Coof Madness

Long time, no touch this issue. Two items:

Today, the excellent Coffee and Covid newsletter sums up the news:

I was able to find one person in the U.S. — little Sally Rodriguez, 3rd grade, from Akron, Ohio — who was surprised yesterday to learn that the CDC’s vaccine approval committee (“ACIP”) met, took public comment, and then voted 15-0 recommending that covid shots be added to the childhood vaccination schedule.

Two of the 15 committee members, who supposedly are experts or something, were wearing a mask ON ZOOM. They actually believe covid travels over the internet. Or maybe they think they can get it from THEMSELVES. Either way, it was a bad sign right from the jump.

Um, yeah. Tinfoil hats make more sense.

The committee, who were all sitting in little boxes in a dystopian version of “Celebrity Jeopardy,” never discussed the fact that covid poses a miniscule risk to healthy kids. The committee never discussed natural immunity. The committee never discussed adverse events.

The CDC hasn’t yet officially added the shots to the standard school schedule, but given the unanimous recommendation from the committee, it’s a sure bet, likely to happen today or tomorrow.

As soon as that happens, the vaccine makers will permanently enjoy liability protection, and the national state of emergency that is currently shielding them can end. So that’s a blessing. Then it will be up to each state whether to follow the CDC’s new guidance and include the mRNA shots in its list of vaccines required to attend school. Many states allow religious exemptions, and a few states have recently eliminated exemptions.

To recap: generally, before 2020 and even with all the interested parties (that would be both the drug makers and the government approval agencies) playing the hell out of our flawed and biased drug approval process, it took 8 to 10 YEARS and about a $1B to get a new drug approved. Now, after less than 2 years OF FIELD TRIALS – that means WE ARE THE GUINEA PIGS – and, effectively, none of the usual trials done before a drug is approved FOR ANYONE, the CDC MANDATES a drug FOR CHILDREN who are, according to their own numbers, effectively at no risk. After two+ years of shouting down, demonizing, and destroying the careers of people who pointed out the bald refusal of the drug companies and the CDC to follow any of the basic science standards (e.g., no ongoing control groups, no double-blinds, no support for adversarial positions (to put it mildly), no serious adverse affects studies, no cost-benefit analyses) they MANDATE this drug FOR CHILDREN.


Another Coffee and Covid reference from a couple days ago:

A new pre-print study published on medRxIV last week titled, “Age-stratified infection fatality rate of COVID-19 in the non-elderly informed from pre-vaccination national seroprevalence studies.” In the study, researchers calculated the current worldwide ‘infection fatality rate’ for covid. You remember the IFR — it’s the ratio of number of deaths to number of confirmed infections.

Back in the day, you could get canceled for comparing covid’s IFR to the flu’s IFR.

The researchers found, for unvaccinated and for previously uninfected, the median covid infection fatality rates were:

  • 0.0003% at 0-19 yrs
  • 0.003% at 20-29 yrs
  • 0.011% at 30-39 yrs
  • 0.035% at 40-49 yrs
  • 0.129% at 50-59 yrs
  • 0.501% at 60-69 yrs

I probably don’t need to say this, but for all cohorts under 50, these IFR’s are far below the flu. For 50-59, the covid IFR is comparable to flu. And, flu IFR’s are also higher for older people, I just don’t have those figures handy this morning.


Long-time readers may recall that, early on in the panic, I (along with many others) pointed out the absurdity of the CFR – Case Fatality Rate. In a disease with a huge percentage of asymptomatic infections, and where even symptomatic infections tended to have very minor symptoms, it is inevitable that huge numbers of infections were going to go unnoticed or otherwise unreported. This, before noting the panicky, highly incented drive to overcount infections and deaths. Put it all together, and I (and, again, many other people) pointed out that simply applying a little logic to the picture, and the IFR, meaning, the chance of anyone dying from a Covid infection, had to be at least an order of magnitude under the scary-sounding CFR.

The above numbers suggest that even that idea was overly pessimistic. A person under 20 stands a three in a million chance (per year, I assume, since these numbers tend to be annualized) of dying from a Covid infection. That’s what we numbers guys tend to call noise, in the big picture. Of all the bad things that can and do happen to young people, this ain’t the one to worry about.

