Covid-19 Diary : Friday 27 March 2020

 

 

I realized I’ve gone almost my entire life without ever typing the word “trillion” before, and now I’m typing it several times a day as if it is an ordinary concept.  Are we really doing the right thing, rushing to throw not “just” billions but trillions of dollars at a problem that currently is more unknown than known?  Some people spend more time wondering which gas station to stop at than some of our leaders have spent deciding what types of social-distancing measures to enact.

We can’t judge the wisdom of what we’re doing if we don’t even understand the limits of what we do and don’t know about the virus problem at present.  Short answer, if you want to skip the lengthy section that follows, is we know a great deal less, as certain fact, than the experts would seem to imply with their confident statements and exact seeming numbers.  Which leads me to the lengthy opening subject.

Lies, Damn Lies, and Statistics

I’m starting to realize a very obscured but vital fundamental truth that has been driving everyone’s perception of every aspect of the Covid-19 virus outbreak.  May I start by providing a story as background.

My 15 yr old daughter is intrigued by and interested in statistics as a possible specialization, or perhaps applying statistics in a field like market research.  Like others, she senses that a proper grasp of statistics might give her access to special secret concealed knowledge that is hidden within groups of numbers and data points.  But is that perception actually true?

I’ve encouraged her interest but have been at pains to explain that while numbers seem exact, their meaning and analysis is anything but exact.  This was vividly illustrated to me decades ago by my Marketing Professor during my MBA.

I was doing some market research for a department store in Dunedin, NZ.  This involved asking shoppers, when they left the store, questions about their visit just then concluded.  We did it outside the client store, and also outside competing stores.  A large competing store complained about this.

My professor suggested that rather than argue about who “owned” people after they had left a store, the easiest answer was to offer to give them a free full copy of the data obtained.  I objected.  “But our client is paying us many thousands of dollars to gain this information, surely it is neither fair nor sensible to give a copy, entirely for free, to their competitor”, I said.

He replied, “David, the raw data is meaningless and has no value.  What we are selling our client is the ability to analyze and interpret the data, and come up with ideas and suggestions from it.”  So we gave a copy of the data to the other store, which continued to trade exactly as it had always done, and we prepared a detailed analysis and suggestions for our client store, which then made major changes to many aspects of their store experience and improved their sales.

Related/implied point – the ability to interpret data is often quite separate from the ability to mathematically manipulate it.

Moving now to the Covid-19 virus rather than small town retailing in New Zealand, we are surrounded by a veritable sea of data, and a much vaster ocean of assumptions.  The numbers by themselves are of little obvious value, and many of the assumptions are nothing more than guesses about what feels right and conforms to the world-view of the person making the assumptions.  As we get more data, our need to assume/guess will diminish, and we can start to more confidently make predictions.  But we’re currently still in an extremely data-inadequate situation.

And now to actually blurt out my realization of this fundamental truth :  Much – maybe even most – of what we currently expect to happen as this pandemic unfolds might be wrong.  Not just a little wrong.  But wrong by a factor of ten or more – like the 25-fold reduction in projected deaths by one of the leading study authors, yesterday.  He justifies this astonishing change by saying the assumptions he earlier made are no longer valid, but that’s an unprovable claim because he refuses to show us how he processes the numbers to arrive at his final conclusions.  I’d also point out that none of the earlier modeling I’ve seen which looked at how to stretch and smooth the curve suggested that adopting the measures that have been adopted would actually lower the height of the curve 25 fold.

Normally, a revision of such an important number, by such a huge factor, needs to be accompanied by an enormous amount more than a couple of lines which boil down to “trust me, I’m an expert, and you’re too stupid to understand what I am doing” (which is close to literally what he said) and a refusal to allow others to review the rationale.

The relevance of this is that much of the measures we are taking that are now costing us trillions of dollars are based on the first version of this person’s model – the version he has now reduced 25-fold.  Has there ever been so much money spent on such a thin line of justification?

When you read an article such as this one (picked at random, just happened to be open in a browser window currently) there should be a huge big disclaimer flashing in neon colors at the top “Note – these numbers might be 25 times too high, and might be 25 times too low”.  Actually, in this exact example, it predicts that “more than 81,000 people could die in the next four months” and then adds that the range of deaths could be as low as 38,000 or as high as 162,000 – in other words, the 81,000 projection needs to be considered as being somewhere between half that number and twice that number.  My suggestion is that even the 38,000 – 162,000 range is probably much too narrow.

