That chart is amazing! Let
me explain it.
There are nine kinds of “lockdown”
measures against the COVID-19 virus, which have been implemented in many
countries of the world. They are: school closures, workplace closures, public
events cancellation, restrictions on gatherings, public transport closures, stay-at-home
restrictions, national travel restrictions, international travel restrictions
and face covering mandates. What the chart shows is an average of an average.
It is the average, over the nine measures, of the proportion of days over the
course of the COVID epidemic since January 2020, that there has been in place a
full restriction. And the chart shows this average for 14 countries in
Western Europe, including the UK.
By a full
restriction, I mean: all schools closed, all “non-essential” workplaces closed,
all public events cancelled, gatherings restricted to 10 people or less, public
transport closed, forced stay at home with only minimal exceptions, mandatory restrictions
in place on internal travel, border closure, or face covering required at all
times when outside the home. These are the restrictions which the Blavatnik
School of Government, based at Oxford University, regard as constituting 100%
lockdown in their respective spheres. And who am I to disagree with them – since
I’m choosing to use their data?
Look at those Irish go! Or
not, of course. An average of almost three out of nine fundamental
freedoms totally denied them, over the course of more than a year? And the UK
isn’t much less bad.
Background
Recently, I’ve been further
developing the “magic spreadsheets,” which help me follow the progress of the
COVID virus. I’ve added the ability to analyze the raw lockdown data provided
by the Blavatnik School of Government. This has enabled me to “slice and dice”
the lockdown data more finely than before. In particular, I can now plot the
levels of individual lockdown measures, and compare them across countries
within a regional group. I’m hoping, in the longer run, to be able to compare
the efficacy of different lockdown measures, both within countries and across
the world.
To prototype these new
facilities, I decided to return to my original selection of 14 countries, forming
the core of Western Europe: Austria, Belgium, Denmark, France, Germany,
Ireland, Italy, Luxembourg, the Netherlands, Portugal, Spain, Sweden,
Switzerland and the UK.
As part of this exercise,
I’ve developed my own metric of lockdown stringency, which I call “harshness.”
In many ways, harshness is similar to the Blavatnik stringency index; indeed, I
calculate it in a very similar way. But there are three differences:
1.
Harshness takes
no account of recommendations, only of actual mandates. If you or others aren’t,
at least in theory, liable to be fined or prosecuted if you break a restriction,
it doesn’t count towards the harshness metric.
2.
Harshness
includes face covering mandates, which Blavatnik stringency does not. And it
excludes “public info campaigns,” on the grounds that you can’t (yet) be
prosecuted for failing to listen to government propaganda! Though I’m sure
Johnson is working on it.
3.
The harshness metric
divides the nine individual policy indicators into two groups: those which directly
impact everyone (workplace closures, gatherings restrictions, stay at home,
travel restrictions, face coverings) and those which only have an impact on some
of the population (schools closures, public events cancellations, public
transport closures, international travel restrictions). And it weights the
former group a little more heavily than the latter.
With hindsight, I now see
two shortcomings in the structuring of the Blavatnik data. One is that the
category “workplace closures” seems too broad. In an ideal world, I would have
separated these into: Office closures (the business can continue to run, albeit
in a reduced mode, through people working at home). Factory closures (the
business must halt temporarily, but stocks of its product are still available).
And closures of “non-essential” shops and services offered to the public (the
products and services are unavailable to customers for the duration of lockdown).
These are in increasing order of severity. And the third of them is, in my
view, far harsher than the first two; impacting as it does not only the workers,
but their customers too, who lose access to goods or services which they, as
individuals, want or even need.
Those of my readers, who like
me sport beards, will appreciate what a problem it is for barbers to be closed
for many months at a time! And anyone who isn’t a fashion groupie, and wears their
clothes to the end of their useful life, will experience the same problem with
clothes shops. Moreover, anyone that wants to decide which shops and services
are “non-essential” for other people is, to put it mildly, arrogant.
The second shortcoming of
the Blavatnik data is that, while it is possible to distinguish national from
regional lockdowns, it gives no information about what fraction of the
population are affected. The Blavatnik people are addressing this, by providing
data broken down to a sub-national level, such as US states, Brazilian and Canadian
provinces, and constituent countries of the UK. So, in the future, I may be
able to use this lower-level data to gain a clearer picture of what has been going
on in each country. But some large and interesting countries, like China, India,
Indonesia and Russia, aren’t (or at least, aren’t yet) covered by this scheme.
