I think of there being basically three extreme possibilities:
Really low R0
We successfully suppress the disease after the protests. As such, while protest-driven infections are a larger percentage of the total, the total number is much smaller, so it doesn’t really matter very much. This is basically the R0 = 0.7 case I mentioned.
High R0, no vaccine in time
Basically everyone gets the disease. As such the protests and other re-opennings have limited direct impact on the number of cases, as you mention. The impact is largely limited to accelerating this, with some effect on hospital capacity and less time to learn about better treatments.
High R0, mass vaccination in medium term
The number of cases keeps growing, then suddenly falls when a vaccination is rolled out. In this case, accelerating the spread is basically the same as delaying the vaccination. Because of the nature of exponential growth, the majority of cases will be just before mass vaccination, so this leads to a dramatic increase in the total number of deaths. (This might be slightly offset by the fact that a higher incidence makes it easier to do clinical trials on vaccines, but I would expect this effect to be small).
In the modelling I assumed an R0 < 1, which is basically a less-extreme version of the first scenario.
Good question!
I think of there being basically three extreme possibilities:
Really low R0
We successfully suppress the disease after the protests. As such, while protest-driven infections are a larger percentage of the total, the total number is much smaller, so it doesn’t really matter very much. This is basically the R0 = 0.7 case I mentioned.
High R0, no vaccine in time
Basically everyone gets the disease. As such the protests and other re-opennings have limited direct impact on the number of cases, as you mention. The impact is largely limited to accelerating this, with some effect on hospital capacity and less time to learn about better treatments.
High R0, mass vaccination in medium term
The number of cases keeps growing, then suddenly falls when a vaccination is rolled out. In this case, accelerating the spread is basically the same as delaying the vaccination. Because of the nature of exponential growth, the majority of cases will be just before mass vaccination, so this leads to a dramatic increase in the total number of deaths. (This might be slightly offset by the fact that a higher incidence makes it easier to do clinical trials on vaccines, but I would expect this effect to be small).
In the modelling I assumed an R0 < 1, which is basically a less-extreme version of the first scenario.