Let’s do some science

The goalposts keep moving. The latest we are being told is that the COVID vaccine keeps the illness from being as severe. We are told that we should trust the science, and I am a facts and figures kind of guy, so let’s do just that.

So we will do what is called a retrospective analysis of COVID data to see if the presence of the vaccine changed the Case Fatality Rate (CFR) of COVID. All figures for this analysis were obtained from this website.

By December 15, 2020, exactly zero percent of the US population had received a COVID vaccine. Also on that date, 17,299,965 Americans had been determined by various tests to have COVID. Of them, 320,309 had died. That means that 1.85 percent CFR.

In the ensuing months, 70 percent of the public received a COVID vaccine between December and September. So how did it do?

As of September 11, 2021, a total of 41,905,818 had tested positive for COVID. As of that same day, 678,866 of them had died. That means that 24,605,853 people tested positive for COVID and of those, 358,557 died from December 16, 2020 to September 11, 2021. That means a 1.46 percent CFR .

Reducing the CFR from 1.85 to 1.46 represents a 31 percent reduction in the CFR. So what caused this reduction in the fatality rate? There are a couple of things that this could point to:

  1. The vaccine does in fact reduce the severity of COVID.
  2. During the first few months of the pandemic, tests were in short supply. For this reason, the only people being tested were those who were the sickest with apparent COVID symptoms. Those who had only mild symptoms, or no symptoms, simply weren’t tested, and this skews the CFR higher.
  3. The medical profession has simply gotten better at treating the illness.
  4. The new strains of COVID are less virulent

In science, you want to eliminate all of the variables except for the variable you want to test (called the independent variable) and the result (called the dependent variable). So how do we do that?

The independent variable is the vaccine. We will isolate that by looking at the 90 day period from June 15 to September 15. During that period, an average of 60 percent of Americans was vaccinated.

To isolate the dependent variable, we will look at a 90 day period immediately prior to the vaccine becoming available. That will eliminate factors 2 and most of 3, which will allow us to examine as pure a set of variables as possible with the dataset we have.

So- let’s do some math. From September 15 to December 15, 2020, a total of 10,343,397 Americans tested positive for COVID. During that same time period, 115,703 died. The CFR for the 90 day period immediately preceding vaccine distribution was 1.12 percent. Now from June 11 to September 11, 2021, a total of 7,697,685 people tested positive, and 63,589 died, for a CFR of 0.83 percent. So there was a 25.9 percent reduction in the CFR between the two time periods.

The conclusion here is that vaccinating approximately 60 percent of the public corelated with an approximate 26 percent decline in the CFR. Note that this does not mean that the vaccine was the cause of this decline. In fact, it implies the opposite. If the vaccine were the cause of this decline, the decline in the CFR should have been larger.

Real scientists would be looking into other reasons for the decline. I am betting that they aren’t.

8 replies on “Let’s do some science”

Real scientists would be doing in depth studies on every possible treatment and transmission mode, instead of issuing public guidance based on assumptions and rules of thumb.
For example, which type of masks, if any are effective? Is 6 feet really enough, and if so under what conditions? How much does surface contact spread the disease versus aerosols? There is disappointingly little information available about these and other vital questions.

Don’t forget Cuomo did his best to make sure the most vulnerable people WERE exposed to Covid during the first pass. And so did several other dem governors.

For calculating CFR the FROM cause rather than WITH cause is used for CFR. So divide the CDC numbers by at least 4 or 5.

Then there is the FROM problem with SARs CoV 2. The case definition for SARs CoV 1 in 2003 was moderate pneumonia symptoms, clouding on chest X-RAY, then and only then a plus positive RT/PCR test. With SARs CoV 2 its just a positive RT/PCR test result. Which in a low prevalence scenario has a 90% false positive. So the FROM numbers for SARs CoV 1 in 2003 (at least outside China) are pretty accurate. The SARs CoV 2 FROM numbers are not.

The IFR by this stage is endemic influenza levels. But only because of the very skewed age demographics. For < 50's it is basically nil.

And if you looks at the cherry picked Phase II results for the Pfizer vaccines the claimed efficacy / mortality rate reductions are statistically impossible. Or rather the confidence interval is so wide as to make the numbers meaningless. There is a very good reason why the usual approval timeline is 4 to 6 years. It takes years not months to get accurate numbers for efficacy etc for viral respiratory infections like human corona viruses.

I wasn’t trying to determine with versus from. I was trying to determine if the vaccine made a difference. What you are trying to do is not only impossible with the data available, it was irrelevant to what I was looking at.

As a math guy of many decades (day job) I’ve been watching the FROM deaths that are viral pneumonia. Year on year changes. Due to data collection artifacts the monthly numbers are high noise until normalized. Not the COVID case numbers which are epidemiologically meaningless. The large majority of WITH deaths were also HSV positive but thats about as relevant as the COVID case criteria. The vast majority of COVID cases are assigned purely on a non valid clinical test criteria. Very high Type I and Type II errors. Not the 95% / 5% that has always been considered acceptable for differential diagnosis but 50% / 90%.

Given the low community R0, low attack rate because of high cross immunity due to endemic HCOV infections (1% general population, up to 10% in children) and the very severe disruption of normal social interaction networks (population interaction events reduced 90% plus) all traditional analysis of short term impact of public health measures will be statically meaningless.

There again, based on the many very basic math errors I am seeing in the published literature ( >80% of all papers) all this is par for the course.

But I’d love to the HAP numbers for SARs CoV 2 viral pneumonia. SARs CoV 2 like SARs CoV 1 looks like its mostly a HAP. Acquired via hospital / heath care facilities.

An interesting question, indeed.

Now, for even more questions, put on your hat of the finest tinfoil, fire up the JHU dashboard, and look at the graphs for various countries in Southeast Asia. Malaysia, Thailand, Vietnam, and to a lesser extent Cambodia: those look mighty anomalous, and to a sufficiently tinfoiled mind, even suspicious.

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