Let’s also understand another thing about exact numbers such as 81,000, or even approximate numbers such as 38,000 – 162,000.  These numbers omit important qualifiers.  The full statement for a range of numbers should be “If all our assumptions are valid, and if our collected data is fairly representative of the population as a whole, then we are XX% certain that the actual number will fall somewhere in this range”.  There are three disclaimers that are not stated in the shortened form of the number :

• If all our assumptions are valid (and what happens if they are not)
• If our collected data is fairly representative of the population as a whole (and what happens if it is not)
• XX% amount of certainty (and how do the numbers change if you increase or decrease your level of certainty/guess)

These three statements are also somewhat circular, because they embody within each of them elements of the other two.  And when you start to see a cataloging of the actual assumptions, and then understand the limitations of the data collected, you also start to realize just how thin a lot of the support is for some of the numbers being given to us at present.  I’m not saying they are wrong, but I’m not saying we need to treat them as incontrovertible gospel truth, either.  It doesn’t matter how many letters after your name, how many lofty academic posts you’ve held, bad data is always bad data.

Certainly, in the case of the modeling for outcomes from our virus outbreak, the first two concepts in the bullet points above are completely guesses currently, with the second one in particular being absolutely an inaccurate and unsatisfactory insufficient data set.  And so all statements such as the original bold statement “could kill more than 81,000 people in the next four months” become so speculative and inexact as to totally betray the confidence with which they are cited.

The problem is that none of the articles breathlessly rushing to get such new numbers and projections into print give a full dispassionate breakdown of the weaknesses embodied within the assumptions and the source and limitations of the data used to breathe life into the assumptions and generate conclusions.

Some of the scientific papers do a better job of explaining their assumptions, maybe even of justifying them, and also explaining the process whereby source data is then processed to generate predictions and projections.  But that doesn’t mean their assumptions are valid, and it doesn’t mean that many journalists, who have never studied statistics, are able to study and critique the material they are reporting as hard and certain facts, or at least, as reasonably precise guesses.

Plus, there are a lot of people who accept and embrace Governor Cuomo’s statement – “This is about saving lives. If everything we do saves just one life, I’ll be happy”. In the case of his state, he himself has costed the sacrifice, at a state level, as being $15 billion.  One can only guess how much the total cost after factoring in the unreimbursed costs and losses to all 20 million people in the state will be.

Is saving one life worth $15 billion?  Governor Cuomo says yes.  Most of us would say no.  And even that equation ignores an important offsetting consideration.  It isn’t just about swapping money for lives.  In the process of saving that one precious life, we’re inflicting massive harm on an untold number of other lives destroyed by their loss of income and loss of savings – destroyed so that some people may commit suicide (this is already happening), others will have poorer quality of life and shortened life spans, and so on.  So it isn’t just the $15 billion vs one life equation.  It is a whole series of equivalences – for example, which is more valuable – saving the life of one person who has 40 years to live, or allowing 20 people to each live two years longer?

But you can’t reason with some people, because it is such an instinctive belief (and sincerely held).  I’ve met people who have conceded they would rather allow an attacker to kill and rape their entire family than to shoot the attacker lawfully in self-defense.  “Nothing can justify the taking of a human life”, they say, while ignoring the fact that by allowing a bad guy to live, they are sacrificing everyone nearest and dearest to them.

And now we’re at the point where subtle deceit sneaks into the picture – these people who strongly and sincerely believe a single life is worth $15 billion feel empowered not only to spend $15 billion per life they save, but also to shade the truth so as to co-opt other people to support them.  It is a vanishingly small reach to go from “I’ll spend $15 billion to save a life” to “I’ll slightly exaggerate the truth to get other people to support me in my life-saving effort”.

Which brings me to my ultimate ugly conclusion.  There are loud groups motivated to push the narrative that this outbreak is beyond-bad, and needs the most extreme of responses to contain it and to save not just one life, but (by their figures) many millions of lives.

Yes, I know that on the other side, there are also vested interests who are keen to rush through what they may feel to be a period of irrational over-reaction and to allow business as normal to resume.

