ChinCOVID: Sturgis “Surge”

You probably heard about the study that found the Sturgis motorcycle rally was a ChinCOVID “super-spreader” event that caused more than 260,000 new cases of ChinCOmmon cold nationwide, “As of August 29,
2020.” I gotta say, that seems unlikely.

The Sturgis rally started August 7.

August 6 ChinC-virus case count: 5, 033,838

August 29 case count: 6,132,996

Increase: 1,099,158

The researchers are claiming that 23.65% — nearly a quarter –of all new cases in the US came from one motorcycle event in South Dakota. Not all the demonstrations/protests/riots/terrorism/campaign rallies/life-almost-as-usual going on in major — large, densely populated — cities across the country.

One rally in a small, isolated South Dakota town.


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COVID-19: January? TRY to keep up.

The CDC now says SARS-CoV-2 may have been spreading slowly in the US in late January.

The first U.S. cases of nontravel–related COVID-19 were confirmed on February 26 and 28, 2020, suggesting that community transmission was occurring by late February.

This is — among other reasons — is why I do not consider the CDC to be a good source for data. Aside from the fact that nation-wide outbreaks occurred that were too widespread and early to be consistent with a mere late January slow spread, we know that there were nontravel-related illnesses well before that.

  • Ohio: Yes, Ohio, far from Washington. Antibody testing found a case dating as early as January 7. Patients in five counties spread across the state.
  • Washington: Two days after Christmas last year; December 27. Nontravel. That’s when she went symptomatic. Exposure had to be a week or two before that, meaning it was spreading mid-December.

Yes, we know that SARS-CoV-2 was widespread in the US by late last year. Three months before the gov noticed, and decided to use it as an excuse for a totalitarian police state.

Do try to keep up, CDC. Here’s a collection of links to case studies indicating extensive — and mostly harmless — exposure long before the lockdowns.

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IHME Model May Not Be Fraud, Exactly, After All

I had a discussion with a reporter who had done a story claiming that there were 32 COVID-19 deaths, and 610 new cases, “in a span of 24 hours in Georgia.”

Since the Georgia COVID-19 Dashboard reported 2 new deaths and 6 new cases (as of 5/13/2020, 1:25:00 PM), I was a little curious where she got her numbers.

It turns out she was just looking at how much the “Confirmed COVID-19 Cases” and “Deaths” total numbers at the top of the page incremented, and apparently assumed all those cases happened in the previous 24 hours. I explained that the increment actually includes cases from different days. Due to delays in testing and reporting by counties and labs, it can be weeks before a case that popped up on 4/27 finally appears on the Dashboard. In fact, I picked 4/27, because yesterday that date did pick up some new cases, making the new peak day (previously the peak day was 4/20).

While older dates are fairly stable now, the numbers for the past two or three weeks can be fluid.

But then it hit me: I’ve called the IHME model fraudulent because they are clearly generating new peaks by lumping days worth of data together and reporting them as occurring on the same day… just like this reporter did.

The reporter is being sloppy, and gives the impression those cases/deaths occurred in that 24 hour period. That’s the kind of reporting that panicked the nation into an unwarranted house arrest. Great for clickbait, bad for informing people.

For IHME — if this is what they’re doing — it’s laziness and incompetence that’s totally irresponsible in an allegedly scientific endeavour.

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This why the IHME COVID-19 model is fraudulent

I mostly avoid the crappy IHME model now, but I happened to see a reference to it today. A quick comparison to reality made it very clear what they are doing.

This is IHME for Georgia, deaths per day; solid red is allegedly the real numbers, and the rest is projected. Note that their total deaths projection is actually fairly close to reality. But also note their curve.

IHME claims Georgia peaked at 100 deaths per day, and continues to have high spikes.

Now this is the actual deaths per day reported by Georgia. Deaths per day gradually increased, accelerated, peaked, and has been declining since mid-April. It peaked at 48.

Why are the numbers so different? Georgia’s graph shows daily deaths. IHME doesn’t; they save up days’ worth of reporting, then graph those days’ worth as occurring on one day. Thus, the total comes close to Georgia’s reality, but they get to generate continuing spikes that, when curve-fitted and projected, give a much higher and extended curve than reality. They “projected” 45 deaths for 4/29 and 4/30, based on that curve. Reality was 7 and 3, respectively.

Falsifying dates of death to generate continuing peaks is fraud. They’ve had plenty of time to look at the real data reported through multiple sources. If it was merely an error, they had plenty of time to correct it. They did not. They continue to use false data. They have no excuse for not noticing that they’re claiming a peak over twice as high as Georgia’s actual peak; 100 vs. 48.

