The Science Fraud That Were The Imperial College and UW Pandemic Forecast Models

Last Updated on February 5, 2022 by Shaun Snapp

Executive Summary

  • The Imperial College and the University of Washington were funded by Bill Gates to produce exaggerated pandemic forecasts that would maximize Bill Gates’ biotech investments.

Introduction

Bill Gates-funded research that was designed to be wrong but fit with the objectives of Bill Gates to exaggerate a pandemic. This is faux research at Imperial College and a lesser-known model from The University of Washington. We will explore the technical problems of these models.

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The Inaccuracy and Non-Replicatability of the Imperial College Covid Model

And the Imperial College model not only proved to be horribly inaccurate, something that could have been predicted by looking at Nail Ferguson’s history of forecasts, but it also has been exposed for being unable to produce the same result between model runs as is explained in the following quotations.

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.”

However, questions have since emerged over whether the model is accurate, after researchers released the code behind it, which in its original form was “thousands of lines” developed over more than 13 years. – Yahoo

This should have immediately been a concern.

Why does a forecast model contain thousands of lines of code? A forecast model will apply proportional values to input data. This does not take thousands of lines of code to do. 

How Do Forecast Models Work?

Let us briefly discuss how a forecast model works.

A forecast model like the one developed by Ferguson would normally work like the following.

One would have a series of parameters that are applied to variables. So it might look like this.

  • A = The size of a country’s population.
  • B = The assumed transmissibility of the virus.
  • C = The mortality per infected individual.
  • D = The average population density of the country, which is then a factor adjusting transmissibility.
  • E = The contagion period of the virus.

And then a host of other variables. These variables would then be multiplied by parameters. Then the model is fed these variables, the variables calculate in a probably lengthy formula, and then there would be an output, which is the estimated mortality. Some of the variables, like the population of the country are established, while others, such as the transmissibility of the virus, are close to unknowns. The very fact that a forecast model would have thousands of lines of code means that the model is overdone with complexity. Ferguson should have studied the work of many forecast experts, that more complex models do not normally beat simple models. Furthermore, there is no previous coronavirus (and covid is called covid-19, as it is the 19th identified coronavirus) that has had anything like the lethality predicted by Fergeson.

Furthermore, is this multi-thousand line code forecast model documented? If so where is that published? Does Nail Ferguson himself even know everything that is in this code?

My guess is that he does not.

And further, that the code is not documented or very little documented. I am personally not interested in reading the documentation of a multi-thousand line code forecast model. That forecast model is bunk, and I am better off starting from scratch because the forecast model designers have already violated a basic rule about keeping the forecast model understandable. It is very well known in development circles that when there is a large amount of code for a straightforward problem, that the code is poorly developed. 

Parameters of Inflection, Transmissibility, and Mortality

Another question is what parameters did Ferguson use for both transmissibility and mortality?  Because whatever he used was very exaggerated. It was known at the beginning of the pandemic that covid was not very deadly.

This is explained in the following quotation.

The one situation where an entire, closed population was tested was the Diamond Princess cruise ship and its quarantine passengers. The case fatality rate there was 1.0%, but this was a largely elderly population, in which the death rate from Covid-19 is much higher.

Projecting the Diamond Princess mortality rate onto the age structure of the U.S. population, the death rate among people infected with Covid-19 would be 0.125%. But since this estimate is based on extremely thin data — there were just seven deaths among the 700 infected passengers and crew — the real death rate could stretch from five times lower (0.025%) to five times higher (0.625%). It is also possible that some of the passengers who were infected might die later, and that tourists may have different frequencies of chronic diseases — a risk factor for worse outcomes with SARS-CoV-2 infection — than the general population. Adding these extra sources of uncertainty, reasonable estimates for the case fatality ratio in the general U.S. population vary from 0.05% to 1%.

However, that was not the assumption of Ferguson’s model as is explained in the following quotation.

The assumed 0.9% death rate (within a range of 0.4% to 1.4%) was tweaked from a smaller estimate in a study of deaths in China by Robert Verrity and others, which found a “case fatality rate” (CFR) of 1.38% among known and tested cases. – CATO

Well, that estimate was off by around (.9%/.02%) or a factor of 45x.

In terms of transmissibility, his estimate was not only entirely out of the norm for what covid ended up being and of previous viruses as is explained in the following quotation.

The key premise of 81% of the population being infected should have raised more alarms than it did. Even the deadly “Spanish Flu” (H1N1) pandemic of 1918-19 infected no more than 28% of the U.S. population. The next H1N1 “Swine Flu” pandemic in 2009-10, infected 20-24% of Americans. – CATO

No Code?

When the paper was published without the model’s code being shared, the entire model should have been greeted with extreme skepticism. The following quotation shows the extreme problem with the sharing of the code.

They finally released their code on April 27, 2020 through the popular code and data-sharing website GitHub, but with the unusual caveat that its “parameter files are provided as a sample only and do not necessarily reflect runs used in published papers.” Put another way, they released a heavily reorganized and generic file that would permit others to run their own version of the COVID model. They do not appear to have released the actual version they ran in the March 16th paper that shaped the US and UK government policies, or the results that came from that model (a distinction that was immediately noticed by other GitHub users, prompting renewed calls to release the original code). – AEIR

This is a major red flag.

