Brief (promise!) D*mn Virus Update

By now, I’ve despaired of convincing anyone who can open their own eyes and look around and is yet not convinced that the COVID 19 panic is and has been from the beginning a fraud. If you can’t see that, I don’t know what I could say to convince you. But, for my own satisfaction:

Way, way back on April 3, using then-available number, basic logic and a little math, I came up with an infection fatality rate (IFR) of around 0.25%, and said that was still probably quite a bit high. About 6 weeks later, the CDC published their “COVID-19 Pandemic Planning Scenarios“. Digging around a bit, their most likely scenario used an IFR of. 0.15 to 0.26%. That means the CDC expects about 15 to 26 out of every 10,000 infected people to die.

Imagine. And they evidently based this on their very ‘generous’ counting of COVID deaths. Now, I’m not some genius sleuth or anything, just pointing out that the data needed to reach these very-much-not-worth-panicking-over-numbers were right there all along, so that even I could reach them. And I’d still bet the CDC IRF is high by a factor of maybe 4. Just a hunch.

Thus: even accepting a world where we all are encouraged to imagine ourselves under dire threat from a disease where 95% of the attributed deaths are among very sick, often very elderly, people with multiple health problems and short, as in months, life expectancies, that risk is still TINY according to the CDC driving the panic. If you’re not in a nursing home or otherwise under palliative care, you are literally under more risk crossing the street than from catching this virus.

Those watching Our Betters decide that rioting over the approved issues immunizes people, while golf or church or a visit to a restaurant is literally courting DEATH for MILLIONS, all while even the ridiculously ‘generous’ death counts plummet, and STILL think the lockup was a good idea and people who don’t wear masks are evil, are not going to be convinced otherwise by math and facts. But I tried.

Charts, because we haven’t done those since they got boring:

Worldometers, as usual. They are the worst-case numbers reporters. John Hopkins and the CDC are always lower by about 10%.

In the US, boy, are they trying to make it seem bad despite all the evidence to the contrary. In a country with 330M people, where close to 8,000 people die on an average day, we’re supposed to cower like rabbits because, with ‘generous’ counting, because around 500 people (and falling), almost all of whom were very sick and most near death before they (may or may not have) gotten infected, are dying while infected per day.

No deaths at all since May 26. Note the hilarious “correction” on May 25, where they can’t say: “we overcounted deaths by a couple thousand, which is about 10% of the total,” Because that would be too easy. Instead, they said:

“Spain: On May 25th, the government decreased the number of total cases by 372 and the number of deaths to 26837. The discrepancy is the result of the validation of the same data by the autonomous communities and the transition to a new surveillance strategy. Discrepancies could persist for several days. We’ve adjusted our figures to reflect the new numbers [source] [source] [source]”

Over. Was over once a) it had ripped through the nursing homes; and b) spring weather arrived.

Stick a fork in it.

Turned the corner yet? Hard to say. Southern hemisphere, but the population is mostly in tropical and subtropical climates – usually hard on airborne viruses. Not sure what’s happening, but remember: 220M people, many living in very poor conditions – kind of like Wuhan tenements. This level of deaths, while certainly tragic on a personal level, is not something to panic over. Will keep an eye on this.


Mexico (pop: 129M, or twice Italy or France) is approaching the Top 6 in deaths (above). Will keep an eye on our neighbor to the south.

Also also: William Briggs took the data in the CDC report linked to above, and produced this chart, showing graphically the about 62M infections generating those 1.7M cases we’ve heard about. Again, it’s that whole functionally numerate thing: if this doesn’t make you guffaw, maybe numbers aren’t your thing?

And Dr. Briggs’ analysis:

As of Wednesday night, and using our standard sources (which exaggerate death counts), there were 1,689,630 reported “cases” (positive tests) and 94,352 reported deaths. The crude CFR was 94,352/1,689,630 = 5.6%. Again, this bug is not killing 5.6% of those with symptoms. The RFR was 0.03%.

The number of estimated actual cases are anywhere from 8 to 30 million Americans. That is, about 2.4% to 9.1% of the US’s population had symptoms or were otherwise cases.

The number of estimated actual infections are anywhere from 37 to 62 million people. That is, about 11% to 19% of the US’s population are already infected.

If actual deaths are lower, then all these numbers will be too high.

The point of all this: to find more cases, all you’d have to do is run more tests – the infection is out there in millions of (asymptomatic or mildly symptomatic) people. Panicking over increased cases is idiotic. Or, to be more generous, shows a lack of understanding of the data.

Making the Sausage: Building and Using Models in the Real World (pt 2)

Something must be done!

THIS is something!

This must be done!

This little unfunny joke reminds me of one of the greatest of Sun Tzu’s teachings: the greatest victory is the battle not fought. When you can achieve your ends without having to roll out the army, that’s the real victory. But, the great Chinese general also points out, there’s no glory in that, since the less enlightened won’t be able to see the victory in the battle not fought. It takes the wisdom and confidence of a great general to have the guts to not fight the battle.

Another bit of real-world experience: the less people know about modelling, the more they expect out of it. The completely ignorant want magic, and, not understanding how models work, get put out when you can’t deliver. In the business world, there’s a great push for predictive analytics. Wouldn’t it be nice to be able to know which deals would go into default before you did them? Or how money costs were going to change over the next decade? Or even what the federal tax rates and rules were going to be?

Like any other model, predictive analytics are an expression of the prejudices of the model builder. Only when what happens in the real world show that the assumptions built into the model conform enough to reality to be useful do those assumptions become anything more than prejudices. Note I’m here talking about *predictive* analytics. The point is that only when they are no longer truly predictive are they be useful -only when they fundamentally say: assuming what has happened in the past continues to happen or happens again, THEN this is what will happen – are they any more than prejudices.

Predicting what will happen based on unknowns, based on things that have never happened before, is fantasy. The Drake Equation (1) is fantasy. Anthropomorphic Global Warming is essentially a fantasy (2). The giant unknowns – what factors causes climate in general – dwarf any supposed science behind a relatively minor greenhouse gas. It could could gain some reality if clear data existed over meaningful periods of time showing the correlation and – big part – accounting for all the other factors, known and unknown, that have caused the climate to change incessantly over a couple billion years. Since that hasn’t happened, and is unlikely to happen any time soon, what we’re left with are prejudices dressed up in math.

