The issue here is that infectious disease epidemiologists don't think anything of a large effect size because they're all about models that constantly mis-predict huge effect sizes. They should be surprised by a large effect, but because they never update their beliefs in response to model failure, they think (predicted but unreal) effect sizes are no big deal.
That's not really true. The example I used was a very large effect size, because it's causal in a way few things are (there's a reason you can use Koch's postulates in infectious diseases, but rarely in anything else).
And these are all things estimated from data, using standard, boring statistical methods like logistic regression.
Dynamical systems models, which is what you're talking about, is a whole different field, and also don't inherently predict huge effect sizes - the last non-COVID one I was working on, for example, was estimating fairly small effect sizes.