This disclaimer by Nail Ferguson is a smoking gun that his model was unreliable and that he was doing everything he could to hide the model from being falsified by independent reviewers. It can also be questioned how much he released the code. It seems Ferguson needed to release code to appease critics. Therefore he came up with a way to release the code, but without releasing it in actual fact.

A Model Designed for Influenza?

This quote on the design of the forecast model is rather shocking.

Last March, Ferguson admitted that his Imperial College model of the COVID-19 disease was based on undocumented, 13-year-old computer code that was intended to be used for a feared influenza pandemic, rather than a coronavirus. Ferguson declined to release his original code so other scientists could check his results. He only released a heavily revised set of code last week, after a six-week delay. – Statmodel

The Imperial College Model Falls Apart Under Independent Testing

As more became understood about this model the more it became apparent why the code file was released this way, as the following quote explains.

Many have claimed that it is almost impossible to reproduce the same results from the same data, using the same code. Scientists from the University of Edinburgh reported such an issue, saying they got different results when they used different machines, and even in some cases, when they used the same machines.

“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. – Yahoo

This means that not a single independent entity reproduced the forecast by Imperial College before Fauci recommended the lockdown to Trump.

How can I be certain of this?

Because the model does not reproduce the same result per model run. This also means that Nail Ferguson would have known this and that he knowingly submitted faulty results. This also explains why Furgeson did not release his code when he reported the result of 2.2 million deaths without a US lockdown.

This means that Nail Ferguson engaged in scientific fraud. 

The quote continues.

After a discussion with one of the Github developers, a fix was later provided. This is said to be one of a number of bugs discovered within the system. The Github developers explained this by saying that the model is “stochastic”, and that “multiple runs with different seeds should be undertaken to see average behaviour”.

However, it has prompted questions from specialists, who say “models must be capable of passing the basic scientific test of producing the same results given the same initial set of parameters…otherwise, there is simply no way of knowing whether they will be reliable.” – Yahoo

What Did The Imperial College Fake Forecast Cost Bill Gates?

The largess of Bill Gates on Imperial College has been impressive. Observe the following quotation.

Like all events in the name of lockdown, Gates’s philanthropic wildfire hadn’t withdrawn. Since 2002, Imperial College London has received grants to the tune of over $280m from the Bill and Melinda Gates Foundation, including a wild $91,494,791 from the foundation last year. Millions have been spent in Imperial’s epidemiology department, where Ferguson works as the head. In November 2016, for example, the Bill and Melinda Gates Foundation granted $5,625,310 to Imperial College to establish a Vaccine Modelling Consortium. The foundation explained the funding was to: ‘improve the quality and timeliness of estimates of the impact of Gavi-funded vaccination programs via the establishment of a Vaccine Impact Modelling Consortium.’

After its establishment, the director of the Vaccine Impact Modelling Consortium would end up being none other than Neil Ferguson. Other members in the consortium included Dan Hogan, from the Gates created GAVI Alliance, Emily Dansereau from the Bill and Melinda Gates Foundation and Azra Ghani, who would later assist him in creating the paper that put the world in lockdown.

Neil Ferguson and Azra Ghani are also both listed as large applicants for grants from the Wellcome Trust, regular partners of the Gates Foundation. In 2017, the pair received $2m worth of funding to for Imperial College to conduct ‘Epidemiology, evolution and control of infectious diseases’.

Neil Ferguson is hardly shy around the Gates foundation. According to Ferguson’s portfolio on gov.uk, he is also a ‘principal investigator’ at the Bill and Melinda Gates Foundation and Gavi, the (Gates created) Vaccine Alliance. Ferguson is also listed as the director of the Centre for Infectious Disease Modelling at the World Health Organisation, and a consultant at the World Bank. Hardly can such factors be left out of account. – The Alternative Column

How About the University of Washington Model?

It should go without saying that any research funded by Bill Gates is going to be false and designed to maximize the fortune of Bill Gates. However, there are many specific criticisms of the UW model, which are explained in the following quotations.

“It’s not a model that most of us in the infectious disease epidemiology field think is well suited” to projecting Covid-19 deaths, epidemiologist Marc Lipsitch of the Harvard T.H. Chan School of Public Health told reporters this week, referring to projections by the Institute for Health Metrics and Evaluation at the University of Washington.

Others experts, including some colleagues of the model-makers, are even harsher. “That the IHME model keeps changing is evidence of its lack of reliability as a predictive tool,” said epidemiologist Ruth Etzioni of the Fred Hutchinson Cancer Center, who has served on a search committee for IHME. “That it is being used for policy decisions and its results interpreted wrongly is a travesty unfolding before our eyes.” – AEIR

Observe that this lack of reliability and constantly changing nature is the same set of problems ascribed to the Imperial College model.

The quote continues.

The chief reason the IHME projections worry some experts, Etzioni said, is that “the fact that they overshot will be used to suggest that the government response prevented an even greater catastrophe, when in fact the predictions were shaky in the first place.” IHME initially projected 38,000 to 162,000 U.S. deaths. The White House combined those estimates with others to warn of 100,000 to 240,000 potential deaths.

Conclusion

The lockdowns were justified based on computer models that had no validity and that had an enormous conflict of interest as they were funded by Bill Gates specifically to exaggerate the pandemic to maximize Bill Gates’ ROI on his investments. Neither Imperial College nor the University of Washington ever declared this financial bias to the people that received their fictitious forecast. Although both entities had a terrible history of pandemic forecast accuracy.