Tuesday, 25 May 2021

COVID-19: World Report, Omnibus Edition (Lockdowns)

This paper is the companion to my recent report on the medical aspects of COVID-19: cases, deaths, tests and vaccinations. Today, I’ll look at lockdowns from a world-wide perspective, following on from my report on lockdowns in Europe a few weeks ago.

As for the previous report, the data I am using, from Our World in Data and the Blavatnik School of Government (both at Oxford University), runs up to May 15th 2021.

Average Lockdown

I decided to change slightly the way in which I average lockdowns to give overall measures of lockdown. Rather than using the Blavatnik stringency figure directly, I decided to calculate separately the average level over all days of the epidemic for each kind of lockdown (schools, workplaces, public events, gatherings, public transport, stay at home, internal travel restrictions, international travel restrictions), then average these eight to give an “Average Lockdown %.” This has the effect of reducing the result compared with the Blavatnik stringency. I also revised the way I calculated the percentage of time spent in full lockdowns, so that it used the same list of eight kinds of lockdown as the Average Lockdown % measure.

The first reason for these changes was to exclude Public Information Campaign, which is not a policy measure but is included in the Blavatnik stringency (and almost always, almost everywhere, contributes 11.11% to it). The second rationale was to exclude any consideration of Face Coverings, which is not counted in the Blavatnik stringency, in either averaging process. It is, however, still possible to assess the stringency of Face Coverings lockdown against the average of the rest.

I’ll start with the top 20 and bottom 20 countries in Average Lockdown percentage over all eight kinds of lockdown and over the entire course of the epidemic. 18 out of the 190 countries which have reported cases have not provided stringency data; so, there are 172 countries in the list from which these selections are chosen:

Ouch! People in Honduras, Libya, Venezuela, Argentina and Eritrea have been locked down, on average, at over 72% on every single day of the epidemic since January 24th, 2020! Their Freedom House ratings out of 100 are respectively 45, 9, 16, 85 (!) and 2 (equal lowest in the world – worse even than North Korea). India is in the top 20, too; but it doesn’t seem to have helped them much recently.

At the other end of the scale, though, Nicaragua, Burundi, Belarus and Tanzania are not much noted for freedom either – with scores of 31, 13, 19 and 40. But Belarus has done well against the epidemic so far; as have Tanzania and Burundi. And Nicaragua too, if I can believe their figures. Moreover, there are three countries in that bottom 20 with Freedom House ratings over 90 – Taiwan, New Zealand and Japan. And, as my previous report shows, Taiwan and New Zealand are in the bottom 20 in both cases and deaths per million population. So, this gives the lie to the notion that stringent lockdowns and success against the virus necessarily go together.

Here’s a plot of average lockdown percentage against Freedom House rating world-wide:

Not much “trend” there at all! I suspect that the places where a government’s general nastiness leads them to lock people down as hard as they think they can get away with, and the places where they are more worried about the economic and psychological consequences of locking down heavily, tend to balance each other out.

And here are world-wide plots of cases and deaths per million against average lockdown percentage:

What those trend lines show is that world-wide, each percent of increase in lockdown (averaged over the eight kinds of lockdown listed earlier) is associated with an increase of 612 in total cases per million and an increase of 13.3 in total deaths per million. The likely reason for the two positive trends is that significant spurts in either cases per million or deaths per million are likely to trigger politicians into locking down harder.

Time in Full Lockdowns

The other metric that I used in the European report was the percentage of days during the epidemic that a country is in full lockdown (100% stringency for that particular measure). This can be applied to individual types of lockdown (schools, workplaces, public events, gatherings, public transport, stay at home, travel restrictions, international). Or it can be applied to lockdown as a whole, by averaging the number of days in full lockdown for all eight of the lockdown types above.

In contrast to the earlier European calculation, I did not try to include face coverings in this averaging, since no government has yet imposed a 100% face mask wearing mandate (everywhere outside the home). But I can still assess the effectiveness of face covering mandates against the average of the rest.

