COVID-19: Americas Report, Omnibus Edition

This is the second of my “omnibus” reports on the statistics of the COVID virus. Today, I’m going to tackle the Americas. That is, the geographical area comprising North America, Central America, South America and the islands off the coasts of North and South America. In this area, there are 35 countries reporting via Our World in Data, which I have divided into three groups:

North America	South America	West Indies
Belize	Argentina	Antigua and Barbuda
Canada	Bolivia	Bahamas
Costa Rica	Brazil	Barbados
El Salvador	Chile	Cuba
Guatemala	Colombia	Dominica
Honduras	Ecuador	Dominican Republic
Mexico	Guyana	Grenada
Nicaragua	Paraguay	Haiti
Panama	Peru	Jamaica
USA	Suriname	Saint Kitts and Nevis
	Uruguay	Saint Lucia
	Venezuela	Saint Vincent
		Trinidad and Tobago

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 March 1^st.

The countries

Here are bar charts of the United Nations HDI (Human Development Index) ratings for the countries in each of the three groups.

It’s interesting that the lowest rated countries in Central America are lower than their counterparts in South America – even Venezuela! And, Haiti apart, the West Indian ratings are very comparable with the South American ones. I did lift my eyebrows at that rating of Cuba. But then, my criteria are not the United Nations’.

In the context of assessing lockdowns, I shall also be using the Freedom House rating. According to Wikipedia, it “measures the degree of civil liberties and political rights in every nation and significant related and disputed territories around the world.” Like the HDI rating, it is a percentage. I won’t show the bar charts here, but the Freedom House rating varies from Cuba (14), Venezuela (16), Nicaragua (31), Haiti (38) and Honduras (45), via the USA (86), to Canada and Uruguay at the top of the tree with 98.

Now, here are the population densities:

Look at those population density figures in South America! Way, way lower than Europe. How can metropolitan greenies live with themselves for endlessly repeating their mantra that the world is over-populated with human beings?

Cases

Again, I’ll start with cases. Here are the spaghetti graphs of total cases per million for each of the three groups:

Apart from the USA and Panama, all these cases per million totals are well below the corresponding figures for European countries. In both North and South America, the countries divide between a group with case numbers high, and another group with them much lower. The West Indies island countries are entirely in the latter group.

The daily cases per million graphs are much less “organized” and more “confused” than their European equivalents:

The epidemic profiles for different countries look to be independent of each other. This reflects that the countries in this area have tended to quarantine arrivals, ban arrivals from certain places, or even close borders altogether, far more than their European counterparts.

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

The slope of the trend line here is very much greater than in Europe. The countries with the higher development indices tend to get many more cases per million; perhaps because they have greater economic activity, and so mixing of people, than the lower rated countries.

Let’s have a look at cases per million versus population density.

Just as in Europe, this looks almost like two separate sets of data. I could almost draw a horizontal trend line for the countries with more than about 100 people per square kilometre, and a vertical one for those with less! This reinforces my view that population density at the national level isn’t a big factor in influencing cases per million.

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

Island nations seem to be generally better at keeping the virus down than continental ones. But they may face some problems, I suspect, once border restrictions have been eased. Controlling the virus in its early stages is one thing; controlling it when the world around is unlocking apace, may prove to be quite another. As the Swedish deputy prime minister said early in the epidemic, “This is a marathon, not a sprint.”

In fact, seven of the bottom 10 in the list are island countries. The other three are Venezuela, Haiti (which shares an island with the Dominican Republic) and Nicaragua. And all the bottom four in the Freedom House rankings – including these three – are also in the bottom seven in cases per million!

Case Growth and Lockdowns

In this area, the weekly case growth graphs don’t show anything of great interest. So, I’ll skip them, and go for the R-rates instead.

There’s an oddity there. That detached line at the bottom of the North American graph is Nicaragua. Geographically, it lies between Honduras and Costa Rica, both of whose R-rates are in the same ball-park as the other countries in its region. It has a president called Daniel Ortega, a former Sandinista; who just keeps on getting re-elected, much in the style of Vladimir Putin. I wonder, perhaps, whether the Nicaraguan figures actually mean anything at all? Let’s have a look at their basic cases and deaths graph:

That gentle curve down since June looks a bit suspicious to me. And where are they in the cases per million rankings, compared with their neighbours Costa Rica (eighth in the list out of 35) and Honduras (pretty much in the middle?) Second from bottom. Furthermore, as we’ll see, their average lockdown stringency is the lowest in the whole of the Americas. And they haven’t closed their borders, though they have at various times screened or quarantined incoming travellers. Curiouser and curiouser. If Mr Ortega and his pals are telling the truth, an awful lot of us in the West ought to be demanding our money back for all the lockdowns we have suffered.

OK, so let’s have a look at the lockdown stringency graphs:

There’s another oddity there. What are the Venezuelans doing? (Theirs is the black line with the bumps near the top of the second graph). Since late September, they have been going through alternate weeks of “Workplaces: Mandatory closed” and “Workplaces: Some closed.” They have also had face coverings required outside the home continuously since Halloween. Mr Ortega gets his low case count by sleight of hand; but Mr Maduro prefers to achieve it by stomping on what little economy and freedom the Venezuelans have got left.