But there’s an imbalance of accountability.  We can never prove the claimed need to spend trillions of dollars as having been wrong.  How could that be done?  We spend trillions of dollars, and either the problem is fixed, which validates the spending, or the problem is not fixed, which suggests we should spend even more.  You really can’t work back and say “Well, actually, we only needed to spend $1.5 trillion, not $2 trillion”.  Besides which, when we’re at a $15 billion per life standard, money has no meaning.

But the people who say “this is all an over-reaction absolutely are accountable for those statements – it would be utterly obvious if we did nothing and things got worse about how tragically wrong that was.  Plus, the harm to their businesses would end up being greater than if they did the heavy lifting up front.

So that means we have one side pushing a “the sky is falling” scenario, while the other side is merely muttering about “balanced responses” and “broader considerations of the costs”.

As for me, I still have no personal idea where on the continuum between “We’re all gonna die” and “It’s just another type of ‘flu” the truth lies.  There isn’t enough real solid data yet – and that is my point.  No-one can confidently assert any type of prediction for the future yet.  It could go either which way.

Here’s that dreadful phrase – “out of an abundance of caution” you probably support social distancing as a prudent and necessary step.  I definitely do, too.  But what is the actual cost/benefit?  If social distancing was free and 100% effective, we’d all eagerly support it.  But if it was even more costly than it is, and totally useless, no-one would be in favor of it.  The truth is somewhere in the middle between those extremes, and no-one knows exactly where.  So how can we create cogent public policy, other than under the nonsense rubric of “if it saves just one life, it is all worth it”?  We need to better understand the complete picture – the pluses and minuses of each of the many different measures we’re rushing to adopt at present.

Case in point – a local supermarket is now restricting people to no more than two pieces of meat per person.  (There are no known national or local shortages of meat.)  They obviously feel they have a good reason for doing this, but do they not realize that one of the outcomes they have created is a need to visit their store more often, with a matching increase in exposure and risk, all because of this seemingly gratuitous policy to stop people from hoarding fresh meat (a ridiculous concept in the first place, because how long can you store fresh meat for?).

What Can We Really Expect?

This is the ultimate truth I’m desperately searching for, every day.  What will really truly happen and when?

As I said, I still don’t know, and the only thing I’m starting to realize is that many of the “experts” also don’t know.  Sure, they are indeed truly experts, but they are only experts in one narrow thing.  This is a weakness that interferes with their ability to think before their single field.

By way of example, I remember when I rented some business premises many years ago.  We had a fire department inspection, and they told me it was illegal to use multi-plug extension outlet boxes – you know, the things that go from a single wall power socket to a power strip with maybe half a dozen plugs on it.  This was a forbidden fire risk.

I duly unplugged all the power strips, causing two thirds of the electronics on everyone’s desk to die, and thanked the Fire Marshal for his advice.  As soon as he left, I plugged them in again, because there’s not an office in the state that doesn’t need and use power strips to funnel the growing number of electric/electronic devices into the always inadequate number of wall sockets.  Maybe there is a fire hazard, but the teensy tiny risk of fire has to be balanced with the huge cost of rewiring entire buildings.  It doesn’t make practical real-world sense, but the Fire Marshal wasn’t charged to make value judgments about real world trade-offs.  He was a “$15 billion to prevent one fire” kinda guy.

So, beware of experts.

I am starting to see some encouraging anomalies in the daily statistics I’m studying assiduously.  Case numbers are nowhere rising as fast as I’d have expected.  But even this perception is very vague and could change greatly tomorrow.  But I’d love to know why it is that the virus has nowhere reached even 1% of a country’s population, and in most of the countries where case numbers are high, the new infection rate is starting to fall.

Is this because of social distancing?  Or is herd immunity coming into play much sooner than expected?  Or is it a weather thing?  Or, or, or?  All of us really only have questions, not answers, at present.

 – A Worked Example

Let’s look at the difference between Sweden and Norway – two very similar countries but with surprisingly different approaches to handling the virus outbreak.  This is a meandering discussion, because I’m writing it as I’m doing the analysis – I did not pick two countries to prove a point, I picked two countries that seemed generally congruent in nature and decided just to study them and go wherever the numbers took me.

The big difference between them is that Norway is in a soft lockdown mode, Sweden is not and has probably the most permissive set of restrictions of any country in Europe.  But Sweden’s case count is just under half that of Norway when expressed in terms of cases per million people.  Why are cases in Sweden not surging, while cases in Norway are diminishing?

It is interesting to now peel the layers off that onion, because each successive layer reveals a new or different truth or context.  At what point do you stop?  At what point do you triumphantly claim to have distilled the ultimate truth?

