COVID-19: Europe Report, Omnibus Edition

In recent weeks, I have been developing the “magic spreadsheets” which help me to follow the statistics of the COVID epidemic, with the aim of significantly increasing the number of countries I am able to look at. This is the first report based on the new technology. It covers the whole of Europe, a total of 46 countries divided into four groups. Here are the groups:

Europe 14	Rest of Western Europe	Eastern Europe (North)	Eastern Europe (South)
Austria	Andorra	Belarus	Albania
Belgium	Finland	Czechia	Bosnia and Herzegovina
Denmark	Iceland	Estonia	Bulgaria
France	Liechtenstein	Hungary	Croatia
Germany	Malta	Latvia	Cyprus
Ireland	Monaco	Lithuania	Greece
Italy	Norway	Moldova	Kosovo
Luxembourg	San Marino	Poland	Montenegro
Netherlands	Vatican	Romania	North Macedonia
Portugal		Russia	Serbia
Spain		Slovakia	Slovenia
Sweden		Ukraine
Switzerland
UK

I’ll end this essay with an assessment of the UK’s performance against the virus to date. I think it’s fair to say that to call my assessment “scathing” would be an understatement.

Looking ahead, I have divided the 189 countries which have reported COVID cases into a total of 20 groups, which I then aggregate together into six “supergroups” as follows:

1.     Europe (Europe 14, Rest of Western Europe, Eastern Europe (North), Eastern Europe (South)).

2.     Americas (North America Mainland, South America Mainland, West Indies (North), West Indies (South)).

3.     Middle East/North Africa (Middle East North, Middle East South, North Africa).

4.     Sub-Saharan Africa (West Africa, Central Africa, East Africa, Southern Africa).

5.     Rest of Asia (North East Asia, East Asia, South Asia, South East Asia).

6.     Australasia and Oceania.

Once again, the data sources are (for epidemic data) Our World in Data and (for lockdown regulations) the Blavatnik School of Government, both at Oxford University. The data I used included figures up to and including February 19^th.

It’s worth noting that there are a number of places for which I cannot show any data. This is because the Our World in Data feed, which I use, now excludes dependencies, such as Gibraltar and the Faeroe Islands. (I am not certain whether or not their statistics will have been folded in to the parent country’s data.) This is a pity, because Gibraltar has the very worst record in the world in deaths per million, and the Faeroes one of the very best!

Scatterplots

This report introduces some scatterplots. These are able to plot any of ten columns of country data (Hospital beds per 1,000, ICU beds per 100,000, Cases per Million, Deaths per Million, Deaths per Case %, Tests per 100,000, Cases per Test %, People Vaccinated %, People Fully Vaccinated % and Average Lockdown Stringency %) against an index column. The two index columns I have chosen to use are:

1.     The UN’s Human Development Index (HDI) percentage rating.

2.     Population densities (in people per square kilometre).

According to Wikipedia: “The United Nations Development Programme (UNDP) compiles the Human Development Index (HDI) of 189 countries in the annual Human Development Report. The index considers the health, education and income in the country to provide a measure of human development which is comparable between countries and over time.”

The countries

I thought I’d start with some bar charts of how the different European countries measure up on the UN’s HDI rating. I’ll also show their populations per square kilometre, as I’m wondering if population density may perhaps be a factor in the transmission of the disease.

Now, here are the population densities:

For population density, Monaco dwarfs the rest. Indeed, the second, third and fourth in its group, Malta, the Vatican and San Marino, are all more densely populated than the Netherlands which comes in fifth! The “density divide” between Western and Eastern Europe is also apparent.

Cases

To the dynamic data. As usual, I’ll start with cases. Here are the spaghetti graphs of total cases per million for each of the four groups:

What comes through here is the contrast between Western and Eastern Europe. In Western Europe, there was a clear first wave, which by the summer had (somehow) been controlled. Then a second wave began in Luxembourg in July and Spain in August. Initially, it spread slowly, with most starting to feel its effects in late September or October. In Eastern Europe, on the other hand, with the (surprising?) exception of Belarus, the first wave was never controlled. Despite a few peaks and troughs, they are all in effect still in the first wave.

