Garbage in, garbage out! Can't build good models with data that's terrible. Inconsistent testing, different reporting methods, lying from governments.
Garbage in policy out
Why it’s so hard to make a good Covid-19 model
91–96 of 96 posts
Re: Why it’s so hard to make a good Covid-19 model
#92I really like the neher lab prediction tool.
You can choose different scenarios and compare it with real data. So you can match the parameters of your model to the actual measured data (number of deaths) and it also has data about the population and the state of ICUs and hospitals that you can use in your prediction.
In the end it is just a tool which can give completely wrong predictions, but you get a felling for what could and could not happen.
Re: Why it’s so hard to make a good Covid-19 model
#93Earlier quoted context omitted.
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…
IMO the IHME/Murray model communication was really confusing, especially considering it immediately became the gold-standard model for the USA. 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 fai…
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Re: Why it’s so hard to make a good Covid-19 model
#94Earlier quoted context omitted.
" 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 " On the other hand, they're still quoting that study, which was very out of date well before the 31st.
Do you (or anyone else) have any resources for a critique/comment of that report? I'm interested since I tried reading it and I think I lacked a finer understanding.
I do not have any specific critiques about it, although I remember seeing the authors had some follow-up work.
But the important caveat about that model is that it assumes everyone does nothing. It has been a rather long time since everyone stopped doing nothing, so even if it is entirely correct, it has not been relevant since well before this article was written.
Re: Why it’s so hard to make a good Covid-19 model
#95Earlier quoted context omitted.
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...
It's hard to use a roadmap with this much variance. Is there anything that can be said with certainty about covid
It's not the variance, it's the worst-case analysis that is helpful. You take decisions that lower the worst-case outcome.
Sure, there is lots that can be said about COVID-19. Epidemiology is a fairly well-studied field, and we have experience with aerosol-transmitted respiratory viruses. We have a pretty good feel for the transmissibility, getting better all the time. Pretty good feel for the disease course, and getting more specific about the different phases of treatment all the time. Pretty good feel for the R0 in many conditions, and the serial interval, and so on.
"Is there anything that can be said with certainty" is a dangerous position to be pushing on the public. It is the very thing that autocrats and dictators try to induce in populations through propaganda and disinformation, so that reason and self-help are surrendered. When there is no way to know truth, truth is what the Leader says it is. And we don't want to go there.
Re: Why it’s so hard to make a good Covid-19 model
#96Earlier quoted context omitted.
It's hard to use a roadmap with this much variance. Is there anything that can be said with certainty about covid
It's not the variance, it's the worst-case analysis that is helpful. You take decisions that lower the worst-case outcome. Sure, there is lots that can be said about COVID-19. Epidemiology is a fairly well-studied field, and we have experience with aerosol-transmitted respiratory viruses. We have a pretty good feel for the transmissibility, getting better all the time. Pretty good feel for the disease course, and get…
I don't think it makes sense to shoot for the least bad worst case. How about the best average case.