But something must be done! We’re all gonna die!

In the business world, the check upon crystal-ball promises is money and reality. The most success has been had in taking well-understood marketing data – through years of gumshoe-level marketing research, we know our ideal prospect has these buying habits and characteristics – then applying Big Data to finding more of those customers. The more you depart from such processes, the riskier it becomes. It certainly can work, but I’ll bet the purveyors of Big Data make sure we hear a lot about success stories, and not so much about the failures. At any rate, the money will soon dry up if there are not results. (3)

But in the world at large, especially if a government is bankrolling the ‘research’ behind a model, these restrictions don’t apply. Ferguson, while still trailing by miles, is now in the race with Erlich for the ant-Cassandra crown: the more wrong he is, the more credible he is assumed to be.

Governments, as giant bureaucracies run, according to Pournelle’s Iron Law, by careerist bureaucrats, will never willingly let a crisis go to waste. Something must be done, and they’ll find the something that most involves government action.

We had no Sun Tzu. A great victory could have been won by doing next to nothing, but an even greater defeat was delivered in its stead. There is no honor to be had, despite the claims of back-patting fear mongers.

Another relevant Sun Tzu saying: If you know yourself and know your enemy, victory is assured; if you know yourself and don’t know the enemy, you may win the battle half the time; victory is impossible if you know neither yourself nor the enemy. Since the enemy – COVID 19 – was (studiously) not known before the war was declared, and who ‘we’ are was unknown, victory as any sane person of good will would understand it was impossible from the get go.

  1. TL;DR version:

“This serious-looking equation gave SETI a serious footing as a legitimate intellectual inquiry. The problem, of course, is that none of the terms can be known, and most cannot even be estimated. The only way to work the equation is to fill in with guesses. And guesses-just so we’re clear-are merely expressions of prejudice.

“Nor can there be “informed guesses.” If you need to state how many planets with life choose to communicate, there is simply no way to make an informed guess. It’s simply prejudice.”

2. From the same source:

“Just as the earliest studies of nuclear winter stated that the uncertainties were so great that probabilities could never be known, so, too the first pronouncements on global warming argued strong limits on what could be determined with certainty about climate change.

“The 1995 IPCC draft report said, “Any claims of positive detection of significant climate change are likely to remain controversial until uncertainties in the total natural variability of the climate system are reduced.” It also said, “No study to date has positively attributed all or part of observed climate changes to anthropogenic causes.”

“Those statements were removed, and in their place appeared: “The balance of evidence suggests a discernible human influence on climate.” What is clear, however, is that on this issue, science and policy have become inextricably mixed to the point where it will be difficult, if not impossible, to separate them out. It is possible for an outside observer to ask serious questions about the conduct of investigations into global warming, such as whether we are taking appropriate steps to improve the quality of our observational data records, whether we are systematically obtaining the information that will clarify existing uncertainties, whether we have any organized disinterested mechanism to direct research in this contentious area.

“The answer to all these questions is no. We don’t.”

3. There are crazy people in business, of course, as in any other group of people, so sometimes the money for a crazy project doesn’t run out before the money in general runs out. But not usually.

Making the Sausage: Building and Using Models in the Real World (pt 1)

Someone somewhere almost certainly financed these…

For about 25 years, I helped finance companies, many of which are household names, use a sophisticated financial modeling tool. I got this gig because I’m good at explaining complicated things. To explain them, I needed to understand them. Thus, I spent years working with the guys who were building the model so I would understand it so I could explain it. I sometimes had direct input, but mostly I saw what was built after the fact. Either way, I had to understand it and how it worked in the real world.

What I propose to do here is walk through how a model works, how people use it, the gotchas, the assumptions, and what a model can and can’t do, illustrated by lightly scrubbed real-world experiences. As I go along, I’ll comment on the Imperial College model (as I understand it – not so much the mathematical details as how it was presented and used) versus how our model was used. This may end up a multi-part series.

Hope the regular readers find this interesting.

First, the users of the model: financial analysts and sales people, generally working for large sophisticated finance companies. The analysts did reporting and what-iffing; the sales people used the model to generate quotes for, generally, large financing deals. Examples: Company A wants a fleet of railcars, maybe worth $100M. They plan to use them over the next 30 years. As a salesperson from Large Finance Company B, I want to propose the following: my company will buy those railcars and lease them to Company A for a certain monthly payment. I will use the model to create a number of scenarios for Company A, showing different options. There will be some back-and-forth, during which time I will use the model to run other scenarios.

*Why* company B wants to purchase and then lease a fleet of railcars to Company A is beyond what I’m concerned with here – it’s some combination of cash flows, tax, and accounting considerations that make (or not) this arrangement appealing to both companies. What I want to do here is give a little taste of how complicated this all can get, and how those complications and uncertainties get addressed.

The first thing to note: there are probably something in the neighborhood of a trillion dollars worth of arrangements like this out there in the world at any time. Individual finance companies can have $100B or more invested in these sorts of deals. Screw up, and you can go out of business, sometimers in spectacular ways. People have careers riding on these deals working out. Therefore, every calculation, every assumption, every number MUST be demonstrated. Our model had hundreds of reports, validating every number and calculation. You could change one number or assumption, and watch that change flow through the deal in the reports. This is a critical distinction: A real-world model used by thousands of people with real money riding on it CANNOT be some sort of secret – giant finance companies had teams of people looking at it, making sure we got it right. Only after years of this scrutiny was the model accepted as an authority, and then only because everybody knew that not only they themselves had verified it, but their competitors had as well! Compare and contrast: Ferguson releases the conclusions of his model first, with only a light gloss on the mechanics. There were no teams of people, let alone teams of adversarial people, vetting his methods and output. He would have been jeered out of the room in my world. (1) Yet, way more than a trillion dollars was in play!

Our financial model had thousands of variables, but they fell into a much smaller number of logical groupings: dates, amounts, treatments, and settings. Anytime anything happens, it happens on a particular date. If the event involves money, it is a particular amount of money. But when money moves around, there are tax and accounting treatments to be spelled out (for every applicable country and state!). Finally, there’s a bunch of other stuff, such as expense allocations, yield definitions, costs of borrowing, constraints on calculations – stuff, and lots of it.