And Our World in Data doesn’t have its data broken down to these lower levels,
anyway.
I’ll preface my remarks on
lockdowns by a brief update on the status of the epidemic in my 14 countries in
terms of cases, deaths, hospitalizations and intensive care.
Cases
So, let’s re-cap on where my 14 European countries are with
regard to cases. All the data I am using was taken on April 10th,
and runs up to April 9th. Though frequently, Blavatnik data will be
incomplete for many countries; it’s not uncommon for the latest records for a
country to be up to two weeks old.
Here are the graphs of total cases per million, and the list
of countries in order:
Here’s the (centrally) weekly smoothed graph of daily cases
per million (in essence, the first derivative of the above):
Here are the weekly case growths (in essence, the second
derivative of the total cases), and the R-rates, which are modelled data but
which I would expect to track the case growth well. And they do, at least until
very recently.
Deaths
I’ll also show the graphs of daily deaths per million:
This is not at all unlike the graph of daily cases, but
shifted right by two weeks or so. Here is the graph of deaths per case since
September:
Deaths per case peak in each country, and then slowly
decline. Until a new variant comes in, or the old variant finds a new “pocket”
of victims to attack.
And here are the lists of countries, with deaths per
million and cumulative deaths per case, both over the course of the epidemic:
Hospitalizations and Intensive Care
Here are the lists of countries by percentage of hospital
beds and percentage of intensive care beds occupied by COVID patients:
And the peak loadings on hospitals and ICUs:
You can see right there where the bottleneck has been, and
still is; in the intensive care units. Portugal, the Netherlands and Spain must
have been engaging in a lot of “Nightingale” style ICU building!
Lockdown Stringencies
To the meat of my missive. Here is the graph of Blavatnik
stringency index by date for each country, with the list of average
stringencies over the course of the epidemic:
Schools Closures
Now, a look at the individual lockdown indicators. First,
schools. Here is the definition from the Blavatnik codebook:
The Blavatnik calculation converts the two indicators C
(the degree of closure: 0 to 3) and G (the geographical scope: 0 for regional, 1
for national) to a percentage P, using the following formula:
where M is the maximum possible value of the
indicator (in this case, 3). The effect of this is to count a regional lockdown
as worth half a point lower than a national lockdown of the same level. There
are, as a result, seven possible values of the resulting percentage:
|
Value %
|
Meaning
|
|
0
|
No restrictions
|
|
16.7
|
Recommended school closure (regional)
|
|
33.3
|
Recommended school closure (national)
|
|
50
|
Required some schools to close (regional)
|
|
66.7
|
Required some schools to close (national)
|
|
83.3
|
Mandatory all schools to close (regional)
|
|
100
|
Mandatory all schools to close (national)
|
Here’s the graph of schools’ lockdown levels through the
epidemic, together with lists showing the current lockdown status of the
schools in each country, and the average level of lockdown over the course of
the epidemic:
The spaghetti isn’t too easy to read, because where two
countries have the same lockdown level at the same time, Excel only shows one
of the colours. You can always pick out the UK, though (pink line), as Excel chooses
to use the colour of a later data series in preference to earlier ones.
You can also pick out those countries which chose
significantly different paths from the others. Notably, Switzerland got rid of
all restrictions from May to October, and Austria did the same from late August
to late October. Whereas Spain has required schools in some regions to close
right through the course of the epidemic. The others all settled, in the
summer, for a period of “Recommended school closing (national).” In practice,
of course, that meant that the schools were open whenever they normally would
have been, unless and until they were ordered to close.
As regards the average lockdown level, of the four worst
countries in deaths per million (Italy, Belgium, the UK, and Portugal) three
are high. Belgium is low, but has recently started to make up for it, by
closing the schools altogether.
Workplace Closures
Here’s the definition of the indicator:
Since the maximum value is 3, the possible percentages are
the same as for schools:
|
Value %
|
Meaning
|
|
0
|
No restrictions
|
|
16.7
|
Recommended workplace closure (regional)
|
|
33.3
|
Recommended workplace closure (national)
|
|
50
|
Required some workplaces to close (regional)
|
|
66.7
|
Required some workplaces to close (national)
|
|
83.3
|
Mandatory all but essential workplaces to close
(regional)
|
|
100
|
Mandatory all but essential workplaces to close
(national)
|
Here are the graphs of lockdown levels over time, and
current and average levels:
A big cheer for the Letzebuergesch (Luxembourgeois), who
abolished all restrictions for a month back in July and August, and are closer
than anyone else to doing it again. And a big jeer for all those, including the
UK, that have mandated closure of all workplaces nationally, except “essential”
ones, for months at a time.
