COVID-19: Understanding the Numbers #coronavirus

Guest post by Neil Lock

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As those acquainted with me will know, long ago I was trained as a mathematician. I’ve forgotten most of the specifics I learned. But I’ve retained the framework; even if it’s a bit rusty. For almost three months now, I’ve been looking at the numbers on the progress of the COVID-19 epidemic. I think I’ve now reached a point where I can put forward some tentative conclusions on how the many and various countries of the world have fared under the cosh of this virus, and why. You can learn a lot from data, if you look at it thoroughly enough!

This (very long) paper is about the data on the COVID epidemic world-wide. It will consist mostly of pictures – like the one at the head – which show the outcomes, to date, from this virus in different countries. It will show lots of pretty pictures on a most un-pretty subject; along with some deductions from those pictures. For those less familiar with the world outside their particular neck of the woods, it may also provide a geography lesson or two. And while I’ll allow myself an occasional acerbic remark about the politics, I won’t dwell on those aspects here; for they demand a whole other essay.

Our World in Data

For my analysis, I used the Excel spreadsheet from Our World in Data [https://ourworldindata.org/coronavirus-data]. It contains currently almost 25,000 records. Our World in Data is a project of the Oxford Martin School, part of Oxford University. Their data is free to use. I’ve used it before in other contexts, and I’ve found it extremely useful.

In essence, this data set gives two or three numbers each day for each country: cases, deaths and sometimes tests. These are also provided as cases, deaths and tests per million of population.

One big advantage of this data set over worldometers.info [https://www.worldometers.info/coronavirus/] is that it includes past history from the beginning of the epidemic. The version of the data, which I used for this exercise, came from June 18th. It includes, for most countries, data up to and including June 17th. This usually represents cases and deaths reported up to the previous day.

Reporting

There are several issues with how the numbers have been reported. First, the records are broken down by territory, meaning that off-shore dependencies like Gibraltar or Puerto Rico are expected to report separately from their mother country. But this has not always been followed. Most dependencies didn’t start reporting their own figures until March 20th or later.

Second, some countries only started reporting when they actually had their first confirmed case of the virus. Moreover, in the early stages of the epidemic, many countries have sporadic missing records. Only around the middle of March did all countries start to provide an explicit “no new cases or deaths” report for those days without a new case or a death.

Third, the national data providers quite often make adjustments to their figures. This can result in huge single-day peaks, or in days with negative new cases, or even negative deaths! And some countries’ figures have caused me to scratch my head. The French figures, for example, have been all over the place ever since I have been following the epidemic. The Ecuadorian figures make no sense at all. And there are many cases of sudden peaks in new confirmed cases over a few days. The most recent example was Sweden, which showed a huge surge in new cases starting on June 3rd. Presumably, due to a large batch of delayed test results?

Fourth, only some of the countries – usually the larger ones – are reporting numbers of tests done. And many of these are only reporting tests weekly, or on an ad-hoc basis.

Fifth, there have been cases of national data providers “re-writing history,” scrubbing out and replacing large chunks of past data. In early June, for example, the UK and the USA wiped out all their data on tests prior to April 26th and May 12th respectively. I suspect this may have been down to a change of units, for example from people tested to tests carried out (which would increase the number of tests recorded).

Sixth, the data has invisible biases. Different countries have been using different definitions of what constitutes a COVID death. A death from COVID is subtly different from a death with COVID, but caused by some other co-morbidity. Moreover, in many countries, cases have been severely underestimated due to limited availability of test kits.

Seventh, there is often, but not always, a weekly cycle in the data. There tend to be more cases reported on Fridays and Saturdays, and less on Sundays and Mondays. This weekly reporting cycle is quite distinct from the 5 to 6 day “wobble,” which is visible in many countries’ raw new cases data, and which the troughs don’t always coincide with the week-end.

All that said, the numbers from Our World in Data are the best I have, so I’ll use them. But to try to work around some of the above problems, in most of my graphs I’ve used numbers of daily cases and deaths averaged over 7 days, from 3 days before the date shown to 3 days after.

The perfect Farr curve?

Time for some pretty pictures at last. Here’s the graph of (raw) cumulative cases for Iceland.

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Isn’t that as pretty a “Farr curve” (symmetrical sigmoid curve) as you could wish for? In 1840, William Farr analyzed a then recent smallpox epidemic in England. He showed that a plot of deaths against time looked very much like the curve of a normal probability distribution, otherwise known as a bell curve. The Farr curve, in which the increasing and decreasing phases are symmetrical and of equal length, is the integral of a normal probability distribution. So, let’s look at Iceland’s (weekly averaged) daily cases (and deaths, too).

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That looks fairly “normal” to me, if a bit jagged at the top. That, so I understand, is how you’d expect the daily cases graph of an epidemic to look, if it was allowed to run its course without any interference, either through public health measures or through importing new cases from outside. Note also how, in Iceland, the deaths have tended to follow some weeks after the cases.

Next, Switzerland.

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That’s a less symmetrical example of a sigmoid curve. In Switzerland, the right tail of the cases graph is a little under twice as long as the left tail. A lot of countries’ cases graphs are similar to this, although in many cases the right tail is significantly longer than it is in Switzerland.

But now, I’ll throw you a curve-ball: Iran.

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That looks more like the back of a camel than a mountain peak! There must be something else in play here. The most likely cause of the second peak seems to have been mass travel for the Eid Al-Fitr holiday towards the end of May, by which time most provinces were out of lockdown.

The worst of the worst

Here are the worst countries in the world in terms of deaths from the virus per million population, as at June 17th.

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Notice that the top nine are all in Western Europe. The USA and Canada are in there too, and three South American countries: Ecuador, Peru and Brazil. South America seems to be fast becoming a “hot spot” for the virus. Apart from Ireland, the remainder are all small dependencies of countries higher up the list: Sint Maarten belongs to the Netherlands, and Jersey, Isle of Man, Montserrat and Guernsey to the UK.

In contrast, here are the countries with the most confirmed cases per million.

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The two lists are quite different, apart from both having San Marino and Andorra near the top. Even Italy, the “poster child” for the epidemic, doesn’t make it into the top 20 in cases per million! As to why the lists are so different, one obvious possibility is that countries which do more tests tend to find more mild and asymptomatic cases, which don’t lead to more deaths. That seems to apply in Bahrain, for example, where they have done over 400,000 tests in a population of 1.7 million.

Western Europe

I’ll look at Western Europe first, since it’s the hardest hit area. Here are the deaths per million.

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Some of the small countries listed here are off-shore dependencies of larger countries. For example, Guernsey is a dependency of the UK, and the Faeroe Islands are a dependency of Denmark. The close dependencies of the UK (Jersey, Guernsey and the Isle of Man) have generally done somewhat better than the UK itself. Dependencies further away from the mother countries have done better still, like Gibraltar and the Danish territory of the Faeroe Islands.

Among the remaining small countries, Andorra, sandwiched between France and Spain, has fared worse than either of them. And San Marino (landlocked inside Italy) has suffered worst of all. But these two disaster areas are outliers. Indeed, small countries which are bordered by bigger countries, such as Liechtenstein, Monaco and Luxembourg, have often done better than their neighbours. Even the Vatican falls into this category, despite its third place in cases per million! And small island countries like Iceland and Malta have done the best of all.

Among the larger countries, Germany is an odd man out. It has far less deaths per million than you’d expect, based on the numbers from other European countries of comparable size. Germany seems to have been doing a better job of tracing the travel histories and contacts of infected people than many other European countries. Indeed, the Germans were among those who alerted the Austrians to the infection hot-spot they had in the Tyrolean resort town of Ischgl.

To show the progress of the epidemic in each country, I plotted total cases per million population (up to June 17th) for each of four groups of countries, from south to north, while including the UK dependencies in the same group as the UK. Spot the Farr curves! It looks as if, the shorter the duration of the epidemic in a country, the more symmetrical the curve is.

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In the last graph, you can see Iceland’s Farr curve in light blue, also the second half of a Farr curve (grey) in the Faeroe Islands. (The first half of the curve is missing, because reporting from the Faeroes didn’t start until 24th March).

Most of the countries have either all but flatlined in terms of cases per million, or reached a state where the new case count is much reduced from its peak, and has become roughly constant. As to the others, Portugal needs a closer look. The UK has clearly “turned the corner,” but is as yet nowhere near flatlining. Gibraltar, too, may repay a closer look. And Sweden… Ah, Sweden.

As an aside, the numbers of new cases for Sweden shown on worldometers.info for the first few days of June don’t match the spreadsheet from Our World in Data; even the latest version. For example, a peak of 2,214 new cases on June 4th appears in the latter, but not in the former, which only shows 1,042 new cases on that day. What’s going on?

A typical example – Italy

Here are two graphs I prepared for Italy, the first European country to be seriously hit by the virus. First, daily new cases and deaths, averaged over the 7-day period. This is much like the Swiss graph in shape, but with a far longer right tail.

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Second, I thought I would look at the ratios between deaths and cases, and cases and tests, over the course of the epidemic. I thought that deaths per case as a percentage would be a useful metric, for two reasons. First, a high deaths per case ratio over a long period is a symptom of a poor health care system, if not also of an unhealthy populace. And second, underestimating the number of cases through a lack of testing is also a sign of a poor health care system. And such an underestimate will result in increased deaths per case.

I also thought that the ratio of positive tests to total tests (“cases per test”) might be instructive, and happily the Italians have provided daily numbers of tests all the way through. In both cases, I’m calculating the ratios of the cumulative counts over the whole period, all the way from the very beginning of the epidemic. That should provide a natural “smoothing,” and allow comparisons to be made between countries, even if some test results are being significantly delayed.

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This pattern is typical of many countries. From the beginning of the epidemic, confirmed cases per test rise fairly steadily to a peak. As the virus takes hold, it becomes increasingly easy to find people who have it. The peak occurs at about the same time as the peak of new cases per day. The percentage of cases per test then starts to fall, even if the number of tests is still increasing or even increasing rapidly, as tests are rolled out to successively less susceptible groups of people.

As to deaths per case, this ratio may initially be high, because many of the very first patients diagnosed were already dying. But afterwards, it rises slowly. In many countries, including Italy, it eventually flatlines. In some, it falls again; but that’s another story.

The sick man of Europe – the UK

In the 19th century, Turkey was labelled by many as “the sick man of Europe.” Since then, this title has been awarded to different countries at different times. But in the context of COVID-19, I think the UK deserves that moniker right now. Here are the weekly averaged cases and deaths.

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The path down the mountainside is long and winding, but at least it’s downward. Note that, unlike Italy where the deaths peak came a few days after the new cases peak, here they were all but simultaneous. That may, perhaps, be because a higher proportion of those who got the virus in March ended up dying quickly, than of those who got it later. And the surge of cases in late May might perhaps be explained by the Bank Holiday week-end.

Now, let’s look at deaths per case and cases per test.

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Hey, where did all that data go? In the version of the spreadsheet from June 1st, there were figures on tests in the UK all the way back to January. By June 17th, they’re gone!

But more interesting is the deaths per case ratio. Whereas in Italy, and in most other countries in Western Europe, this number seems to converge towards a constant from below, in the UK it overshot, going to 16% before dropping back to 14%. This suggests, perhaps, that the virus may have found more “low hanging fruit” – older people, and those with serious co-morbidities – in the UK than in other places. Or, maybe, that the unusually warm weather for much of the UK during the period had an effect of slightly lowering the lethality of the virus.

In the daily cases graph above, there’s a detail at the left of the graph, far too small to see on that scale; namely, the beginning of the epidemic. So, I devised a third graph to show this. It shows the ratio of (weekly averaged, to avoid enormous early spikes) daily cases each day to the previous day, as a percentage. The Excel formula gets quite complicated, because you have to deal with days with new cases next to days without new cases. I decided to give +100% to a day with cases which follows a day without, and -100% to the reverse. Here’s the result for the UK.

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As you see, the UK has had two separate phases of the epidemic. The first began in early February, shortly after the first case was reported on January 31st. There were 9 cases in total in this phase. There were then no new cases for a while; the raw data shows no new cases from February 14th to 23rd inclusive. At the end of February, a new rash of cases appeared, until on March 2nd the count of total cases jumped by over 50%, from 23 to 36, in a single day. This is the day which I assigned as the “onset date” for the UK; an idea I’ll discuss in the next section.

But right now, a few more interesting graphs from Western Europe. First, Sweden.

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I am tempted to say, in Hamlettian fashion, that Sweden’s case numbers have jumped from “To peak or not to peak,” to “Something is rotten in the state of Sweden.” That said, the Swedes have ramped up their testing considerably in the last few weeks, so some of the recent rise may just be down to finding a higher proportion of the mild or asymptomatic cases that were already there.

Next, Portugal.

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The Portuguese were doing OK, until the beginning of May. Since the middle of May, the new cases have been increasing pretty much linearly. Now, Portugal began to ease its lockdown restrictions on May 4th, with small shops re-opening. And on the 18th there was a further easing of restrictions, including re-opening restaurants, cafés and some schools. It seems reasonable that these may have caused the subsequent slow rise in new cases.