After that, the virus in each country seems to be proceeding at more or less its own pace. This can be seen even more clearly by looking at the graphs of daily new cases per million:

It isn’t just Belarus that shows up differently from the others here. Moldova, for example, shows five or perhaps even six peaks, and is on the way to another one:

That suggests that the dynamics of this virus are a whole lot more complicated than just “first wave” and “second wave.” It will be interesting to see what the rest of the world has to show.

Let’s have a look at a scatterplot of cumulative cases per million against HDI rating:

The countries with the very highest HDIs – Finland, Norway, Iceland – have relatively low case counts. Maybe in these places it’s more a matter of population density? But these countries apart, I was expecting a larger positive trend, on the grounds that countries with higher HDIs will tend to have more international travel, and so be more vulnerable to re-seeding of the virus.

Let’s have a look at cases per million versus population density. I capped the densities at 1500 people per square kilometre, so Monaco and Malta are represented by those two points very close together at the far right:

This looks almost like two disparate sets of data. There is a low density set on the left, of countries with less than about 200 people per square kilometre. To which set, I might be tempted to fit a trend line from the origin towards that point at the top (Andorra). Then there is a higher density set of countries, in which the cases per million don’t seem to depend much if at all on the population density.

It looks as if, above a certain population density, the national density is not a big factor in case numbers; other factors like testing régime and containment policies are more important. Even though observations, both from my own local area and from the Netherlands where I used to live more than 40 years ago, suggest that local population density can significantly spur case activity. In particular, high-rise living is not conducive to avoiding COVID.

Lastly for cases, here is a list of all the countries, ordered by cumulative cases per million:

A high number of cases per million isn’t necessarily either a good or a bad thing. It may represent a strong program of testing (as in Luxembourg), or alternatively a high rate of transmission (as in Andorra). A low number, on the other hand, means (probably) that the virus has been contained, for now. But that doesn’t necessarily mean that it will stay contained – as witness what happened in Belgium in October.

Case Growth and Lockdowns

I don’t usually show the graphs of weekly case growth right from the beginning of the epidemic. This is because much of the data is highly confused. The clear-sighted may find themselves dazzled by bright spaghetti, and the colour-blind will have difficulty picking out any salient features at all. But I’ll break my rule on this occasion, because of the interesting things that happen towards the right of the graphs:

The Eastern European graphs show a decline in the size of the peaks of weekly case growth, starting in October or November. The Western European graphs would show something similar, if you took out the wild excursions in Ireland, Spain and Monaco. What could have caused this? Lockdowns? Let’s look at the record:

These graphs show the series of “copycat” lockdowns imposed across Europe around late October and early November. But in Eastern Europe particularly, the decline in the peaks of weekly case growth had already started prior to that time. In my thinking, at least, the jury is still out on that one. I think lockdown efficacy can only be addressed on a local basis.

Meanwhile, we can look at the R-rate, the number of infections each infected person passes on to others. While this does give a far clearer picture than weekly case growth, I’m reluctant to set too much store by R-rates, as they are modelled, not based directly on measured data. And they are modelled in different ways by different countries, as shown by some (for example the UK) being very jagged, and others (for example Sweden) far smoother.

Again, that looks inconclusive to me.

Now for a histogram the UK politicians won’t like. I’ve added to my spreadsheets a calculation of the average level of lockdown in each country (on a per-day basis), since the beginning of the epidemic:

You can see most of the expected suspects near the top! Ireland, in particular, has been trigger-happy on the lockdowns all along. But at this point, I’m more interested in the bottom. Finland is dead last in Europe in cumulative cases per million, Belarus is seventh from bottom, and even Estonia is in the bottom half. In the cases of Finland and Estonia, it looks as if the first wave was controlled without much of a harsh lockdown, but the second wave is proving larger. Belarus, I’ll take with a pinch of salt; but it looks from the Blavatnik data that the only mandatory lockdown measure they have taken is complete closure of the borders since October 29^th. If only all of them had done that back last March… Sigh.

