Many, such as @youyanggu, make the inaccurate conclusion that statutory restrictions, such as NPI mandates (i.e. masks, distancing, etc. orders) are equivalent to effective restrictions (i.e. voluntary use of masks independent of government order)
They are not
In the US in particular, state-level mandates are entirely toothless and unenforced.
To believe that people not inclined to wear a mask will wear one because of a toothless order is ridiculous.
Likewise that those inclined to wear one will stop once the state order is lifted
A good example is the wide gulf between statutory tax rates and effective tax rates.
Anyone who believes that, say, General Electric paid 35% of its income in taxes in 2017 (the statutory rate), for example.
2. Oxford stringency restrictions are qualitative assessments of legal mandates. They do not capture effective compliance (i.e. mandates are ineffective if unenforced)
3. Unemployment levels are a function of state response. It is an error to assume that there should be a relationship between Oxford stringency assessments and unemployment levels for all kinds of reasons
Michigan currently has the highest deaths per day (per capita) of any US state.
It is an instructive case in both the effectiveness of lockdowns as well as the non-seasonality of COVID
Michigan, like many states in spring of 2020, had an explosive first wave. Lockdowns were put in place but not soon enough to prevent the first wave from generating the highest deaths per day of all three weeks
But notice how the sharp uptick in deaths is followed very quickly
by a sharp downtick.
This is a shape similar to what Belgium experienced -- a very "pointy" (steep and high) initial wave of deaths
This is a common pattern that we see when:
a. There is a comprehensive attribution of COVID deaths -- for instance Belgium confirmed COVID cases
There are those of us who have worked with "the data" for some time, but who are not data scientists
And there are those who know all about data, statistical analysis, etc. but who have little "boots on the ground" experience
There is a natural antagonism between the camps
Which is counter productive
The boots on the ground folks, of which I include myself, need to get over their natural fear of being talked down to by the scientists
And the scientists need to acknowledge that those of us who have been slogging through the shit for decades...
probably gained some insight along the way
Demographic data and health data is UNBELIEVABLY dirty, balkanized and full of characteristics that make it very difficult to coalesce -- example: longitudinal data recorded across different time frames
1. NPIs are not limited to hand washing and wearing a mask. They also include telecommuting, seating capacity restrictions, school closing, etc. all of which directly affect the indoor transmission of the virus
2. NPIs do not eliminate transmission, they modulate it down. Intrinsically less transmissible diseases, such as influenza, can expect to be more heavily modulated by NPIs than COVID thus it is not surprising that NPIs eliminate a greater amoung of influenza infection than COVID
Monday morning report. N.B. Many locations have either limited (USA) or no (Sweden) reporting of infection and death statistics over the weekends.
This means that Monday's numbers usually produce a false picture of the situation with infections and deaths underrepresented.
1/US States ranked by COVID death rate (avg of last three days). The Dakotas continue to be the deadliest place in the United States. My home state, Indiana, has dropped from sixth place to tenth in today's rankings.
2/World locations ranked by death rates (avg over past three days). Czech Republic is leading (up from North Dakota) with North Dakota currently second deadliest place on Earth.