As with schools, among the worst performing countries the
UK, Italy and Portugal have been very strongly inclined to close workplaces;
and Belgium has joined them. There certainly looks to be some degree of
correlation between locking down workplaces and COVID deaths per million. Of
causation, though, I shall not speak; not yet, anyway. Nor shall I speak of
economic consequences; that’s for another day.
Public Events Cancellation
In this case, there are five possible percentages:
|
Value %
|
Meaning
|
|
0
|
No restrictions
|
|
25
|
Recommended public event cancellations (regional)
|
|
50
|
Recommended public event cancellations (national)
|
|
75
|
Mandatory public event cancellations (regional)
|
|
100
|
Mandatory public event cancellations (national)
|
A plaudit for the Danes for being at the bottom of the
averages league. Another plaudit for the Letzebuergesch for showing willing
recently. But the attempts by Ireland, the Netherlands and Luxembourg to unlock
their public events last summer proved ill-fated. And the Swiss unlock in October
seems, even to me, to have been a bit rash.
Gatherings Restrictions
Here’s the definition of the Gatherings indicator:
In this case, there are nine possible percentages:
Value % | Meaning |
0 | No restrictions |
12.5 | Restrictions on very large gatherings (>1000 people) (regional) |
25 | Restrictions on very large gatherings (>1000 people) (national) |
37.5 | Restrictions on large gatherings (101-1000 people) (regional) |
50 | Restrictions on large gatherings (101-1000 people) (national) |
62.5 | Restrictions on medium size gatherings (11-100 people) (regional) |
75 | Restrictions on medium size gatherings (11-100 people) (national) |
87.5 | Restrictions on small gatherings (<=10 people) (regional) |
100 | Restrictions on small gatherings (<=10 people) (national) |
Here are the graphs:
Belgium, the UK and Portugal are all in the top four at
ruining people’s social lives. Interestingly, Italy is at the bottom this time
round. But all the average figures are bad.
The countries to try unlocks during the summer were Italy,
Spain, Switzerland, Luxembourg and, very briefly, Ireland. The ones trying
recently are Luxembourg again, and Austria. That strange looking inverted spike
near the top right was a “Christmas window.” But overall, most countries have
been totally locked down for gatherings of any size since November.
Public Transport Closures
There are five possible percentages:
|
Value %
|
Meaning
|
|
0
|
No restrictions
|
|
25
|
Recommended public transport closures (regional)
|
|
50
|
Recommended public transport closures (national)
|
|
75
|
Mandatory public transport closures (regional)
|
|
100
|
Mandatory public transport closures (national)
|
Here are the graphs:
While there has been much talk of closing public
transport, only Ireland and, briefly, Italy have actually gone ahead and done
it. Most countries have settled on “Recommended public transport closures
(national)” for many months at a stretch. Which, if my neck of the woods is
anything to go by, means that public transport is running normally, but almost
no-one is using it. That’s an awful waste of money, is it not?
Switzerland, though, has not imposed any restrictions on
public transport at any stage during the epidemic. It’s good to know the Swiss
trains are still running on time! Even if there’s almost no-one in them.
Stay at Home Restrictions
There are seven possible percentages:
|
Value %
|
Meaning
|
|
0
|
No restrictions
|
|
16.7
|
Recommended stay at home (regional)
|
|
33.3
|
Recommended stay at home (national)
|
|
50
|
Required stay at home with exceptions (regional)
|
|
66.7
|
Required stay at home with exceptions (national)
|
|
83.3
|
Mandatory stay at home with minimal exceptions
(regional)
|
|
100
|
Mandatory stay at home with minimal exceptions
(national)
|
Could there be a bit of a cultural divide here? From the
individual’s point of view, stay at home mandates are probably – along with
workplace closures – the harshest restrictions of all. But it seems that German
speaking countries, and predominantly Protestant countries, have been more
reluctant to impose them hard than the more strongly Catholic and Latin ones.