Tests

Here, I’ll simply show the graphs of tests per hundred thousand for each of the countries. You’ll see that some countries set great store by testing (such as Luxembourg, Denmark, Slovakia and Cyprus), and others do not (such as the Netherlands, Ukraine and Albania).

Hospitalizations

The further you get from the core of Western Europe, the patchier the hospital data gets. Here, first, are the numbers of hospital beds per million for each of the countries:

Here are the numbers of hospital beds (per thousand, this time) plotted against the UN’s HDI rating:

That downward trend is interesting. In Europe at least, the more developed a country is, the less hospital beds it tends to have, relative to its population!

Here are the numbers of hospitalized COVID patients per million in each of the countries:

Again, the difference between the epidemic patterns in Western and Eastern Europe becomes obvious.

Here are the percentages of hospital beds currently occupied by COVID patients in the Europe 14 region (blank bars are those countries that do not report data):

In the rest of Western Europe, only Iceland, Finland and Norway report this data, at 0.8%, 0.6% and 0.4% respectively. Here’s the data for Eastern Europe:

So, the six worst affected countries in terms of hospital bed occupancy by COVID patients are Spain, Portugal, the UK, Slovakia, Italy and Latvia.

Intensive Care

If hospital data is patchy away from the centre of Europe, data on Intensive Care Units (ICUs) is even more so. Here are the numbers of ICU beds per million in each country:

This time, the trend between HDI rating and number of ICU beds goes the other way:

Here are the numbers of COVID patients occupying ICU beds, per million population:

As to the occupancy percentage of ICU beds by COVID patients, here are the numbers from the Europe 14:

The Portuguese must have been doing some emergency hospital building!

In the rest of Western Europe, only Finland is reporting, and the figure is just over 5%. In Eastern Europe, Czechia is reporting 93%, Estonia 19% and Slovenia a staggering 104%. That’s all the data I have on ICU occupancy. So, the worst hit ICUs right now are in Portugal, Slovenia, Spain, Czechia and the UK, in that order.

Deaths

To the final curtain. Total deaths per million in each region are as follows:

It looks very much as if there is a “race to the bottom” going on here. The UK is closing in on the Belgian world record holder (among countries with populations bigger than a few tens of thousands). Slovenia and Czechia are catching him up, too. And watch out for the “dark horses” of North Macedonia and, coming up on the wide outside, Slovakia.

Here’s the league table of deaths per million among my 46 European countries. UK politicians may wish to look away at this point:

I won’t bother to include the daily deaths per million graphs, because they look very much like the cases per million graphs, just displaced to the right by three weeks or so. Thus, it’s time for the scatterplot of deaths per million against HDI rating:

That’s encouraging. While European countries with higher HDIs tend to get more cases per million, they also tend to get less deaths per million. There must be at least something good about this thing called “development,” of which the UN speaks.

So, I’ll pass to deaths per case. Now, if there is one COVID metric on which to judge a country’s health care system, this one is it. Poor testing, poor hospital care, insufficient (or poor) intensive care when needed; all these will increase this metric. I’ll leave aside all considerations of deaths per case at different stages of the epidemic, and simply go for the jugular. That is, deaths per confirmed case, as a percentage, over the whole course of the epidemic. As a European league table. I can almost hear the UK politicians saying “Ouch!”

I’m nearly done now; but I see that I haven’t yet discussed an important subject. That is…

Vaccinations

After the final curtain, the encore. I left vaccinations until last, because they are new on the scene, and there is no way as yet to tell by observation whether or not they are having an effect. Another month, hopefully, will tell.

I will not graph the total vaccinations; which, to me, is a virtue signalling number rather than a significant statistic. Rather, I decided to show “people vaccinated” (one or two jabs) and “people fully vaccinated” (two jabs). As with hospitalizations and ICUs, the data becomes a bit patchy once you go beyond the core of Western Europe.

The UK, Malta and Serbia (and perhaps Latvia) seem to have gone out of the blocks like sprinters. So, let’s look at the numbers of people fully vaccinated with both jabs:

So, it looks as if the UK and Latvia have taken the strategy of getting the first jab out to as many as possible as soon as possible. Whereas everyone else is concentrating on getting as many as possible as fully immune as possible. This latter strategy, I think, is saner; for it gives the best protection as quickly as possible to the most vulnerable.