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In Gibraltar, the epidemic has had two, or perhaps three, phases; the first being close to a bell curve. It seems possible that the recent new outbreak was caused by relaxation of lockdown; and in particular by re-opening the border for those who live in Spain and work in Gibraltar.

Onset Dates

When the epidemic in a particular country has had only one phase, it’s quite easy to assign an onset date. This I define as the first day, after the very first day on which cases were recorded, on which the (raw) new case count increases by 50% or more over the previous day. In Italy, for example, the first three cases were reported on January 31st. Then on February 22nd there were 14 new cases, and on the 23rd a further 62. I therefore assigned February 22nd as the onset date for Italy. If the country has had multiple phases of the epidemic – like the UK and Singapore – then there’s an element of judgement in choosing which phase represents the onset.

After the onset, the case count climbs exponentially for a while, sometimes doubling in around 3 days. But this lasts no more than a week; one “wobble” cycle of the virus. After that, it settles into a state in which the day to day increase is still significant, but generally decreasing. You can see that in the graph above for the UK.

Here’s my list of onset dates up to and including 14th March:

  • 03 Jan: China (though there had been cases reported earlier)
  • 17 Jan: Thailand
  • 23 Jan: Japan
  • 25 Jan: Taiwan
  • 26 Jan: Australia, South Korea
  • 31 Jan: Vietnam
  • 21 Feb: Iran
  • 22 Feb: Italy, United States
  • 25 Feb: Bahrain, Kuwait
  • 26 Feb: Iraq, Oman, Spain
  • 27 Feb: Sweden
  • 28 Feb: Austria, France, Germany, Norway, Switzerland
  • 29 Feb: Georgia, Iceland, Israel, Netherlands, Romania, Singapore
  • 01 Mar : Algeria, Azerbaijan, Pakistan
  • 02 Mar : Belgium, Ecuador, Finland, Lebanon, Qatar, San Marino, United Kingdom
  • 03 Mar : Czech Republic, India, Russia
  • 04 Mar : Belarus, Denmark, Portugal
  • 05 Mar: Chile, Ireland, Malaysia
  • 06 Mar : Argentina, Botswana, Brazil, Canada, Estonia, Greece, Saudi Arabia, Slovenia
  • 07 Mar : Egypt, Hungary, Indonesia, Luxembourg, Macedonia, Palestine, Philippines, Poland
  • 08 Mar : Afghanistan, Latvia, Malta, Slovakia, South Africa, United Arab Emirates
  • 09 Mar : Bulgaria, Costa Rica, Maldives, Peru
  • 10 Mar : Albania, Dominican Republic, Somalia, Tunisia
  • 11 Mar : Lithuania, Moldova, Panama, Paraguay, Serbia
  • 12 Mar : Armenia, Brunei, Cyprus, Liechtenstein, Mexico, Morocco, Sri Lanka
  • 13 Mar : Cambodia, Congo, Croatia, Jamaica, Turkey, Ukraine
  • 14 Mar : Andorra, Bolivia, Senegal, Trinidad and Tobago

Now that’s interesting. Seven countries, all in Asia except for Australia, had the virus in January. Then everything went quiet for 3 weeks or so, until on February 21st-22nd the epidemic went viral (no pun intended) in three countries: Iran, Italy and the USA. Then it was all over the Middle East and Western Europe inside 10 days, and all over the world inside three weeks.

There’s a school of thought, which posits that an “Italian strain” of the virus has spread more effectively and caused more deaths in the countries and US states it reached than the original “Chinese strain.” But the above suggests to me that the distinction, if there is one to be made, should perhaps be between the “February strain” and the “January strain.” The February strain could just as easily have come to the USA directly from China, as via Italy. Particularly given that it first appeared soon after the end of the (extended) Spring Festival holiday in China.

Deaths per million versus onset date

I thought that a scatterplot of deaths per million population against onset date might be instructive. In allusion to the well-known “Hockey Stick,” I call it the “Football Boot.”

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This does, indeed, show that almost all the worst affected countries first “went viral” in a short period from February 21st to about March 7th. Superficially, there appears also to have been a second wave around the third week of March. But the “tongue” of the boot – those countries that have both high death rates, and onset dates around that time – are all dependencies. So, this is an artefact of those countries not starting to report their numbers separately until that time.

Interestingly, all the countries which first reported cases before 21st February have very low deaths per million. Moreover, up to 19th February, there had been only three deaths reported from the virus outside China: in France, Japan and the Philippines. Two were Chinese citizens; the third had just returned from Wuhan. The hypotheses that the February strain of the virus was able to transmit from human to human more easily than the January strain, or that the February strain was more lethal than the January strain, cannot, I think, be ruled out on this evidence.

World cases and deaths

Before I look at regions and countries of the world beyond Western Europe, I’ll show the cases and deaths graph for the world as a whole.

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You can see the first phase of the epidemic on the left, separated from the second by a couple of weeks of relative calm, in which only China was finding significant new cases. The resemblance of the cases curve through March and early April to a Farr curve is also striking. Even though it’s in the daily cases, not the cumulative totals as the Icelandic Farr curve was!

All that said, the Farr curve starts to go off base in April. After having all but levelled off, it starts to wobble, then to rise again. I wonder why? A third phase, perhaps, on a longer timescale than the first two? As we’ll see a bit later, yes, that’s what it is. And the countries it’s impacting include some very large and populous ones, like India, Pakistan, Bangladesh and Indonesia. That’s potentially worrisome. How long it will last, and how far up it will go, I have no idea.

But something interesting pops out of the graph of world-wide deaths per case.

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That significant decline since late April in the ratio of (cumulative) deaths to cases might mean that the virus has taken most of the available “low hanging fruit” from aging Western polities. Or that it is weakening. Or that it is reaching places like tropical Africa, where the conditions – heat and humidity – are not so conducive to its survival and spread. Or that roll-out of testing is finding more and more mild cases, that don’t end in death. Which? I don’t know.

Since I earlier suggested “deaths per case over a long period” as a potentially useful metric with which to judge individual countries’ health systems, I’ll also list the worst deaths per case ratios. Remember, if your country is high up in this table, that’s a black mark against its health system.

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North America

Time to set off on a tour of the rest of the world. I arbitrarily divided the world into nine regions: Western Europe, Eastern Europe, North America (mainland), West Indies, South America, Middle East and North Africa, Asia, and Australasia and Oceania. I’ll start in North America.

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That doesn’t look too good for my American friends. Here are the cases per day for the USA.

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It looks as if it may be a long, slow path down from the high plains! Though that would be easier to judge, if the figures were broken down by state. After all, the USA is in some ways 50 separate countries. American friends might care to do a similar exercise to this one on a state by state basis, if the data is available. But the deaths per case ratio is far lower than in Western Europe, about 6%; which is good.

Canada, in contrast, looks to be on the mend.

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And here are the daily cases and deaths from Mexico. Not good, I fear.

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The West Indies

I grouped together all the, mostly small, countries on islands in and around the Caribbean Sea under the heading “West Indies.” Here’s the league table.

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I won’t follow up on any individual countries in this region. But what is very notable is that six of the top seven countries in the region in deaths per million (the Dominican Republic being the exception) are dependencies. One belongs to the Netherlands, three to the UK and two to the USA. It seems plausible to me that the cases in these countries were sparked by travellers from the mother countries. Support for this idea comes from the onset dates for each of these six countries, which were all between 23rd and 28th March.

South America

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We’ve heard lots of bad news coming out of Ecuador. And I’m not sure I believe any of their figures at all. Here are their raw cumulative case counts.

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Yes, that’s right, the total cases go down at least twice during the second week of May. The Ecuadorians can’t even work out how much trouble they’re in! So, let’s try Peru.

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Inconclusive; a couple more weeks will tell.

Brazil’s daily cases look as if they may just about have peaked, so the same applies to them. But they are currently running at about 90% positives per test (cumulative) – suggesting that their test kit resources are nowhere near up to scratch. Their deaths per case, though, show a strong decline. That’s probably good.

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The Chileans are in trouble, with cases still going up. Not to mention deaths.

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Eastern Europe

Back across the Atlantic, let’s take a look at Eastern Europe. I’ve included Russia here rather than in Asia, because most of the Russian cases have been around the Moscow area.

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So far at least, Eastern Europe has been hit considerably less hard than Western Europe. In Moldova though, daily cases are on an oscillating but upward trend, and there was a recent spurt of new cases, a bit like Sweden on a smaller scale. So, there may be trouble brewing here; and, perhaps, in some other Eastern European countries.

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Here’s the Russian data.

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It looks as if the Muscovite daily new cases may have peaked. But Russia is a big country, so there’s still a long way to go.

Middle East and North Africa

In this group, I’ve included the Arab and Muslim countries, from Pakistan, via Iran, Turkey and the Gulf, to Africa as far south as the Sahara Desert. I’ve excluded remnants of the former Soviet Union, except Armenia which has a close relationship with Iran.

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We’ve already met the camel from Iran. Armenia’s graph looks a bit like Mexico’s, but more jagged. In contrast, here’s Kuwait.

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The epidemic looks to be on the way to being contained in Kuwait, and the deaths per case ratio is low. It looks as if these guys know what they’re doing, even though cases per test are still going up. I’d guess they already have relevant experience, from dealing with MERS.

Turkey, on the other hand, shows a more European style profile, but cases have started to creep up again.

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But there’s worse yet in the Muslim world. Pakistan has had a recent spurt of new cases.

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So, too, has Saudi Arabia, after it had gone down for a while. I guess the drop may have been due to the fasting month Ramadan, which I’m told the Saudis take very seriously. And the second rise is probably due to Eid Al-Fitr again, the festival at the end of Ramadan.

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Two more countries in this area are of interest. Yemen has the worst deaths per case ratio in the world, over 22%. And Qatar has the highest number of cases per million in the world.

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That doesn’t say much for the Yemeni health care system, but at least the absolute numbers are still small for a country of 30 million.

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Qatar is top of the “world league” in terms of confirmed cases per million population. Like several other countries, it has had a two-phase epidemic. One began in early March, at the same time as the outbreaks in Europe. The second, bigger outbreak started about three weeks later. At the other end of the epidemic, they seem to have turned a corner, although the proportion of tests proving positive is still going up. Moreover, the deaths per case are minuscule compared with Western Europe or the USA. I’m told they’ve had quite an aggressive program of contact tracing since early in the epidemic; so perhaps this may be how they achieved these results.

Bahrain has one of the most aggressive virus testing programs, per million, in the world. Worldometers puts it second only to the United Arab Emirates in countries with populations over a million. But Our World in Data doesn’t have any data on tests in the UAE; sigh. So, here’s Bahrain.

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They may or may not have reached their peak of daily cases. But if they really are “over the hump,” they’ve done well.

Sub-Saharan Africa

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Where is (or are) Sao Tome and Principe? I hear you ask. It’s a small group of islands off the western coast of Africa, near the Equator. Now, their cases and daily deaths data, when weekly averaged, make it look like they have had a series of epidemics, each lasting a week or so. However, if you look at the raw data, you see a number of large single-day bursts.

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If we can believe the data, and those really are three big clusters, all quickly snuffed out after a single day, then maybe the virus doesn’t survive easily in the conditions there – high heat and humidity? But how did the virus get there in the first place? Perhaps the outbreaks might have been started by visitors; it’s an oil-rich area, so there may be Westerners jetting in.

Djibouti, on the other side of Africa, seems to have much more reliable data collection. And it does show a multi-outbreak pattern, including an almost perfect bell curve on the first outbreak. It’s a big port, with lots of international traffic, and regularly has Western soldiers passing through. I think this supports the idea of the virus dying out, and later being re-introduced.

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But South Africa, unfortunately, still has a near exponential new case count.

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Asia

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All these death rates are minuscule, compared with the hardest hit regions of the world. But, even within such an exclusive club, you can see immediately that some of the countries closest to China – Thailand, Taiwan, Vietnam – have unexpectedly low death rates.

Here are the Maldives. Again, a multi-peak epidemic, with fast-dropping tails, suggesting that the virus doesn’t enjoy monsoon conditions too much.

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So, we come at last to the source of our woes, China.

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Nothing to see here, perhaps? Apart from one huge adjustment on February 13th, it’s not unlike a bell curve. But what about those blue bits further to the right? They look like several small clusters, each of which is relatively quickly snuffed out. That’s very clear in the daily growth chart.

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Maybe the Chinese now have a high degree of immunity to this virus? In which case… their recent case figures may even be truthful. Pity about the human transmission bit.

Now, why not compare China with its neighbours, as I did for Western Europe? Here’s the data for China and the six other countries, whose onset dates were in January.

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Vietnam seems to have shrugged off the virus as if it didn’t even exist. China and Taiwan have it under control, and Thailand very nearly so. But I wouldn’t be surprised if people in these countries already had some level of immunity to this virus. Perhaps via SARS? Or might there have been some small “pre-releases” of the new virus from China even before January?

