> I looked at various models over the summer knowing that while they may be wrong, they may be useful. But they did not help much at all for understanding trend

> I looked at various models over the summer knowing that while they may be wrong, they may be useful. But they did not help much at all for understanding trends and relationships. Take for example models that have been modified for mask mandates. During the summer, most of them had trended down to zero with masks “on” but the opposite has since happened. Most of the models I've seen seem to be focused on generating long-term predictions. The big questions people wanted answered at the beginning were "how long will the pandemic last?" and "when will we hit the peak?". And IF you can calibrate your model correctly, for those questions, SIR and its relatives make sense. But those aren't the questions we should be asking. For public policy decisions, what we really want to know is "is the epidemic spiraling out of control?". For answering that question, a simple exponential fit does well enough in the short term. Why aren't more models focused on the short-term, when long-term predictions heavily depend on finicky human behavior? > There is a genuine amount of circular reasoning where models are used to validate social distancing efforts which in turn are incorporated into revised models. Do you think this reasoning amplifies or reduces social distancing efforts?