This chart shows the respective number of new reported cases per day in both countries, with Sweden’s adjusted to reflect that it has almost twice the population of Norway.  (Even without this adjustment, Sweden still has fewer cases).

That is a clear difference in case numbers, so we need to try and understand why there is a difference.  Not only is it a significant difference, it is also the opposite of what you’d expect due to the stronger social distancing laws in Norway.  If these laws work as seems intuitively the case, we would surely see Norway’s new case count drop with respect to Sweden’s new case count.  The laws took place on 12 March, but ignore the sudden drop in new cases at about the same time – it would take 5 – 10 days for the impact of the new law to reflect in new case counts.

The first thing we note is a surge in Norwegian new cases reported on 10 – 13 March, before dropping back to where a straight line extrapolation from the earlier dates might suggest.  What caused this temporary surge?  We can’t find out, but imagine something significant caused a brief blip in case counting.

There’s also a two day surge at the end which is surprising.  Is this an anomaly?  Or is there a specific reason for this sudden surge, too.

My point is that we can’t even look at the raw numbers without feeling a need to adjust and explain them for any perceived unusual or one-off events that might be distorting an underlying picture.  If there are explanations for the two surges in Norway’s cases, we’d end up with a much closer matching set of numbers.

Is the difference between the two countries a weather related issue?  We’d love to find indication, anywhere, of weather sensitivity, because that could give us reason to hope for a seasonal respite come our summer.  But if we were looking for that, we’d not find it here.  The average high and low temperatures in Stockholm and Oslo are almost identical (to within about 1°F in February and March).

Is it an urban/rural issue?  That question embodies an assumption there’s a difference in spread between urban and rural, but it seems reasonable – there are more high-density social-contact situations, with more other people, in urban than rural areas.  82% of Norwegians live in urban areas, whereas slightly more Swedes live in urban areas (87%).  So if this was a factor, it might have been expected to very slightly boost Sweden’s numbers, but because the two countries have an urban/rural split more or less within 5% of each other, it is unlikely to have much impact, no matter what difference there might be between urban and rural risks in general.  No explanation under this concept, either.

So, how is it that Sweden has little more than half the spread of cases as does Norway?  We’re still looking for a huge mystery factor.

Another possibility might be that Sweden is doing less testing and Norway is doing more.  This would mean Norway is finding more people, sooner, and with weaker degrees of infection, whereas in Sweden, people only get counted when they are more sick and needing hospitalization.

We checked, and it seems that as of about 27 March, Norway has tested about 74,000 citizens, and we estimate (based on actual numbers for 25 March) that Sweden has tested about 28,000 citizens.  Sweden’s policy is to restrict testing to people suspected of having the disease, Norway is an “equal opportunity” tester.  So, unsurprisingly, while Norway has a positive test rate of about 0.5%, Sweden’s is higher – 0.8%.  And it does seem very likely that by doing nearly three times as many tests, Norway would uncover more cases.

This also points us to another imprecision.  The concept of a “case” is not as clear as it should be, because some cases are entirely undetected.  What is the ratio between detected and undetected cases?  That of course depends on how the testing is done, but other than that, we have absolutely no idea other than “best guessing”.

We like the link between more testing and more cases counted.  We’re starting to get to what feels like a valid explanation.  But, so far, we’ve been focused on the new case counts alone.  Perhaps we should also look at death rates, because death rates are much less subjective, and see if we can fit this into our explanation or not.

I’ll not do another chart because there are fewer deaths to count and a harder pattern to show.  But, looking at death rates does uncover a very different set of numbers.  Total deaths in Norway to date are a mere 19, whereas Sweden has 105, or, expressed in constant population terms, 56.  So Sweden actually has three times the per-head-of-population death rate of Norway.

Assuming similar standards in health care, and similar levels of general health and age in the two countries (which we feel are acceptable assumptions without checking the realities for this example), the substantial difference in death numbers does seem to support that Sweden’s case counts are being under reported, and that’s a suggestion that is confirmed by the actual count of tests being done by both countries.

If death rates are more or less the same in both countries, then case rates should be more or less the same too.  The fact that Sweden’s counted case rate is much lower seems to be the result of the country doing very much less testing.  Which creates a nice pair of ways to reconcile the difference in numbers.

