"If it looks like you're overreacting," Dr. Anthony Fauci said in March about the developing COVID-19 response, "you're probably doing the right thing."
So how long before people think you're overreacting (answer: not long), and how do you remove the "probably" from "doing the right thing"?
In lieu of certainty, you project. You build statistical models with whatever data you can acquire and update them as better information comes along. Such models aren't definitive but can guide preparations.
We see something similar with weather forecasts — the splay of spaghetti lines on a map that converge as a hurricane approaches landfall, or disagreements among models over how much snow will fall where, as was the case leading up to Minnesota's most recent April affront.
The new strain of coronavirus has a lower fatality rate than those that produced SARS or MERS, but it's also harder to contain. It's deadlier than the seasonal flu by an order of magnitude (1.4% by one estimate, compared with 0.1%) and more ecumenical in its targets. Some who are infected have minor symptoms; others, none, but each must entertain the possibility of a terrible outcome. Thus, economy-crushing restrictions have been put in place to impede the disease. Doubts about these actions and the models that guide them are to be expected. With all that in mind, let's look at the basics.
What model is Minnesota using?
The Minnesota model, now on its second iteration, was developed by the University of Minnesota School of Public Health and the Minnesota Department of Health. It combines data from elsewhere with information specific to the state. Some places are using similarly unique models, while others seek consensus from a range. Is the first or second approach better? Yes.
Ideally, the Centers for Disease Control and Prevention or the National Institutes of Health would have provided an official national model, but that's now a task for the future. It's too late this time.