Culling The Herd: The Coronavirus Death Rate And The Conservative Herd Immunity Con.
What is the death rate from Coronavirus, or for that matter any disease? The traditional formula for this is quite simple.
(Confirmed Deaths ÷ Confirmed Cases) X 100 =Fatality Rate Percentage.
This is the formula regularly used for everything from the flu to the Coronavirus. However, it is not good enough for a conservative narrative seeking to frame sheltering in place as an evil-commie-lib-socialist plot aimed at wrecking the wonderful capitalist utopia brought to us by the Trump regime. It’s not good enough because it produces a death rate much too high to justify reopening society.
Let’s take New York, the hottest spot for Coronavirus in the nation, or for that matter, in another manifestation of American supremacy, the world. As of this writing, New York has almost 280,000 confirmed cases and about 21,400 deaths for an eye popping death rate of nearly 7.7%. We can’t have that. So we need to adjust the numbers.
Adjusting the Numbers: The Flawed Methodology
In this case the key to adjusting the numbers, to bring down that percentage, is to increase the denominator. Argue that the confirmed cases understates actual cases, and by a lot. Then use the estimated actual cases instead of confirmed cases in the equation.
An example of this approach is Kevin McCullough’s Townhall article with the inflammatory title Antibody Testing: Proves We’ve Been Had! I encourage you to read it as an example of this flawed methodology. If you accept Mr. McCullough’s reasoning, you have indeed been had.
Mr. McCullough cites a New York study that found 13.9% of those tested had Coronavirus antibodies. This implies they contracted coronavirus, symptom free, and now have immunity from it. While such immunity is not actually proven I shall for this discussion assume it exists. When reviewing Mr. McCullough’s article, click through to the article he uses as the citation for the New York study. When you do two things will be made quite clear in that article.
- Those tested were not representative of the general population of New York. The tests were done in grocery and box stores, so by definition these were people out and about. In the words of Governor Cuomo: “These are people who were out and about, shopping, they were not people who were in their home, they are not people who were isolated and not people who were quarantined who you could argue, probably had a lower rate of infection because they wouldn’t come out of the house.”
2. The same article McCullough uses as his source also states that the number of deaths in New York is greatly understated because it includes only deaths in hospitals and nursing homes. It notably excludes what is believed to be a significant number who die in their own homes.
Even though McCullough’s own source makes clear the sample tested was not representative (by failing to include groups with likely lower infection rates), McCullough nevertheless treats it as representative. He extrapolates the 13.9% to the entire population to determine the actual number of infections is 2,500,000.
Even though McCullough’s source also makes clear the number of deaths is understated McCullough nevertheless uses the current official number of deaths that fails to include those who died at home. Surely if one is going to extrapolate an estimated number of “actual cases” for the denominator, then consistency (and intellectual honesty) would require also extrapolating an estimated number of actual deaths for the numerator, particularly when you know that number is greatly deflated. Alas, such intellectual honesty was lacking in Mr. McCullough’s approach.
McCullough takes the best for his argument approach of an estimated 2.5 million infected, and the also best for his argument known to be deflated death count, and plugs that into the formula. He then concludes the real fatality rate “is actually .75%.” He compares that favorably to the seasonal flu’s fatality rate, ignoring the obvious confirmed apples to estimated oranges problem. Many also get the flu without interacting with the health system and being formally diagnosed. If you applied his method to the flu it too would have a lower fatality rate than is reflected in McCullough’s statements of the flu’s fatality rate.
The Culling: What It Takes To Get To Herd Immunity
Mr. McCullough talks about herd immunity, arguing millions are already immune and we are closer to herd immunity than we realize. Getting to herd immunity requires culling a certain percentage of the herd, a bad thing if you are among those culled, or someone you care for is. McCullough does not tell you how big that culling would be, even applying his own rosy (and wrong) numbers. I will.
If you apply McCullough’s 0.75% to the U.S. population of 330 million you get 2.5 million Americans “culled” from the herd, as in dead.
I accept that number as somewhat high because at some point herd immunity confers significant protection to even those not immune. That point, for the record, is about 70% of the population, or over 200 million Americans. So applying McCullough’s own (wrong) 0.75% fatality rate to the more than 200 million Americans required to contract the virus to confer herd immunity means around 1.5 million dead Americans. That’s not anything like the flu.
Another point here. We can describe the confirmed cases in New York as those who actually get sick. They have symptoms that prompt the test. In New York that is about 280,000 cases. Again, accepting McCullough’s figure of 2.5 million New Yorkers who have gained immunity from having the virus, that means that about 11% who get the virus get sick. Applying that to the 200 million Americans required to gain the benefit of herd immunity means about 22.5 million sick Americans, of which (as explained above) about 1.5 million would die. Again, that’s not comparable to the flu.
Perhaps that’s an acceptable loss to some, but at least be up front about the carnage you are talking about. What’s more keep in mind that the flawed start to McCullough’s method means his 0.75% is wrong, likely much higher, and that the deadly consequences of returning to normal are also much higher.
In the real world, there is no sign of herd immunity. Yesterday (April 24th) saw the biggest single day increase in new coronavirus cases yet.