https://aatishb.com/covidtrends/
This way of presenting the statistics helps visualize the deflection point.
(With that, countries can't be compared at the relative point where the deflection happens, because the axes show absolute cases, not cases per population, so small countries appear to more left than larger countries. Yet, you can easily visualize the deflection point, and also see the changes in testing procedures (sudden jump downward, then back to the original slope.)
We've got to be a bit careful of scaling by population, as that can distort or misrepresent the situation in other ways.
One example. Consider two populations identical except that, one has 10,000 people, one has 1,000,000 people, both being infected with a disease with the characteristics of Covid-19 by a single 'patient zero'. For the first few days and weeks they will have identical numbers of infected, dead, recovered etc. Once the 10,000 person country has reached a point where effects show of 'proportion of population affected' show up, only then are the characteristics of the small and large population infection statistics going to diverge. The small population will show slowing while the large population will continue to grow at an unencumbered rate until it too hits the point where 'proportion of population' effects begin to show.
Instinct suggests to me that scaling by log
n population (
n to be determined) might be a better way of doing it as the other processes involved are exponential ones BUT I have not thought it through in detail so I may be talking out of my arse.