So does this mean that in truth, Sweden’s less strict approach to social distancing is a failure?  Even a disaster?  Quite possibly it might.

If we consider Norway’s enactment of strict distancing requirements on 12 March and give that ten days to start to take effect, we see that on 22 March, Norway has 7 deaths and Sweden has 22.  So in the five days since then, Norway’s deaths have increased 271% and Sweden’s have increased 477%, almost twice as much.

But we’re still talking small numbers and a short time frame.  Do we have enough data to be able to make a definitive statement about the beneficial impacts of Norway’s social distancing approach compared to Sweden’s?  For that matter, other than observing the nature of the two sets of laws, how actually do we evaluate each of the different restrictions to identify ones that work, ones that don’t work, and – even more obscured – additional measures that might be needed?

Might the lower death rate also be a result of the higher testing and faster ability to provide care, rather than the social distancing?  That’s a valid possibility, too.

Are there other as yet unconsidered factors?  Almost certainly!

Our conclusion at this point is a very vague “social distancing is good”.  But can we leap from that vague feeling to anything more substantial?  Can we come up with a prediction of how many deaths to expect in the US, either with or without social distancing?  Or can we use the numbers and different social distancing methodologies to now create a table of measures ranked from most to least effective?  No to pretty much all of the above.

Or, how about a cost comparison that says “the economic cost for each life saved in Norway compared to Sweden is  $????”?  Do we have any idea at all how many zeroes that number should have?  Not really.  We don’t know either the cost of Norway’s measures (compared to Sweden’s less strict measures) nor do we really know for sure exactly how many lives have been saved as a result.

We also don’t know, from either set of numbers, the really vitally important numbers – how infectious the disease is and how lethal it is.  Those are the most important two numbers of all, and they remain obstinately obscure.

Statistics

Today sees Liechtenstein drop two places, with Gibraltar racing past and now appearing on our list, and Switzerland also now overtaking the microstate.  The seven states of any size with the highest infection rates are :

• San Marino/223 cases/the equivalent of 6,572 cases per million people
• Vatican City/4/4,994 (unchanged)
• Andorra/267/3,456
• Faero Islands/144/2,947
• Iceland/890/2,608
• Luxembourg/1,605/2,564
• Gibraltar/55/1,632

Here are the top six major countries, showing death rates per million of population in the country.  Netherlands took back its place that France “stole” yesterday, and Iran slipped two places down to the bottom of this list :

• San Marino/21 total deaths/the equivalent of 619 deaths per million (no change for two days)
• Italy/9,134 deaths/151 per million
• Spain/5,138/110
• Netherlands/546/32
• France/1,995/31
• Iran/2,378/28

To put those numbers into context, the death rates per million in US/UK/Canada are 5/11/1.

For major countries and/or outbreaks, and in general :

	Same Day Last Week	Yesterday	Today
Total Cases	275,864	529,614	594,791
Total Deaths/Percent of all Resolved Cases	11,398/11.0%	23,976/16.3%	27,255/17.0%
Active Cases (ie not yet died or cured)	172,554	382,258	434,530
US Cases/Deaths/Case rate per million	19,643/263/59	83,672/1,209/253	102,568/1,607/310
UK Cases/Deaths/Case rate per million	3,983/177/59	11,658/578/172	14,543/759/214
Canada Cases/Deaths/Case rate per million	1,087/12/29	4,043/39/107	4,757/55/126
Worst affected major country/case rate	Italy/778	Switzerland/1365	Switzerland/1,494
Second worst country affected	Switzerland/649	Italy/1333	Italy/1,431
Third worst	Spain/460	Spain/1236	Spain/1,406
Fourth	Norway/361	Austria/767	Austria/855
Fifth	Austria/294	Norway/621	Norway/696

Italy has already dropped from first to second place in terms of prevalence of cases, and it seems likely might fall further to third place in the next day or two.  We’re very surprised at the continued terrible results coming out of Switzerland.  Perhaps we could make that a case study-investigation for tomorrow.

Here’s the zoomed out version of my US daily death chart, comparing the number of Covid-19 deaths to other causes.  As you can see, the Covid-19 cases have lifted well clear of the bunch of lower incidence causes, and now is challenging the remaining five major causes.  At the current rate of growth, Sunday or Monday will probably see it exceed the incidence of stroke-related deaths and emphysema, then it will take big moves to reach Alzheimer’s, then cancer, and maybe ultimately heart disease.