The other three countries are all well past their peaks of daily cases, with cases increasing roughly linearly. Let’s take a closer look at one, South Korea.

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You can clearly see the two phases of the epidemic, January and February. And the February strain of the virus was more harmful than the January strain; indeed, most (60%) of the South Korean cases are said to have come from the same cluster. It’s also noticeable that, for a month or so starting in the middle of March, the daily case count stubbornly refused to go down.

The South Koreans have been assiduous throughout on contact tracing and isolation, and on testing. But they still haven’t completely beaten the virus. As shown by the continuing new cases in May; caused, we are told, by a single new cluster.

In contrast, elsewhere in Asia, Bangladesh’s new cases are still trending strongly upwards.

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Japan’s graph is like Switzerland’s in overall shape, but with a sharper peak.

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In recent decades, Singapore has taken over from New York as “the cross-roads of the world.” It’s very close to the Equator, so it’s hot and humid; and the Singaporeans are zealous about health matters. So, I expected to see a multiple-phase epidemic, perhaps a bit like Djibouti. And that’s what I got. A preliminary phase of the January strain; then the February strain brought a rise to a big peak; then two (or maybe three) further minor peaks.

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Indonesia particularly interests me, because I worked in Bandung, Java for three months back in 1983, and I loved the place and the people. So, how are they doing? Not very well, I’m afraid.

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Too early to tell, in my opinion. There may be a Ramadan effect here, too. But the deaths per case have dropped significantly since their peak.

Last, but very much not least, since it’s the second most populous country in the world: India.

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In India, the new cases don’t look to be anywhere near peaking yet. That’s not good news. But the deaths per case have begun to decline, suggesting the heat and humidity effect may also be at work here, though not yet strongly. India (like the USA and Russia) is a big and very populous country, so there’s still a distance to go.

Australasia and Oceania

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Only two things to say. One, the Northern Mariana Islands and Guam are both US dependencies. Two, I know how paranoid the Aussies and New Zealanders are about letting anything biological into their countries from outside; and it shows in the results here.

Who has done well, and who has done badly?

In Asia, several countries close to China (and China itself, if we can believe their numbers) have done well at containing the virus locally. They must have well used what they learned from SARS. Clearly, the key time for controlling a virus like this is the very beginning of the epidemic. Contact tracing and isolation seem to be the important factors in stopping the initial clusters of infection from spreading. If you lose that first battle, the war will be long and bloody.

On the other hand, at least two Asian countries, India and Bangladesh, still have substantially rising daily new cases. Indonesia has not yet peaked. And all three have big populations.

Some Middle Eastern countries, particularly in the Gulf area, have also done well; again, probably due to their experience with MERS. Pakistan, Saudi Arabia, and Iran and neighbouring Armenia are showing cause for concern. But North Africa seems relatively unaffected, perhaps due to a combination of heat and low population density.

Africa south of the Sahara seems to offer conditions that are not very favourable to the virus. Most African countries are, therefore, getting off relatively lightly so far, except for South Africa. I’d expect the same would apply to tropical Central and South America. That leaves, as the most vulnerable places: Europe (including Russia), North America north of the tropics, and South America south of them.

In the Americas, the countries currently causing concern are Mexico, Chile, Brazil, Ecuador and (a little bit) Peru. US new cases have peaked, but there’s still a long slog ahead.

In Eastern Europe, there are so far generally less cases and deaths than further west. But some countries, like Moldova, may suffer a rockier road than others. And Russia still has a long way to go.

In Western Europe, in every country except Sweden, new cases have now peaked. But the UK government Twitter feed (how amateurish!) reported 1,346 positive tests on June 18th. And the previous day’s count was 1,218; more than double Germany or Italy on the same day. There’s still lots of work to be done.

In Western Europe as a whole, the Nordic countries, except of course Sweden, have done best. The Germanic countries are next best. Germany in particular has done very well in light of its size; likely due to relatively good contact tracing in the early part of the epidemic. The Catholic countries in south and central Western Europe, the UK, and the Netherlands, have done worst.

Two small European countries have suffered disasters (San Marino, Andorra). But others (Liechtenstein, Monaco, perhaps even Luxembourg) have been more successful at keeping the virus at bay than their neighbours. Small, geographically close dependencies (like Jersey) have tended to do better than their mother countries, but not hugely. Small, remote dependencies (Faeroe Islands, Greenland, Gibraltar) and small island countries (Iceland, Malta) have done best of all.

The relative success of many small countries, and the disasters in others, suggest that for a virus like this, containment measures are best carried out on the scale of tens or at most hundreds of thousands of people. That means towns and cities, not large countries or even US states. The Austrians, I think, got it right when they quarantined the ski resort Ischgl.

Moreover, I don’t think it makes any sense to shut down normal daily life in areas which have few or no cases. Nor to close parks. If you want people to “social distance” from each other, why ban them from the very spaces in which they have a chance to get away from other people? Nor, indeed, does it make sense to force symptom-free people, with no known connection to anyone with the virus, and who have not recently returned from somewhere infected, into isolation.

To conclude. Who will win the “wooden spoon” for the country that dealt with the virus worst? In Western Europe at least, only three horses are left in that race: Belgium, Sweden and the UK.

The next question is, what will happen as the lockdowns are lifted? The example of Portugal suggests that new cases may start to rise again, but not catastrophically. I plan to wait a few weeks, and then to re-visit what has (will have) happened post-lockdown.

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168 thoughts on “COVID-19: Understanding the Numbers #coronavirus

  1. We here in the USA are well past the incubation time for the protests and riots to have caused a spike. Here is New Jersey as of today https://www.nj.gov/health/cd/topics/covid2019_dashboard.shtml It is noteworthy that 50% of our states deaths took place in long term care facilities eg nursing homes. The New Jersey case curve is very Italian-esque. Is it possible that there is some genetic factor at play other then the ABO thing driving the variation globally?

    • Stupidity of governors seems to be inherited, often with the offspring being dumber than parents.

      Also, genetically our East coast virus strain is said to be more like the Northern Italy strain.

    • Lots of graphs and numbers. Here is a number, 453,000, maybe, world wide fatalities to date. There are almost 8 billion people on the planet. That folks is not even close to the max of fatalities from the seasonal flu.

      • Or not even close to the 1968 Hong Kong ‘flu out break. This is a panic to change behaviour globally and impose draconian laws. Look at what is going on in the UK. One commenter has labelled it a “plandemic”.

      • In my whole long life I have yet to know anyone who died from the seasonal flu. I think the numbers on death from flu are wildley overestimated, maybe to encourage governments to buy (often useless) vaccines for their populstions.

        • Looked up death rates for Australia a month or so back.

          (One every three and a bit minutes in case you were wondering and wanted context for the virus death rate)

          From the Australian Bureau of Statistics website flu was listed about 12th as biggest killer in Australia, very close to Intentional Self Harm. The big difference was the mean age for flu deaths was in the 70s, while Intentional Self Harm was 44.

          So, in answer to the first point, seasonal flu seems to kill older and by implication weaker people, so unless you know a lot of elderly then you may not know any flu deaths.

          The observation to be made is Intentional Self Harm just kills, and, in Australia at least, has killed and will continue to kill magnitudes more people than Wuhan did.

          Now the discussion about if Lockdown did or did not save thousands of lives is one I shall avoid for now, but I would like to make a comparison to the reaction levels. Wuhan? Significant changes forced on nations to save projected lives. Mental Health? Proved to kill from all age groups EVERY year? Encouragement and awareness, but no enforced regulations.

          Make of that what you will.

        • You are correct. Ask any doctor if he has lost patients to seasonal influenza and the number is usually zero or one patient.

          Flu deaths are grossly overstated using models, not a list of patient’s names.

        • Influenza has many mutations if the virus divided into 4 groups that some are more severe than others. People do not actually die From Influenza nor any Coronaviruses like SARS-CoV-2 that the disease is called COVID-19, or Rhinoviruses that are the Common Cold. All Virus Germs have to have a host to live off of their Respiratory System – making viruses parasites – that actually use the other germs in your body like Bacteria used for digestion or in your mouth and nose – that every animal has – as their means to replicate and will even use your own Immune System that sends Plasma as White Blood Cells to replicate, as these viruses being attacked then feed off of everything it comes into contact with. What people are actually dying from is not the virus itself. People are dying from Pre-Existing Health Conditions that are causes of Immunocompromised health like Diabetes, HIV/AIDS, Cancers, Heart Disease, Bronchitis, Pneumonia, etcetera… Because as the COVID-19 multiplies in the Lungs and Bloodstream it reduces the Oxygen that every cell in every organ needs to be healthy and by these viruses filling your lungs the blood cannot get the Oxygen they require. Next comes Hypoxia because your Liver cannot get the Oxygenated cells it requires producing high levels of Carbon Dioxide and in your Lungs the Carbon in your normal Bloodstream cannot exchange with the Oxygen at these low levels and the virus is clogging the lungs where that exchange takes place making toxic levels of Carbon Dioxide. Your veins and arteries start to collapse at the same time your blood is getting thicker and stickier that it clots up and your Heart Rate slows down. Your Diaphragm is weakened as all the muscles lack oxygenated blood and your breathing becomes labored even more. That whatever Health Conditions you have become increasingly worse. You can be very healthy and catch a Cold Virus and this COVID-19 will make it worse as it feeds off of that Cold Virus and your coughing and sneezing that usually helps the body expel the Cold Virus just stops as Dry Coughing starts as the COVID-19 takes over causing what I just described.

          Hypoxia is Carbon Dioxide Poisoning and the Lack of Oxygen thickening your bloodstream that affects your brain as thinking becomes difficult, balance is lost, vision is blurry, taste is lost, your toes become reddened and inflamed and splotches of reddening on your skin from the lack of oxygenated Red Blood Cells occurs…and this can happen from just wearing “Masks” because they reduce the 19.6% Oxygen your body requires and you are rebreathing your own Carbon Dioxide.

        • You are correct, Bjorn.

          Just like the EPA’s PM2.5 alleged deaths, influenza deaths are estimated and/or modeled.

      • I recall seeing (but did not check to verify) that seasonal flu on average claims more than 800,000 annually. So CV19 has to date claimed about half, or just over, the number of people claimed by the flu each year.

        It is a nasty virus, but it has been over hyped. It only required the protection (and isolaton) of the elderly, the vulnerable, and keeping out of the national health care system (in which I include care homes and nursing homes). Western governments failed terribly in that task. Indeed, had they just protected the elderly, care homes etc, the number of fatalities would have been halved, or thereabouts.

        Unfortunately, it will take a generation to pay for this failure.

  2. Interesting, however many of this data, needs taking with a few Kg of Sodium chloride.
    It’s more than possible, that the UK death figures, were massaged to keep the daily deaths below that magic 1,000 mark.

      • Yes, Joel, or perhaps “cosh” in the sense of a blackjack or similar rude device. No hyperbolic functions, then.

        • Jorge, I did indeed mean the kind of “cosh” generally used to hit people over the head. But thank you for your sinhing example of reading the article carefully!

          • It appears that all those minutes spent doing The Times cryptics (with a biro whilst standing atop a bowling ball) were not squandered.

            Thank you, Neil.

          • Your article and graphics were excellent…

            Except, I dearly wished you showed all of the graphs at the same scale, first.
            Including exploded inserts to show the details where you delved into details would then be informational.

            As it was, I had to keep jumping the article up and down to remind myself which groups of countries were at which scale, e.g.:
            Deaths per 1M cases, “United States” – “350”. Scale was up to “400”.
            Deaths per 1M cases, “Ecuador” – approximately 225. Scale was up to “250”.
            Deaths per 1M cases, “Sao Tome and Principe” – approximately 55 on a scale up to “60”.

            Deaths per 1M cases is supposed to inform the audience with comparison at a defined constant. Changing scales ruins that comparison, visually anyway. Your commentary describing each graph is excellent.

            All of the graphs appeared to change scale, any at the same scale appeared to be accidents.

            After spending several years as a budget manager in a large organization, I learned to despise graphs even though the bosses loved them.
            But then, none of the bosses were interested in accurate representation; especially when presenting said graphs to their Vice Presidents.

          • “jorgekafkazar June 21, 2020 at 9:05 am
            It appears that all those minutes spent doing The Times cryptics (with a biro whilst standing atop a bowling ball) were not squandered.

            Thank you, Neil.”

            Agreed!
            And I like the image Neil’s use of “cosh” brings to the topic.

  3. Thank you for putting this together.

    I believe that demographics play a major role, particularly related to your “low hanging fruit,” and besides quality of the health care system, the health of the citizenry is a major factor. Besides dietary deficiency of vitamins and minerals, over consumption of carbohydrates, especially the sugar fructose, leads to all kind of comorbidities that degrade immune function.

    Additionally, previous occurrences of other corona virus diseases may have imparted immunity to some.

    • “Additionally, previous occurrences of other corona virus diseases may have imparted immunity to some.”

      Elderly people, who typically seldom go out, may not have had as many opportunities to be exposed to other varieties of corona viruses, thus might have had less immunity or none at all.

