Friday, 15 May 2020

Are lockdowns effective? (a bit nerdy)

UPDATE (17 May): This post was picked up by Toby Young on Lockdown Sceptics, here, scroll down, I'm mentioned by name. And some folks have asked if they could re-post to which I say “fine” as long as you link back to this post. Cheers.
If Lockdowns were effective, you would expect a correlation between the stringency of the lockdown and the number of cases or deaths per million. For while correlation does not (necessarily) imply causation, the opposite is true: causation does mean correlation. Hence: the stronger the lockdown, the fewer the cases or deaths per million. That’s the theory.
I did the figures, based on a “stringency index” put out by Oxford University. 100 being the most stringent. And I did the correlation between this and the cases/deaths/ million, which come from Worldometer, run out of Johns Hopkins U.
Long and short: there is no correlation.
Go here to view the spreadsheet
A figure of less than 0.6 means little or no correlation. Here the figures are 0.36 for correlation between stringency and deaths/M and 0.18 for same and infected cases/M.
UPDATE (31 May): The latest figures are: 0.28 and 0.01. So, over time, the case for Lockdown has become even more tenuous.]
So, there is -- at least according to the figures we have so far-- no correlation between the strength of the lockdown and the number of cases and deaths per million. This could change, and I’ll keep an eye on it.
By the way, they two yellow shaded are those with low levels of Lockdown (according to Oxford University). Our own Hong Kong is at the middling to lower end of stringency, but world-best in terms of cases and deaths per million.
ADDED (17 May): Singapore would likely have higher “Stringency Index” as it’s implemented “circuit breaker” measures since the Oxford figures which are as at March 30. Also: I’ve just noted Taiwan is very low in Stringency and ought be shaded yellow. My bad. Indeed, Taiwan has low Lockdown stringency and the lowest figures in the world for cases and deaths per million.
It seems to be the case that high levels of cases and deaths/million led to severe lockdowns, rather than the severe lockdowns leading to lower levels of each. And note also: that the top/best performers above, in terms of deaths per million (which the spreadsheet is sorted on), are all in Asia and Oceania. The top seven.
ADDED (18 May): The Lockdown Skeptic they couldn’t silence. Story of Medium.com suppressing Aaron Ginn's article, after it had had 2.4 million views. You can still read it here on WebArchive, and I see no reason why it ought to have been suppressed. It is simply some data, with some conclusions drawn. You may agree or not with the conclusions, but that ought to be allowed for debate, not simply suppressed. Especially when the US is making such a thing of the suppression of early Wuhan cases.
ADDED (20 May): This is my spreadsheet of the dates of lockdown, as defined (by me) as being the date that the Oxford U “stringency index” for the country in question went above 50. And the date of Peak infections, taken from Worldometer. I don’t want to make too much of this as there are many factors involved, such as the extent to which people did or did not follow the lockdown strictures, the measurement of the cases, etc. etc... Still, it’s interesting that the dates vary so widely. You’d expect them to cluster around 14 days, the incubation period. But they vary from 6 to 61 with an average of 28 days between the Lockdown and the peak of cases.
The live spreadsheet is here