As for regular ‘flu, combined with pneumonia, Covid-19 is now running at over twice the daily death rate of those two combined, and of course, tomorrow’s another day with likely another increase.  And the day after, and the day after.

But I still hear commentators dismissively comparing Covid-19 to regular ‘flu, and saying “We’ve had less than 1000 deaths from Covid-19, compared to every year, more than 25,000 from regular ‘flu, what is all the fuss?”.  What a shame they don’t read these daily articles!

Who Should Pay?

Airlines are some of the most unethical companies out there.  They’ll almost literally lie, cheat or steal so as to separate you from more of your money than is ethical or necessary.

I say this because of what we are now seeing, all around the world, with airlines trying to avoid giving you back your money for tickets you bought and paid for on flights that the airlines can no longer operate.  Now, of course, we all understand that the airlines are suffering enormously in this terrible scenario, and if you subscribe to the school of thought that says “don’t take the last roll of toilet paper off the shelf in case the next guy truly does need it more than you do” then perhaps you’ll be okay letting the airlines illegally keep your money.

But if you’re also financially impacted by the virus and everything that is happening as a result, you might instead think that your first responsibility is to yourself and your family, not to a mega-company company somewhere else – a mega-company that is already getting a share of a $50 billion govt bailout plus assorted other handouts, a mega-company that should have been prudently storing cash and assets for such events, but which instead has been on a drunken spending spree, buying back its own shares (how good an investment has that proven to be) and generously distributing dividends to shareholders and bonuses to executives.

The situation is simple.  Under US law, if an airline cancels a flight, it must give you a refund of every penny you paid.  Everything you paid for the ticket, for baggage, seating, anything and everything you paid must now be refunded.  Yes, the airline can bargain with you and ask you to accept a travel voucher for a larger amount than the refund, but it can not force you to accept that.

There is also no justification for an airline saying “yes, we’ve canceled your flight, but don’t worry, we’ve found an alternate flight six hours later (or earlier) that we’ve shifted you to instead, so you don’t qualify for a refund”.  If your flight no longer operates, you qualify for a refund.

If the airline is slow to give you the refund, or outright refuses, complain to your credit card company and dispute the charge.  The airlines hate that, because they not only have to then refund you the money, but they may also get charged a dispute fee penalty by the credit card company too.

EU airlines are trying to get government permission to no longer be obliged to issue refunds.  Until they get that permission, stand fast.  We’d also point out that now is a really bad time to be accepting travel vouchers instead of cash, because there’s a measurable risk that some airlines may go bankrupt.  What happens to your travel voucher then?  It may become worthless.

Here’s a great article that sets out your rights if an airline cancels your flight.

Timings And Numbers

As I’m starting to sense, there’s an increasing divide in opinion as to what the future holds.  Strangely, the entire main stream media seems to be eager to portray the worst possible numbers, to the point that whereas barely a week ago they were lauding Dr Birx, now they’re launching ad hominem attacks against her because the facts she dispassionately recites and the opinions she offers diverge from their own.

Here’s a nice article about her current view of the future.  Apologies, should they be necessary, for citing a source that is clearly right of center.  But they’re not making the news, they’re not even interpreting and shading it.  They’re simply passing the raw data through to us (watch the video clips – the only source you can trust these days is direct from the lips of the person), while the main stream media becomes more aggressive at censoring what it terms “the lies coming out of the Presidential Daily Briefings”.

Closings

It is almost amusing to read that China has now decided to bar the entry of foreigners to China.  Whereas this outbreak started off with China protesting when other countries restricted Chinese visitors, now China in turn has imposed a blanket ban on everyone, everywhere, coming to China.

One could note that a blanket ban is at least not susceptible to charges of bias!  And the reason is totally sensible and understandable – if the Chinese numbers are to be believed (and I can’t overstress the importance of that “if”) then China has almost entirely 100% eradicated the virus, with new cases being almost exclusively from people coming in from other countries.

So, a sensible policy, short term.  And there, of course, is the problem.  How long can China forbid any and every foreigner from coming to China, no matter what the reason?

At least China and all other countries allow their own citizens to return home.  But not so much, Highlands, NC.

The small town has just passed another even stricter resolution that includes :

No-one allowed inside any retail stores.  You can order in advance and do curbside pickup.

No-one allowed inside any restaurants.  You have to call in advance and order then wait for curbside pickup or delivery.