The way the model is used: Setup: an expert or a small group of experts from the finance company would sit down with me, or some other expert on the model, and we’d walk through the options. Sometimes, it was a simple process, we’d get the basic knocked off in an afternoon, and after a few iterations over the course of weeks or months, they’d be happy with how it worked for them. Other times, options were defined and redefined and tweaked over years and even decades – these were the largest companies, with the biggest teams of experts, and a budget for customizations.

Once the model was set up to incorporate the assumptions and preferences of a particular company, the analysts and sales people could use it to forecast, report, and put together proposals. No two companies made exactly the same assumptions, or defined key elements such as yields exactly the same way.

What this taught me: You must define terms! Every day terms critical to understanding what the model is telling you don’t mean the same thing to different groups or even individuals within a group. The million dollar question is: how much do you need to charge in order to make this deal work? You can measure this in cash, in simple rates, in more complex yields, in risk-adjusted returns over forecast money cost curves, and on and on. Each approach, each step of each approach, requires definition – what are you measuring? Against what?

Sometimes, even the experts don’t know. Sometimes, there are interesting interactions – change this, and the change will cascade through the numbers in sometimes non-obvious ways. Expectations will sometimes be wrong, where a change that looks like it should move the needle one way instead moves it the other way, because the interactions were not clearly understood.

Yet, at the end of the day, we could prove out every number. We could never have licensed copy 1 if we could not.

Even more than the usual skeptic, I turn a gimlet eye on any claims that a model has shown anything. Models show what you want them to show. You either spell out all the assumptions, demonstrate all the interactions, answer all questions, and stand up against your critics (we had ours) or you STFU.

Finally, the way you build a model like ours is through constant interactions with real-world experts and constant iterations. You get everybody’s input and feedback, and thus get everybody invested. There is no reason to trust a model in any field that has not gone through that process. Otherwise, the model is just your prejudices, your assumptions, and your blind spots. Plus math. Such a model merely tells us what you think and what you’d like to believe, nothing more.

  1. To be completely honest, there would probably be management types in the room who did not really understand the issues. Who knows how they would react, but the numbers guys would have gone nuts.

We’ll Never Know

One of us? The evidence seems somewhat fragmentary.

I’ve sometimes said that the correct scientific answer to most questions is: we don’t know. This is so because a) most interesting questions do not fall under the purview of science; and b) the highly conditional nature of all true science usually leaves room for reasonable doubt even in the relatively small subset of questions where science can be brought to bear. In the first group fall such timeless questions as “what am I to do with my life?” “Does life have any meaning?” “Should I marry this person?” “What should we name our child?” These are really important question for which science isn’t much help.

In the second group, fall such questions as: “When did human beings arise?” It is thought that creatures one could argue were human arose maybe 4 million years ago. Maybe. Modern humans, people who could pass unnoticed at a cocktail party given a shower, a shave, decent clothes, and a zipped lip, maybe 500,000? Maybe a million years ago?

And you know what? We’ll never know the answer. We’ll never eliminate reasonable doubt. We’ll never dig up every fossil, never be very confident of our taxonomy or dating of such fossils we do dig up, and in general, will never be confident we have enough information to be sure, even under the relaxed standards of reasonable doubt. (1)

It gets much worse when we don’t even have the tools of science to play with. We could probably trace the decline of Western thought in all fields through Scripture scholars. I’m sure all this predates Luther, but he is the one the most people will have heard of. First off, he really was a scripture scholar – he learned the original languages, and, like everyone in the monasteries where he was educated and which he later fought to have shut down, really knew his Bible. He is reported to have answered a question about his translation by saying: “Tell them Luther says it is so!”

Be that as it may, over the next 3 or 4 centuries in the West, Scripture scholars came up with one interesting interpretation after another, untethered purposely at first, but then as a matter of habit, from tradition. One might say it became a tradition to reject tradition. But since scholarship is, at its roots, tradition, eventually they become unbound by any rules.

Yet, at least up until modern Universalist Unitarians at the turn of the 19th century (2), each separate take on Scripture was considered a life-or-death matter. As hard as it is to imagine today, there was a time when a good Presbyterian passionately believed all Methodists were damned. There was a time when every new take entailed the founding of a new religion, the one true religion.

That was tiring. Sure, *Catholics*, enslaved to tradition, were of course damned. But, eventually, Protestants came to be more and more tolerant, in the sense of not attaching much value to the particulars of your beliefs, so long as you paid your respects to Jesus (Whoever He was) and were a good person.

And – here we are. Of course, the urge toward orthodoxy, the drive to find right worship, is more basic to us humans than any merely intellectual understanding. Like the parts of the water balloon you’re not pressing on at the moment, an obsession with right worship, and, even more important, on membership on the team doing the right worship, swell up and become dogma precisely when more intellectual dogmas are most denigrated.

A paean to uncertainty, with a nod to William Briggs, who (literally) wrote the book on it: The reason we need to be circumspect, the reason we need to act on principle rather than pretending to be pragmatic, is that we know so little. When I say I’m choosing a particular end the goodness of which justifies certain questionable or even horrible actions, I’m kidding myself. I don’t know if my actions will bring about the end I claim to seek, and, based on all experience up to now, I will never know. This is particularly true especially if that end is inherently vague if not utterly fantastic, as all Utopias necessarily are.

If I can’t know – and the future is always unknowable – I am acting on some other basis, whether I care to admit it or not. The wise understand that, to be free, we must know why we act. We must embrace our principles. To do otherwise is to embrace slavery.

