There’s a paper out claiming that black infant infant mortality is much higher when they’re treated by white doctors, rather than black doctors.
Could it be that MCAT scores have negative predictive value?
No, there’s a simpler explanation: the report is nonsense. A metaphorical cee-gar to the first person to explain why.
And the next question is: why do the pinheads that authored this paper have jobs?
A number of epidemiologists and virologists did not expect to see significant adaptive evolution for increased transmission in covid-19, and continue to argue against that hypothesis. Vincent Racaniello, well-known virologist, takes this position. So does David Dowd, an infectious disease epidemiologist at Johns Hopkins.
They think that chance can drive a new variant with no transmission advantage to high frequency, even when there are many cases ( tens of thousands or more) . The B.1.1.7 variant went from ~1% to a big majority of cases in England.
I’ve just run some sims ( with the sort of transmission dispersion observed in cov-19). R (fitness) = 1, in both cases. I propagate 100 times in each run. Variant A starts out with 5,000 cases, B with 2000. How often did B catch up with A? 6 out of 100 runs.
How often did B catch up with A when A started out with 50,000 cases and B with 20,000 cases? zero, out of 100 runs.
Start out with 2000 B and 50,000 A, same fitness: how often did B catch up? zero out of 100 runs.
Start out with 2000 B ( with a fitness of 1.05) and 50,000 A with a fitness of 1: How often did B catch up? 100 out of 100 runs.
The top curve shows the relative frequency of B.1.1.7 as a function of time in Denmark. Dowd can look at that and believe it is a random fluctuation. Wow.
Lloyd Fredendall was a general in the American Army in WWII, serving in North Africa.
He is known primarily for being a fuck-up. His early career may have been a sign: he dropped out of West Point twice.
He commanded II Corps in its advance into Tunisia, so his relative competence mattered.
He had a weird habit of talking in his own private slang. He called infantry units “walking boys” and artillery “popguns.” Instead of using the standard military map grid-based location designators, he made up confusing codes such as “the place that begins with C.” His subordinates had trouble understanding what the hell he was talking about.
He spent lots of effort building an underground fortress ( his headquarters) 70 miles behind the front lines and spent most of his time inside it, rather than visiting the front lines and talking with his commanders.
Tactically, also a mess: he split up units and scattered them widely. Which turned out poorly (Kasserine Pass).
After Kasserine Pass, Ike fired him. But how did Fredendall get anywhere in the first place, and why did removing him take so long?
Well, the most talented people didn’t much go into the American armed forces in those days, least of all the Army. The Army wasn’t prestigious, wasn’t well-funded, wasn’t very meritocratic. Promotion was slow, pay was lousy. The US Army was about the size of the German Army while it was still obeying the Treaty of Versailles – but the Black Reichswehr was an elite, taking only the best, secretly preparing for der Tag. Every sergeant was ready to be a captain. The US Army was not like that.
The Army leadership all knew each other. Most were West Pointers. It was fairly easy-going.
Put to the test in WWII, we found out that our generals often weren’t very good. Ike himself had to learn an important lesson: how to fire people, including old friends. After a while American leadership became fairly good at that, for example when Nimitz fired Ghormley.
The Soviets already knew how to fire people ( sometimes with extreme prejudice) but Stalin learned to judge by performance and fire people intelligently: promote the winners, fire ( and sometimes execute) the losers. Act as if winning is the most important thing.
Generally, the governing classes in the US, for the last generation or two, has not acted as if they think that winning, actually achieving your goal, is very important. Promotion follows failure: indeed, being right when almost everyone else is wrong just shows how undesirable you are. Iraq is a good example.
Covid-19 is another example. The professionals weren’t very good, aren’t very good. They didn’t know a lot of important, knowable things. Probably the most talented people were going into something other than epidemiology or virology.
We don’t have to make them unpersons, don’t have to send them to Kolyma. We don’t have to pull out their teeth and fingernails. There’s no reason to put on a black leather jacket and shoot them in the back of the head. That would be wrong.
But we can fire them. And we should.
As many virologists have stated, their expectation was that the evolution of noticeably higher-transmission variants of Cov-19 was quite unlikely.
There is solid evidence that this has now happened at least three times (D614G, A222V, and B.1.1.7) with at least two others likely ( in South Africa and Brazil).
They failed in an important aspect of their job.
Once upon a time, I was talking to a young engineer about some new wrinkle in solar concentrators. He was enthusiastic: he thought that with a little effort, you could focus sunlight enough to generate a temperature higher than that of the Sun itself.
I said ” Nope. ”
Theory is your friend. Correct theory, that is.
These graphs show the results of evolutionary experiments by Richard Lenski, in which a bacterial species ( e coli) has been evolving under constant conditions for many years: tens of thousands of generations.
These bacteria were not perfectly adapted to the experimental environment, and so there is selection for changes that allow them to do better in these conditions. Adaptive change is rapid at first and slows down with time, as the culture approaches an optimum phenotype. Fitness increases rather like the logarithm of time.
The probability of a beneficial mutation fixing is proportional to the advantage it confers. Large-effect beneficial mutations are more likely to fix and dominate the early phase. As the bacteria get closer to an optimum, the possible gain from a beneficial mutation is smaller, and so those smaller-effect beneficial mutations ( the only ones possible) are less likely to fix. Thus they take longer to fix (on average they need to occur many times before succeeding) and they also fix more slowly, since their growth advantage is small.
relevance: a new virus in humans is like the situation near the origin of graph B. The virus is not yet close to an optimum, so change is fairly rapid – particularly if the virus is infecting vast numbers of people ( like covid-19) which greatly increases the number of copies of the virus and thus the chance of favorable mutations ( Fisherian acceleration). Favorable to the virus, that is.
