In 2009 I posted on some predictions of the spread of the H1N1 virus which turned out to be off by two orders of magnitude. I wrote "I always worry that bad predictions from scientists make it harder to have the public trust us when we really need them to." Now we need them to.
We find ourselves bombarded with predictions from a variety of experts and even larger variety of mathematicians, computer scientists, physicists, engineers, economists and others who try to make their own predictions with no earlier experience in epidemiology. Many of these models give different predictions and even the best have proven significantly different than reality. We keep coming back to the George Box quote "All models are wrong, but some are useful."
So why do these models have so much trouble? The standard complaint of inaccurate and inconsistently collected data certainly holds. And if a prediction changes our behavior, we cannot fault the predictor for not continuing to be accurate.
There's another issue. You often here of a single event having a dramatic effect in a region--a soccer game in Italy, a funeral in Georgia, a Bar Mitzvah in New York. These events ricocheted, people infected attended other events that infected others. This becomes a complex process that simple network models can never get right. Plenty of soccer games, funerals and Bar Mitzvahs didn't spread the virus. If a region has hadn't a large number of cases and deaths is it because they did the right thing or just got lucky. Probably something in between but that makes it hard to generalize and learn from experience. We do know that less events means less infection but beyond that is less clear.
As countries and states decide how to open up and universities decide how to handle the fall semester, we need to rely on some sort of predictive models and the public's trust in them to move forward. We can't count on the accuracy of any model but which models are useful? We don't have much time to figure it out.