  4. As those acquainted with me will know, long ago I was trained as a mathematician. I’ve forgotten most of the specifics I learned.

    I am so glad. I trained as a physicist, among other skills, and i’ve forgotten just about all of it. Even some of the basics. I put it down to dementia but maybe, just maybe, it’s old bloody age 🙁

    • Nah. Your memory just prioritizes stuff. If you don’t use a memory, it puts it in the back of the Lost Ark warehouse in your head. You add more and more memories to the warehouse as you get older, but there’s only that one guy with his little hand truck, so it takes him longer and longer to fetch stuff.

      Einstein used to say, “Never memorize anything you can look up.”

      I’m in my eighth decade, now, but I still have a memory like one of those big grey animals.

      • jorgekafkazar
        June 20, 2020 at 1:25 pm

        “Einstein used to say, “Never memorize anything you can look up.””
        ——————-

        “How do you know you have ten fingers!” 🙂

        cheers

        • I never count fingers, toes, ears, teeth or arms or legs.
          Why bother?

          If I lost one, I’m sure I’d notice. Without counting anything.

    • Sometime in his 40s, my father pointed out to me that people remember way less than 10% of what they learned in school. So, forgetting the vast majority of what you learned in school doesn’t have to be attributed to old age, or dementia, or anything else like that. It’s just a matter of use it or lose it.

    • I too trained in physics but i’ve been doing computer stuff for 43 years since. I can remember the details of every system I’ve built but was horrified to realize the other day that i can’t take a derivative.

    • Stephen, for me it tends to be “use it or lose it.” The concepts and techniques I have had to use (or re-learn) in the meantime are still fresh in the mind. The rest, not so much.

  5. After the 8 points are made about the dismal quality of the data–I again have no idea why one would go one step further in trying to analyze it. This is quite a spectacular analysis, thorough and lengthy–but based before it even started, on admitted seriously flawed data; what value can it have? Who draws conclusions from it?

    Just as with Christopher Moncton’s treatment a few days ago–I remain baffled. To state all the ways in which the data should all be disqualified, and then state but it’s the best I had so I went ahead and did it anyway–well, we better hope that bridges are designed and built with better accountability. Once the preamble to this article is removed or not read, someone is going to make decisions based on the excellent appearance of the following report. Would you drive over a bridge built that way? Would–or do–you accept global warming calculated from that kind of data?

    Note that Christopher Moncton just a couple of days ago declared from his analysis that Britain has better managed the pandemic and the US less so; this treatment shows the United Kingdom with a higher number of Covid-19 deaths per million population than the US. That requires either a new definition of ‘better managed’ or an admission that maybe the data is just too flawed to draw any conclusion from it–other than that we seriously need, before the NEXT pandemic, a more consistent and accountable method of reporting data.

    I hold no hope that this will be done, based on history; nothing will change until every qualified mathematician declares ‘I can do nothing with this data, and I won’t, and these are the reasons why’.

    • Real data tends to be messy. Only in the world of climate “science” can the concentration of CO2 be expressed to hundredths of a ppm or temperature to hundredths of a degree C.

    • Len
      Ok then let’s just do nothing. That may work for you, but not for me. I use it an indication with the available data, but I clearly understand the limitations.

      In the head post about Aussie and New Zealand good at stopping biological things into the countries. Yes that is correct, but only farm related. There was nothing at all, I mean nothing, in place at border entrance until a few days before lockdown.

      New Zealand’s lead up to the lockdown on 26th March was almost zero. Testing only started a few days before. On the 13th March I rang the ministry of health, and the covid hotline to find a testing site, there was nothing. New Zealand went into one of the strictest lockdows, with 350 reported cases, one in hospital, no testing, after watching the covid expansion globally for over three months.

      Let me be clear, New Zealand was lucky, not well managed. The government over reacted buy immediately following the UK, which reacted to the world’s most successful failure, Neil Ferguson’s model forcast of impending room.

      • Hmmm–well, let me respond this way: I can list three excellent but conflicting analyses–that by Willis Eschenbach, by Christopher Moncton, and now by Neil Lock; which one should guide the doing of something? They all differ because of the poor quality of the data, do we know which one is right? I don’t. They all treat the data–as it exists–in exemplary ways. But the data is no good.

        I do indeed think something has to be done–that which was not done following every single influenza pandemic that the planet has endured previously, so that New Zealand and everyone else knows what to do next time, what works and what doesn’t–collect consistent data with no political interference from which everyone’s mathematical treatments yield the same result. So far we’re just doing what economists do when all stacked end-to-end–‘they still point every which way’.

        But as money and politics has grown to control everything that should be independently scientific, I won’t hold my breath for this to happen following this pandemic either. We creep ever so consistently towards an Idiocracy.

        By the way, unrelated–does ‘Ozonebust’ hint of some displeasure over data concerning the ozone-hole-scare?

        • Len. Your last sentence……YES.
          Not displeasure, simply a completely different interpretation from the very detailed data over 40 years.
          Regards

        • Len Werner: “collect consistent data with no political interference.”

          Yes, that is what should be done next time. Indeed, that is what the WHO’s reporting system was (supposedly) designed to do. On reflection, I think it has actually worked better than I initially expected. Ecuador and France were the only countries where I said, “I don’t believe this,” and the issue with Sao Tome is not the numbers themselves, but their timing.

          But under the current political system, there will always be those wanting to massage numbers like these for their own purposes. I suspect that might be the cause of the recent goings-on with the Swedish numbers. As long as that continues, no-one will ever be able to do a perfect analysis of such data. But that, in my view, is no reason not to try as best we can.

    • Len:
      At its base level, the COVID-19 pandemic is a field biology exercise and most of such data contains a lot of “noise”. I have been studying the pandemic at a more “micro” level, looking at county information important to the Pensacola area, which includes some counties on the east coast of Florida and the I-10 Corridor between Orleans Parrish in Louisiana to the western Florida Panhandle. My work is as a field biologist and not as medical professional or economist. Here a few findings:

      1, The data are highly variable by county and even within the county.
      2. The counties around Pensacola reported their first cases in mid-to-late March. Reason: the area is relatively far from major international and national transportation hubs so it took some time to reach our area. Pandemics travel the way we do; ship, plane, train, automobile, etc.
      3. Mitigation strategies did help dampen the spread and now that those are relaxing our new case numbers are rising.
      4. New case numbers initially peaked in April for our area and have declined since; however, they did not decline to anywhere near zero and have remained consistently higher than many expected. Also, daily new case values in several locations are as high or higher than the April numbers. Some are calling this second peak the “Second Wave”. That is incorrect. The rising numbers at this time may be a rebound, spike or surge but the Second Wave is a term for the seasonal increase, which is likely to happen this coming fall/winter. (An insect-borne disease can get a second wave with the next generation of the insect vector, usually in the spring/summer.)
      5. The relationship between number of tests and number of cases is not straightforward, at least in the local data. Complicating factors include a variable % positive test rate as sampling moves from a nonrandom distribution to a more random distribution, a possibly variable incubation time (just my speculation) and a variable lag time from sampling to analysis to reporting, which has been as high as two weeks. Data on the various State Department of Health websites are uploaded daily but all of the reported cases are not from the previous day.
      6. Many of us are interested in the mutation rate of SARS-CoV-2, which has not been nailed down by any means. This article notes a difference between the east coast and west coast epidemiology and
      in the State of Florida, more cases are reporting but the mortality is lower. We will not complain. Also, of interest: what are the long-term effects of COVID-19?
      7. Another interest: antibody test results.
      8. Returning to more new cases, the benefit is to increase Herd Immunity, which is only a few percent currently. Anecdotal data suggests the disease is moderating in severity as indicating by a lower death rate but that could be related to more positive tests in younger, or healthier individuals and the improved identification and isolation of compromised people. Also, the medical community is gaining ground on successively treating sinker patients.
      9. We all wait for a vaccine but nay questions will remain about effectiveness and duration of protection.

      These are a few of the field biology observations from the 70+ reports that I’ve written for the local community (and which have spread beyond). We absolutely need to take this opportunity to study the COVID-19 pandemic with our modern tools, as this is the first in about 50 years. and the next could be much worse.

      • 9. We all wait for a vaccine but nay questions will remain about effectiveness and duration of protection.

        Do you expect a vaccine and why?

        • Derg:
          Pardon my typo which should have been “many” rather than “nay”. As for an effective vaccine, I’m not sure; our medical researchers would know more. A number of candidates are in testing, or about to be, and the expectations are high but Coronaviruses have not been an easy target to subdue. We should know much more by the end of the year. Questions will be: How effective? How effective against the virus mutation rate? How long does protection last? What side effects, short-term and long-term and for various age-groups? (Early indications are that at least some of the vaccine candidates are well tolerated with only minor side effects common to many injections like some redness, minor swelling and soreness around the injection site but long-term side effects will be an ongoing study. Much to learn about this virus and COVID-19.

  6. Re the small countries, could their varied #s be simply due to the fact that they have small populations, and statistically that results in a spread? I’m reading “Thinking, Fast and Slow” by Daniel Kahneman. On p109-111 he discusses the incidence of kidney cancer by US county. Turns out the counties with the lowest incidence are small, rural, sparsely populated, and largely Republican. But so are the counties with the highest incidence. He concludes (rightly, I’m sure) that this is mostly random variation, because the absolute number of kidney cancer cases in such counties is likely low. Could be the same for small countries and covid?

    • I would guess that small countries occupying both the best and worst ends of the spectrum may be at least partly a matter of luck. If you get a big cluster, like San Marino or Andorra (or New York City, if it was a country!), you’re in trouble. If you don’t, like Iceland or the Faeroes, you’re OK. The level of contact tracing is important too, though.

  7. The head poster here shows a great emphasis on tracking total numbers of cases, though it’s been said many times on this blog that this number depends critically on the amount of testing done in any locality? In particular, in concluding that Sweden is supposedly a badly managed country on this, the writer manages to speak out of both sides of his mouth, first saying that case numbers prove something bad for Sweden, then conceding that this may just be due to more testing, then saying later that Sweden is one of the worst three countries..

    If you ask me, Willis Eschenbach’s analysis is far more trusty than this, showing daily deaths for Sweden being well past their peak, same as for a lot of other places

    • I tend to agree that the case numbers are not that useful because of test/testing rate differences.
      Deaths is more useful, but even there you have wide variance in what gets reported as a COVID-19 death, and changing policies on this.

      However, you have to put a stake in the ground somewhere.

      • The tests used today presumably have better reliability than those used earlier, though there are still numerous test kits being used from several different vendors. None has been validated as would be the normal practice.

        As important, if false positives exceed false negatives, then more testing will find more cases even if no one is actually infected.

    • The problem with counting deaths rather than cases is that you are always several weeks behind. It will be interesting to see whether the recent surge in Swedish cases translates in the end to an increase in deaths. But that won’t be visible for two to three weeks.

        • I re-worked the four comparison graphs of European countries using deaths rather than cases. It didn’t make any difference to my conclusions. Apart from making me notice that the Swedish reporting system shows hardly any deaths on Mondays, and the Spaniards did a big negative adjustment in late May and have reported almost no deaths since. Of course, I won’t be able to judge the Portuguese and Swedish situations, the two I am most interested in, for another 3 weeks or so.

          • Will, one difficulty of trying to assess effects of “social distancing” is that there are different kinds of social distancing. Your essay treats public transport use as the measure of social distancing. But that is very dependent on where you are. Where I live (about 35 miles from London), public transport use fell essentially to zero within a couple of weeks of the lockdown. Helped, I will say, by major engineering works which meant there were no trains to London anyway.

            On the other hand, I am very skeptical of whether the “two metre rule” has had any effect at all. But I don’t know how you could test this.

          • Ok, then improve it, find a better way to quantify social distancing. The point is that you can’t just assume that social distancing measures had a major effect on the epidemic, you need to demonstrate it. I, like a number of others, have shown I can’t find any major effect. If you think there is one then you need to show it, not assume it.

  8. Case data is junk because it depends on how many are being tested and in what contexts. Deaths is the only good data (though it has its faults), ideally excess deaths all causes by date of death not report, though failing that Covid deaths by date of death is good. Infections should be inferred from deaths not cases.

    If you compare when populations adopted social distancing to the date when their death curves began to slow down or to their overall death toll you will find no signal of the social distancing in the curves or data. This is the analysis that needs to be done to prove the efficacy or otherwise of social distancing measures on the epidemic. I have made a preliminary effort here. https://conservativewoman.co.uk/why-social-distancing-is-worse-than-useless/

  9. No offense intended, but what an extraordinary waste of time due to the fact that severe Covid-19(84) is primarily an iatrogenic disease. For some inexplicable reason, respirators were chosen as the primary treatment, but unfortunately they create the very disease they are meant to treat.

    Dr. Paul Mayo, perhaps New York City’s most illustrious critical care doctor expressed the risks [of ventilators] pithily: putting a person on a ventilator creates a disease known as being on a ventilator.

