How Billions of People Got Scammed By Neil Ferguson's COVID-19 Warnings of Millions Dying

Billions of people across the planet continue to cower in a fear that was ginned up to a large degree by forecasts made by British epidemiologist Neil Ferguson that millions would die from COVID-19.

Even now as the scam is slowly being revealed, here in San Francisco the fear is thicker than the city's famous summer fog.

Most people won't even get in an elevator car if someone else is in it. Couples take walks with no one within a hundred feet of them but they wear masks.

And The San Francisco Chronicle reports:

As both state and local shelter-in-place orders are showing signs of relaxing, residents are starting to think about eventually getting back to work and what that may look like. But with social distancing being a part of life now, many may choose to avoid crowded BART trains, Muni buses and even casual carpools with strangers, and instead opt to get to work in their own vehicles. A new study reveals how this may affect Bay Area traffic — and it's not good.

The new study from Vanderbilt University projects a dramatic rise in traffic in the Bay Area caused by a rush to single-occupancy vehicles after the shutdown. It could potentially increase the morning commute alone by up to 42 minutes.

To be sure, there are other bad actors in the fearmongering beside Ferguson, including Trump advisor Anthony Fauci and his sidekicks Deborah Birx and Scott Gottlieb. But early on, they all banged the "dead are coming" drums based on Ferguson projections.

I spotted Ferguson as a scammer right out of the gate. When his report first came out, I wrote:

Common Dreams has published an article by staff writer Jake Johnson that begins:

An alarming scientific report compiled by British researchers and shared with the Trump White House warns that, in the absence of drastic and coordinated government action, the novel coronavirus could kill as many as 2.2 million people in the United States alone.

The "scientific" report was written by epidemiologist Dr. Neil Ferguson. et al. and published Monday by the Imperial College of London,

It contains this major qualification  (my bold and highlight): 

In the (unlikely) absence of any control measures or spontaneous changes in individual behaviour, we would expect a peak in mortality (daily deaths) to occur after approximately 3 months (Figure 1A). In such scenarios, given an estimated R0 of 2.4, we predict 81% of the GB and US populations would be infected over the course of the epidemic. 

Got that? The paper makes its 2.2 million death claim by making the assumption that no measures will be taken to counter the virus and the R0 stays at the absurdly high level of 2.4. These are dishonest assumptions (especially since behavior has changed in the US dramatically). So why is someone publishing such a dishonest paper?

But it turns out his scamming may have gone on at an even deeper level.

A professional coder, Sue Denim, who has written software code for over 30 years (including 8 years at Google as a senior software engineer working on Maps, Gmail and account security), has been digging into the code Ferguson used to make his absurd forecasts.

Or at least, she has been digging into the part of the code that Ferguson's Imperial College will release. Curiously, they won't release the entire code that he used for his forecasts. But there is enough that was released that should completely destroy Ferguson as a competent scientist.

Here are her key insights:

[What has been released] isn’t the code Ferguson ran to produce his famous Report 9. What’s been released on GitHub is a heavily modified derivative of it, after having been upgraded for over a month by a team from Microsoft and others. This revised codebase is split into multiple files for legibility and written in C++, whereas the original program was “a single 15,000 line file that had been worked on for a decade” (this is considered extremely poor practice). A request for the original code was made 8 days ago but ignored, and it will probably take some kind of legal compulsion to make them release it.  Clearly, Imperial are too embarrassed by the state of it ever to release it of their own free will, which is unacceptable given that it was paid for by the taxpayer and belongs to them.

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The model.  What it’s doing is best described as “SimCity without the graphics”. It attempts to simulate households, schools, offices, people and their movements, etc. I won’t go further into the underlying assumptions, since that’s well explored elsewhere.
Non-deterministic outputs. Due to bugs, the code can produce very different results given identical inputs. They routinely act as if this is unimportant.

This problem makes the code unusable for scientific purposes, given that a key part of the scientific method is the ability to replicate results. Without replication, the findings might not be real at all – as the field of psychology has been finding out to its cost. Even if their original code was released, it’s apparent that the same numbers as in Report 9 might not come out of it.