Here are the top and bottom 20 in time spent under full lockdowns:

Ouch again! Hondurans, Venezuelans and Libyans have been, on average since January 24th 2020, under four or more full lockdowns out of eight. The Irish are the worst hit in Europe, having been under an average of three full lockdowns out of eight since the epidemic began.

At the other end, China is right down there, third from bottom. I suppose this may be because, in such a large country, full national lockdowns would not be appropriate while the virus is only active in certain provinces. Russia, Indonesia and Brazil are probably down in this group for similar reasons. But India, for some reason, is not.

Here are the plots of cases and deaths per million against average time spent in full lockdowns:

The respective gradients are +702 cases per million and +13.2 deaths per million, both per 1% of time spent under full lockdowns.

Effectiveness of Lockdowns World-wide

When I addressed the effectiveness of lockdowns in Europe, I was using slightly different measures of what constituted a percentage point of average lockdown, and of what constituted a percentage point of full lockdown. I have also switched to using deaths per million rather than cumulative deaths per case, since this is the metric on which the politicians will be judged. Towards the end of the paper, I will, therefore, need to re-work those figures in order to compare the European situation with the world-wide one I will present below.

But I decided to continue with my methodology of plotting cases and deaths per million against stringency for each of the individual kinds of lockdown, then comparing the slopes of the trend lines with the plots above, which give the trends of cases and deaths per million against the average over all eight kinds of lockdown. The method is seat-of-the-pants and lacking in statistical rigour, surely; but it does let me get a feel for the effectiveness of different lockdowns when compared with the average.

I put the results into four graphs, the first of which looks like this:

The blue bars represent the actual trend line gradients, in this chart in cases per million per percentage of average lockdown stringency (averaged over all eight kinds of lockdown, and over the course of the epidemic). The grey bars are the result of subtracting the gradient (+612 cases per million) of the trend line on the graph of cases per million against my new “average lockdown %.” Where a grey bar stretches to the right, this means that a mild lockdown of this kind is less effective in controlling cases per million than other kinds of lockdown. Where a grey bar stretches to the left, this means it is more effective.

In contrast to what I found in Europe, world-wide it seems that face coverings are more effective than any of the other measures in controlling cases per million. These are closely followed by international travel restrictions, internal travel restrictions and public transport closures. Stay at home mandates and school closures are less effective, and restrictions on gatherings, cancellations of public events, and workplace closures, are less effective still.

Here’s the corresponding graph for trends in cases per million against % of full lockdowns:

On a world-wide scale, it seems that even restricting gatherings to the full lockdown level of 10 or less has little effect, and cancelling public events does little more. The most effective full lockdown is stay at home, followed by border closure, followed by the four others in quick succession.

Here are the corresponding graphs for deaths per million:

For controlling deaths per million world-wide, the most effective full lockdown measures are international travel restrictions and public transport closures. These, along with face coverings and internal travel restrictions, have the most effect when the lockdowns are relatively mild. Locking down workplaces is of little utility unless it is a full lockdown; and restrictions on public events and gatherings are surprisingly ineffective.

Specific Lockdowns

Next, a look at which countries have favoured which particular kinds of lockdowns. In most cases, I’ll show only the top 20 lists, as the bottom 20 often has many of the same countries.

It seems that Middle Easterners and Central and South Americans, in particular, tend to prefer to close schools rather than lock down something else.

Strict workplace closures, the worst kind of lockdown of all from the point of view of the general public, are strongly favoured by the usual suspects like Venezuela, Honduras, Eritrea and Libya. But also, by many European countries. Ireland in second place, the UK in sixth, and Italy in seventh fully deserve wooden spoons. Interestingly, China is up there, though it rarely uses full lockdowns.

A bit of a mixed bag; but Italy leads, and Honduras is up there again.

France has been hardest of all on gatherings, closely followed by Monaco and (I repeat myself) Honduras. But Belgium, Portugal, the UK and China are all in the top 20.