Perhaps Catholic dominated countries have less concern for the individual than others?
Notice how popular the “Required stay at home with
exceptions” restriction has been. In the UK at least, this lets you go out for food
shopping or exercise, but not for anything else. That’s no way to live for
months at a stretch, is it? But that’s what they’ve done to us.
Travel Restrictions
There are five possible percentages:
|
Value %
|
Meaning
|
|
0
|
No restrictions
|
|
25
|
Recommended not to travel (regional)
|
|
50
|
Recommended not to travel (national)
|
|
75
|
Mandatory restrictions on internal travel (regional)
|
|
100
|
Mandatory restrictions on internal travel (national)
|
Here are the graphs:
The worst culprit on this one is very obvious! The UK had
a national 100% lockdown in place on travel outside the immediate local area
for 81 consecutive days, from January 5th 2021 to March 26th.
There were also 51 days of 100% lockdown during the first wave of the epidemic.
Note also those strange looking hatched patterns. The main
culprit here is Portugal, which has had a habit for a while of imposing 100%
lockdown on Friday to Sunday, and releasing it for Monday to Thursday. The
French have been doing something a bit similar, but on a smaller scale.
International Travel Restrictions
This one has no regional flag, so it uses a simplified
formula:
There are five possible values:
|
Value %
|
Meaning
|
|
0
|
No restrictions
|
|
25
|
Screening of international travellers
|
|
50
|
Quarantine international arrivals from high-risk
regions
|
|
75
|
Ban on international arrivals from some regions
|
|
100
|
Ban on international arrivals from all regions, or
border closure
|
Here are the graphs:
Now, the Letzebuergesch can be excused for not closing
borders so much. They are, after all, a land-locked country. And according to
Luxembourg’s National Institute of Statistics and Economic Studies, 44.9% of
Luxembourg’s workforce is made up of cross-border workers.
But the second from the bottom should gobsmack you. Of all
the countries you would have expected to close borders hard and early to
prevent importation of further strains of the virus, the UK, being in essence an
island, ought to have found it easier than the continental countries. But look
at this:
It’s the dark brown line that counts here. The UK didn’t
impose any COVID border controls at all until June! And it didn’t start
banning arrivals from virus hot-spots until Christmas Eve. Madness! And not
only medical madness. It was also political madness for a supposed democracy to
lock voters down with harsh restrictions on internal travel, while letting
foreigners come in as they pleased!
Face Covering Mandates
In this case, there are nine possible percentages:
|
Value %
|
Meaning
|
|
0
|
No restrictions
|
|
12.5
|
Face covering recommended (regional)
|
|
25
|
Face covering recommended (national)
|
|
37.5
|
Face covering required in some places (regional)
|
|
50
|
Face covering required in some places (national)
|
|
62.5
|
Face covering required when with others – (regional)
|
|
75
|
Face covering required when with others – (national)
|
|
87.5
|
Face covering required everywhere outside the home
(regional)
|
|
100
|
Face covering required everywhere outside the home
(national)
|
Here are the graphs:
Plaudits to the Swedes this time, for not imposing any face
mask mandates at all. I’ve found no hard evidence that cloth face masks, worn
by the general public, have any measurable effects against the spread of the
virus; and I spent quite a bit of time looking. And, as I’ve written before, I
personally find face masks unhealthy, uncomfortable and demeaning.
Minor credits to those countries, such as the Netherlands,
which have not imposed any nation-wide face mask mandates. Brickbats for the
rest, and most of all for the Spaniards, the only country in my 14 to require
mask wearing (in some regions) whenever the individual is with others.
Harshness
Now, it’s time to describe my new “harshness” metric. I look
at this from the point of view of the human individual. I seek a numerical
estimate of inconveniences and loss of rights and liberties, caused by these
measures to an “average” adult member of the population. In an ideal world, I’d
like to be able to weight each regional restriction by the proportion of the population
affected, to give a kind of national “inconvenience and nastiness level.”
As the first step, for each of the nine lockdown indicators
I covered above, I classify the indicator value on a given day into one of
three severities:
1.
Very Harsh – an unreasonable inconvenience, or a
clear infringement of human liberties, particularly if carried on continuously
over a long period.
2.
Harsh – an inconvenience or indignity, or a less
serious infringement of liberty; but you could still get prosecuted if you
disobey it. Or, perhaps, someone like a shopkeeper could get prosecuted for
allowing you to disobey it.