Back to science. Let’s end with another quotation from that excellent Crichton Caltech address:

Let’s think back to people in 1900 in, say, New York. If they worried about people in 2000, what would they worry about? Probably: Where would people get enough horses? And what would they do about all the horseshit? Horse pollution was bad in 1900, think how much worse it would be a century later, with so many more people riding horses? But of course, within a few years, nobody rode horses except for sport. And in 2000, France was getting 80% its power from an energy source that was unknown in 1900. Germany, Switzerland, Belgium and Japan were getting more than 30% from this source, unknown in 1900. Remember, people in 1900 didn’t know what an atom was. They didn’t know its structure. They also didn’t know what a radio was, or an airport, or a movie, or a television, or a computer, or a cell phone, or a jet, an antibiotic, a rocket, a satellite, an MRI, ICU, IUD, IBM, IRA, ERA, EEG, EPA, IRS, DOD, PCP, HTML, internet. interferon, instant replay, remote sensing, remote control, speed dialing, gene therapy, gene splicing, genes, spot welding, heat-seeking, bipolar, prozac, leotards, lap dancing, email, tape recorder, CDs, airbags, plastic explosive, plastic, robots, cars, liposuction, transduction, superconduction, dish antennas, step aerobics, smoothies, twelve-step, ultrasound, nylon, rayon, teflon, fiber optics, carpal tunnel, laser surgery, laparoscopy, corneal transplant, kidney transplant, AIDS…… None of this would have meant anything to a person in the year 1900. They wouldn’t know what you are talking about.

Now. You tell me you can predict the world of 2100. Tell me it’s even worth thinking about. Our models just carry the present into the future. They’re bound to be wrong. Everybody who gives a moment’s thought knows it

Michael Crichton’s 2003 address to Caltech

Science is in many ways a heavily hedged bet: under precisely specified conditions, performing the specified steps will get you the predicted results. Don’t follow the rules, and all bets are off. We should be more amazed than we are that it works as well as it does in the real world. And science is the best we can do in this vale of tears. (3)

  1. In one of many long-running, acrimonious debates among paleoanthropologists, where whether or not you got invited to conferences or got your stuff published in a specific journal depended on what position you took on some arcane proposal, some wag quipped: never is the battle so fierce as when the stakes are so low. It matters not one whit whether, say, some subspecies of Australopithecus is or is not in the main line of human descent or is some ended branch (any species that doesn’t end in us being some sort of tragedy, it seems). But try telling that to the parties involved. And – NOBODY WILL EVER KNOW FOR SURE!
  2. Think it was Vonnegut who quipped: “Unitarians don’t believe in anything. I’m a Unitarian.”
  3. Apart from metaphysical necessary truths, of course. But those are in some sense not of this vale of tears….

Three Things, and a Thought

h/t to Don at Zoopraxiscope for links to these two essays:

Richard Grenier, “The Gandhi Nobody Knows

Michael Crichton, “Aliens Cause Global Warming” (PDF)

I had read years ago about what a creep and weirdo Gandhi was, so this first article just reinforced a vague opinion. The second, though – wow. Regular readers here have possibly noticed my occassional reference to the Gell-Man Amnesia Affect:

“Briefly stated, the Gell-Mann Amnesia effect is as follows. You open the newspaper to an article on some subject you know well. In Murray’s case, physics. In mine, show business. You read the article and see the journalist has absolutely no understanding of either the facts or the issues. Often, the article is so wrong it actually presents the story backward—reversing cause and effect. I call these the “wet streets cause rain” stories. Paper’s full of them.

In any case, you read with exasperation or amusement the multiple errors in a story, and then turn the page to national or international affairs, and read as if the rest of the newspaper was somehow more accurate about Palestine than the baloney you just read. You turn the page, and forget what you know.” 

– Michael Crichton (1942-2008)

So, before this essay, I knew Crichton as a really smart guy who wrote some fun novels and coined a very useful concept – and had the sense of humor to ask Nobel Prize winning physicist Murray Gell-Mann (who is the sort of person Crichton would have lunch with) if he couldn’t attribute it to him, since it sounded better that way.

But this essay is now my second-favorite Caltech address, after the epic Feynman one I often quote, the ‘Cargo Cult Science‘ address. Crichton is also a very good writer (surprise), so where a large part of Feinman’s appeal is in his folksy bluntness, Crichton is a pleasure to read just for the nice English – and the hammer he throws down. Dude ain’t buying it:

To an outsider, the most significant innovation in the global warming controversy is the overt reliance that is being placed on models. Back in the days of nuclear winter, computer models were invoked to add weight to a conclusion: “These results are derived with the help of a computer model.” But now large-scale computer models are seen as generating data in themselves. No longer are models judged by how well they reproduce data from the real world increasingly, models provide the data. As if they were themselves a reality. And indeed they are, when we are projecting forward. There can be no observational data about the year 2100. There are only model runs.

This fascination with computer models is something I understand very well. Richard Feynmann called it a disease. I fear he is right. Because only if you spend a lot of time looking at a computer screen can you arrive at the complex point where the global warming debate now stands.

I’d love to quote pretty much the whole thing. He starts with a withering – and familiar to readers here – criticism of Drake equation. Oh, heck, here it is:

What is the Drake Equation? - Universe Today
looks scientifilicious….

This serious-looking equation gave SETI an serious footing as a legitimate intellectual inquiry. The problem, of course, is that none of the terms can be known, and most cannot even be estimated. The only way to work the equation is to fill in with guesses. And guesses -just so we’re clear-are merely expressions of prejudice. Nor can there be “informed guesses.” If you need to state how many planets with life choose to communicate, there is simply no way to make an informed guess. It’s simply prejudice.

As a result, the Drake equation can have any value from “billions and billions” to zero. An expression that can mean anything means nothing. Speaking precisely, the Drake equation is literally meaningless, and has nothing to do with science.

Next, he makes a possibly topical observation:

Stepping back, I have to say the arrogance of the modelmakers is breathtaking. There have been, in every century, scientists who say they know it all. Since climate may be a chaotic system-no one is sure-these predictions are inherently doubtful, to be polite. But more to the point, even if the models get the science spot-on, they can never get the sociology. To predict anything about the world a hundred years from now is simply absurd.


Just as we have established a tradition of double-blinded research to determine drug efficacy, we must institute double-blinded research in other policy areas as well. Certainly the increased use of computer models, such as GCMs, cries out for the separation of those who make the models from those who verify them. The fact is that the present structure of science is entrepreneurial, with individual investigative teams vying for funding from organizations which all too often have a clear stake in the outcome of the research-or appear to, which may be just as bad. This is not healthy for science.

