I'm not a modeler but I work in an industry where models are used extensively for forecasts of future demand on public infrastructure. I have never once seen a model output that was 100% accurate, but in most cases they provide reasonable estimates based on prior trends and current known conditions.
Models for biological phenomena are probably very tricky simply because the "current known conditions" are often a large black hole, but "prior trends" in other comparable situations can at least provide some reasonable guidance on future conditions. Maybe the range of potential outcomes is wide, but in a rapidly-changing situation at least the model can be re-calibrated and updated constantly.
I see a COVID-19 model as not unlike a hurricane tracking model. It will never be totally accurate, but the forecasts are reasonably accurate for emergency preparedness purposes.
Per your point, it sounds like the field you work in involves some kind of stochastic modeling, thus generating imperfect but nevertheless helpful outputs.
Biological models are far more problematic beyond the reasons you cited. Simply put- nature is complex beyond our wildest imaginations.
The reason global warming models will always fail is that it’s not just the physics of weather, but the biological effects of countless living plants and animals, the holistic effect of all nature, as it were, on the climate. The sheer number of variables, much less how to correctly weight them, is beyond our present scientific understanding.
So to me, the Wuhan models have far more dissimilarities than similarities to hurricane tracking models where biological agents are not significant.