Today’s Essay by William Briggs

No time to write anything, but can link to stuff you should read. Like this:

All Those Warnings About Models Are True: Researchers Given Same Data Come To Huge Number Of Conflicting Findings

Some brave souls handed out a massive dataset to a bunch of sociologists, and asked them to use it to determine some very sociological-sounding relationship: “Whether “more immigration will reduce public support for government provision of social policies.””

Hilarity ensued.

I’ve loved reading Dr. Briggs since I first came across his blog many years ago, because, as a pro, a real mathematician and a scientist, he gives succinct and accurate statements where I, the amateur (in the best sense of the word, I’ll own) have only scattered and sometimes overbroad thoughts. For example, here he sums up the fundamental, inescapable problems with modeling:

There are many warnings about models we examined over the years, you and I, dear readers. Two that should have stuck by now are these:

1. All models only say what they are told to say.

2. Science models are nothing but a list of premises, tacit and explicit, describing the uncertainty of some observable.

The first warning is easy to see, and it goes some way in removing the mysticism of “computer” models (that a model was computed still impresses many civilians). Every one of those 1,253 models was a computer model.

The second warning I can’t make stick. Let me try again. By premises I mean all the propositions, or assumptions, observational or otherwise, that speak of the observable. This also includes all premises that can be deduced from the premises.

Even before I spent a couple of decades working with a particular mathematical financial model, a million plus lines of code describing all the cash, tax, and accounting implications of a given transaction, I knew instinctively that models just describe a perfect world that lives only inside the model builder’s head. After asking clients a thousand times to specify what they wanted to model to do, and telling them a thousand times that, no, the model output doesn’t predict the future other than saying what will happen IF a giant list of assumptions hold exactly as specified, I knew two things:

  1. A well-built model can be very useful if used intelligently by people who understand how it works;
  2. Almost nobody understands how models work.

The pros I worked with understood that any transaction can be blindsided by some random unpredictable or at least unexpected event with financial implications. “Risk” was thus built into the model – but only insofar as such risk could be measured. But since very well compensated and trained professionals have been working to identify and quantify risk for centuries now, there’s a certain level of confidence that models like the one I worked with can be useful. But an 8-point quake hits California? Krakatoa goes off again? Some drooling imbecilic pushes the Big Red Button while reaching for his tapioca? Hey, all bets are off.

Slightly more subtly: while any number of individual disastrous events may be vanishingly unlikely, taken all together, it’s all but inevitable that some unexpected disaster or other will in fact happen sooner or later. This observation hardly rates as science – it’s more like history, or common sense. But it is nonetheless real.

Anyway, read and enjoy.

Round 2: What’s More Foodie?

A. Homemade pastrami on fresh homemade ciabatta rolls, with mustard, sauerkraut, and sharp cheddar?


B. Pulled pork from a beautiful Boston butt off a backyard raised pig, on the same ciabatta rolls, with a homemade Carolina honey mustard sauce?

I mean, c’mon, man. We’re living the good life. More relevant info: actually had a brisket off a locally raised grass fed cow, but there are drawbacks: a huge fatty brisket is kinda what one wants for pastrami, not necessarily a beautiful but lean one off a grass fed cow. Pastrami still remains the best, highest use for brisket, so not a loss by any means, it’s just that – I think – pastrami anticipates a low quality cut, and this was hardly that. No matter – it was fabulous.

We got that lovely grass fed brisket from my daughter’s father in law, and since I was making pastrami anyway, I got a Costco slab o’ beef. Pastrami making is one of those things where, once you’re making any, you might as well make a lot. Brined in a new recipe for me, one including a significant amount of sugar, and it was frankly wonderful. BUT – I went oven-baked, not smoked, because I don’t have a smoker and failed to make arrangements with some people I know who do. It works fine, but I’m going to need to run the side-by-side with a smoker some day…

The grass-fed one was absolutely delicious. But I’m not sure the Costco one, especially the point, was not as good, and more ‘traditional’ – marbled, fat cap, etc. Couldn’t go wrong here.

The Boston butt was about as beautiful a cut of pork I’ve ever seen: lovely red, even marbled. And it cooked up wonderfully.

So, yea.