But, most of all, all the town’s “taxpayers living out of town” – ie, people with a second home in Highlands – have now been banned from coming to Highlands.  Anyone who contravenes this ban will be charged with a class 2 misdemeanor.

Question to Highlands, NC :  Are you crediting back the land tax payments from everyone you have banned from coming to the property they own and pay you taxes for the “right” of ownership and possession?  How does someone who lives in town 183 days a year get “privileges” while someone who lives 182 days a year not have the same rights?

We’re all in favor of movement restrictions in general, and recognize the value in them.  We’re also uncomfortable with the constitutional issues such restrictions pose, and we feel this blanket ban goes way too far.  Keeping strangers out – maybe.  Keeping land owners out – no.

Shortages

Ummm, could we have it back again now, please.  Canada gave a huge amount of medical equipment and supplies to China – 16 tons in total, in the second week of February.  That was very kind of Canada, but apparently, no-one stopped to wonder if they might need it themselves, little more than a month later.

It is true that China has now started donating medical supplies to other countries.  And sometimes selling it, too.  But you want to be careful before accepting vital important supplies from China, or, at the very least, quality-control it carefully.  As Spain has found out.

One looming shortage – politicians.  Britain now has both its Prime Minister (Boris Johnson) and Health Secretary confirmed as having the virus (and Prince Charles too, but I’ve yet to find a person who considers that to be a bad thing – most people wish that his self-isolating would keep him away from phones and the internet, too).  Noting the large amount of contact politicians have with each other and the general public, this is not altogether surprising, and one wonders how many other people are in the same “cluster” of infections as the PM and Health Secretary.

Logic?  What Logic?

Virus?  What Virus?

The administration is planning to start tracking the virus on a finer-grained basis than at present.  Currently, we have reasonably clear distinctions between states, although even that is not exact (the more you dig into every statistic, the more it reveals itself to be a clumsy result of assumptions).  How would you, for example, treat a person who lives in Washington State, vacationed in Idaho where they caught the virus, then moved on to Montana for the next part of their vacation and discovered their illness?  Which state are they counted against?  And for the second part of that answer, they then travel back to Washington to recover.  Which state are they counted against now?

The plan is to go from the 50 states to the 3142 counties and other similar administrative districts.  Ostensibly, this is a great idea.  But we think it also magnifies the “corner case” situations like the WA-ID-MT example above, and the finer we drill down into geographic data, the fuzzier it becomes.  Sure, some people live in one state and work in another, but lots more live in one county and work in another.

In addition, there are no magic boundary lines, either around states or counties, and the virus is highly mobile.  It (or, to be exact, an infected person) can go right around the world in a single day.  Our sense is that this is a precursor to declaring “safe” and “not safe” regions in the country, but that only works if the safe and not safe regions fully fence themselves off from each other.  As soon as you have the slightest amount of leakage between safe and not safe, the virus will flow into the safe area.

It also becomes harder and harder to control access to smaller and smaller areas.  It is possible to control access to an entire state, but there are probably almost as many access ways in and out of a single county as there are of an entire state.

We also note that some cities have many times more people than some entire states.  Hello, Wyoming – the entire state of WY has a population of 578,000, spread among its 23 counties.   The least populous counties, either in Wyoming or other states, have about as many people in them as a single apartment block in our cities.  The least populous county, as of 2016, is Kalawao Co in HI, with a population of 11.  Loving Co, TX, is next, with 67.

So, an interesting idea, and will give some more information, but shouldn’t be over-valued or, ahem, over-used.

Medical

Related to the article about Chinese virus tests sent to Spain is this article about general levels of test accuracy.

A nice note from a reader, also a physician, suggested that people not only eat plenty of organic fresh or frozen fruit and vegetables, but also go easy on the alcohol.  It is an immune system suppressant.

Money

After three days of strong recovery, the Dow stumbled on Friday, dropping a substantial 4.1% and closing at 21,637.

Other

Thanks to reader Tom, who sent in this link to streamed plays from the National Theatre in the UK.  The shows will be streamed on YouTube.

Keep in mind that YouTube has adjusted its settings so now all streams automatically default to a lower quality than before, due to concerns about “running out of internet”.  But you can increase the stream rate by going to settings.

Please stay happy and healthy; all going well, I’ll be back again tomorrow.

 

Please click here for a listing of all our Covid-19 articles.