1. Thanks to Steve Hays for sending me this article by Dr. John Ioannidis. Ioannidis is a Greek physician and epidemiologist. He's most famous for (rightly) taking to task academic scientific publications and showing how the vast majority of them are basically bad science and/or bad statistics. Ioannidis' article on the coronavirus is worth reading. As an ID physician said about the same article: "Excellent piece by @METRICStanford on CFRs, the calculus of social distancing, and how lack of data is compromising decisions."
2. Similarly I earlier pointed out that Nate Silver (whose day job is predicting political elections on his 538 website) brought up some limitations in coronavirus models too.
3. I think epidemiological models might parallel meteorological models. That is, I think epidemiological models attempting to predict how bad the coronavirus will be might be similar to weather reports trying to predict the weather. We can't accurately predict the weather 100% of the time. Let alone in every place in the world. Sure, there may be general accuracy, but weather reports notoriously get things wrong too. Just my general impression, but I'm no epidemiologist or meteorologist or statistician or the like.
Of course, this doesn't necessarily mean we shouldn't bring an umbrella if the weather report predicts it's going to rain, but it also doesn't necessarily mean we should. It depends on a number of factors. Such as the further out the weather forecast is, the more unreliable it is likely to be. It's normally more accurate to predict it will rain this evening than it is to predict it will rain tomorrow or one week from now or one month from now.
4. I guess the only sure thing about predictions regarding the weather, political elections, the future impact of disease on populations, what the stock market will do, and who will win the Super Bowl is that there's bound to be someone in the end who will say: "See, I told you so!" :) And sometimes they might even be right. Sometimes.
