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.

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 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.