    In other words, the rhythmic mechanical force of pressure upon alveoli triggers an out-of-control immune response, i.e. cytokine storm, which can lead to organ failure and death. At best ventilators exacerbate lung inflammation caused by the virus; at worst they create the hyper-inflammation that leads to organ failure and death.

    When hospital patients need assistance breathing and are placed on a mechanical ventilator for days at a time, their lungs react to the pressure generated by the ventilator with an out-of-control immune response that can lead to excessive inflammation

    Lungs respond to hospital ventilator as if it were an infection
    https://www.sciencedaily.com/releases/2012/07/120718172835.htm

    This is not some fringe hypothesis; numerous doctors are now expressing their misgivings and concerns about routine use of ventilators for Covid-19(84).

    • As i understand from what I’ve read, ventilators are used to get amounts of oxygen above what are normally given with the nose tube (which has a name I don’t remember). It seems that hospital staff don’t like to administer high levels of O2 through the nose because of the “blow back” into the room when the patient is not inhaling.

      When I was a kid in the ’40’s and ’50’s. Patients who were being given oxygen, where in something called an “oxygen tent”. As you might image, this was a less than cubic meter coving over the head and shoulders, with a flexible, clear plastic front, which provide access to the patients head. It looks to me like this would be a far more humane way to administer high levels of oxygen, while avoiding the room contamination problem. We need to bring back 1947 technology!

    • What about cough suppressants? I don’t think a patient can cough with all those tubes in there nose and mouth.

      • That significantly increases the risk for ventilator associated pneumonia (VAP), which is a major source of increased illness and death.

    • Jan E Christoffersen
      June 20, 2020 at 11:49 am
      ——————

      Maybe you are right,
      but contemplate this;
      as far as known, no any dead chinese body ended up in a bin.

      Maybe they are lying, but still no evidence of the Chinese dumping their dead in the bin, as far as I can tell.

      Do you know this to be contrary as stated here for you?

      And then, what kinda of lies and deception would run far and wide in consideration of covering up and nullifying the grotesque of dead bodies of people dumped in bins, in developed rich and highly industrialized countries?
      How does this add up?

      “The impaled have no rights or means to moan or complain about the speak in the eye of the other.”

      The simple question here in the given subject consist as to whether and what group one belongs!

      cheers

      • Imprisoned Muslims and dead Muslims say otherwise.

        China’s government is the worst and the most evil on the planet.

        Thanks Bushes, Clinton’s and Obama 🙁

        • I think you are overstating the case. We have some pretty evil governments in the world. North Korea springs to mind. Venuzuela has to at least be in the running as does Iran. I’m sure readers here can think of many more contenders for the worst and most evil title.

          • China is the 2nd largest economy in the world.

            The others you mentioned are bad, but the world needs to be afraid of the Chinese communist.

    • I should have added that the last death estimate I have seen for China is 4,600, which dates from two months ago or so. It must be 1 or 2 orders of magnitude higher. Has anyone seen a more recent figure?

  10. Always interesting to me is how treatment plans are not even considered. To me the approach to treatment needs to be reflected into the infection to death ratio. Or maybe a better set of metrics is infection to treatment plan to death ratio. Not easy to attain those metrics yet they are the most important to those who are infected.

  11. Very good analysis and reflects the work I have been doing with the worldometers data.
    One thing is obvious Mortality rates are one of the biggest variables and one thing you haven’t mentioned is the biggy, Medicine.
    The approach to medicine is also a massive variable, with many countries ignoring the WHO, CDC, FDA and NHS and trying (many successfully) non standard medicines. As in they may not be anti-virals.
    There is also quite a disparity in start dates and “onset” dates reflecting how it spread throughout the world.

  12. Neil, If I could add comment to your sentence:
    “Or might there have been some small “pre-releases” of the new virus from China even before January?”

    I heard something on the BBC yesterday (19th) about analyses of sewage waste samples from Milan and Turin, dating to Nov 2019 , which had revealed traces of Covid 19 virus. Of course contamination of lab samples is always a possibility, but given the business traffic between China and the North Italy manufacturing centres, there is a suggestion that Covid 19 was present in China at least 2 months before its existence was formally recognised. Furthermore, cases of severe respiratory distress and subsequent fatalities in Europe during that preliminary period would surely have been attributed to seasonal flu and pneumonia, if the existence of a novel virus strain, originating in China, was as yet unknown to hospital staff and coroners.

    • Even if true (presence of virus in Italy in November), the number of cases must have been low enough that they did not show up in excess death monitoring.

      Contamination of samples is a possibility as you say, but so is insufficient selectivity of tests.

  13. Why the whole tohuwabohu?
    It’s just a flu that kills the elderly.
    It cleans the population from the weak.
    I understand that most of politicians are in the risky age, but is the reason to spoil the life of the society?

    • Two replies to that simplistic analysis
      1 Yes it kills the elderly but infects from age 20 up many of whom need hospital care overwhelming the systems as happened in New York and Italy
      2 Two large scale studies in Italy and Wales by two Scottish universities found surprising years of life left to older people ( the point being the old have survived whereas the unhealthy dont much get to be old)
      The study estimated the years of prospective life snuffed out early by the virus

  14. Re: the early test data for the UK.

    My suspicion is that this was removed because the early tests were carried out using Chinese test kits, which proved to be wildly inaccurate. The data was basically junk, so removing it makes a lot of sense (to me).

  15. Interesting post Neil. Thank-you.

    The plots assume the population’s biochemistry and immune system of each the countries is the same.

    Why did you not plot China? or South Korea?

    It is a fact that those people who are Vitamin D deficient (blood serum 25(OH)D330 ng/ml).

    So, in the case of India.

    The first wave of Indian cases, would be rich people who live and work indoors in air condition buildings (and who travel and are in contact with those who travel). These people are more likely to be Vitamin D deficient.

    The second wave of Indian cases, would be poor people who live in very, crowded conditions and who travel by foot, train, and so on. These people are less likely to be Vitamin D deficient. Pollution is also a factor as it blocks the UVB that is necessary for the body to create ‘Vitamin’ D.

    As China is a very populated country they may be giving their people Vitamin D via their diet. This would enable China to hide how they are stopping the covid in populous cities, from the Western countries.

    Neil’s Summary of the Indian Data.

    Last, but very much not least, since it’s the second most populous country in the world: India.

    In India, the new cases don’t look to be anywhere near peaking yet. That’s not good news. But the deaths per case have begun to decline, suggesting the heat and humidity effect may also be at work here, though not yet strongly. India (like the USA and Russia) is a big and very populous country, so there’s still a distance to go.

    This is the Indonesian Study that shows Vitamin D deficient people have 19 times greater chance of dying of covid than Vitamin D normal people.

    Patterns of COVID-19 Mortality and Vitamin D: An Indonesian Study

    https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3585561
    With reference to normal cases, Vitamin D insufficient cases were approximately 12.55 times more likely to die (OR=12.55; p<0.001) while Vitamin D deficient cases were approximately 19.12 times more likely to die from the disease (OR=19.12; p<0.001).

    This is an analysis of the prevalence of Vitamin D deficiency in the US and it finds the that Vitamin D deficiency 'correlates' with skin color.

    Correlates means there is a direct correlation with how dark your skin is and how Vitamin D deficient the person is.

    In the US the death rate among Blacks is twice that of the general population. That makes sense as 82% of the US black population is Vitamin D deficient as compared the 42% of the general population, and 62% of the Hispanic population. The Hispanic covid death rate is roughly 30% higher than the 'white' population's covid death rate.

    https://www.sciencedirect.com/science/article/pii/S0271531710002599?via%3Dihub

    Prevalence and correlates of vitamin D deficiency in US adults

    If a country were to act on the Vitamin D deficient Vs Covid death rate, information they could reduce their death rate by more than a factor of ten by let say putting Vitamin D in a common food or better yet by getting everyone to take 4000 UI/day which is what is required to get almost the entire population to Vitamin D normal.

    • There are many questions around India.

      Will it end up more like the U.S. and Brazil or China? Since it’s already ahead of China, it may end up with the most cases and challenge the U.S. and Brazil for greatest number of deaths. India though has a median age of less than 30 and Brazil’s is about 33 and the U.S. about 38.

      In the end, I see India with most cases followed by Brazil and the U.S. In deaths, U.S. probably ends up with the most because of its older population followed by Brazil and India. All three are in the running for these dubious distinctions.

    • It is not always the mathematics and physics that are important to remember. We must recognize BeeEss when we see it, climate science being the best example.
      However, wrt to Covid, how many time were we give bad advice and information by the top medical “experts” in WHO, USA, and Canada. I have essentially quit listening to the “updates” as they are useless with NO medical ADVICE.
      The above reference to Vitamin D is typical of the omission. This is 2020 and we should know enough about Vitamin D to ensure the people in care homes are not deficient. The daily briefings should not it.
      Then we have HCQ. As soon as Trump mentioned it, there was a political based stampede to ensure that we were told it is useless. In the last day or two another USA medical group announced it was stopping the testing. They were likely doing it wrong anyway.
      As some doctors noted, it has to be given early and in combination with zinc.

    • “a country… could reduce their death rate by more than a factor of ten by…getting everyone to take 4000 UI/day”

      But that would be a tacit admission of their incompetence in accepting the pitifully low RDA (600 iu/15mcg). It’s also not certain that 4000 IU/day is sufficient. In Canada some jurisdictions (British Columbia, for instance) have routinely refused testing for serum D3 level with the response “just supplement”. And Health Canada prohibits the sale or importation of pills containing more than 1000iu/25mcg of D3. So to get 4000iu/day you have to swallow 4 pills. That’s a lot of unnecessary and costly filler.

  16. Analytic speculation on elaborately presented completely unreliable, unverified, ‘dirty data’ is simply a fatuous waste of time. One figure it would be amusing to have out of all this (tho probably meaningless) is # of deaths / # of “cases”. It’s impossible to eyeball it from these graphs, but it looks here to be less than 1 death in 1000 cases.

  17. It is the virus from Hell….released by the CCP….France built the lab so the CCP would promise not to develop any weaponized viruses….the USA contributed millions to operate it….the parking lots of hospitals in Wuhan showed a spike in numbers of vehicles in August 2019 (satellite photos) and computer searches for some symptoms of the virus spiked at the same time. Some people have had it for 2 months or more….virus has been detected in eyes of some…one autopsy revealed virus in spleen…very contagious….Chinese would not have shut down major portion of economy for any other reason than stopping the spread.

    • A more compelling reason for their draconian response was to terrorize the population of Wuhan and shut down the recent protests there over the wicked air pollution. The CCP greatly fears the rebellion in Hong Kong spreading to the rest of China. And it worked, not only there, but in Hong Kong as well.

  18. Very good summary and I found it interesting to compare the different countries. What I came away with though was that countries/states that protected/isolated their elderly the best were those that handled the pandemic the best. Those that did not isolate the elderly with health issues failed miserably. Final stop.

    I also have an issue with the term “pandemic”. It seems to be a misused term for what we are seeing. A pandemic is when someone in your immediate family passes from the disease. This is not a pandemic for me. I don’t have anyone in my family who has died from the disease. I don’t even know of anyone who has died. I don’t even know of anyone who has gone to the hospital for covid care. I don’t even know of anyone who has had the disease or anyone who may have had passing symptoms. This is not a pandemic.

    But if you are old and have health issues, it can be dangerous. But then again, very few of those who are old and with health issues have died. But they do make up the greatest share of the deaths.

  19. Lies, damn lies and mathematics.

    It’s well recorded that covid19 tests are useless, case statistics are useless, and death statistics almost useless. Why do people like Christopher Monkton, Judith Curry and Neil Lock insist that they can get a meaningful result from fake data?

  20. If you run a scatter plot using the deaths/Mi^2 on the US data you find this is a population density issue. If I normalize the data to Tulsa OK as a 1 then I am 27 times more likely to die in Dallas county and 700x more likely to doe in Kings County. All data from USAFacts.org.

  21. I don’t think the data can be aggregated meaningfully. Population averaged data hides focal hotspots like New York or the province of Quebec, where public health mismanagement killed people.

    In my home province, Alberta, Canada, they have tested a lot and have published a lot of granular data. The average age of death is 83. 75% of death have 3 or more co-morbidities. Another 14% have 2 or more. But if you are healthy and under 60 the risk of anything more than a bad cold is tiny, and if under 40, almost zero. Respiratory infections have always killed old, sick people, my parents included.

    Old, sick people should be protected. Everyone else should be let out of their cage. Freedom is not free, and catching the occasional cold is a reasonable price to pay.

    • I’ve tried to tell that to several FaceBook friends who are nurses.
      They of course throw back 1 or 2 cases of some 44 year old man or a 56 year old woman who apparently were healthy but died anyway of COVID-19 ARDS. They are arguing from their emotions and anecdotal cases. While I’m giving them the cold hard stats that say the cases they cite are rare.
      It’s like trying to argue economics or police and racial issues with a Trump-hater who is suffering from chronic TDS. Rationality and logic departed their brains long ago.

    • This what I was thinking.

