[M T]

Ultimately, restrictions should be set not based on level, but on growth rate.  If R > 1, then you need to tighten up restrictions until R < 1. 

I believe that is a wise strategy, but who would support it?  I get the feeling any restriction is questioned by the well-educated and mostly tuned-in members of this board.

If you look at the graph of R as calculated on https://ca-covid-r.info/  (The archived Nov 3 content shows month boundaries on the charts of state's R)
you will not see plateaus of R at -0.3  or +0.5.  Instead one sees steady slopes with inflection points. 
I've been trying to understand why we don't see plateaus instead.  Is it because there is some hysteresis built into the calculation, or maybe into the whole system of infections & testing?  Or is it sorta an elastic effect by the population bending the policies more and more after a while.

The steady slopes are weeks long.
I would have thought policy settings resulted in a more-or-less flat R during the policy's effective date.

If you did change policies at, say, 1.2 (turn on restrictions), 0.9 (turn off restrictions), using this calculation of R, it might be interesting to see what would have happened.

[M T]

Ultimately, restrictions should be set not based on level, but on growth rate.  If R > 1, then you need to tighten up restrictions until R < 1. 

I believe that is a wise strategy, but who would support it?  I get the feeling any restriction is questioned by the well-educated and mostly tuned-in members of this board.

If you look at the graph of R as calculated on https://ca-covid-r.info/  (The archived Nov 3 content shows month boundaries on the charts of state's R)
you will not see plateaus of R at -0.3  or +0.5.  Instead one sees steady slopes with inflection points. 
I've been trying to understand why we don't see plateaus instead.  Is it because there is some hysteresis built into the calculation, or maybe into the whole system of infections & testing?  Or is it sorta an elastic effect by the population bending the policies more and more after a while.

The steady slopes are weeks long.
I would have thought policy settings resulted in a more-or-less flat R during the policy's effective date.

If you did change policies at, say, 1.2 (turn on restrictions), 0.9 (turn off restrictions), using this calculation of R, it might be interesting to see what would have happened.