I agree that it's been somewhat circular in this regard. However, that it is circular does not mean that it's wrong. It means that the test of whether it was good or bad was faulty and we learned less than we should have.

You claim, and I quote, that models' "unwise and ill-considered use caused great harm to society".

But what you have talked about above doesn't come anywhere close to supporting your claim. You didn't even support the claim that the models were wrong! All you did was make a claim again without evidence (!!!) that they weren't validated thoroughly enough.

The difference between "this caused great harm to society" and "I claim but do not show that they weren't properly validated against evidence" is immense.

If you aren't going to take seriously the need to check in with reality in the case of Covid, why even try to use it as an example? Why not drop the claim about the consequences and instead just talk about how the methodology was shaky (if you care to even document that it was shaky instead of just claiming it)? Or if you already have, why not cite it (if it checks out)?

It's not like the proper way to use epidemiological modeling is a topic that nobody else has thought about. Heck, there are even papers on the exact topic, like, for instance, https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-023-15936-w!

So why are you seemingly so reticent to actually engage with the actual data on the topic?