
sgt101 wrote: post #41

Earlier quoted context omitted.

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?

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

#52

Post by jvanderbot »

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…

This is not as absolute as is being presented. We can speak about spreads and aggregate behavioral patterns just fine if we take times to infinity and zoom out far enough. The next few weeks or localized transmisson (which is "impossible" to model) are problematic, agreed.

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

#53

Post by mcguire »

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.

"Meanwhile, a report from Imperial College London that made headlines for its dire, modeling-based forecasts predicted about 2.2 million U.S. deaths from the coronavirus, if nobody changes their everyday behavior."

On the other hand, they're still quoting that study, which was very out of date well before the 31st.

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

#54

Post by watwut »

guscost wrote: post #51
watwut wrote: post #49

Earlier quoted context omitted.

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.

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

#55

Post by [deleted] »

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…

[deleted]

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

#56

Post by tomrod »

paganel wrote: post #32
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…

That's right, difference between the date of specimen collection and date of announcement. General approach is to announce as soon as positive confirmation hit. There is a vintage to this data. Source: working in this dataspace as we speak.

Keep up the crowd-sourced wisdom HN, it helps prevent us data science folks in thick of it from keeping blinders on!

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

#57

Post by mcguire »

3fe9a03ccd14ca5 wrote: post #38
jeromegv wrote: post #29

Earlier quoted context omitted.

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.

Most of North America was also covered by ocean at some points.

Neither of which is spectacularly good for the North American economy.

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

#58

Post by jefftk »

CodeWriter23 wrote: post #40
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.

I can't tell what you think should have happened differently here

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

#59

Post by [deleted] »

watwut wrote: post #48
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…

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.

[deleted]

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

#60

Post by tunesmith »

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 population centers deciding to relax mitigation before they've vastly increased testing capacity.

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