“…cries out for the separation of those who make the models from those who verify them.” That’s it exactly. Ferguson never had his programming and numbers checked by a disinterested party. Instead, a known panic-monger with a history of hysterical – and hysterically wrong – predictions of doom, was allowed to be be judge, jury, and executioner of his own scheme. INSANE.

The appropriate disinterested parties would be numbers guys. Data analysts. Model builders. Scientifically literate experts in the scientific method. Expressly not other epidemiologists, even less politicians and journalists.

Finally, also years ago, I read somewhere that the origin of the English word ‘slave’ was Slav, that so many Slavs were enslaved that it became a brand name, as it were. Well, check this out:

The oldest written history of the Slavs can be shortly summarised–myriads of slave hunts and the enthralment of entire peoples. The Slav was the most prized of human goods. With increased strength outside his marshy land of origin, hardened to the utmost against all privation, industrious, content with little, good-humoured, and cheerful, he filled the slave markets of Europe, Asia, and Africa. It must be remembered that for every Slavonic slave who reached his destination, at least ten succumbed to inhuman treatment during transport and to the heat of the climate. Indeed Ibrāhīm (tenth century), himself in all probability a slave dealer, says: “And the Slavs cannot travel to Lombardy on account of the heat which is fatal to them.” Hence their high price.

The Arabian geographer of the ninth century tells us how the Magyars in the Pontus steppe dominated all the Slavs dwelling near them. The Magyars made raids upon the Slavs and took their prisoners along the coast to Kerkh where the Byzantines came to meet them and gave Greek brocades and such wares in exchange for the prisoners.

“The Cambridge Medieval History,” Vol. II, 1913, via

That would be 1/2 of my ancestory. Note that Slavs were sold to – Africans. If this reperations nonsense comes to pass, I’m getting in line.

Urgent Book Review: Unreported Truths about COVID-19 and Lockdowns: Part 1: Introduction and Death Counts and Estimates

Clarissa’s Blog reports that, due to public pressure, most notably by Elon Musk, Amazon, after at first rejecting the booklet without comment or recourse, today published Alex Berenson’s criticism of the COVID 19 overreaction.

Unreported Truths about COVID-19 and Lockdowns: Part 1: Introduction and Death Counts and Estimates is the first of a planned series of booklets about the wild panic-mongering surrounding the virus and initiating the lockdown. Berenson says he decided to publish this as a series of booklets because he wanted the information out now, and it takes time to compile and format it.

Short and sweet: buy this booklet now. Read it – it’s short. It’s the first part of all the data and analysis we’ve been batting around here for the last 3 months. It’s got the links, to all the official sources and reports from the major players. No sources that should be in the slightest controversial.

Berenson starts out from the same position as most people: that news out of Wuhan and especially Italy was very concerning. I gather the pictures were even worse – I don’t consume news lest it consumes me, so I didn’t see them. Then, he saw the numbers on age distribution in the March 16 report from the Imperial College and had, as Agent Smith might say, a revelation: old people were about 100 times more likely to die of COVID 19 than young people. Yet, this stunning fact had been ignored by the media. What was going on?

So he dug through the numbers. Readers here are already familiar with what he found: wildly overstated death rates, wildly inaccurate models, bait and switch on the lockdown, wildly uncertain, but almost certainly over-aggressive, counts of deaths and cases. And a psychotic resistance from the press to report on any of it.

Of course, he, like me, acknowledges that COVID 19 is a nasty bug for some people. An outbreak can be nasty to the old and frail. It’s not like it’s not a real disease or anything. Of course it is. It’s the wild, animal refusal of many terrified sheep to see any context that’s the worst feature, I won’t say of the disease, but of the panic surrounding it. That panic has already caused more harm than anything the virus could ever do.

The numbers are all there, in public, available to anyone. All it takes to see the lies is a willingness to look. The links are all there.

For those who find the more technical analysis at William Briggs’ blog sometimes daunting, this would be a good read. Berenson, a professional writer, presents the information in a pithy, readable way I can only aspire to. I’d say send copies to your panicked family and friends, but, alas! the panicked cannot be reasoned with.

The stupid Amazon reader won’t let me cut and paste, even for a review, so you’ll need to buy it and read it. It’s only a couple of bucks, and HAS ALL THE LINKS I got tired of including all the time. Do it now!

I eagerly await part 2.

More d&mn Virus

Clarissa’s blog states some numbers:

Across the US, 2,6% of all COVID deaths are of people under the age of 45.

In Massachusetts, only 14,8% of deaths are under the age of 70.

In Minnesota, 81% of all COVID deaths are in nursing homes.

In Connecticut, 6,3% are under 60 and 18,8% are under 70.

In PA, there are more deaths over the age of 95 than under the age of 60.

Worldwide, there are more deaths over the age 100 than under the age 30. Obviously, the number of people over the age 100 is massively lower than that of the under-thirties.

My comment (longer than her post. Pithy, I ain’t):

Also, it’s not like nursing home populations are homogenous. The majority of the people stuck there are going to die sooner rather than later. A 2010 study in SF showed 80% of nursing home patients died within a year of being admitted. but the average stay is still 2.2 years – because some, especially those with dementia and Alzheimer’s (and little else) typically live 5-10 years.

What you end up with is a churn: there’s a constant flow of patients who die soon – that SF study showed a median survival time of 3 months(!) for men and 9 for women. Those poor souls show up with one foot on the threshold of St. Peter’s gate, and pretty much promptly step over. BUT – as, anecdotally, I see when I go caroling at the same nursing home year after year, SOME residents live for many years, skewing the average stay high. The median ‘stay’ is like 6 months; the average is 2.2 years.

The takeaway: while some elderly people who would have otherwise lived a few more years no doubt died of COVID 19 in nursing homes, I’m betting – and that autopsy video you posted bears this out, where all 12 victims were extremely ill before they caught the virus – that mainly the virus is doing little more than accelerating the deaths of extremely sick people, if even that. The sad truth: people in nursing homes are put there to die; in the old days, if an 80yr old died, the cause of death was ‘old age’, with a nod, maybe, to the cold, flu, infection, or other otherwise minor illness that pushed them the last inch over the finish line.

