[M T]

So, I found a very interesting dataset, the Oxford Government Response Tracker.  Basically, it tracks government action in response to the coronavirus.  The stringency index is based on Eight of the policy indicators (C1-C8) record information on containment and closure policies, such as school closures and restrictions in movement (the variables are: school closing, workplace closing, canceling public events, restrictions on gatherings, public transport closings, stay at home requirements, restrictions on internal movements, and international travel controls).  They show the relationship between the index and number of cases:

Uh, a country that is (say) 100x the population gets the same vertical scale on the left as the small country???  The scale on the right is some artificial scale, but I'd guess it is intended to be something proportional to the population.   So, if Lichtenstein did nothing and had all 38K population get sick, they would have looked ok just because the gray plot didn't go that high.  China would look worse, despite it having, what, 4 orders of magnitude more population.

Oranges versus barrels of apples.

And while someone is making this stuff up, how many countries in this set had 50 (or more) different regions that had different rules applied at different times? (Italy had some of that.)  Are these stringency steps when the last region applied them, or when the first did?

This needs a lot of work to pass the "smells like BS" test

Remind me, who on this board pointed out that any statistic based on quantity of positive tests was pretty much meaningless?  Or do we just pull that card out of our sleeve when we want to discount an outlier but ignore it for all the non-outliers.  (We have the same card for deaths, by the way.)

[M T]

So, I found a very interesting dataset, the Oxford Government Response Tracker.  Basically, it tracks government action in response to the coronavirus.  The stringency index is based on Eight of the policy indicators (C1-C8) record information on containment and closure policies, such as school closures and restrictions in movement (the variables are: school closing, workplace closing, canceling public events, restrictions on gatherings, public transport closings, stay at home requirements, restrictions on internal movements, and international travel controls).  They show the relationship between the index and number of cases:

Uh, a country that is (say) 100x the population gets the same vertical scale on the left as the small country???  The scale on the right is some artificial scale, but I'd guess it is intended to be something proportional to the population.   So, if Lichtenstein did nothing and had all 38K population get sick, they would have looked ok just because the gray plot didn't go that high.  China would look worse, despite it having, what, 4 orders of magnitude more population.

Oranges versus barrels of apples.

And while someone is making this stuff up, how many countries in this set had 50 (or more) different regions that had different rules applied at different times? (Italy had some of that.)  Are these stringency steps when the last region applied them, or when the first did?

This needs a lot of work to pass the "smells like BS" test

Remind me, who on this board pointed out that any statistic based on quantity of positive tests was pretty much meaningless?  Or do we just pull that card out of our sleeve when we want to discount an outlier but ignore it for all the non-outliers.  (We have the same card for deaths, by the way.)