The epidemiological model that drove the world to shut down was ‘buggy’

Early in the Wuhan virus’s trajectory, the British government announced that it was going to go for a herd immunity approach (that is, the approach Sweden eventually used). Because the government is Tory, the left-leaning media insisted this would kill every Briton.

These arguments gained weight when epidemiologist Neil Ferguson introduced his model showing that anything other than a total lockdown would kill over 500,000 Britons and 2.2 million Americans. Britain and America instantly stopped in their tracks. Only now, months later, are we learning that Ferguson’s model was a buggy mess.

The British Telegraph reports that the disgraced epidemiologist, who broke quarantine for his sex life, had a fatally flawed model:

The model, credited with forcing the Government to make a U-turn and introduce a nationwide lockdown, is a “buggy mess that looks more like a bowl of angel hair pasta than a finely tuned piece of programming”, says David Richards, co-founder of British data technology company WANdisco.

“In our commercial reality, we would fire anyone for developing code like this and any business that relied on it to produce software for sale would likely go bust.”

[snip]

“There appears to be a bug in either the creation or re-use of the network file. If we attempt two completely identical runs, only varying in that the second should use the network file produced by the first, the results are quite different,” the Edinburgh researchers wrote on the Github file.

The same article notes what many in conservative circles knew long ago, which is that Neil Ferguson has a track record of making inaccurate, apocalyptic predictions:

Concerns, in particular, over Ferguson’s model have been raised, with Konstantin Boudnik, vice-president of architecture at WANdisco, saying his track record in modelling doesn’t inspire confidence.

In the early 2000s, Ferguson’s models incorrectly predicted up to 136,000 deaths from mad cow disease, 200 million from bird flu and 65,000 from swine flu.

At PowerLine, John Hinderaker looked at Minnesota’s equally buggy modeling, which drove bad epidemiological and economic decisions, and learned something fascinating: The primary model was written over a three-day weekend by recent college graduates:

Before Friday, March 20, Marina Kirkeide, who graduated from the University of Minnesota College of Science and Engineering in 2019, was a School of Public Health part-time research assistant working on HPV transmission for Kulasingam. On a gap year before starting Medical School at the University in fall 2020, Kirkeide also had a second job as a lab tech at St. Paul’s Regions Hospital. That Friday, Kulasingam called her and two other research assistants and asked if anyone was available to “work through the day and night” to get a COVID-19 model to Governor Walz the following Monday. They all jumped at the chance.

“I don’t think a lot of researchers get to work on something over the weekend and have public figures talk about it and make decisions based on it three days later,” says Kirkeide, who had to leave her hospital job to focus solely on modeling. She feels the responsibility of such a big project, too. “[In this situation] you don’t have the time to validate as much as you normally would. You want to get it right the first time. And your work has to be really, really quick.” 

Models based upon hypothetical data are just guesswork in fancy costumes. If you want useful information, you need to look at actual data. Daniel Horowitz looked at a Dutch chart drawn up based upon several thousand blood samples and drew solid, not hypothetical, conclusions:

Study this chart for a few minutes and take in all the data – from the asymptomatic/mildly symptomatic rates to the hospital and fatality rates divided by age. You have to get to the 50-59 age group just to reach a 0.1% fatality rate, the level often cited as the overall death rate for the seasonal flu. Those are all lower odds than an individual has of dying in a giving year of any cause and in the case of an average 50-year-old, five times lower.

They didn’t test kids under 20, but their fatality rate is likely near zero.

While the Netherlands is an entirely different country, it has actually experienced a 30% higher death rate per capita than America. So the numbers are likely not any higher here for those under 70, especially because the macro serology tests showing a 0.2% fatality rate (but grossly distorted by the death rate of those over 80), as well as what we are seeing in prisons and ships in younger populations, seems to harmonize with this data. A brand-new study from France also shows very similar estimates of fatality rates, at least for those under 60.

Back in the 1970s, Americans still had enough common sense to react with dismay, rather than reverence, when news broke about a $50,000 academic study concluding that mother’s milk is good for babies. Today, our deference for academia and expertise is so overwhelming that we’ll willing accept anything they tell us, common sense be damned.