      I was trying to figure out if there was a point to all the curves. If statistics do not help me make a personal choice, they are a waste of my time.

      I am retired and live full time in a motorhome. Since the panic-demic I have travel 5000 miles and nine states. For example, I drove through Grand Canyon National Park late in the day and then set up camp. The next day I went back and the park was closed.

      Arizona has some interesting statistics showing age for deaths and setting where infected. There is no basis for closing remote parks.

      Another state I traveled through was closed by order of the governor. The remote part I traveled through had no cases. An interesting statistic I noticed was traffic deaths had been reduced for the period had been reduced more than the covid-19 deaths.

      In a third state I went sailing. The governor was opening the state. Boating was allowed but I could not use the campground because that county had a high number of deaths due to nursing homes and a packing plant. Since I was sailing alone there was no one from the packing plant or nursing homes with me.

      Part of enjoying freedom is being responsible for yourself.

  22. Neil Lock’s 22 Feb date for the US is “late” by at least 3 weeks, maybe 4 weeks, of the actual SARS-CoV-2 virus seeding in the US and then early cryptic spreading in communities in California, Washington, and New York City.

    So, what I have come to realize about this COVID-19 pandemic and how it started is the “first confirmed cases” were NOT reflective of the actual virus seeding going on.

    The CDC by end of May was realizing this was in fact the case:

    Evidence for Limited Early Spread of COVID-19 Within the United States, January–February 2020
    “From January 21 through February 23, 2020, public health agencies detected 14 U.S. cases of coronavirus disease 2019 (COVID-19), all related to travel from China (1,2). The first nontravel–related U.S. case was confirmed on February 26 in a California resident who had become ill on February 13 (3). Two days later, on February 28, a second nontravel–related case was confirmed in the state of Washington (4,5). Examination of four lines of evidence provides insight into the timing of introduction and early transmission of SARS-CoV-2, the virus that causes COVID-19, into the United States before the detection of these two cases. First, syndromic surveillance based on emergency department records from counties affected early by the pandemic did not show an increase in visits for COVID-19–like illness before February 28. Second, retrospective SARS-CoV-2 testing of approximately 11,000 respiratory specimens from several U.S. locations beginning January 1 identified no positive results before February 20. Third, analysis of viral RNA sequences from early cases suggested that a single lineage of virus imported directly or indirectly from China began circulating in the United States between January 18 and February 9, followed by several SARS-CoV-2 importations from Europe. Finally, the occurrence of three cases, one in a California resident who died on February 6, a second in another resident of the same county who died February 17, and a third in an unidentified passenger or crew member aboard a Pacific cruise ship that left San Francisco on February 11, confirms cryptic circulation of the virus by early February. These data indicate that sustained, community transmission had begun before detection of the first two nontravel–related U.S. cases, likely resulting from the importation of a single lineage of virus from China in late January or early February, followed by several importations from Europe.
    source: https://www.cdc.gov/mmwr/volumes/69/wr/mm6922e1.htm?s_cid=mm6922e1_w

    The California patient who became ill on February 13 likely had contracted the virus 3-5 days earlier, at least February 10th. The other California resident who died on February 6th suggests that person contracted the infection 10-14 days earlier in late January. How many people did they expose before their own symptoms and some degree of isolation? We’ll never know, but you can reasonably guess with an R0 of 2.5 it was 4-6 persons. Then how many of those 4-6 persons passed it on? Mathematically speaking it’s another now 32-88 more persons infected. So by mid-February it was cryptically spreading when no testing was being done.

    So, my personal conclusion is there was hell of a lot more early seeding of this virus throughout communities in the US, UK, and most of Western Europe than realized/admitted or evident from the documented cases.

    It is this early, undetected seeding (cryptic circulation, likely asymptomatic and low-symptomatic infections in healthy people before the virus got a foothold in places like nursing homes and exploded into severe and lethal cases) that explains the vast difference between say New Zealand and the UK now. UK, namely places like London, Manchester, New York City are cross roads to world for Asia and the rest of Europe and the America. Christchurch and Auckland are not cross roads in any sense.

    And we have indications that there may have even been infected individuals in the US and France in late December.
    France:
    https://www.livescience.com/coronavirus-france-patient-zero-december.html
    US-Washington State:
    https://www.livescience.com/covid-19-like-illness-december-washington.html

    Bottomline: There was lot more seeding throughout those regions than realized. That is also much more likely given that we now know there are many asymptomatic infections which leads to community dispersal than realized. And at this point in the pandemic, contract tracing is simply a waste of resources that accomplishing very little in suppressing further spread of this virus in the Americas and in Europe.

    • Opps, excuse my math faux pas, 4-6 infections leads to 10-15 new infections at an R0 of 2.5.

    • Joel,

      I didn’t show the daily growth plot for the USA, but if you look at the raw data you’ll see that there were 15 cases there prior to February 22nd. There were no new cases from the 15th to the 20th, then one on the 21st, then suddenly 19 on the 22nd, more than doubling the total cases. That was why I assigned the 22nd as the onset date – of the main epidemic, not of the first small wave. The pattern was very similar in the UK, as I showed you; there, the onset date was March 2nd, on which the total case count ramped up from 23 to 36.

      As to early seeding, I agree; I think there was a lot more of the virus around in February and before than appears in the official statistics. I myself fell ill on January 30th with what could well have been mild symptoms of COVID-19. The first “official” case in the UK was found on that day, and reported the next.

  23. All this data is very interesting, but I rarely see what I think are the most important statistics: (1) the number who have had the disease, but are now well, and (2) the number who are currently ill.

    • With so many contract SARS-CoV-2 infection and remain asymptomatic and then recover with antibodies, that is an almost impossible number (number who have had the disease) to know. And even if you know what it is today, tomorrow it will be some number higher.
      And your definition of “ill” is an important detail. Many people who feel ill stay home and recover and were never tested while actively shedding viable virus. They only go to the hospital and get a confirmed diagnosis if they are really seriously “ill”. So the definition of “ill” matters greatly.

    • Interesting that you bring up the issue of numbers of recovered cases, old engineer. Most countries have been publishing these figures throughout the epidemic on worldometers, but they aren’t in the Our World in Data spreadsheet – presumably because they aren’t “official.” The UK and the Netherlands decided some time in April that they wouldn’t actually try to publish these figures at all. Spain and Sweden followed suit more recently. I wonder why? Political reasons, probably.

  24. Neil, I really want to applaud your time and effort for putting this together. You’ve certainly done the best you can given the constraints of the data and reporting inconsistencies between regions.

    However, it’s dangerous to draw as many conclusions as you have with so much emphasis on “cases” as the showcase variable in the analysis. Cases are a function of testing, plain and simple.

    Case in point, you discuss calculation of “onset” as a feature of cases. And yet we unequivocally known that some countries didn’t start testing until late in this cycle, meaning you’re flying completely blind until that very first test is administered.

    Here’s an extreme example: imagine a country in the late phases of an epidemic, quite far along the sigmoid/Gompertz “tail” of hospitalizations, deaths, symptomatic cases or any other objective variable (excluding, obviously, cases). If they have never tested for the pathogen before, but then STARTED testing late in this epidemic cycle, their reported “cases” would perfectly simulate the “head” of the Gompertz curve. You’d be forgiven for thinking you caught the start of infection, when it might have actually been weeks earlier.

    In other words, you can’t see the start of something if you didn’t start looking until it was already half over.

    There’s also qualitative analysis offered here around countries that are “doing well” or “not doing well” based on their case counts. By following that logic, you’re falling into the trap of passing praise/judgement on something a government can simply dial-up or down by changing their testing policies.

    Still, you’ve put a lot of work into this, which is appreciated by many, I’m sure.

    • SteveB, thank you for your kind words.

      In a number of countries, of which Iran is an example, the first case and the first death were simultaneous. They may not have been doing much (if any) testing up to then, but they certainly did test those cases! Also, I designed the onset date to catch the first significant increase in cases, rather than the first case itself. You’ll see that, in many countries (including Iran), the onset date is the day after the first case.

      I appreciate that, by intentionally restricting tests, a government could keep the case count down, and so the severity of the problem hidden… for a while. That’s why I also tracked deaths per case. That particular skulduggery would show up as a bump in deaths per case. (Maybe that explains why France is so high in that table?)

  25. Dr. Fauci says the American people don’t believe science.
    Well, that’s a good thang since science is about KNOWING not believing.

    Part I
    For instance, we KNOW from US CDC data (science loves data) that 80% of Covid-19 related deaths were in people over 65 years of age with underlying health problems: cardiovascular, COPD, diabetes, high blood pressure, obesity, etc. On death’s doorstep, as Trump correctly observed.

    We KNOW from data that Japan has the highest percentage of 65+ at 27% but has not yet acquired 1,000 Covid-19 related deaths.

    We also KNOW from US CDC data that HALF of the Covid-19 related deaths occurred in NYC and four states: NJ, NY, MA, PA.

    We KNOW from data that the top ten states for Covid-19 related deaths were responsible for over 70% of them.

    What can we conclude from these data-based scientific observations?

    That Japan respects its elderly and does not warehouse them as cash cows and neglect them in badly run (BLUE) nursing homes and milk them for Medicare and Medicaid payments.

    That Covid-19 was not a threat to the majority of a young and healthy nation and the shutdown, social distancing clown show and economic cluster f** that destroyed fitness clubs and movie theaters was unwarranted and irresponsible.

    Part II
    We KNOW from widely published, copied and cloned K-T and NASA atmospheric heat (power flux) balances for the greenhouse effect to perform as advertised requires the earth’s surface to radiate as an ideal black body, upwelling “extra” energy for the GHGs to “trap” and “back” radiate.

    We KNOW from the laws of thermodynamics that conservation of energy says “extra” energy does not, cannot exist.

    We KNOW from the laws of thermodynamics that the non-radiative heat transfer functions of a contiguous participating media, i.e. atmospheric molecules, and experimental demonstrations, the gold standard of science, that ideal, black body emission upwelling “extra” energy does not exist.

    Therefore:
    We KNOW the greenhouse effect does not exist,
    We KNOW that GHG warming of the atmosphere does not exist,
    We KNOW that man caused global warming does not exist.

    Part II
    Of course, knowing has always fought an uphill battle against believing.
    That is also why science and religion are and always will be totally incompatible.

    • I just heard Fauci tell Americans that he had to lie to them on 60 Min about masks being useless

      toooooo save the medical community from running out of masks..

      Fauci is a Douche

  26. Just to touch on something Scissor said earlier, I don’t go to the UK very often, but I am always shocked (and I don’t shock easy) by the level of ill health of both young and old there.

    Diabetes and obesity are through the roof.

    From the post: “The French figures, for example, have been all over the place ever since I have been following the epidemic.”

    That’s completely normal, it helps keep us confused, bewildered, and easy to manipulate.

    • To Climate Believer
      Well if your post is from the US here are some data that suggest otherwise
      USA Deaths per million from Hypertension 74, Percentages of Population with T1 and 2 Diabetes 11 % and Obesity 36%
      UK same issues -the rates are Hypertension 25 per m , Diabetes 3.9% and Obesity 28 % of Population

      Sources WHO Health Rankings 2017

      • From Diabetes.org.uk :
        The number of people living with diabetes in the UK has soared by 59.8 per cent in a decade.

        From 2019 stats NHS.uk:
        29% of adults classified as obese, this is an increase from 26% in 2016.

        In Scotland where I spent a lot of my youth, some 40 years ago now, it’s getting pretty bad.

        Gov.scot:
        Overweight (including obesity) prevalence was lowest among young people aged 16-24 (36%). A significantly higher proportion of those aged 25-34 were overweight (55%), with further increases with age up to age 65-74. More than three quarters of those aged 65-74 were overweight including obese (78%), and all age groups above 45 (except for 75+, 68%) had a prevalence of over 70%.

  27. Neil,
    Thank your for this excellent post.
    There is an update on onset dates.
    In Italy ,it is now being reported that sewage samples from Milan and Turin taken on December 18 last show traces of the virus were present two months before the first reported case there in February this year.
    The discovery backs up reports from round the world that the coronavirus was well established before it was identified, helping to explain how it became so widespread.

  28. The data from the UK is very patchy from area to area while some areas have been hit hard by the virus others much less so even though there is only a few miles between them. I don’t think we can draw a conclusion that it is just killing unhealthy people, lets say that I am an old unhealthy person, then I am being spared simply because of where I live also some very old people have recovered from COVID-19. I would like to get on with my normal life now and this is not the Spanish flu it is just another SARS virus outbreak which we have had in recent years and eradicated. I have worked throughout this outbreak and have never been ill and I am not afraid of catching it. What we have is the MSM trying to scare us that we are going to get COVID-19 when the reality is we are more likely to die in a car accident or get struck by lightning then catch this virus.

  29. A data set for the US if any are interested.

    https://covidtracking.com/data/

    Breaks it down by tests, positives, negatives, total tests, hospitalizations(the most chancy at the state level), and deaths(all are on an as available as different states have different requirements). Available as a google doc, csv and other formats.

