In this April 18, 2020 article from Issues & Insights, Micheal Fumento makes the case that epidemic modeling should be ignored for having any value in prognostication.
With each new pandemic over the last 50 years, the pattern has been the same–wild predictions of death followed by a reality far short of the predictions. This was true of AIDS, Ebola, Avian flu, and SARS. Here are some key points:
The avian flu strain A/H5N1, “even in the best-case scenarios” was to “cause 2 (million) to 7 million deaths” worldwide. A British professor named Neil Ferguson scaled that up to 200 million. It killed 440. This same Ferguson in 2002 had projected 50-50,000 deaths from so-called “Mad Cow Disease.” On its face, what possible good is a spread that large? (We shall return to this.) But the final toll was slightly over 200.
In the current crisis the most alarming model, [The Imperial College Study] nay probably the most influential in the implementation of the draconian quarantines worldwide, projected a maximum of 2.2 million American deaths and 550,000 United Kingdom deaths unless there were severe restrictions for 18 months or until a vaccine was developed. The primary author: Neil Ferguson. Right, Mad Cow/Avian Flu Fergie.
Then a funny thing happened. A mere nine days after announcing his model, Ferguson said a better number for the U.K. would be only 20,000. The equivalent would be fewer than 80,000 American deaths. Technically, that U.K. number was buried in a table in the report under what might be called “a fantastic case scenario.” But could that reduction possibly reflect a mere nine days of restrictions? No.
Then suddenly Fauci announced a flat figure of “more like 60,000,” the same number the CDC says died of flu two years ago. Probably not coincidentally, until quite recently the agency said there were 80,000 flu victims that year, before lowering it to 61,000 – presumably because people were using that figure to compare to COVID-19 deaths. In any event, the 1968-1969 “Hong Kong flu” killed an estimated 100,000 Americans, or 165,000 adjusted to today’s population.
Then Fauci finally said it. “I’ve spent a lot of time on the models. They don’t tell you anything.” A few days later CDC Director Robert Redfield also turned on the computer crystal balls. “Models are only as good as their assumptions, obviously there are a lot of unknowns about the virus” he said. “A model should never be used to assume that we have a number.”
The only “model” with any success is actually quite accomplished and appeared in 1840, when a “computer” was an abacus. It’s called Farr’s Law, and is actually more of an observation that epidemics grow fastest at first and then slow to a peak, then decline in a more-or-less symmetrical pattern. As you might guess from the date, it precedes public health services and doesn’t require lockdowns or really any interventions at all. Rather, the disease grabs the low-hanging fruit (with COVID-19 that’s the elderly with co-morbid conditions) and finds it progressively harder to get more fruit.
The models essentially have three purposes: 1) To satisfy the public’s need for a number, any number; 2) To bring media attention for the modeler; and 3) To scare the crap out of people to get them to “do the right thing.”
Assuming it’s possible to model an epidemic at all, any that the mainstream press relays will have been designed to promote panic. Take it from Fauci, who early on so eagerly employed them – they are to be ignored. Now and forever.
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