Since they also know governments were using their model to make plans to deal with the outbreak, and knowingly provided — are still providing — a falsified model, I think a criminal investigation is warranted.

Added:For the slow, here’s a simplified example of how counting incidents on the wrong day skews the model. The vertical axis is Cases. The horizontal axis is Days.


In this example, we had ten total cases, with 1 case occurring per day. That is graphed in black. The curve is flat.

But using IHME’s methodology…

Day 1: They report 1 case.
Day 2: skipped
Day 3: They added days 2 and 3, and reported both on day 3.
Day 4: skipped
Day 5: skipped
Day 6: Added days 4, 5 and 6 together, and reported all on day 6.
Day 7: skipped
Day 8: skipped
Day 9: skipped
Day 10: Added days 7, 8, 9, and 10 together, and reported all on day 10.

A much different curve, showing cases increasing now. But the total number of cases is the same.


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IHME Model: Stephen King has been fired

The IHME COVID-19 model, notorious for “projecting” disease stats that look more like an apocalypse model than reality, has been adjusted once again. This time, though, instead of “fixing” it to make hindcasts even worse, they actually revised it such that numbers are now into the merely exaggerated.

I first noticed the model’s issues when examining March 31 “projections.” I chose that date because it had become the present, and thus a useful test of the model’s accuracy.

It failed. It’s death projections weren’t too bad, but the hospital beds needed numbers were insane.


3/31 projected beds: 95,581

3/31 actual beds: 38,743

For giggles, earlier this month (April) they revised the model and the 3/31 prediction (a hindcast, mind you) became…


With this latest, post-Stephen King, revision, 3/31 now hindcasts 41,070 beds needed vs. the actual 38,743 that I estimated. That’s much more reasonable.

The total projected deaths looks more like what I expect, too: 60,415.

Don’t get me wrong. For those that do get COVID-19 and go sour, it’s bad. Very bad. But that’s also true of the seasonal flu (which has already killed more people than COVID-19 is projected to kill). And the people who are particularly vulnerable to SARS-CoV-2 are the same people particularly vulnerable to the flu.

COVID-19 looks less like what most people see as a pandemic, and more like an extended “flu” season (yes, I know influenza and corona viruses aren’t the same thing).

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IHME Model Revisited

So how’s that IHME COVID-19 model doing today? When last we looked, it appeared to be run by Stephen King

For April 5, it projected:

  • Beds: 179,267 (126,649-225,921)
  • Deaths per Day: 1,529 (1,228-1,790)
  • Total Deaths: 9,893 (9,023-10,646)

Reality says:

  • Beds: 37,589*
  • Deaths per Day: 1,030
  • Total Deaths: 9,536

* I’ve estimated that using Georgia’s 19.3% of cases needing hospitalization. I applied that figure to US total cases (333,173), then subtracted the 17,177 recovered cases and 9,536 dead, neither of which need hospitalization for COVID-19 anymore.

DPD comes in almost 200 fewer than the low end of IHME’s range, but nearly 500 below the mean, which they’ve inflated by about 50%. Total deaths predicted is still high, but the range includes reality.

But beds are pure science fiction (there’s nothing scientific about that). They projected 4.77 times as many beds as appear to be in use. On this planet/spacetime continuum, at any rate.

Amusingly, they tweaked the model since my last reality-check. They had “predicted” that 95,581 beds would be needed on March 31. Now it claims we needed 107,638 (90,119-122,430) beds that day. With hindcasting getting worse, I have zero confidence in forecasting.

Holy shit, guys. If you’re going to change your model, change it to get closer to reality, not farther.

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I think we can safely say that model is flawed

The University of Washington’s Institute for Health Metrics and Evaluation has a model that projects potentially useful data like US COVID-19 deaths, and hospital beds and ICU units needed.

So how good is it?

For today, it had predicted:

  • Beds: 95,581
  • ICU: 19,638
  • Daily Deaths: 719 (638 – 781)
  • Total Deaths: 3,710 (3,629 – 3,772)

Meanwhile in the real world:

  • Daily Deaths: 439
  • Total Deaths: 3,580

As you can see, reality was not only considerably lower than the prediction, reality didn’t make into the low end of the predicted range. (ETA: I see the numbers have been updated. Daily deaths is now 648 which does bring it into the low end of predicted. Total deaths increased to 3,789.)

Beds and ICU units are tougher to figure. If anyone has a link to US data for those, drop it in comments below. But I can use Georgia as a proxy.