Here’s the league table of full vaccinations:

Amazing! The UK is third from last, among those countries that report data, in delivering the second dose! Latvia is only one place higher. Oh, and Denmark is up near the top. Again.

How well has the UK done?

As an Englishman, currently resident in England, I feel a need to assess the performance of the UK over the COVID virus so far.

·       Fourth out of 45 European countries in deaths per million from COVID. Second only to Belgium among countries with comparable populations (over about 10 million). And on the way to catching up.

·       Seventh out of these 45 countries in cumulative deaths per confirmed case. Second only to Italy among countries with comparable populations.

·       Seventh out of 42 European countries in average level of lockdown through the epidemic. Second only to Italy among countries with comparable populations.

·       Only 16^th out of 46 European countries in cases per million, even though the UK is one of just seven of those countries which have done more tests than their populations.

·       Third in Europe in current percentage of hospital beds occupied by COVID patients.

·       Fifth in Europe in current percentage of ICU beds occupied by COVID patients.

·       While the UK is top out of 33 European countries in people vaccinated per million, it is third from bottom in people who have had both vaccinations.

And that doesn’t count things which don’t show up on the graphs. Like health secretary Matt Hancock misrepresenting the results of new cases versus tests back in May, on which he was caught out by Sir David Norgrove, chairman of the UK Statistics Authority. The political skulduggery and persistent alarmism of SAGE, the (supposedly) advisory group whose remit is to provide “scientific and technical advice to support government decision makers during emergencies,” but which seems to have made itself into the driver of government policy on COVID. Leading to the fiasco of the (well-intentioned) “tiered lockdown” system being abandoned after only a few weeks in operation, and replaced by a harsh national lockdown, which SAGE seem to have every intention of keeping going as long as they can. And beyond even these, the government have granted immunity (as opposed to indemnity) to vaccine manufacturers against prosecution for harmful side-effects of their vaccines.

As the Guardian reports (https://www.theguardian.com/politics/2021/feb/21/lockdown-easing-in-england-key-dates-and-phases-in-the-roadmap), the “road map” out of this fiasco looks like a Churchillian combination of “blood, toil, tears and sweat.” Small shops still to be closed until some time in April. The middle of May, at least, before hairdressers can re-open and those of us with beards can get them trimmed again. And there isn’t even a date given for gatherings like my brass band rehearsing (which we could do back in October, while still in Tier 1).

We want our money back! Along with our social lives, our economy, and our rights and freedoms. Overall Rating: F-.

Oh, there is talk of “reforming the NHS.” But I say, reforms will not be enough. A revolution is needed. The NHS in its present, politicized form must be scrapped, and a proper health care system built in its place. The sacred cow has reached the end of its useful life.

The problems are not with the doctors and nurses out in the field. The problems are with the bureaucrats and politicians, and the academics and others that advise them. We need to de-politicize health care. To separate its financing and its provision; perhaps on the German model, or something like it. To hire some trustworthy public health advisors from countries which have done relatively well against the virus, like Denmark. And to sack SAGE en bloc.

 

COVID-19: Local Report, February 16th, 2021

Until now, all the reports I have done on the COVID-19 virus have been at a national and international level, comparing different countries’ performances against the virus. Today, I’m going to focus on new COVID cases reported over the past few months. And, particularly, on a small swathe of South-East England around my home.

A few weeks ago, I found a convenient source of data on new cases in England, broken down by borough. It is here: https://electionmaps.uk/covid19-tier-map. The data shown is weekly, in units of cases per 100,000 population over the course of a week. That means I have to multiply by 10/7 to convert to my preferred unit, cases per million per day (weekly averaged). Each data point covers a week from Wednesday to Tuesday. The data begins from the week ending October 13^th, and is usually (but not always!) there by the Thursday after the Tuesday to which it refers.

