Quote:
Originally Posted by Axel Foley
Garick, this seems like something you might enjoy.
https://www.sydney.edu.au/news-opini...hme-model.html
In regard to the IHME model for COVID-19 deaths:
Seventy percent of US states had an actual death rate outside the 95 percent prediction interval for that state, casting doubt on whether the model is suitable to inform COVID-19 resource allocation.
Quote:
Originally Posted by Garick
Sadly, that likely means that people will just run off to another model, rather than accepting that we don't have the data we need to be able to accurately model this thing yet.
Quote:
Originally Posted by Axel Foley
Found another article that points out a potential flaw seen by critics:
https://www.statnews.com/2020/04/17/...s-critics-say/
IHME uses neither a SEIR nor an agent-based approach. It doesn’t even try to model the transmission of disease, or the incubation period, or other features of Covid-19, as SEIR and agent-based models at Imperial College London and others do. It doesn’t try to account for how many infected people interact with how many others, how many additional cases each earlier case causes, or other facts of disease transmission that have been the foundation of epidemiology models for decades.
Instead, IHME starts with data from cities where Covid-19 struck before it hit the U.S., first Wuhan and now 19 cities in Italy and Spain. It then produces a graph showing the number of deaths rising and falling as the epidemic exploded and then dissipated in those cities, resulting in a bell curve. Then (to oversimplify somewhat) it finds where U.S. data fits on that curve. The death curves in cities outside the U.S. are assumed to describe the U.S., too, with no attempt to judge whether countermeasures —lockdowns and other social-distancing strategies — in the U.S. are and will be as effective as elsewhere, especially Wuhan.
I had been wondering why they were showing such a sharp decline rather than a fat tail on the graph, and if they are heavily relying on data from Wuhan to predict the effects of social distancing in other places then it makes sense.
1) hate hate HATE statnews.com, as they are pretty much a shill for the biotech industry
2) The IMHE model is simply a curve-fitting model. It doesn't even purport to be mechanistic. I suspect that when all is said and done the modeling geeks are going to have a long and contentious debate about modeling approaches. Various time series approaches are amechanistic and just try to hit targets. We may reset to mechanistic approaches. e.g., "big data" doesn't care WHY it's right, just that it's right. It'd be nice to see that change, since I see it in my own workplace.
3) arxiv is about as close to fake news in academia as one can get. Frankly, it's irresponsible to post pre-reviewed papers and make it freely available with essentially no caveats. Do you want science to never be trusted again? Because this is how science becomes not trusted, by circumventing peer review. I mean jesus at least submit to plosOne if you want it out fast.