61–70 of 96 posts
Because there's no downside to the authors for overpredicting deaths and resource use, and _a lot_ of downside for underpredicting. So all the "bad" things get taken into account, and all the "good" things are ignored.
One thing I've reinforced in my view of the world is that common sense is very uncommon indeed. "2 million deaths" my ass. I hope people reconsider their trust in other models that are "hard to make".
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…
But recently, we have become quite good at weather predictions 7 days out, and even for 14 days are now significantly better than chance.
Disease models can be useful even in the absence of predictive power. Getting any one of a few dozens assumption wrong can throw your prediction off by orders of magnitude. But it still allows you to, for example, explore how sensitive the epidemic is to different policy alternatives.
Besides: what are the alternatives? As long as you do anything, that action is based on some “model” of how the world works, how people behave, what value you assign to competing objectives. Writing that model-in-your-Head down or implementing it in software is strictly better than not doing so: it forces you to be explicit about the assumptions you make, it allows people to cooperate, it forces them to be specific in any criticism, it is a far better tool to communicate your reasoning to people affected by it, it deals in real numbers and will quickly expose any significant oversights you might otherwise miss, it’s accuracy can be measured and thereby improved...
> Why it’s so hard to make a good Covid-19 model Because there's no downside to the authors for overpredicting deaths and resource use, and _a lot_ of downside for underpredicting. So all the "bad" things get taken into account, and all the "good" things are ignored. One thing I've reinforced in my view of the world is that common sense is very uncommon indeed. "2 million deaths" my ass. I hope people reconsider thei…
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.
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.
> Why it’s so hard to make a good Covid-19 model Because there's no downside to the authors for overpredicting deaths and resource use, and _a lot_ of downside for underpredicting. So all the "bad" things get taken into account, and all the "good" things are ignored. One thing I've reinforced in my view of the world is that common sense is very uncommon indeed. "2 million deaths" my ass. I hope people reconsider thei…
2 Million deaths was without all the drastic measures we've taken. I'm having a hard time understanding what exactly you are suggesting we (society) should have done here. "Used our common sense" to do what?
I think once we're through this, you will see that there wouldn't be 2 million deaths no matter what, although the measures did help, of course.
The current IHME models get routinely revised downward by a lot (to 1/3-1/4th the initial figures) even though _they assumed "measures" right from the start_. As of this morning, the projected fatalities in the US dropped again by a quarter, to 61K, with the lower bound at 31K. My prediction? We will be nearer to the lower bound. Why do I think that? There are fewer than 10K "serious" cases in the US, and over the past week intubations in NY have dropped so precipitously, Cuomo doesn't mention them in his pressers anymore.
In general we've seen doubling periods that seem to suggest 5-6 days, but occasionally as fast as 3 days, unclear how distorted those numbers are by testing and mitigation and misattributed deaths.
If it has a natural R0 of 6, then it means that an infection will infect 6 others within that contagion period.
If we're thinking R0 is not 2-3 but is instead 5-6, then to make consistent with the doubling periods we are seeing, it'd mean that people are contagious for a longer period of time than we first thought.
That being said, I'd pay good money to hear his latest numbers...
Earlier quoted context omitted.
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…
CT Bergstrom (UW bio prof in communication with IHME team) lays it out in his rapid peer review here: https://twitter.com/CT_Bergstrom/status/1243837050253496320
The uncertainty intervals are pretty confusing even to educated users trying to understand the results in good faith. The entire model used some version of the Wuhan intervention as a prior, and only the uncertainty in fitting curves to that prior is represented in the intervals.
The problem is that the overwhelming majority of the uncertainty in actual outcome doesn't come from the curve-fitting uncertainty, but rather what prior to fit a curve to.
The IHME model seems OK for what it does, but I'm baffled as to how it become the most-cited tool that we have. It's totally inflexible, and pretty confusing in terms of what it actually represents. Its overestimates are now being used as a bludgeon against people that take the virus seriously, which IMO is a major issue and I think wrong, but difficult to refute given how inflexible and confusing the model is.
I had a lame model that is currently predicting lower death rate but in a similar trend [1]. In this model my assumption is the lockdown to continue for 45 days. The result which I regret to have seen shows a scary number of 600k deaths after 39 days. So I'm hoping for something spectacular to happen such as vaccine, a drug, the sun etc that I would use to change my prediction.
Earlier quoted context omitted.
2 Million deaths was without all the drastic measures we've taken. I'm having a hard time understanding what exactly you are suggesting we (society) should have done here. "Used our common sense" to do what?
So what you're saying is Donald Trump saved 2 million lives, then? :-) I think once we're through this, you will see that there wouldn't be 2 million deaths no matter what, although the measures did help, of course. The current IHME models get routinely revised downward by a lot (to 1/3-1/4th the initial figures) even though _they assumed "measures" right from the start_. As of this morning, the projected fatalities…
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 know how/when this ends. We might still be in the early days.