The map below shows the borough in which I live (Waverley) in blue, and its seven neighbours in yellow:

Starting with the big borough to the north, and moving round clockwise, they are: Guildford, Mole Valley, Horsham, Chichester, East Hampshire, Hart and Rushmoor. The boroughs are all of roughly similar populations, but they have very different population densities:

Here are the cases per million per day, up to February 16^th:

Look at the far right of that graph. All eight boroughs are now down below the WHO’s “endemic” threshold of 200 cases per million per day, which is the point below which unlocking ought to be very seriously considered. This is true even for Rushmoor, which has been by far the hardest hit borough on the area. Note, also, that the case counts labelled with 16-Feb-21 actually refer to the week from February 10^th to 16^th inclusive. A snapshot taken today would probably give substantially lower figures again.

Now compare the case counts on the far right of the graph with those on the far left. We’re pretty much back where we were in October, aren’t we? And, prior to the “circuit-breaker” lockdown which began on November 5^th, my borough and the ones to the south of it were in Tier 1; the rest were in Tier 2.

And yet, SAGE, the advisory body whose antics during September and October led to the November lockdown in the first place, were even in late January wittering about “a third huge spike in deaths unless inoculation cuts transmission significantly.” The Telegraph article at https://www.telegraph.co.uk/politics/2021/02/19/lockdown-when-end-uk-rules-what-covid-restrictions-review-england/ quotes one of their sub-committees as saying: “even in a best-case scenario, in which vaccines stop 85 per cent of transmission in those vaccinated, lockdown would have to be kept in place until the end of May to prevent another significant spike in deaths.”

This is typical of SAGE. When you look hard at it, SAGE seems to be just a clique of “woke” alarmists. And it seems less interested in its supposed remit of advising government on scientific matters, than it is in directing government policy over the epidemic. Moreover, SAGE’s policy of choice seems to be: Lock down for the sake of locking down! I wrote a fairly detailed analysis of the composition and behaviour of SAGE up until the middle of October here: https://misesuk.org/2020/10/17/eighty-six-sages/.

But I have news for SAGE, for Johnson, and for all other pro-lockdown junkies: UK weekly averaged COVID deaths have been on a downward trend for a whole month now!

As to the Reproduction Rate (R-rate) of the virus, that has been below the “magic threshold” of 1 continuously since about January 9^th. It is now down around 0.7, a level not seen since the “second wave” began all the way back at the beginning of July. As shown here:

By the way, that’s evidence that the lockdowns since late November worked! But whether such a high level of lockdown was actually necessary is debatable, since the improvement in the R-rate began in the middle of December, well before the third and heaviest lockdown began. I’ll give Johnson the benefit of the doubt on that one for now. Though I will review it when next I come to assess the UK’s COVID performance against its European neighbours.

But the message that leaps out from the graphs above is that there is no good reason to continue the current level of lockdown on a national scale. The obvious and sane course of action is to return within days to the tiered lockdown system that was in place during October and (briefly) during December. Put individual areas into tiers appropriate to their latest case counts, have “Tier Four” (effectively, a local lockdown) available if necessary, and we should be fine.

And yet, Johnson is procrastinating, if not also prevaricating. Looking at https://www.telegraph.co.uk/news/2021/02/19/when-boris-johnson-announcement-roadmap-end-lockdown-ease-restrictions-time/, it looks as if nothing at all will happen until at least March 8^th. And they say: “Lockdown is unlikely to be eased significantly until daily COVID cases are in the hundreds, compared with more than 10,000 a day now.” If the current week-to-week decline in the number of new cases (which has been at about 28% since the New Year) continues unchanged, that would take about 8 weeks from now by my calculation. And yet, they are talking about schools and shops possibly re-opening in March! The two, put together, make no sense at all. Meanwhile, there is talk of another blitz on face mask wearing and social distancing when the shops do re-open, just as there has been in the supermarkets since January 11^th.

It looks as if all the good work that so many of our MPs – including Jeremy Hunt, my own MP – did back in the autumn in order to prevent SAGE overcooking the goose, has now been thrown away. Since November, Johnson’s policy on COVID seems to have been entirely dictated by SAGE. It’s hard to avoid the thought that, since Dominic Cummings left, Johnson has only been listening to the last person or committee who talked to him. And SAGE will rank very high indeed in Johnson’s mind on that score.

