Neil Ferguson, the “expert” epidemiologist from the Imperial College in the U.K., is back in the news… and not in a good way.
As a reminder, Ferguson almost single-handedly served as the catalyst for causing fear and panic about COVID-19. And his research led to lockdowns around the world.
Ferguson predicted that 2.2 million Americans would die from COVID-19 in a worst-case scenario. He also predicted that 510,000 would die in the U.K. without a lockdown. Even if steps were taken to stop the spread, he claimed 250,000 in the U.K. would perish.
Those estimates have turned out to be remarkably wrong.
In the U.S., mortalities from COVID-19 are now at 51,495, a mere 2.3% of what Ferguson predicted. Ferguson’s prediction was nearly 43 times higher than current numbers.
(Note: This mortality number [51,495] is lower than what we see in the mainstream media. The National Vital Statistics System, which tracks all U.S. deaths, started stripping out flu and pneumonia deaths. Interested readers can review the numbers daily here. The total is still inflated, but it is closer to reality than what we are reading in the news.)
And in the U.K., mortalities are now at 32,692… nowhere near 250,000.
Of course, any loss of life is heartbreaking to see. But the actions taken because of Ferguson’s research have already caused more than $10 trillion of economic damage and growing. Yet the model Ferguson used was not shared or peer-reviewed.
But due to intense pressure from the academic and political communities, the code and assumptions used to determine these grossly inaccurate predictions were released a few days ago.
A team at Edinburgh University has been analyzing the software.
Get this – every time the code is run, it produces different outputs, even with the same inputs. It produces different outputs if it is run on different computers.
There is no differentiation between different demographics. An asymptomatic child’s ability to spread the virus is assumed to be the same as an infected nurse or doctor at a hospital.
Even Ferguson’s assumptions on mortality rate are grossly overstated. He assumed in the U.S. that the number of mortalities would be 0.9% of those who contracted COVID-19.
Swedish epidemiologist Johan Giesecke, who is highly critical of Ferguson, has a current estimate of the mortality rate at 0.1%. And as I wrote yesterday, we are seeing data in the U.S. that suggests the mortality rate may be as low as 0.03%.
And the model itself is roughly 15 years old. That got me thinking… What was Ferguson’s track record of predictions for previous viral outbreaks?
In 2009, Ferguson predicted that as many as 65,000 would die in the U.K. from the swine flu outbreak. The actual number was less than 500.
In 2005, Ferguson predicted that as many as 200 million people could be killed worldwide from the bird flu outbreak. Not a typo – 200 million. Reality? Less than 1,000.
In 2001, Ferguson predicted that as many as 150,000 would die in the U.K. from foot-and-mouth disease. Only 200 died. But six million animals were culled, and farmers lost billions of farming revenue.
With a track record like that, why would anyone listen to Ferguson at all?
One of our biggest societal challenges is figuring out what information sources we can trust. We are flooded with conflicting information.
My team and I spend countless hours every day sifting through information, checking and double-checking sources to figure out what’s true and what’s biased.
Needless to say, knowing what is happening is critical to understanding investment implications.
Be sure to keep reading The Bleeding Edge for our findings. Now to our insights…