Thus, even if everything was done right, as you described, chances are all that would have happened was that the ‘curve’ of deaths in nursing homes would have been ‘flattened’. As was always inherent in the math, the same number of people would have died, just spread out a little more. Applying this to the whole population, OTOH, only guarantees that the virus hangs around for longer and longer – until as Gavin Newsom clearly hopes, flu season starts up again, and an airborne virus that would have died out in the spring is given a second life.

The video mentioned above, in which a doctor describes a German report on 12 autopsies done on COVID 19 victims:

It’s a bit long and over-detailed for us non-specialists. The key points, from my perspective, are at 1:00 in, where he says some calming (to the rational mind) things about outcomes (although, since his numbers seem to be more case-based than population based, about 400% less calming than they should be), and most especially at 2:30 on, where he discusses the characteristics of the 12 poor people who died. Average age: 73; condition: all 12 were in extremely poor health BEFORE they caught the virus. All 12 already had one foot in the grave.

It’s a good video, but illustrates my main point in all these blog posts about the virus I keep throwing up: we humans are bad at assessing risk, and wildly, recklessly, and disastrously overestimate the risks of COVID 19. One could even say, based on the evidence, that we’re incapable of assessing risk: once frightened, we rush to embrace any bad news, and are incapable of integrating good news. Here, this doctor, a charming, even-handed fellow, even faced with the cold, hard reality that, with very few exceptions, COVID 19 doesn’t kill anybody UNLESS the victim is already very sick, drones on about the need to understand risks. The risks are in front of him; he even explains them, but he doesn’t understand them; the understanding he seems to be seeking will not change the risk profile of Joe or Jane average American one iota.

What risks? If you aren’t quite old and very sick already, you’d be almost better off wearing a helmet 24/7 just in case a meteorite were to hit you in the head.

The COVID Numbers You MUST Know

File:Lots of math symbols and numbers.svg

Basic, basic math, and therefore, evidently, beyond the grasp of virtually all doctors, journalists, politicians, pundits, and all us terrified sheep:

  • Percentage of people in the US in nursing homes: approximately 0.4%
  • Percentage of COVID 19 deaths in nursing homes: approximately 60%
  • COVID 19 death rate doctors are throwing around today: 1%

Here’s the math-is-hard part. Since the US is approaching 100,000 reported COVID 19 deaths, we’ll use that nice round number to make it easier:

  • 60% of 100,000 is 60,000. That’s about how many COVID deaths have taken place among nursing home patients – 0.4% of everyone
  • 40% of 100,000 is 40,000. That’s how many COVID 19 deaths have taken place among everyone who isn’t a nursing home patient – 99.6% of everyone.
  • US population is about 330,000,000. Multiplying the total population by 0.4%, we find about 1.3 million nursing home patients in the US . (The CDC agrees)

There are some fancier philosophical considerations that figure into any fancier math, but let’s keep it simple: let’s weight the quoted fatality rate by populations in and out of nursing homes. We’ll start by establishing a ratio based on the (wild, maybe) assumption that everyone is equally likely to catch the virus:

  • 60,000 deaths divided by 1.3 million nursing home patients = 4.615%
  • 40,000 deaths divided by 328.7 million everybody else = 0.012%

Of course, not everyone has caught the virus yet, so we need to change these imaginary deaths rates based on everybody already having caught it to reflect the 1% death rate being tossed about these days. What if everybody catches the virus, and the 1% death rate holds, but split between the nursing home patients and everybody else? That would mean 3.3 million Americans die, up somewhat from the 100K we’re approaching. In this scenario, using the numbers up above:

  • 152% of all nursing home patients will die. That seems unlikely.
  • 0.4% of everybody else will die. That’s 4 out of every 1,000.

Four out of 1,000 is bad. If a person had 250 non-nursing home acquaintances, on average 1 would die of COVID 19. If, again, EVERYBODY gets infected, and 1% is the real death rate.

But nobody any more is predicting 3.3 million deaths. Trends suggest we’re very close to topping out at a little over 100K. But, hey, what do I know? Here’s where you get to plug in your favorite guess of how many Americans will, in fact, die of COVID 19, and see what happens to your chances if you aren’t in a nursing home:

Let’s start with the popular guess of half a million dead. If you’re anticipating 500,000 COVID 19 deaths (which would require a wild reversal of all observed trends followed by an explosion of deaths – but hey, never mind, we’re assuming it’s your number to pick) then the likelihood of a non-nursing home patient dying is:

  • 500,000 divided by 3,3 million is right around 15%
  • 15% of 0.4 is 0.06%.

That’s well above shark attack levels, but within bad flu season levels. Comparable to ‘die in a car accident on my way to work’ level risk. Do you panic over driving to work and demand the government destroy the economy in order to save you? No? If you had 1,167 non-nursing home acquaintances, you’d lose 1 to COVID 19, on average.

Putting in more plausible (but still likely far too high) numbers, like 200,000, gives even lower likelihoods. 200,000 deaths mean you have a 0.024% chance of dying if you’re not a nursing home patient, and so on.

Of course these numbers are just first-pass, get a feel for the landscape bogies. To get a more scientific estimate of the likelihood of any person dying of COVID 19, we’d need a team of actuaries to work on this. Actuaries are the highly trained mathematicians who estimate risk for insurance companies. Unlike us laymen, actuaries are really, really good at understanding risk. They divide people into groups or strata based on a number of risk characteristics, which characteristics are in turn based on years and years of experience and experimentation. Insurance companies have billions and billions riding on the accuracy of the numbers the actuaries come up with, which numbers are revised constantly in response to real-world outcomes. They’re good at their job.

The first thing an actuary would do is look at risk categories, and notice that 95% of the COVID deaths are of people already sick, generally very sick. Almost all are also very old. Sick, old people – the kind of people who end up in nursing homes. For otherwise healthy people, the risk of COVID 19 really is just background noise. (1)

People with no feel for numbers just cannot grasp how ridiculous this panic and resulting lockdown over COVID 19 are. YES, we should have done more to protect the elderly and sick; YES, it would have been prudent to restrict travel from hotspots; PERHAPS locking down NYC for a couple of weeks would have been prudent. But locking down suburban California or rural Montana and anywhere that isn’t a nursing home, hospital treating COVID patients, or perhaps megalopolis with decades of history of criminal mismanagement? For months on end? Shutting down small businesses and putting millions out of work – on purpose? INSANE.