Here’s the list of average lockdown stringencies:

Uruguay and Dominica stand out as having controlled the epidemic quite well to date, yet having a relatively relaxed lockdown régime. But, of the bottom five in the Freedom House ratings, two, Honduras and Venezuela, are right at the top in average stringency. Cuba is about a third of the way down. The other two, Haiti and Nicaragua, are near, and right at, the bottom. Haiti’s data, though, looks to my eye much more trustworthy than Nicaragua’s.

Let’s see what happens if we plot average lockdown stringency against Freedom House rating:

Inconclusive. But what if we take out Nicaragua (that point way below the others, near the far left), on the grounds that none of their data is at all believable?

Cherry-picking? Moi? But cut out the incredible Mr Ortega, and it looks as if governments with (supposedly) greater concern for the freedom of ordinary people have tended to lock down less hard. But not by all that much.

Tests

I won’t show the total numbers of tests per hundred thousand, as except for the USA and a brave attempt from Chile, these are all way below European levels. Cases per test, however, show a more interesting picture:

Generally, cases per test on the American mainland are considerably higher than in Europe. That suggests a persistent shortage of test kits in many countries in Central and South America. West Indian cases per test, for those few countries that report, are comparable with, or a bit higher than, Europe’s. Except for Cuba at the bottom. I wonder why?

Hospitalizations

This section will be a lot shorter than the corresponding one in the European report, because only Canada and the USA are actually reporting hospitalizations. I will, however, show the scatterplot of number of hospital beds per thousand versus UN HDI rating:

In contrast to Europe, in this area there is a positive correlation between HDI rating and provision of hospital beds.

Here are the hospitalizations per million plotted over time, for Canada and the USA:

This contrasts the three-peak epidemic in the USA with the two-peak Canadian one. Peak hospital bed occupancy by COVID patients has been 14.4% in the USA and 5.2% in Canada. I won’t bother with the ICU data, as they are almost in direct proportion to the hospitalizations.

Deaths

Total deaths per million in each region are as follows:

These graphs show how differently the epidemic has taken its course in different places. In North America, there are three groups: the very bad (those with over 1,000 deaths per million), the bad (the rest of the believable ones) and the ugly (Nicaragua). There are big gaps between these categories! In South America there’s a somewhat similar divide between bad and very bad; but the threshold between the two is lower, about 600 deaths per million. But both continents have, on average, less deaths per million than Europe. In the West Indies, deaths per million are generally lower yet than on the mainland.

Here’s the league table of deaths per million:

Deaths per million against HDI rating:

As with cases, the higher the HDI rating, the higher the deaths per million.

Now for cumulative deaths per case. As I’ve said before, if there is one COVID metric on which to judge a country’s health care system, this one is it. High means bad.

Ouch! Mexico comes out the worst, and by a long way. Its health care system is known to have been plagued with problems in the past; maybe they are there still. Ecuador and Bolivia have both had some nightmares, too. These three all show more deaths per case over the course of the epidemic than even the worst European country (Bulgaria).

And Cuba is right down there. This means either that they’re lying, or that the Cuban health care system is among the best in the world. Maybe that’s why the UN rates them so highly?

Vaccinations

Here are the equivalent graphs to the ones I gave for Europe last time round.

There are no figures for full vaccinations in the West Indies yet. Looking at the above graphs, the Chileans are making a brave attempt (and they are also adopting the UK strategy, of getting the first jab out to as many as possible as quickly as possible). But the USA is way out ahead of anyone else. If there is anywhere in the world where the effects of vaccination might be showing up already, it’s there. So, here’s their graph of weekly case growth, stringency and R-rate:

I find the evidence inconclusive. The drop in R-rate in the new year was too soon after the first vaccinations to have been caused by them. Give them another month, though, and we may be seeing either a drop in the R-rate and weekly case growth at a constant stringency, or a drop in stringency without an increase in the R-rate or in weekly case growth. There should be a significant drop in deaths per case, too. But the USA won’t be the easiest place to see these things. One, because it’s so big. And two, because it is in effect 50 disparate countries, each with their own rules.

To sum up

With the exceptions of the USA, Panama and perhaps Canada, countries in the Americas – particularly the island ones – are not yet as “far through” the epidemic as in Europe. One of the main reasons is that they have tended to close borders more stringently than the Europeans. If the vaccines work as advertised, these countries may end up with significantly lower deaths per million than the Europeans. If not, they will have a lot of work to do. No judgement is possible yet on whether or not the vaccines are positively affecting the virus statistics.

The countries with the lowest Freedom House ratings – such as Cuba, Venezuela, Nicaragua, Haiti, Honduras – tend to have either the very highest, or the very lowest, average lockdown stringencies. The Nicaraguans are, in my opinion, lying about the progress of the epidemic in their country. But the Haitians seem to have controlled the virus well with a relatively light lockdown régime; and if their data is a fabrication, it’s a very good one. Venezuela and Honduras are, very definitely, not places to be right now.

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.