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 the code produces critically different results, even for identical starting seeds and parameters.

I’ll illustrate with a few bugs. In issue 116 a UK “red team” at Edinburgh University reports that they tried to use a mode that stores data tables in a more efficient format for faster loading, and discovered – to their surprise – that the resulting predictions varied by around 80,000 deaths after 80 days:

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That mode doesn’t change anything about the world being simulated, so this was obviously a bug.

The Imperial team’s response is that it doesn’t matter: they are “aware of some small non-determinisms”, but “this has historically been considered acceptable because of the general stochastic nature of the model”. Note the phrasing here: Imperial know their code has such bugs, but act as if it’s some inherent randomness of the universe, rather than a result of amateur coding. Apparently, in epidemiology, a difference of 80,000 deaths is “a small non-determinism”.

Imperial advised Edinburgh that the problem goes away if you run the model in single-threaded mode, like they do. This means they suggest using only a single CPU core rather than the many cores that any video game would successfully use. For a simulation of a country, using only a single CPU core is obviously a dire problem – as far from supercomputing as you can get. Nonetheless, that’s how Imperial use the code: they know it breaks when they try to run it faster. It’s clear from reading the code that in 2014 Imperial tried to make the code use multiple CPUs to speed it up, but never made it work reliably. This sort of programming is known to be difficult and usually requires senior, experienced engineers to get good results. Results that randomly change from run to run are a common consequence of thread-safety bugs...

But Edinburgh came back and reported that – even in single-threaded mode – they still see the problem. So Imperial’s understanding of the issue is wrong.  Finally, Imperial admit there’s a bug by referencing a code change they’ve made that fixes it. The explanation given is “It looks like historically the second pair of seeds had been used at this point, to make the runs identical regardless of how the network was made, but that this had been changed when seed-resetting was implemented”. In other words, in the process of changing the model they made it non-replicable and never noticed.

Why didn’t they notice? Because their code is so deeply riddled with similar bugs and they struggled so much to fix them that they got into the habit of simply averaging the results of multiple runs to cover it up… and eventually this behaviour became normalised within the team.

Get a load of this bug:

 In issue #30, someone reports that the model produces different outputs depending on what kind of computer it’s run on (regardless of the number of CPUs).

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 Although the academic on those threads isn’t Neil Ferguson, he is well aware that the code is filled with bugs that create random results. In change #107 he authored he comments: “It includes fixes to InitModel to ensure deterministic runs with holidays enabled”.  In change #158 he describes the change only as “A lot of small changes, some critical to determinacy”.

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 Much of the code consists of formulas for which no purpose is given. John Carmack (a legendary video-game programmer) surmised that some of the code might have been automatically translated from FORTRAN some years ago.

For example, on line 510 of SetupModel.cpp there is a loop over all the “places”  the simulation knows about. This code appears to be trying to calculate R0 for “places”. Hotels are excluded during this pass, without explanation.

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Conclusions. All papers based on this code should be retracted immediately. Imperial’s modelling efforts should be reset with a new team that isn’t under Professor Ferguson, and which has a commitment to replicable results with published code from day one.

On a personal level, I’d go further and suggest that all academic epidemiology be defunded. This sort of work is best done by the insurance sector. Insurers employ modellers and data scientists, but also employ managers whose job is to decide whether a model is accurate enough for real world usage and professional software engineers to ensure model software is properly tested, understandable and so on. Academic efforts don’t have these people, and the results speak for themselves.

It will take many years but, eventually, the absurd COVID-19 panic of 2020 will be exposed. It is based on sloppy science across the board, and with promotion by some scientists who should know better but appear to have agendas different than truth and sound science.

Meanwhile, the masses, in totals of billions across the globe, live in fear of a virus that kills nowhere near the number that Ferguson projected with code that probably couldn't forecast a snowstorm as the snow was falling.

 -RW