What we have driving the panic are theories – highly dubious, emotion-packed theories. The ‘models’ are nothing but those theories and panic dressed in bad math and run on a computer – as anybody familiar with model building and data evaluation could have told you. (We certainly tried.)

Theories include the efficacy of general lockdowns – any science behind that? Cost benefit analysis? Social distancing – ditto? Nope – 6 feet is a magic distance in all cases; lockdowns save lives without costing other lives. We people who ‘believe’ Science! just *know* these things. To question them is heresy, punishable by burning at the stake!

And the government at various levels suddenly has the duty and power to unilaterally shelve constitutionally guaranteed rights of assembly and religious freedom, and effectively seize the property of business owners without trial or compensation, and throw millions out of work, due to a duty to manage risk just discovered lurking in some emmenation from a penumbra somewhere….

Bottom line: if you’re terrified of catching and dying from COVID 19 and don’t live in a nursing home, or are otherwise quite sick or very elderly, you need to start wearing a helmet at all times. NASA estimates 12-15 meteorites strike earth’s surface every day, after all. Sometimes, a lot more. A guy even died, once. An American woman had a meteorite barely miss her head, and leave her with a terrible bruise. Think of the children!

  1. In case you’re wondering, yes, I briefly worked as a number cruncher – like, adding up numbers from reports on a 10-key, very bottom of the totem pole – in an actuarial department as my very first real job out of college. I balked at the amount of work needed to get my math chops up enough to advance, and changed careers. Math really is hard, sometimes.

A Political Observation & Some More Charts

The top 6 countries by COVID 19 deaths are shown below. These 6 countries barely represent 10% of the world’s population yet account for 56% of all cases and 72% of all deaths:


A political observation, something all these countries share:

USA – election of a ‘right wing’ administration revealed the vulnerability of the culturally and politically dominant Left, which, predictably, completely lost its mind. Now, under the COVID noise, indictments are coming down against the 3rd tier of Leftist operatives.

UK – contrary to the wishes of the politically dominant Left, the nation voted not only to reject globalism and leave the EU, but then enabled the despised non-Leftist Boris Johnson to become Prime Minister and form a government.

Italy – The anti-globalist 5 Star Party came to power in 2018.

France – Macron, a lifetime Socialist Party member, has been under siege by the Yellow Vests for over a year. The Yellow Vests are a mixed bag, politically – hey, it’s France – but include ‘populist’ anti-government factions. While Macron is often portrayed as pro-business, a cursory look at what he’s up to shows he’s pro giant global business, and pursues pretty much textbook Gramsciite social destruction policies.

Spain – a history of conflict, often violent, between Communists (under whatever name) and more conservative elements. ‘Right wing’parties have been making gains in recent years.

Brazil – Jair Bolsonaro’s election in 2019 put in power pretty much the Left’s nightmare candidate. As Wikipedia sums up: “He is a vocal opponent of same-sex marriage and homosexuality, abortion, affirmative action, drug liberalization and secularism. In foreign policy, he has advocated closer relations to the United States and Israel. During the 2018 presidential campaign, he started to advocate for economic liberal and pro-market policies.”

Each of these nations has a Left that’s having its power threatened. Somehow, that correlates remarkably to more COVID 19 deaths…

Now for some charts. Have COVID 19 daily deaths counts fallen off a cliff? Why, yes, yes they have: (All charts from Worldometers)


This is probably as low as Italy will fall, as far as daily death counts go, as they count COVID 19 deaths very liberally, and, with an older population, they will not lack from nursing home patients with the sniffles checking out. But, on a populations 60M+, we’ve reached the statistical noise level of deaths.


Ditto. Also note that the declines started in early April – with the arrival of spring, which is what any sane person would have predicted.


French data is extremely noisy – again, hey, they’re French – but the overall decline is still there. Those spikes are all related to reporting lumpiness. Tiny numbers.


So here’s the one country, of the six ‘leading’ country, where the decline is not evident from the graph. Brazil has over 200M people; 750 deaths are, as always, personal tragedies, but, statistically? Barely registers.

I’ll keep more of an eye on Brazil.


Even the birth palce of COVID Panic Porn is clearly on the way out. This data shows an odd weekly cyclicality: down, down, down, off a cliff, down, way up, repeat. Reporting quirk? I’d assume so.

As noted previously here, England has implemented a policy of listing COVID 19 on the mandatory reporting list, along with the Plague, Mad Cow Disease, anthrax – because a flu-like infection fatal well under 1% of the time is just like those things. The net result: as the infection inevitably spreads, more and more people who test positive will show up in the counts regardless of COVID 19 actually contributing to their deaths, or, indeed, despite showing no symptoms at all.

So, realistically, we’ve reached bottom unless the UK changes its reporting rules. It may even go up.

Finally, the US:

Same pattern, same issues as with the UK data. Given the political investment in keeping the lockdown and fear going by political conmen like the reptilian Newsom, I’d guess this is about as low as the daily counts will be allowed to go.

I think I just insulted reptiles.

Bloodletting, Lockdowns, and Other Adventures in Medical Theory

The term science as used over the years means two related but distinct and consistently confounded activities. The first and most ancient meaning: Science is an organized, systematic approach to a subject. Under this rubric are such modern fields as Political Science, most of Economics, and Philosophy, as well as Chiropractic Medicine, Astrology, and Scientology. The key is an organized, systematic approach. Such an approach appeals not to controlled experiment or repeatable observations, but merely to logic built upon certain more or less reasonable assumptions supported by a set of anecdotes. Sciences in this sense may or may not comport well with reality.

It is important to note that science in this first sense is often, maybe even generally, the best we can do, and can be useful. Economics is very useful, for example, even if it is supported almost entirely by a large (and convincing!) set of anecdotes. It has not been proven experimentally, for example, that socialist governments *must* *always* impoverish their subjects, but the lack of a clear counterexample sure does give one pause, to say the least.