3.
None – either no restrictions, or merely a
recommendation which will not be enforced.
Here is the list of conditions which I classify as Very Harsh
or Harsh for lockdowns at the national level:
|
Indicator
|
Value(s)
|
Severity
|
|
School closing
|
3: Mandatory all schools to close
|
Very Harsh
|
|
2: Required some schools to close
|
Harsh
|
|
Workplace closing
|
3: Mandatory all but essential workplaces to close
|
Very Harsh
|
|
2: Required some workplaces to close
|
Harsh
|
|
Cancel public events
|
2: Mandatory public event cancellations
|
Very Harsh
|
|
Restrictions on gatherings
|
4: Restrictions on small gatherings (<=10 people)
|
Very Harsh
|
|
3: Restrictions on medium size gatherings (11-100
people)
|
Harsh
|
|
Close public transport
|
2: Mandatory public transport closures
|
Very Harsh
|
|
Stay at home requirements
|
3: Mandatory stay at home with minimal exceptions
|
Very Harsh
|
|
2: Required stay at home with exceptions
|
Harsh
|
|
Travel restrictions
|
2: Mandatory restrictions on internal travel
|
Very Harsh
|
|
International travel controls
|
4: Ban on international arrivals from all regions, or
border closure
3: Ban on international arrivals from some regions
|
Very Harsh
|
|
2: Quarantine international arrivals from high-risk
regions
|
Harsh
|
|
Facial coverings
|
4: Face covering required everywhere outside the home
3: Face covering required when with others
|
Very Harsh
|
|
2: Face covering required in some places
|
Harsh
|
As the second step, I deal with restrictions at the
regional level. I follow in essence the same procedure as the Blavatnik people
do, deducting half a point from the indicator value if the restriction is
regional. In almost all cases, this turns a Very Harsh severity into Harsh, or
a Harsh severity into None, if the restriction is regional rather than
national.
As the third step, I use my classification of the nine kinds
of measures, and so their corresponding indicators, into those which directly impact everyone (workplace closures,
gatherings restrictions, stay at home, travel restrictions, face coverings) and
those which only have an impact on some of the population (schools closures, public
events cancellations, public transport closures, international travel
restrictions). I then score each of the nine as follows:
·
Very Harsh, impacts everyone – 4 points.
·
Very Harsh, impacts only some – 3 points.
·
Harsh, impacts everyone – 3 points.
·
Harsh, impacts only some – 2 points.
There is, of course, a degree of subjectivity both in the
classification of the indicators and in the points allocation. For example, my
classification of public transport closures reflects that, living on the outskirts
of a small town, I’m not a big public transport user, even though I have a free
bus pass. Moreover, other people might come up with different weightings from
mine.
All that having been said: I add up the points for the
nine indicators, divide by the maximum possible score (32) and multiply by 100
to get a percentage.
I’ll show, first, the spaghetti graph of harshness against
time for my 14 countries:
Superficially at least, it is very similar to the
corresponding graph for stringency, but a bit more spread out vertically. In
Europe, harshness values tend most of the time to be lower than stringencies.
This is partly because for the “public info campaigns” indicator, which is almost
everywhere and almost always at maximum (one of the few things political
governments are genuinely good at is propaganda!), I have substituted the face
coverings indicator, which to date (even in Spain) has never risen above Harsh
severity. And partly because only a couple of the countries have gone so far as
to close public transport nationally.
Here are the corresponding lists of current and average
harshness values:
Comparing this with the ordering of average stringencies
above, most of the countries have similar ranks in both. But interestingly,
Belgium comes fourth in harshness, but only tenth in stringency. On a brighter
note, though, the Swedes go the other way. While eleventh out of the 14 in
stringency, they are dead last in harshness.
Harshness versus Stringency
Lastly on the subject of harshness, I’ve modified one of
my pre-existing graphs to show stringency and harshness on the same scale:
You can see how, through the first lockdown in the UK, the
harshness line was well below the stringency one. And in the relative calm of
the summer, it was still further below. Since the big lockdown at the start of
this year, though, the harshness has risen so that it is very nearly up to the
severity. Although the latest lockdown has had only a slightly greater
stringency than the first, its harshness on the people has been considerably
higher than the first. Indicating, I suspect, that the SAGE “scientists” – who
don’t seem to have very much concern for the ordinary people of the UK – have
won the power struggle they started back in September. Since November, Johnson has
been simply taking orders from them; and as a result, we’ve all been hurt far more
than was necessary.