For public transport, I think it’s better to use the full lockdowns list, because anything below 100% lockdown is merely a recommended closure or a regional one, not a mandatory national closure.

It seems to be Middle Easterners who like to lock down public transport hardest, followed by Central and South Americans.

It’s the Central and South Americans – including Honduras – who like to force people to stay at home for long periods. China, India and Pakistan are in there, too.

For restrictions on internal travel, I’ll again show the list by full lockdowns, as any less-than-full lockdown here is only a recommendation or regional, not a nation-wide mandate:

Many of the usual suspects again; and Ireland gets a dis-honourable mention, too.

As to international travel restrictions, I’ll show both the top and bottom 20, as there’s shame in being in the bottom 20 if the country’s overall record against the virus is poor:

Australia, New Zealand, Canada – these guys got it right in the early stages. As did Vietnam. In the hall of shame on this one are: Bosnia, Andorra, Mexico, Brazil and the UK.

And, last but not least, face coverings:

It’s south-east Asia which leads on this one. It seems to have done Laos and Singapore no harm; though in Brazil and Peru, at least, it doesn’t seem to have done any good at all.

Comparison with Europe

I thought I would re-work the European numbers from my earlier paper under the new averaging conventions. So, here are the results per percentage of average lockdown, in the same format as above:

Which lockdowns work?

In Europe, face covering mandates and school closures have a negative effect on both cases and deaths per million, relative to the average! Whereas world-wide, they go the other way. Why such a big difference with face masks? Is it cultural – Western people don’t know how to wear face masks effectively? Or, perhaps, could it be that once the virus reaches a certain level of penetration in the population, face masks are a hindrance rather than a help?

Gatherings restrictions seem to work in Europe, but not world-wide.

There is much better agreement on the other lockdowns. To control cases per million, international and internal travel restrictions are the most effective – and closing public transport, if you actually go as far as doing it nationally. To control deaths per million, you need the same three; international and internal travel restrictions, and public transport closures. And when it comes to 100% lockdowns, full workplace lockdowns are effective – but expensive.


Opher Goodwin said...

Really interesting Neil. It seems that there are huge cultural difference which make a big difference. Ill-disciplined countries suffer most!
If the rules are not abided by they can't make a difference.
I suppose another factor is how stringently they might be enforced. Very difficult to tell.

Neil said...

I suspect the difference may be how much confidence people have in their governments. Whether for good or bad reasons. The Nordics seem, mostly, to have confidence for good reasons. (But then, the Nordics are the ones who aren't enforcing face masks!) The Chinese, I'm not so sure.

I do plan to look at the variation in these figures in different parts of the world. It'll be a while, though.

Sluggo said...

Very good work, Neal!
Several other factors include climate (average humidity, average temperature, and even average wind speeds)in certain geographical areas. Locally in my area, cases initially occurred earlier and were much higher in numbers and faster spreading in large apartment buildings that shared a common HVAC system.
Side question, was anyone bombed with a large load of spam messages from Opher's site yesterday? It was obvious by the poster's fabricated names that it was probably due to a bot.

Neil said...

Hi Bill, and thanks. Good to have former WriterBeaters talking to each other again!

When I looked at temperature and humidity, initially it seemed that the virus spread less well in tropical Africa than elsewhere. I'm coming to think this may be a spurious correlation, and that the driver of virus transmission may just be people mixing. If you have larger distances between places, and less contact with the outside world, the virus may find it harder to get a footing.

I think you're right about large apartment blocks. Very early in the epidemic (April of last year), I was looking at figures from the Netherlands where I used to live. There are two suburbs of Rotterdam across a river from each other; one low-rise, the other high-rise. The low-rise suburb had about half the cases of the high-rise; but there wasn't much difference between them in population density. It isn't the population density that matters, but how much you meet people (for example, in lifts).

And no, I haven't received any spam from Opher's website.