An old virus in humans, say measles ( > 1000 years old) is closer to an optimum: change is much slower.
It seems that most professional virologists are used to viruses that have been around for quite a while – understandable, since new viruses do not sweep through the human race every year.
You could have predicted the emergence of new higher-transmission variants of covid-19 from this theoretical perspective. I did, arguablywrong did, probably others have as well. But virologists did not.
There’s a new variant of cov-19, the UK strain, that is something like 70% more infectious than previous strains. At this point its lethality seems about the same as earlier strains.
Virologists, most of them, did not expect this. I did.
Let’s consider some simple examples. Imagine that you have someone – a single individual – with what we will call a normal strain, A. On average, in the current situation, it has an R0 well above 1.
What is the most likely outcome? Most likely, he doesn’t spread it to anyone, and it dies out. Overdispersion: most people don’t do much spreading, and a few do a lot. Let’s say that 20% of those infected do all of the spreading. Right off the bat, 80% of new strains die out, just because of this pattern.
Now, imagine that we simultaneously introduce two new strains, A and B with a 50% greater R0%. for each, a single individual.
There are four possible outcomes: A spreads widely, B spreads widely, both and A & B spread widely , both A and B disappear.
Most likely both will disappear (~64% chance) .
There’s a fair chance that A will spread while B is lost, and a moderately larger chance that B will spread while A is lost.
There is essentially zero chance that both will spread widely: even if both manage to avoid being lost by chance in the beginning, B will grow faster than A and replace it.
So, suppose you introduce one person with A, and one person with strain B: can you judge the relatively infectivity by which one succeeds? No – there’s a significant chance that the less-infective one will win out.
Now consider a situation in which A is already common, and a single case of B is introduced. what are the possible outcomes?
2. B replaces A – happens if B is lucky enough to get past the risk of extinction when rare. But once it gets up to a few hundred copies, it will surely replace A.
What can we conclude if B is rapidly replacing A ( as has been the case with the new UK strain)?
That it surely has significantly higher transmission, significantly higher R0.
Many virologists thought this very unlikely, and some said that you could never know that a new variety had higher transmission from mere incidence data: you must understand the biological mechanism. Are they correct? Obviously not.
Why did they think that a new, more transmissible variant of COVid-19 was unlikely? I would say there are several reasons. One, they typically deal with viruses that have been around for a long time, like measles ( > 1000 years) . An old virus is going to be pretty well-adapted to to humans. Probably it’s at a local optimum, where small changes would reduce infectivity. But you don’t expect that high degree of optimization in a virus that’s brand new in humans: while spreading to very many people, more than 100 million, greatly increases the chance of transmission-increasing mutations. Fisherian acceleration.
Like most biologists and MDs, most virologists don’t know any theory, and in fact don’t _believe_ in theory. For this they occasionally pay a price.
Was thinking about various factors that influenced the discovery of America – by which I mean Columbus’s voyage. Not Saint Brendan or Madoc, not the Phoenicians, not the Romans, not the Solutreans. Not Basque fishermen. Not Leif Ericsson or Bjarni Herjólfsson either, because, although they really did get there, the information didn’t spread far. The same for Polynesian contacts – they did visit South America, but since they themselves were isolated from the Old World, it didn’t go anywhere.
First, they had cheap, reliable ships that were up to the job. I emphasize cheap.
Columbus and European civilization _knew_ there was land on the other side – China, if nothing else. They knew the world was round. Unfortunately for Columbus, potential backers also knew how _big_ the world was, and how very far it was to China ( he had a fruitful delusion about this.)
One factor must have been the discovery of a number of useful islands in the Atlantic: the Azores, Madeira, the Canaries ( rediscovered, really – already inhabited and known to the Romans). An expedition that merely found another Madeira would have more than paid for itself. Legend placed plenty of other islands out in the Atlantic, from Antillia to Huy-Braseal.
Could Columbus have known about Leif Ericsson and Vinland? Just barely possible: there were people in Iceland that remembered Vinland, but the story wasn’t generally known in Europe. And even if he had heard, he was going far south of their discoveries.
People have occasionally wondered if sea beans had something to do with it. Sea beans, or drift seeds, at least the ones we’re interested in, are buoyant seeds come from tropical plants in the New World. They can float long enough to reach the shores of Western Europe. When you found a sea-heart on the beach, something that clearly did not originate in Europe, surely that unknown land felt a bit more real.
You might also be interested in my booklists from 2014, 2016, 2017, 2018, and 2019.
Credentials don’t make an incorrect argument right, and the lack of them can’t make a correct argument wrong. The track record tells you more – George Green and Srinivasa Ramanujan ( and Freeman Dyson) did what they did. In that sense, degrees don’t matter.
But they can give you signals of greater or lesser utility. Ph.Ds in math or the hard sciences prove you have some brains – not necessarily that you will make good use of them, or that you’ll be useful, but sure, you probably have some brains. Or at least you once did. Doesn’t necessarily mean that you know much outside your specialization, or have much sense. Although you might.
What about a Ph.D. in psychology? it doesn’t mean that you can’t have some brains, but its predictive value isn’t very high.
An M.D.? Again, doesn’t mean that you can’t be smart, but, usually, not born puzzle solvers. Significantly overrated by both the general public and holders as an indicator of general omnicompetence.
Ph.D. in education? On average, it predicts that you’re dumber than someone with a B.A in education, already below the general college average.