For comparison, here are the corresponding graphs for
Belgium, the worst hit country as far as deaths per million are concerned, and
for Sweden, the most relaxed in terms of lockdowns:
Notice how, on the Belgian graph, the harshness tracks the
stringency very closely, while in Sweden the harshness is considerably and consistently
lower than the stringency.
Full Lockdowns
To conclude this missive, I’ll look at how much time people in
different countries have spent under full national lockdowns. That is,
100% stringency (or harshness – the two coincide at 100% stringency) in a
particular indicator or measure. My “% Time in Full lockdown” metric is
calculated by summing the number of days since the start of believable data on the
epidemic (which I take as January 24th, 2020, when the first
confirmed case was found in Europe) that the harshness of a particular
indicator has been at its maximum possible value of 100%, then dividing by the
total number of days since the start of the epidemic, and expressing the result
as a percentage.
The average harshness, above, tells us which countries’
politicians and their hangers-on like to lock us down hardest overall. In
contrast, the graphs below should give an idea of which particular kinds of
lockdown each of them most like to hit us with.
I haven’t bothered to show the lists for public transport,
stay-at-home or face coverings. For public transport, only Ireland and Italy
have reached full lockdown, for 16% and 5% of the time respectively. Only Italy
has been all the way to full stay-at-home lockdown; for 5% of the time. And
none of the 14 countries, not even Spain, has reached the maximum possible
harshness on face covering mandates.
Great kudos is due to the Swedes for their reluctance to
lock down on the things which really hurt the people under them. Only on public events have
they locked down with any gusto; and while they have restricted gatherings to
10 or less people for 30% of the duration of the epidemic, that percentage is
still lower than for any other among my 14 countries. Let’s give a big cheer for
our Swedish friends!
At the opposite end of the scale, the Irish have taken
almost every opportunity they can to hurt their people. On workplaces and on
travel restrictions, they are top of the list for the longest time spent in
full lockdown; on schools, they are second; and they are fifth out of 14 on the
length of time for which gatherings have been restricted to 10 or less people.
The Italians are top of the list for schools and public events, and second on
travel restrictions. The Belgians have chosen to lock down harshly on
gatherings, but have been relatively middle-of-the-road on most of the others.
And then there’s the UK, which fully deserves to inherit the
appellation of “the sick man of Europe.” The UK comes third out of 14 on the
time spent in full lockdowns on schools, workplaces and gatherings, and fourth
on public events and travel restrictions. To add insult to injury, both the UK and
Ireland have failed to close their borders fully at any point during the
epidemic. Something which even the Germans, far more centrally located in
Europe and with long land borders, did manage to do, if only for two months at
the start of the epidemic. The UK, of course, was three months late in taking
any border measures at all.
In conclusion
It remains only for me to repeat the chart with which I
came in, the “average of the average” percentage of time spent in full lockdown
for all the nine measures. To juxtapose it against the chart of countries by deaths
per case. And to pose two questions:
1.
Why, of the top four countries of the 14 in the
proportion of time spent in 100% lockdowns – Ireland, Belgium, the UK and Italy
– are three also in the top four in deaths per case?
2.
Why are the bottom three of these 14 – Sweden,
Luxembourg and Denmark – also in the bottom four in deaths per case?
Oh, but you may say, Sweden and Luxembourg are at the top of the 14 in cases per million! True; but irrelevant. Lockdowns are supposed to minimize new cases, and by implication the deaths they lead to. Even in their proponents’ wildest dreams, lockdowns can’t help anyone who already has the disease. So, they can’t influence the deaths per case ratio; unless, perhaps, the virus is raging so wildly that large numbers of people are dying of it before they can get to hospital. And that hasn’t happened in Europe. Has it?
Could it be that the governments, that found that they were doing poorly against the virus, panicked and locked down as harshly as they thought they could get away with, without regard for the rights and liberties of the people they are supposed to serve? Or, perhaps, that these poor performers realized that their health systems were failing, and decided to paper over the cracks? Or is there something else going on, that we aren’t being told about?
I’ll leave you, dear reader, to contemplate for yourself the relations between authoritarian government and poor performance against the COVID virus.