  30. Those charts are very hard to read. I have to break out my magnifying application to read them. There’s plenty of room on the page to make them larger. Please do so in the future, or provide a link to a full-sized version.

  31. The numbers I want to see but never do are the degree of illness on those that test positive. Raw numbers of positive tests are meaningless if you do not include that. Are those numbers not known, or are they not made available because it goes against the narrative our leaders want to convey?
    Other numbers I would like to see is the length of stay in hospital and the result, either discharged or died.
    To get a complete picture we need to know:
    Number of positive tests
    Number of positives that have 1) no symptoms 2) slight symptoms 3) full symptoms w/o hospital
    Number of positives that recovered
    Number of positives were admitted to hospital
    Number of those hospitalized that recovered and were discharged
    Number of deaths

  32. Do you know if positive antibody tests are included in these positive result totals? It would be nice to separate those out since that would indicate a past infection and not an accurate look as to how the virus is spreading now.

  33. I am going to mention once again that it appears to me that the recent rise in new cases in southern states in the US is mirroring the rise in new cases in other nations similarly situated at lower latitudes in the NH. It is doubtful that this is coincidental. The strongest clue for that is the rise in cases in California. The increase in new case numbers for California is taking place all in Southern California. Some of that may be due to the outbreak in Mexico which is now in full swing. That could partly explain why Southern California, Arizona, and Texas are seeing new cases in the thousands per day.

  34. “Isn’t that as pretty a “Farr curve” (symmetrical sigmoid curve) as you could wish for? In 1840, William Farr”

    When you see the author mention Farr , you know for certain he has not read the literature on the failure of nonmechanistic approaches.

    Next, forget looking at country data unless the country is small and compact.

    Nothing about epidemic data is “normal” or easily spatially averaged.

  35. Since ‘cases’ range from asymptomatic to death, the numbers are completely meaningless. The only statistics that would be useful would be demographic analyses of mortality, that might illuminate differences between countries. A far better use of the author’s time.

  36. Neil
    I have just completed a twenty nation analysis
    10 Western ie 9 European plus the USA
    10 Eastern – 8 Asian plus Australia and NZ
    I used Death rates per million which do vary in method of recording but did not use cases because of even more unreliability
    However the differences between the death rates of the Western group ( mean 463 deaths per million) were so much greater than the Eastern group ( 2.9 per million ) at 9 June that I thought any concerns about death rate recording methods were second order
    A bench mark was the rating of 195 nations preparedness for a pandemic made in October 2019 made by the Global Health Security Index in October 2019
    The US was ranked no 1 the UK no 2 yet both are in the high death rate western group
    Being prepared is one matter but acting promptly is essential yet that was what neither the US nor the UK did
    The star at this was Taiwan which monitored internet chatter in China in December and on December 31 started testing and quarantining travellers from Wuhan
    Australia was not far behind picking up stories of a new virus on 20 January and closing its borders to travellers from China
    on 1 February copping criticism from both WHO and China
    My study examined a number of demographic data including population density, median age and percent 65 plus, plus percent morbidities of hypertension, diabetes, obesity, and smoking rates Based on analysis of data from many sources I concluded these did not explain the great differences in death rates I then checked quality of health care systems as evaluated by the GHSI Again no apparent explanatory power as the US system was ranked No 1 out of 195 world wide and the UK no 11 with 8 of the high death rate nations being ranked in the top 20
    Even more paradoxical was that Vietnam with no recorded deaths had its health care system ranked 74 th

    My conclusions are
    1 The Taiwanese success with a death rate of 0.3 per million reveals the value of having active bio security intelligence monitoring
    2 Have competent medical advisers who do not as they did in the UK second guess what the politicians want to hear
    3 Have means of coordinating actions by Governments, Health care systems, immigration and border controls all ready to be activated quickly
    4 Act quickly particularly on border controls which can be graduated ie Taiwan tested travellers from China before resorting to more stringent border controls
    The lessons from both Vietnam and Taiwan are that if a nation acts early on border controls it has less need for more stringent internal controls such as lockdowns The UK throughout March April and May had no border controls with some 3.1 million airtravellers coming through Heathrow alone in March- many from China
    5 Because many cases are asymptomatic to the virus its smart to test early and widely particularly of internal prospective vectors such as delivery drivers health and aged in care workers
    6 If lockdowns seem required start with public gatherings and when it comes to industry start first with those where workers are in close contact with the public
    Australia did not close down manufacturing, mining, building and construction industries but achieved the same low death rate of 4 deaths per million as New Zealand which closed down all industry at higher economic cost
    7 And test all passengers on cruise ships which seek to dock as Australian experience was they can be hot beds of infection with just one ship whose passengers were allowed to disembark untested in Sydney causing 21 deaths out of a national total of 102
    8 Have leaders communicate regularly with the public
    In Australia’ s case a national cabinet of federal and state leaders was formed supported by an advisory committee of federal and states chief medical officers whose leader always stood with the Prime minister to comment nightly on progress
    Neil Congratulations on such a large scale analysis its the best I have seen
    A request -could I use some of your analyses with of course full attribution to you and if so how would you wish that referenced?
    Thomho Melbourne
    Australia

    • Thomho, you can just link to this page, that’s enough for me.

      Good point about the lack of UK border controls during the crucial time. But expecting returning passengers to isolate now is simply shutting the stable door very hard when the horse has been away for months.

  37. So many graphs, so little validity.

    Sorry, but I am in … the-data-sucks-so-bad-who-in-thier-right-mind-would-spend-this-much-time-trying-to-message-any-reality-from-it … camp.

    First, how do we really know how many “confirmed cases” really had the virus?
    Second, how do we really trust that all countries of the world had the same standards of recording cases and deaths?
    Third, how do we really know how many people REALLY had the virus who never tested for it, because the symptoms were so mild?

    And there are other questions that contribute to my pessimism.

  38. So, testing was low initially and numbers of cases/deaths were low. Then testing increased due to fear and the numbers rose and peaked. Now the numbers of testing and cases has shrunk. Who’d a thought that!

  39. It would be nice if the data set included the daily number of all cause deaths. That would help with the reporting bias issue.

  40. Neil,
    Nobody seems to be checking population density as an important variable for Covid 19 deaths
    Notice that San Marino, Belgium and UK all have significantly higher population densities than other countries in Europe.
    In the USA where it is easier to make comparisons try plotting out deaths/million against population density per state. With one or two exceptions you will find a good straight line correlation.
    If everyone is squashed together in a small space the virus spreads quickly. Why is nobody looking at this as a metric?

    • “If everyone is squashed together in a small space the virus spreads quickly. Why is nobody looking at this as a metric?”

      scientists who run agent models look at this in detail.

      amateurs at websites? are not interested in the truth. they are interested in supporting their preconceptions

      • “Steven Mosher June 21, 2020 at 12:59 am

        …are not interested in the truth. they are interested in supporting their preconceptions…”

        That is hilarious! Punctuation is a problem with this one…

        No-one knows what you are trying to say. Oh, wait! Yes we do, it’s bollox as usual.

    • Neil, I did look at population density as a factor several weeks ago. The problem I found is that it isn’t the population density of a country (or state) as a whole that matters, it’s the local population density.

      When I looked at the numbers for the Netherlands (which provides very good stats on numbers of COVID hospitalizations per population in each municipality), I found that the worst hit areas were almost all out-in-the-sticks places in the south-east of the country. At that time (late March) the densely populated Randstad in the west was relatively little affected. It looks as though the initial epidemic in the Netherlands was fanned by the crowds at Carnival celebrations (week-end of February 29th) in the Catholic parts of the country.

      Later, the virus spread to other parts of the country too. But I noticed that even neighbouring places, with similar population densities, could have very different hospitalization rates. The (low-rise) municipality outside Rotterdam where I used to live had, and still has, only about half as many hospitalizations per population as the neighbouring high-rise one; even though the two are similar in numbers of people per square kilometre. It looks to be close-quarters living which increases the risk, not population density in itself.

    • Neil
      I checked population density as a possible explainer of death rates in my study
      It is the case that COVID-19 being highly infectious, then human proximity facilitates the spread of the virus, so that other things being equal you will get more infections in high population-dense cities (and conversely in remote areas very few eg 139 rural local government areas in Australia recorded no infections or deaths)
      But it is not inevitable that high-density living need result in high rates of infection and deaths
      The 8 Asian nations I examined have large cities with population densities equal to those of European cities or New York yet had low death rates (see my post above)
      E.G. population per square kilometer Tokyo 6,158 Hong Kong 6,670 Taipei 10,000 Seoul 16,000
      are all similar to London 5,177 Madrid 5,400, New York 10,194
      So it resolves down to how quickly nations took action and how they did it that most matters in affecting death rates

  41. Like Thomho I have performed a lot of analysis on the SARS-COV-2 data. I have written 4 articles for Actuaries Digital on it. In my last article I concluded that Australia and New Zealand had done well not just because they closed their borders relatively early but because the natural immune levels of their populations were at a high point at the end of their summer when the virus hit. This is due to high levels of Vitamin D from sunlight exposure. Countries that have done relatively well either their populations also had naturally high Vitamin D3 levels or as in the case of Nordic Countries and Germany provided supplementary vitamin D3 in staple foods. I also cautioned that a second wave could happen in southern hemisphere countries whose populations enjoyed plenty of sunshine and open air activities. This we are starting to see in Argentina and South Africa as winter progresses and we may yet see in Australia and New Zealand. How communities respond to viral infections is also important. In many countries in Asia people will naturally wear a mask if they believe they have a upper respiratory tract infection so it was easy for the governments of those countries to ask their populations to wear these in indoor public places. In Thailand air conditioners were turned off and windows opened. This was so sensible as fresh air is the easiest way of diluting the virus in the atmosphere.
    The world will be a different place after this pandemic. People will be much more concerned about their immune systems, fresh air, social distancing and not passing infections on to others than we have in the past. Lets hope that what we have learnt remains ingrained in our lives and those of future generations.

    • If low vitamin D levels are indeed a contributory factor this would explain the high UK rate. Being relatively far north the natural levels of vit. D would be low due to the low sunlight, this would be expected to have a greater effect in the immigrant communities (BAME) who have even lower levels of vit. D (well documented years ago among hijab wearing women in the UK). The COVID data from the UK would appear to bear this out, cases among BAME about 50% higher than white residents in the same areas. You’d expect there to be a difference between the UK and the nordic countries for the same reason.

  42. Your reasoning about Portugal is wrong. In the last two months, tests are done in a completely different way from the beginning. In early March, a friend of mine had symptoms but she was only tested 5 days later, after she was already feeling good. In the last weeks, there have been several cases where one person started having symptoms in big companies, and so they sweep tested like hundreds, finding sometimes like two hundred people testing positive but no symptoms. These numbers are not comparable at all, and explain most of the different tail distributions.
    It should be no surprise to WUWT readers that most statistical analysis with BAD data will leave us nowhere…

    • Luisa, my apologies for failing to respond to you in a timely fashion. And thanks for your report on the ground situation in Portugal. I suspect your comment was held in moderation for a while, and by the time it was published my “eye radar” on this thread was already below it.

      What you say makes an even better case for opening up normal life again soonest. And once the proportions of already immune and recovered are ascertained and made public, there will (should) be hell to pay for anyone that tried to cook the books.

      But in my view, analysis of data which, though admittedly bad, contains attempts by honest people to provide good data, is better than “analysis” on no data at all – like climate models.

  43. Isn’t the lesson that rather than locking down whole countries we use the lessons they learnt in time of the plague in Europe ans iso;ate infected people/locations? That way there’s something left for the majority who survive unscated. The UK had isolation hospitals in the 1950s I spent several months in one.

    It came as no surprise to see the UK and its NHS described as amateurish. The NHS has been underfunded and badly managed for decades. Having lived in France for a number of years the contrast between the two health services is quite marked. Results of blood tests, Xrays, MRI Scans are given to the patient normally at the time and at worst within a day or two. In the NHS thay can take weeks and the patient rarely sees them. In recent years I have had poor experience fron=m the NHS, a son sent home with an undetected collapsed lung, and a 3 year old grandson waiting 7 seven hours without food or drink for an “emergency” operation on an injured ear. I would regard any data from the NHS as not fit for anything.

    It was a bit unfair highlighting Yemen as performing poorly, I would imagine CV19 is pretty far down their list of problems it is just one of the Four Horsemen visiting that part of the world.

    https://www.bbc.com/news/world-middle-east-29319423

    • Yes, I agree that the way to restrict the spread of the virus is to isolate only individuals with the virus, and places that have become infection hot-spots. As I said, I think the Austrians got it right when they quarantined Ischgl.

      And yes, the NHS has been badly managed for decades. But in my view it’s not so much that it’s underfunded, as that the funds it gets disappear into a black hole, and don’t reach the people at the sharp end.

    • The NHS has long since been about “managing” expectation rather than actual health care…

      I feel for the ground staff that have to deal with “management”.