Early in the Wuhan virus’s trajectory, the British government announced that it was going to go for a herd immunity approach (that is, the approach Sweden eventually used). Because the government is Tory, the left-leaning media insisted this would kill every Briton.

These arguments gained weight when epidemiologist Neil Ferguson introduced his model showing that anything other than a total lockdown would kill over 500,000 Britons and 2.2 million Americans. Britain and America instantly stopped in their tracks. Only now, months later, are we learning that Ferguson’s model was a buggy mess.

The British Telegraph reports that the disgraced epidemiologist, who broke quarantine for his sex life, had a fatally flawed model:

The model, credited with forcing the Government to make a U-turn and introduce a nationwide lockdown, is a “buggy mess that looks more like a bowl of angel hair pasta than a finely tuned piece of programming”, says David Richards, co-founder of British data technology company WANdisco.

“In our commercial reality, we would fire anyone for developing code like this and any business that relied on it to produce software for sale would likely go bust.”

[snip]

“There appears to be a bug in either the creation or re-use of the network file. If we attempt two completely identical runs, only varying in that the second should use the network file produced by the first, the results are quite different,” the Edinburgh researchers wrote on the Github file.

The same article notes what many in conservative circles knew long ago, which is that Neil Ferguson has a track record of making inaccurate, apocalyptic predictions:

Concerns, in particular, over Ferguson’s model have been raised, with Konstantin Boudnik, vice-president of architecture at WANdisco, saying his track record in modelling doesn’t inspire confidence.

In the early 2000s, Ferguson’s models incorrectly predicted up to 136,000 deaths from mad cow disease, 200 million from bird flu and 65,000 from swine flu.

At PowerLine, John Hinderaker looked at Minnesota’s equally buggy modeling, which drove bad epidemiological and economic decisions, and learned something fascinating: The primary model was written over a three-day weekend by recent college graduates:

Before Friday, March 20, Marina Kirkeide, who graduated from the University of Minnesota College of Science and Engineering in 2019, was a School of Public Health part-time research assistant working on HPV transmission for Kulasingam. On a gap year before starting Medical School at the University in fall 2020, Kirkeide also had a second job as a lab tech at St. Paul’s Regions Hospital. That Friday, Kulasingam called her and two other research assistants and asked if anyone was available to “work through the day and night” to get a COVID-19 model to Governor Walz the following Monday. They all jumped at the chance.

“I don’t think a lot of researchers get to work on something over the weekend and have public figures talk about it and make decisions based on it three days later,” says Kirkeide, who had to leave her hospital job to focus solely on modeling. She feels the responsibility of such a big project, too. “[In this situation] you don’t have the time to validate as much as you normally would. You want to get it right the first time. And your work has to be really, really quick.” 

Models based upon hypothetical data are just guesswork in fancy costumes. If you want useful information, you need to look at actual data. Daniel Horowitz looked at a Dutch chart drawn up based upon several thousand blood samples and drew solid, not hypothetical, conclusions:

Study this chart for a few minutes and take in all the data – from the asymptomatic/mildly symptomatic rates to the hospital and fatality rates divided by age. You have to get to the 50-59 age group just to reach a 0.1% fatality rate, the level often cited as the overall death rate for the seasonal flu. Those are all lower odds than an individual has of dying in a giving year of any cause and in the case of an average 50-year-old, five times lower.

They didn’t test kids under 20, but their fatality rate is likely near zero.

While the Netherlands is an entirely different country, it has actually experienced a 30% higher death rate per capita than America. So the numbers are likely not any higher here for those under 70, especially because the macro serology tests showing a 0.2% fatality rate (but grossly distorted by the death rate of those over 80), as well as what we are seeing in prisons and ships in younger populations, seems to harmonize with this data. A brand-new study from France also shows very similar estimates of fatality rates, at least for those under 60.

Back in the 1970s, Americans still had enough common sense to react with dismay, rather than reverence, when news broke about a $50,000 academic study concluding that mother’s milk is good for babies. Today, our deference for academia and expertise is so overwhelming that we’ll willing accept anything they tell us, common sense be damned.