I’ve already written about the lag between policy change and changes in cases and deaths. That summary is pertinent to understanding the table I’ve created below.
It’s very tricky to work on this because of so many variables coming into play, such as what percentage of customers are allowed in a business, what types of businesses are allowed to be open, what kind of public response will occur, and compliance with various measures. There are also fixed variables that play a role such as population density to consider as well. As such, I’m only going to do some very rough estimates by state. Some states haven’t put any kind of stay at home restrictions/advice in place.
After the state name, next column with a “x” indicates that policy varies around the state. The NYT is the source of the state action information.
Cases – This is roughly the date at which I expect some of the first cases to start showing up in the data related to reopening or easing up on business restrictions. However, it may not be immediately identifiable because of both variability in case identification as well as increased testing. It should be noted that I primarily tried to use stay at home orders for this date, but in some cases, reopening business dates needed to be used. This date may also be based on a projected reopening date, which is also subject to change since these can still change.
Deaths – This date reflects roughly when deaths are going to start emerging from the new cases.
Impact – This is the date by which trends in the data should be clear if things were done too quickly.
There are three places with exceptions. Illinois and DC still have restrictions in place, South Dakota has not had any, although tribal nations within the state have had different rules in place by their tribal governments.
Obviously its too soon to make sense of any data emerging by state right now in comparison to more restrictive orders. However, I thought it might be useful to get some dates set to analyze whether or not my argument for lag times was justified. Honestly, I would really like to be proven wrong and see things considerably improve.