This is no way to run a country. We want our lives back! We want our economy back. We want our shops back. We want our pubs back. We want our social lives back. We want to be rid of those damned masks. We don’t want any “vaccine passport,” except perhaps as a temporary measure for international travel only. We, the ordinary people of England, have been patient – too patient, I think – for almost a year now. And our patience is nearing its end.

 

The Snow White Goose

Back in May, I published some photos of a white goose that inhabits my local lake. And an aggressive one, too. I've been back there a few times during the recent cold snap, and here are some more photos, of her, her lake-mates and scenes in the vicinity.

Me? Aggressive? Never!

If he stays down there too long, that will be his swan-song

This was how the weather was...

...but it looked much better than it was.

"Children won't know what snow is!" (Dr David Viner, 2000).

And this was just a dusting, compared what they had with a few miles east

But swans, geese and a coot all know what ice is

So does the white goose

I'm the anserine Messiah! I can walk on water!

COVID-19: Europe Report, January 2021

(No, your eyes do not deceive you. Those figures above 100% are just as real as any other government statistics!)

This is the third monthly update to my end of October report on the COVID-19 situation in 14 Western European countries. Again, the main data sources are the same as before: Our World in Data and the Blavatnik School of Government, both at Oxford University. The data I used was taken on February 3^rd, and it included figures up to and including February 2^nd.

This month, I have updated my “magic spreadsheets” to show also data on numbers of COVID patients in hospital and in intensive care, and numbers of vaccinations. I have also noticed that one particular country, Denmark, seems to have been quietly establishing itself as probably the most successful against the virus among all these countries. It seems that, unlike in Hamlet’s day, something is not rotten in the state of Denmark! So, I will be putting a special focus on Denmark in what follows.

Country Comparisons

I’m going to be calculating, among other things, percentage occupancy rates of hospital beds and intensive care unit (ICU) beds with COVID patients. So, I thought I would start by showing the numbers of hospital and intensive care beds per million population for each country. A figure of hospital beds per 1,000 population is included in the Our World in Data feed. However, it has not been updated since the beginning of the epidemic. Looking at the Wikipedia article https://en.wikipedia.org/wiki/List_of_countries_by_hospital_beds, I see that the numbers from Our World in Data broadly match the 2017 figures shown there, although some may be older. For ICU beds, I have simply used the numbers from the Wikipedia article.

Here are the numbers of hospital beds per million population:

That shows up something weird. The further away a country is from the geographical centre of Western Europe, the less hospital beds it tends to have relative to population! What might have caused that? Strong Northern constitutions? Healthy Mediterranean climate? Or a history of relative poverty in the south? I don’t know. But I will note that Denmark comes second to last in this classification.

Here are the numbers of ICU beds. The data is from 2018 or earlier; so, it excludes any emergency ICUs, which have been created as a result of the COVID epidemic.

Cases

Once again, here’s the spaghetti graph of new cases per million in each country:

The spaghetti doesn’t look very appetizing, does it? But what is noticeable is that, since July, there has almost always been one country with a significantly higher new cases per million count than all the rest. In July, it was Luxembourg (which, being the smallest country, will always tend to show more volatility in cases per million than the others). In August and September, it was Sweden. In October, Belgium’s new cases per million count went to an unprecedented peak. Then Luxembourg took over again, until the middle of December. Briefly, the Netherlands was at the top, until overtaken by the UK close to Christmas Day. Then Ireland, having been at the very bottom of the graph through November and much of December, soared to the top, with a peak almost as high as Belgium’s. Most recently, it is Portugal whose cases have risen to a high peak. Here’s the histogram of current (last reported) cases per million per day. Guess who’s at the bottom?

One deduction I make from all this is that virus hot spots tend to move around Europe, seemingly at their own pace. Another is that the Irish lockdown, which started in October – earlier than anyone else’s – even if it solved the immediate problem, still left a huge pool of infectables, who then became infected all together around the New Year.

