Such nonsense!
I had to model a system that was required to track geosynchronous satellites which actually describe a small pattern in the sky that nearly repeats every sidereal day. Rain fades are a problem. "Nearly" is the key because you don't actually know whether the diminution of signal is due to atmospheric problems or satellite drift.
The software to control the tracking had to be subjected to more difficult conditions more often than might occur in the real world.
Sometimes models work. Sometimes they don't. The problems arise when someone is trying to model a system with too many variables and/or too many unknowns. The existence or planetariums proves that a model and the real thing can be pretty damn close.
ML/NJ
I think we are saying the same thing. Complex systems are complex because they have too many variables and/or many unknowns. The detail in which I speak are the variables and knowns. If you have complete detail you could be the real thing. Think of scientific experiments where you try to simulate the real world in a closed environment. That is sort of a model isn't it? Make it closer to the real world by adding more details. It's still a model, but closer to the real thing. Keep doing that to infinity, and you have the real thing, hypothetically. That of course is not feasible, particularly in complex systems so we settle, as you put it, "nearly" enough. Which is also correct.
Bottom line at the moment, people don't know enough about the ChiCom flu, thus you have unknowns. It is also a complex system that is being dealt with - humans and their environment.
You are right to say, models work sometimes and other times they don't. I agree.