The second sense, the sense in which Newton and Feynman are scientists in a way Machiavelli and Sun Tsu are not, rests on its claims working in the real world. Valid controlled experimentation and replicable observations *demonstrate* what they claim; technological applications of these claims reduce them to truths, albeit conditional truths in the physical world. Physics and Chemistry and related fields are sciences in this second sense.

BUT – here’s the huge caveat: saying Chemistry is a science is not the same thing as saying that everything any particular chemist claims is science. And, a little more subtly, there can be well-founded scientific claims embedded within the claims of areas that are science, generally, only under the first definition.

As discussed ad nauseum on this site, in fact, the main original reason I started blogging in the first place: We owe allegiance to and are morally obliged to accept, however conditionally, only the claims of science in the second sense. Why? Because science in the sense of a systematic study of a topic must be based on more or less credible assumptions. A reasonable person may reject those assumptions. Phrenology has a clear subject matter and a well-defined approach. All you have to accept is that the shape of a man’s skull tells you important things about his temperament and intelligence.

Does it? Now is where a little skill in science, what I call scientific literacy, comes into play: even with the meager science chops I have, it is easy to imagine what a valid scientific (in the second sense) study to determine the relationship between skull bumps and personality would look like. Double blind, large populations, carefully spelled out rules of observation and measurement, elimination, as far as possible, of judgement calls about temperament, off the top of my head. Then, once again based on my simply having read a bunch of science, the second part: if such a study existed, any serious Phrenologist would *lead* with that study, and all the follow up studies such an interesting and useful science would of course generate.

But they don’t. Therefore, fraud, or at least, self-delusions.

The situation becomes more murky when there is plenty of anecdotal evidence to support claims, such as the innumerable people who will testify that probiotics or homeopathy have healed them. Good people of good will, no doubt. But do they? I don’t see anything like valid scientific studies to support these and many similar claims. If they existed, anybody steeped in science in the second sense would lead with them.

But they don’t. Does this mean they are wrong? No. It simply means an honest man owes them no allegiance, and that a scientifically literate man is all but compelled to note the lack of any real science to support the claims. If science in the second sense is being claimed, then a scientifically literate man is obliged to call foul.

Mostly, I assume, and it generally seems to be the case, that there’s little if any harm in most such claims. Go on a keto diet and get you probiotics from a tube, if you want, certainly none of my business. But sometimes…

And – a lot of other such stories. What I want to talk about today is that even radium water was supported by anecdotes and scientific theories. That golfer who died from drinking radium water – recommended by his doctor! – spent years telling anybody who would listen how good it was for him, improved his energy, and all in all made him feel great – right up until cancer rotted his bones out.(1) And the manufacturer had a theory – Radiation hormesis – that sounded pretty good to many people. It sounded like science, and, being scientifically illiterate, the average Joe doesn’t even know the right questions to ask.

Back to bloodletting. It’s customary to think doctors back in the old days were really stupid – bleeding people, thinking intentional blood loss was going to cure them? When bloodletting was done enough, it in itself would kill you! How stupid do you have to be?

Not stupid at all, it turns out. Doctors well into the 19th century still used it.

They had a theory. They still do. Some patients did recover, and, no doubt, credited the bloodletting as the cure to all who would listen. The nice doctor would tell the poor sick person they needed bloodletting with stone certainty; friends and relatives might testify to its effectiveness.

But those ignorant days are past, right? Doctors today rely on science in the second sense, well-understood, thoroughly tested practices supported by multiple rigorous studies…

Unfortunately, nope. For example, we older people are often prescribed several drugs at the same time. Drugs have a range of effects on people; few are universally effective: some people respond as advertised, but some just don’t respond for reasons that are poorly understood, if understood at all. And drugs interact. Sometimes, the interactions are well known, usually because they killed a number of people. Failing that, it’s unlikely that the drug interactions have been studied at all, let alone subjected to rigorous, scientifically valid research.

Think about it: a drug company spends many millions of dollars getting a drug tested. All in, it takes in the neighborhood of a billion dollars to get a drug to market. That drug – any and every drug – may and probably will be used in conjunction with any number of other drugs (and alcohol, pot, and goodness knows what else). To *scientifically* answer the question: how does this drug interact with aspirin? Tylenol? Blood pressure meds? Serotonin uptake inhibitors? and on and on, would take billions and billions of dollars. And then how about with both blood pressure meds and antidepressants together? And so on.

To require the level of testing to determine the risks (and, who knows? benefits) of combining multiple drugs would simply put the drug companies out of business. No one could afford the level of testing needed to figure all this out. No more new drugs for you! (2)

Thus, when people say medicine is more art than science, they ain’t kidding.

The topical application of all this: doctors are trained and formed in this world. They are not, and, insofar as they are practicing in the real world, cannot be, scientists in the second sense.

So, we are assured – we were, weren’t we? – that lockdowns were needed, because science! Nope, no science was involved – doctors had a theory, like they had with bloodletting and radium water. You think there are rigorous scientific studies proving lockdowns, on the whole, improve things? Has anyone even dared propose that such studies exist? Social distancing save lives! It’s science! 6 feet! 5 feet doesn’t cut it, and 7 feet is clearly excessive! Nope. Somebody has a theory, like ringing church bells to disperse the aqueous humors causing the Plague (it was perfectly logical science, in the first sense of the word).

Bottom line: at best, an increasingly credulity-defying position to hold, lockdowns and social distancing were reasonable guesses based on some untested and unverified theory held by doctors, doctors who are not scientists and are as likely as anybody else to be scientifically illiterate. No overall assessment of risk was performed, as in, do the likely costs of lockdowns and social distancing exceed the assumed, theoretical, benefits?

Nope. It’s theories all the way down – at best. No real science involved, nothing that would compel an honest, scientifically literate man to assent to these measures. More likely, it’s political corruption refusing to let a crisis go to waste, even if they have to manufacture the crisis.

It is so clear now that the costs of lockdowns and social distancing exceed by far any benefits that it is sheer evil perversity that any remain in effect. We need to explore ways to make the panic mongers and political operative pay.

  1. As the Wall Street Journal put it: “The Radium Water Worked Fine Until His Jaw Came Off”
  2. Whether this would necessarily be a bad thing overall is an open question. It has to be!