This was the response to asking for your proof that lockdowns cause more harm than no doing so, and your assertion of an inevitable "huge spike" in infections.
Looking at this paper, it seems to be a study of 131 counties and their calculated R rates and an attempt to extract the impact of applying and releasing various NPIs on the R rate.
As a proof it leaves much to be desired.
Here are some problems:
1. It uses "confirmed case" numbers in between April and Late July 2020, which is a metric which is even now considered to be unreliable, and certainly was at that time.
2. There is no consideration that the test rate will have substantially changed over the data collection period, so will have had an impact on the data being used.
3. Of the 131 countries, a large number should be considered to be poor sources of reliable data.
4. There is no stated weighting method to combining the data, from these sources (population size, density etc)
5. There is no consideration of the stage on infection, between countries.
6. There is no stated method for extracting the impact on R for the various NPIs. It is just stated!
7. Observation of the graphed trends of R vs. NPI status, for most countries does not show any correlation between release of NPIs and R. The time period is simply too short for such an effect to be visible. Have these non-effects been included, or rejected?
8. The time period used is not enough to model even 1 wave of catch-release-effect.
The paper does show that applying NPIs, reduced R, and releasing NPIs increased R. Also shows that the time to maximum effect differs between apply/release.
It does not seek to justify the view that applying NPIs has an overall harmful effect. Or.. "Huge spike" as you put it.