Estimating R0

Excellent article by Andy Larsen in the Salt Lake Tribune entitled "This is one key metric Utah should track as the state considers easing coronavirus restrictions"; it's on estimating R0 in real time. It includes estimates (with confidence intervals). Do note that the big data coder is more sophisticated than the epidemiologists. The latter view R0 as a constant, which isn't very applicable to COVID-19, since the whole idea of social distancing is to make R0 go down. I doubt epidemiologists are unaware of either point, but the jargon of the field has yet to catch up. Anyway, here's Rt on a state by state basis:

More importantly for SUU economics students, it includes links to the Python code to run the whole thing.

Of course, you need to get into the safer range (R0 less than 1), and then stay in that range. Utah did well on the first one, but not the second:

 

The article is also the first I've seen to point out that Smithfield executives gave perverse incentives to workers that contributed to their outbreak. That one is going to be in ManEc texts for decades to come, I'm sure (and I'm not sure how I missed it so far).

You could read a lot into the rankings of states, noting that some that want to "re-open" are still at the bad end. But, there's also some that dealt with their outbreaks swiftly and decisively, like Washington, that are still at the bad end. But here's the thing: no one wants to turn out like New York, but New York