The movement of the hottest spots from country to country is shown even more clearly by the graph of weekly case growth from the start of the epidemic:

Hospitalizations and Intensive Care

Now for some new graphs. Here are the numbers of COVID patients occupying hospital beds, per million of population:

You can see the differences between the first and second waves here. Although in most countries the cases are way higher in the second wave, hospitalizations are only slightly higher. The Spanish figures (dotted line; because they don’t report at week-ends) suggest to me that they got the second wave even before Belgium did. Switzerland and Germany, unfortunately, are not reporting hospitalization figures. Oh, and look at those Danes (grey line). They’ve been bumbling along at or near the bottom, ever since they started reporting hospitalizations back in May.

Re-casting the latest figures as percentages of the total hospital beds available, the order looks like this:

20% to 25% of available beds taken up by COVID patients is surely a major slice of all hospital beds in a country. But it doesn’t look, to me, to be a show-stopper. Yet, at least.

Now, let’s look at the ICU patient counts and percentages:

What’s this? Portugal at 173% of ICU beds occupied? Sweden at 132%? Big trouble – even though they surely must have done some emergency building of new or temporary intensive care units. UK at 88%? Spain 79%? Ireland 69%? Aha, it looks as if this is where the blockage is.

Meanwhile, the Danes are sitting pretty, relatively speaking, at 32%.

Deaths

Here are the figures for deaths:

That’s Belgium with the first two big peaks. Of the recent disaster zones, the bigger peak is Portugal, and the smaller but wider peak is the UK. Meanwhile, where are the Danes?

If you look at the figures in terms of total deaths per case over the course of the epidemic, they look like this:

One interesting question is: Is the new strain of the virus, as has been claimed by some, more deadly than the original one? Here is the graph of deaths per case, offset by 21 days, since late May:

The peaks in the left part of the graph are unreliable, due to reporting issues at that stage of the epidemic. But the general trend until October is downwards, from a (much) higher percentage in March and April. If the new strain is more lethal, then I think it must have appeared first in Italy at the beginning of October (21 days before the peak). The peaks further to the right suggest that, when the new strain first hits a country, it is more lethal than the older one, which by that time will have declined far from its original virulence. But after that, its deadliness tends to decrease with time; just as happened with the first strain.

Vaccinations

So, to the latest European political football: COVID vaccinations. These are reported in three ways: total vaccinations, people vaccinated (one or two jabs) and people fully vaccinated (two jabs). I’ll show only the last two of these, measured as percentages of the population:

Guess who’s at the top on that second graph? Yes, Denmark. Followed by Italy, the UK and Germany. There’s a divide between two strategies here. The UK is trying to get the first jab out as widely as possible, whereas Denmark, Italy and Germany are trying to get as many people as possible fully vaccinated. I’d have gone with the second strategy, on the grounds that the most vulnerable people are very much more vulnerable than any of the other groups, so deal with them first. But then, I’m not a politician. It will be interesting to see what – if any – changes result in the virus reproduction rates in different countries as the vaccine roll-out proceeds. My guess is that we probably won’t be able to see much effect until March.

How has Denmark done so well?

So, what have the Danes done right? Is it the strong Viking constitutions? Some secret ingredient in their pastries? Or is it possible that the Danish government, or the set-up of their health system, may have made some contribution to it?

Here’s the graph of weekly case growth, stringency and R-rate.

The lockdown measures they have taken seem to be similar to other European countries. Though for much of the time, according to the Blavatnik measure, Denmark has been among the more lightly locked down countries in the area. Indeed, through November and December, Denmark was at the very bottom of the lockdown stringency list.

Currently, Denmark is in 10^th place out of my 14 countries at 70%. Only France, Switzerland, Belgium and Luxembourg are lower. But even if recent measures made a difference – and that fall in the reproduction rate suggests that they did! – that doesn’t explain why Danish deaths per case are so low.

The Danish health care system is essentially run by regional and local governments. It is well funded at 10 per cent of GDP (in 2018); similar to the UK. And despite the low number of hospital beds, the hospitals are particularly well funded, at 43% of all health expenditure. Notably, in 2015, the Danish government took active steps to de-centralize decision making in the health care system. I suspect this may have some relevance to the strength of their performance (so far) in the COVID-19 epidemic.