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

Executive Summary

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

Introduction

Bill Gates funded research 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-Replicability of the Imperial College Covid Model

The Imperial College model not only proved to be inaccurate, something that could have been predicted by looking at Nail Ferguson’s history of forecasts. It also has been exposed for being unable to produce the same result between model runs, as 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 generally 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. When the model is fed these variables, the variables are calculated in a probably lengthy formula, and then there would be an output, the estimated mortality. Some variables, like the country’s population, are established, while others, such as the virus’s transmissibility, are close to unknowns. The 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 generally not beat simple models. Furthermore, no previous coronavirus (and covid is called COVID-19, as it is the 19th identified coronavirus) 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?

I guess that he does not.

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

Parameters of Inflection, Transmissibility, and Mortality

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