Several states, including Georgia, embarked on an unexpectedly political experiment late last month. Despite not hitting the benchmarks established by the federal government for scaling back social distancing measures, they were going to do so anyway, echoing President Trump’s desire for a return to economic normalcy even while rejecting the safety guidelines Trump ostensibly espoused.
An intense debate over the decision erupted, with critics suggesting that those states would see a spike in new coronavirus cases, given the renewed ability of the virus to spread. Those supporting the decision figured that no spikes were likely or, perhaps, that they could be contained.
So what happened? On Wednesday morning, Axios offered a provocative answer: States that reopened were seeing fewer new coronavirus cases.
Aha! The question is settled in Trump’s favor. (The reopen-the-economy Trump, that is.) Except that the reality of the situation is a bit murkier than the headline suggests.
Axios compared seven-day averages of new cases on a state-by-state basis, pitting the figure on May 4 to that on May 11. There’s nothing erroneous about that method, certainly, it’s just a somewhat limited consideration of one specific variable. It also depends on a fairly vague definition of “high-risk states.”
So let’s consider this more broadly. The guidelines set up by the White House coronavirus task force consider two metrics to measure the spread of the virus: new cases and the density of positive tests. So we will do the same. To pool the states into ones which might be expected to see new spikes (due to reopening) and those which might be expected to see declines in cases (from staying closed), we used the April 30 iteration of the New York Times’s map categorizing each state. That gave us three groups: States which were reopening, those about to reopen and those remaining closed.
Those categories have shifted since, but given the up-to-two-weeks window in which symptoms of the virus emerge, we get something of a snapshot of how things changed from April 30 to May 12, about two weeks later.
Using this methodology, we get a different picture than what Axios presented. On average, states which were opening at the end of April saw an increase in new cases. Closed states saw a relative decline. That’s an average of the states, mind you; about half of the reopening states saw a decline. Overall, though, there was an increase.
This is related to the concurrent increase in completed tests. More tests, the more likely you are to get more confirmed cases. In each category of state, the number of tests being completed each day has generally increased. (In Montana, the increase has been through the roof.) Notice, though, that there was a bigger average increase in closed states than in open ones — and a decline in new cases in those closed states.
As you might then expect, the rate of positive tests in closed states declined more than the rate in open ones.
This metric is a bit trickier, though. Instead of comparing the relative increase in new cases or tests, we’re comparing the relative percentage-point shift in positive cases. We can view that either as an average of states (the average of the changes in Alabama, Kentucky, etc.) or as a cumulative change across all states (meaning that we add up the number of tests in each state each day and compare that to the number of positive tests in all of the states).
You can see how the method yields different results when looking at the open states. Thanks to Mississippi, the average of each state’s seven-day average shows a slight increase in the number of positive cases. But overall, the number of positive tests in those states as a group declined. In states that remained closed, though, the rate of positive tests declined much more.
So what’s the takeaway? The takeaway is that it’s complicated. That while it’s not clear that states which reopened their economies paid no price, it’s also not clear that they saw big spikes in new cases.
And that, at least, is good news.