  44. Neil, I’ve been in Vietnam since Jan 20th. I don’t think there was any preexisting immunity in play here but more the speed and ruthlessness of the official response to the virus, built up from the harsh and relatively recent encounters with SARS and MERS viral pandemics. They used aggressive track and trace coupled with centralised quarantine for positive symptom-displaying cases and isolation for entire county level areas where cases came from. There was also an early 3 week long civil lockdown.
    They have also foc saved the life of a UK commercial pilot working for Vietnam Airlines who was on life support for an extended period of time and who lost 90% lung function at one point but has now recovered enough to be taken off a ventilator.
    They took all the right steps, very early and deserve congratulations.

  45. How is Sweden doing, really?

    Here are three highly informative videos about how Sweden has handled the virus.

    One is from a woman who lives there and describes what daily life is for her. It is interesting that Sweden has not locked down but has closed all their libraries and museums and galleries. So you can go the pub and the gym but you can’t peruse books or look at art.

    https://www.youtube.com/watch?v=92R0bnW0S_4

    Swedish Education

    Sweden did not close their primary schools but in March Swedish high schools, universities and adult education centres were recommended to close and adopt “distance education” by Prime Minister Stefan Lofven. The move was aimed at limiting the pace coronavirus was spreading and was recommended by the Health Authorities: “Students from high school and upwards are not to be in school but stay at home. Schools for children for grade 1-9 and kindergartens were to remain open for the time being,” Lofven said. The Head of the Swedish Public Health Agency, Johan Carlson, said the agency did not consider it was time to close primary schools or kindergartens, though there was a rise in the dissemination of the virus. Carlson said it was likely the closures would be in place for several months. (One primary school, Campus Manilla, was closed in March because an 8-year-old had the virus.)

    The next video is a broader look at how Sweden is doing compared with other countries.

    The interesting point here, which the Daily Mail and assorted Sweden-bashers always miss is that (1) most Swedes trust their government and (2) most Swedes follow the advice of their experts because they have not been “got at” by politicians and any shrieking hysterical “we’re all gonna die!” media. There is also the not-so-little matter of every Swedish politician admitting that they have made mistakes and saying what happened and how they are going to improve things.

    https://www.youtube.com/watch?v=-Noc6IhjQT8

    Overall, despite the higher death rate, I would say the Swedes have got it right. They have not trashed their economy (which they admit it is down 7%), their rights and freedoms and social lives in the hopeless task of fighting a glorified flu bug. To date, I have not seen anyone’s data on how many people die every year from the flu without all the accompanying yelling and screaming and putting that up against what has happened this year. This bug might be more infectious, but last year’s version if it was still around would have killed almost as many. So what we are talking about here is not stopping the virus killing people but the attempt to prevent the increase in deaths, which is an entirely different idea.

    The third video is from Sweden’s second largest city, Gothenburg. It is a 10-minute stroll around the city sometime in the afternoon and shows everyone that has been suffering under lockdowns for months what they are missing (normal civilized life) and what they have lost (their freedom and their rights).

    Why have we had lockdowns inflicted on us?
    Because of articles such as this:
    https://www.thesun.co.uk/news/11300219/swedens-coronavirus-approach-schools-pubs-open-catastrophe/

    Now ask yourself: where would you rather live?

    • If you think Sweden got it right then consider the following:-

      1Death rates per million of Population 4 May 274 9 June 465 -annualized growth rate 17% along with the USA 17% for the same period Both are equal worst in the growth of deaths of ten Western nations in that period

      2 Swedish Chief Epidemiologist who advised against Lockdowns, now says “too many people have died
      if we were to encounter the same disease again, I think we would settle on doing something in between what Sweden did and the rest of the world has done ” Statista 6 April

  46. As many others have remarked, all cause mortality is pretty much the only reliable set of data.

    ‘The Basic Research Question. “Did countries show an alarming excess in total deaths during the ‘Corona’ period of March to May 2020?

    The Answer: Alarming excess? No. Nowhere. Any excess? Some places. In a review of twenty-four countries in Europe, we see no mortality-excess outside the normal range in six countries; mild excess in eleven countries; and significant spikes in seven countries. In only two or three (of the latter seven) will the full magnitude of the mortality-excess double that of their own late-2010s flu spikes, with the impact softer on a longer time horizon (see the final summary section for list of countries by how much the Corona-associated excess compares to their own 2010s flu spike excesses).

    Of those countries with mortality excesses, many have entered below-average mortality following the end of their spikes. I expect this will continue and will be seen in every country that showed a spike, given the age-condition profiles of those who died in this flu wave (over 80 and in poor health). I will update this post July 2 and would expect to see countries that had significant excess-mortality (especially Sweden) to show below-average mortality for June.’

    https://hailtoyou.wordpress.com/2020/06/16/against-the-corona-panic-part-xiv-total-mortality-data-in-europe-now-confirms-the-wuhan-coronavirus-was-comparable-in-magnitude-to-flu-waves-of-the-2010s-the-panic-and-lockdowns-are-fully-discredit/#more-7151

    • Tim, it would be good if you compared the MOMO deaths figures with past years (or, perhaps, an average of past years) and plotted those against the COVID figures for each country, in a way that ordinary people could understand. I’m sure Anthony and Charles would publish such a paper!

      • The problem being that there is zero confidence in covid figures in any country, even Sweden, certainly in UK. All causes mortality are the only reliable numbers available.

        By the way, I can claim no credit whatsoever for the quotation and link in my comment.

        The pen name of the gentleman in question is E.H. Hail, email: hail_to_you@mail.com

        He is certainly doing some great work!

    • Tim
      The only excess deaths data I have seen is for the UK which came in at 950 per million population at mid June
      The recorded death rate per million of same date was 638 per million, suggesting that the latter underestimate actual deaths ( Reported Economist 20 -26 June

  47. The missing link?

    The case for all government actions has been to “flatten” hospitalizations. The concept of “excess deaths” presented as “saving” lives. Along with fears of lack of medical staff, lack of beds, lack of ventilators, et al., which ramped up fear and drove hysteria. And they emphasized it was not to prevent cases, just to plateau the load.

    If one eliminates the outliers, which, in fact, may possibly conceivably perhaps flattened the load, the graphs presented show no such thing. They all ramp up, plateau, then decline to the extent that makes a mathematician using the probability theorems and the Bell Curve absolutely shout with joy. Once very obvious, but neglected conclusion, is there simply was not and has now and overwhelmingly likely will never be a “flattening” as predicated. At least not with “cases”.

    The glaringly missing component are hospitalizations and their analysis. As in how many were put into hospital beds versus being patted on the head and told to go take an aspirin and drink plenty of liquids? And of the hospitalized, how many were hooked up on the death machines (ventilators)? Then what reserves the hospitlal(s) had in terms of beds, ICUs, etc.

    Going forward, that’s the only relevant factor since the public policy practice should be to make certain one can deal with the peaks as measured against treatments.

    Note: without a truly 90%+ universal respiratory vaccine, handling peak outbreaks with a battery of treatment (including the HCQ-combo) is the only other area public health experts might be helpful.

  48. Generally there seem to be a number of additional contributions to high infection and particularly high death rates. I am from the UK so will mention those examples.
    1. Proximity to a large number of transiting people for increased transmission. (e.g. Heathrow Airport, London)
    2. Population density. (England has 1050 people per square mile and 84% of all in the UK live there. UK density misleading. 8 million people around London alone)
    3. Older population. (UK people are regularly living to be over 100 now and the average age is c.45 with 1 in 5 being over 65)
    4. Mixed age households. (UK mixed age households on the rise in the last decade as c. 1/3rd of under 35’s end up back at home with parents)
    5. Due to good health services, (regardless of how bad some may say they are) large numbers of people of all ages living with conditions that become ‘co-morbidities.’ But particularly true of older people.

    Was this perfect storm for some countries compared to others? You can cut the data cake a lot of different ways.

  49. Disaster? Sounds pretty normal for the life and times of mankind to me. Results may vary, so argue that to death?

    I await a perspective that looks to addressing the disease rather than restricting everyone.

  50. Analysis of this data, the models used and the fact that these “models” were constantly “Modified” should open the eyes and give serious pause to ANYONE that believe that the Climate Models are worth “two cents.” Look at the economic disaster they produced. Look at what they showed (in the first weeks) and was not acted upon – Nursing home deaths. Look at what the predicted and were absolutely, positively INCORRECT.

    Just like a space probe can not be programmed to go to the Moon, Saturn, Pluto or the edge of the solar system with no further human intervention, adjustment and or modification the COVID models were periodically “Adjusted” to get somewhat meaningful results, that caused these problems. This should clearly show that the Climate models are pure assumption, supposition and prognostication.

  51. What is most suprising.
    The FIRST wave, everyone seems to believe happened in FEB-MARCH, was actually the SECOND wave.
    That makes utter nonsense of pretty much everything that has been said and debated.

    There is now clear and emerging evidence the FIRST wave was (shown in sewage and retested samples from December for Pneumonia), actually started in late autumn last year in China, and clearly infected many in Turin, Milan and Paris in DECEMBER (as usual with mild symptoms and lots of asymptomatic cases).

    I have clear and anecdotal evidence of unexplained severe pneumonia which affected children in ural RUSSIA in November-December, resulting in shutting down whole city’s schools for weeks (inc members of family and friends in Moscow).

    The SECOND wave was actually what everyone cited as the FIRST wave and killed many more (like the spanish flu did!), and we are now into the far less deadly 3rd wave.

    In this case immunity is far more widespread than claimed, which explains the low mortality in Scandinavia and Russia/ ex-USSR countries.

  52. You had fun with that Neil! Not sure how accurate some of it is though. All the figures have been politically massaged. There is now evidence that it all started much earlier and testing is all over the place and inaccurate. That makes measurements and comparisons very dodgy.
    But an interesting read with much to talk about.

  53. You noticed some strange data around Sweden. It is as the government is reporting in very strange ways. Today for example, they found 1500 new confirmed cases of COVID-19. But the report on Worldometer says 343. Yesterday they found 1710 new cases. The report on Worldometer was in the 100s.

    What they do, is to report a small number today, and then adjust the history.If you go in on the history for Sweden, you will find most days somewhere between 1200 to 1500 new cases. But if you read the reported cases each day, you see some hundreds.

    • Thanks, Mats. I looked at the Swedish stats again. Worldometers (or whoever they are getting their data from) are clearly mis-reporting Swedish cases. I have a spreadsheet of their figures against Our World in Data’s for this month so far. There is no comparison. It doesn’t look like smoothing to me, but I’ll have to investigate that further.

      The deaths figures do seem to tally, with Worldometers always a day ahead. But the cases… no. Sweden seems to shut down on Sundays and holidays, and in particular for the recent Midsummer Festival (hope you enjoyed it!). According to Our World in Data, there were no new cases or deaths reported for June 20th to 22nd inclusive; I can well believe that. But Worldometers are showing completely different numbers.

      I’ll put together a post-script to this article, hopefully tomorrow.

      • Sweden decided to stop all reporting during weekends and red days. They also removed the daily cases from the health ministries website (FOHM). They are giving a total, and the details per small regions, but no longer a daily summary. The total deaths are lagging with up to 30 days (they report the true day for the death to happen. If you look at the daily figures, you can see them moving when they are up to 30 days old.

        • I can still see new cases (sjukdomsfall per dag) in the FOHM website linked from the Worldometers page for Sweden. And the numbers match the Worldometers numbers.

          Perhaps the FOHM (and so Worldometers) are reporting cases against the day they were found (presumably, the day the test was done)? While One World in Data is using the “official” figures (which presumably come through the WHO?), and putting them against the day the new case was officially reported? In which case, the totals ought to match in the end. I checked the deaths figures for Sweden for June – they match perfectly, with Worldometers always one day ahead of Our World in Data. I also cross-checked against the UK, and there’s no discrepancy – Worldometers is always one day ahead of Our World in Data for both cases and deaths. So the problem seems to be arising only for Swedish cases.

          • Yes, I should have expressed it differently. You can view them in Swedish FHOM web page, in a graph. But it is very hard to get them into a tool like Excel, they are not in a table.

            The historical numbers match the Worldometer historical numbers. But the daily reported numbers into Worldometer are not the same as what is later found in the history. I have given example from two days in a row for Sweden for that. Almost all days are nowadays in that way. I have followed Worldometer and Sweden since start. It was not like this before. Sweden used to report number of cases confirmed during today.

            I think Sweden is the one that changed. This started 2-3 weeks ago, when number of cases per day more or less doubled. And the effect is, that if you only look at Worldometer summary page, Sweden look like it has a fourth or fifth of the daily cases they actually have and report.

          • Actually just saw this:
            Todays report in Worldometer: 303 reported cases by Sweden (there is actually a line in Worldometer telling per country what they reported that day).

            But Sweden have 1050 new cases confirmed today. So tomorrow, likely the graph for today in the Worldometer history will be much closer to 1050 than 303.

  54. And today Sweden did it again. 1642 new cases. 230 of them reported in Worldometer. The rest added as history. The total is kept about right, but the visible daily reporting is missing like 1000-1500 cases. Per day.

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