Least to most disruptive/damaging social distancing measures

At the top are the least damaging measures such as testing, hand washing, and checking our body temperature. After that we start to get more draconian such as travel restrictions, closing of sporting events, schools, and most businesses.

To make wise decisions, we need to better understand how effective each measure is in terms of both the damage it causes and benefit it provides.

Statewide Stay-at-Home measures were put in place as an emergency measure

When most statewide stay-at-home measure were put in place during mid to late March, there was a growing fear that we would “end up like Italy” and that we were on the precipice of bodies lining streets across the United States if we didn’t take action. People felt a real sense of panic about an impeding disaster and pulled the emergency brake lever to just stop nearly everything in its tracks.

We had a lot less data back then, so a study of those March decisions can be evaluated at some point in the future. What’s more important is that now that we have some data, and actually a lot more data, what have we learned from taking these measures?

Statewide closures obviously worked, right? It’s not so simple.

Many people point to NY as evidence that statewide closures work. While NY has certainly mitigated unchecked growth of the virus, we can’t yet say for sure how much of that was from cancelling events, or washing more hands, or wearing masks, or staying at home, etc. We may have “common sense” intuition, but analyzing data is usually a superior approach.

The only real way to know how effective shutting down has been is by comparing two similar situations in which one shut down and one did not. Since we don’t have a bizarro parallel universe of New York that didn’t shut down, we can at least compare the actions of all the different States and see what outcomes the different decisions had.

Let’s compare speed to shut down to expected death rates

So one way to visualize effectiveness of shut downs is to compare how quickly States shut down to the total number of currently expected deaths, normalized to population size of course.

According to Tomas Pueyo:

In this theoretical model that resembles loosely Hubei, waiting one more day creates 40% more cases! — Flattening the Curve

That certainly sounds scary! One day adds 40% more cases? With a situation like that, we should all shut down as quickly as possible.

Let’s see what actually happened…

I graphed the projected total deaths (past and future) using the IHME model versus how long it took a State to shut down from the time it started seeing people dying (when it reached 0.5 deaths per million).