Georgia has 818 COVID-19 patients hospitalized, and 3,817 total cases. The United States has 180,789 total cases, making Georgia 2.1% of the total. If Georgia’s beds are also 2.1% of total beds, then…

38,743. A far cry from 95,581; a bit more than a third of the prediction.

Some studies have shown that roughly 80% percent of COVID-19 infectees are asymptomatic or quite mild cases. That would leave 20% who might need hospital beds: 36,158. Which is fairly close to my proxy estimate of hospitalizations, which works out to 21.4%.

Gloom and doom doesn’t help. Sure, you can use worst case projections to plan… for the worst, but when a model’s best case is nowhere near lower reality, it’s time to scrap the model, and start over.

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An Open Letter to Professor Steven P. Grossman

TO: Steven P. Grossman (
SUBJECT: An Open Letter to Professor Steven P. Grossman


Regarding your opinion column:

The right to bear arms is not absolute

You should be ashamed of yourself. Deliberate false equivalence in a professor should be grounds for a university investigation of whether your anti-rights position has colored your actions as dean.

Do you think it is permissible to yell “fire” in a crowded movie theater in order to create a panic? How about whether it is legal to speak to a crowd and tell them to go out and shoot the first police officer they see, or homeless person or teacher?
While virtually everyone accepts such a common sense limitation on the First Amendment, there are those who argue that anyone who proposes limitations on the possession of guns is an opponent of the Second Amendment’s right to bear arms.

I see what you did there. When an ignorant layman does that, I’m willing to consider the possibility that it is attributable to mere… ignorance. When a law professor equates MISUSE/ABUSE of a right to simple, lawful, and harmless exercise of right, I know it’s purely malicious.

You pretend that lying and threatening are the First Amendment equivalents of the Second Amendment right to POSSESS a tool.

Before you wrote that despicable screed did you fill out a federal form and ask permission from the government to buy the computer on which you composed the column? Did you undergo a prior restraint background to prove your innocence before even obtaining the inanimate tool you used to exercise your First Amendment right to voice that opinion? Did you use a 1980s Intel 80286 computer limited to 768K RAM, because only the military needs a high speed, high capacity Pentium with 8 GB?

Did you get a license to possess your mouth, just in case you might lie to a student — or Baltimore Sun readers? Did you undergo a background check to possess your typing fingers?

Do University of Baltimore School of Law students undergo background checks before purchasing textbooks, or writing class papers?

Yes; threats, lies, incitement to violence are abuses of free speech rights, and we punish people for that. Likewise, assault and murder using firearms are abuses of the right to keep and bear arms. As a law professor I would expect you to know that those are also unlawful and we punish those offenders.

Buying and possessing a firearm differs not from buying and possessing a computer, telephone, megaphone, pen, or pencil. The ABUSE that must be controlled is not the same thing as possessing a tool with the potential to be abused.

Reasonable laws limiting the possession and sale of certain guns are clearly not violative of the Second Amendment. Such laws include but are not limited to those banning weapons, such as the AR-15 designed for combat…

If you believe that modern AR-pattern semiautomatic rifles were designed as military weapons (despite the fact that no nation on the planet generally issues semiautomatic rifles to its regular troops), then how do you square banning them with the precedent of U.S. vs. Miller, 1939, in which the Supreme Court found that short-barrel shotguns could be regulated because they had not been shown to be a weapon used by the military? The Court specifically said that the Second Amendment does protect the possession of military arms. An honest law professor would know and admit that.

Your disdain for basic, constitutionally protected human rights disgusts me.


Carl “Bear” Bussjaeger

Wintemute/UCDavis DUI v. Later Crime Study: Methodology

You know the one; the anti-rights mainstream media is hyping it as demonstrating a strong link between prior DUI convictions and later serious violent crime offenses by law-abiding firearms owners.

I’ve already addressed the horrifically Orwellian privacy violations* by Wintemute and his junior police-statists. That distracted me from my original intent to look at his methodology. Looking at Wintemute’s methodologies is always an exercise in morbid fascination.

As usual, he abused the process to generate a foregone anti-rights conclusion. Continue reading

Another bogus gun viol.. Wait. WTF?

I saw this today.

Drunk drivers more likely to commit violent gun crimes in California, study finds
Gun buyers in California convicted of driving under the influence are at greater risk of committing a violent crime or a firearm-related offense, a group of researchers at UC Davis found in a broad study that tracks gun purchasers over the span of a decade.

Well, that sounds bad, right? Maybe we should make a DUI conviction a prohibited person qualifier.

Or maybe we should look at the study and see what they really found. Continue reading