PROFESSOR Graham Medley, joint chairman of the modelling committee for the Government scientific advisory body Sage, has exposed the slippery practices its members have been permitted to develop and then use via the Government to control the Covid response narrative.
Even after Spectator editor Fraser Nelson’s persistent questioning of him on Twitter was made more widely available, the public still hasn’t been shown just how significant Medley’s admissions were. Nor have politicians admitted to being duped or, if aware, of being part of the modelling scam.
The London School of Hygiene and Tropical Medicine, where Medley is Professor of Infectious Disease Modelling, produced horrendous scenarios (which everyone uses as forecasts) on December 11 on the likely effect of the Omicron virus variant on the UK. It reported as its base case that there would be 25,000 further deaths by April 2022 with no additional measures.
The investment bank J P Morgan has pointed out that these scenarios were based on the Omicron variant being as lethal as the Delta variant. Yet in its country of origin, South Africa, the real-world early data strongly suggested Omicron was much milder in its effect.
Asked by Nelson why that mildness was not taken into account, Medley said there was no point in including it, as ‘decision-makers are generally only interested in situations where decisions have to be made’. That is, of course, to introduce measures.
He went on to say that ‘there is a dialogue in which policy teams discuss with modellers what they need to inform them with their policy’.
In other words, the policy is driving the interpretation and use of the data, not the other way round – which is the way we have been sold the workings of the science advisory teams and the basis on which most people would assume that science data is used.
Medley was blithely admitting that his disease modellers were biased by the input criteria given by the Government. In other words, the model scenarios were not a neutral assessment of the likelihood of disease spread or death numbers.
Instead, they were deliberately pessimistic scenarios that they allowed to be broadcast as official forecasts by the Government and the media, both of whom were acting in their own interests.
They were scaremongering salvoes for the Government to use against the population, dressed up as ‘the best science’ in a way which gave convenient cover to the politicians.
The result was corrupt and costly, but the modellers gained repute and no doubt funds for their employers, while the politicians could claim action wasn’t down to them, but was instead from these brilliant Sage people.
Medley said that the modellers knew about the pessimism bias all along. The trouble is that they didn’t ensure that the public knew, or that the politicians told the truth about the models. They hid behind the small print in which only the initiated would admit to the extreme uncertainty built into them.
The public health statistics analyst and commentator Chris Snowdon highlighted the nonsense of Warwick University’s recent model which, amongst other wholly unlikely scenarios, was showing Omicron deaths in the UK reaching almost 3,000 per day before the end of this month. The latest datapoint, which is for December 31, shows 203 deaths from or with Covid-19.
If we look for other evidence of the corruption of science through the use of computer models, then we find it in the enigma of the absence of real-world evidence for the dire forecasts of (supposedly) man-made climate change. Here, the myriad models have again proved to be wrong, and for the last 40 years to boot.
Since 1979, global temperatures have barely risen at all, yet most of the models were forecasting at least a one degree Centigrade rise by now.
In the parlance of the trade, the models are all running hot. In other words, the models created since the Man-made global warming issue became the dominant narrative for governments and the vested interests in the early 1980s, all have forecast much hotter temperatures than we have actually experienced. The models were wrong, and excuses have been made.
But policies based on supposedly unsustainable, but actually imperceptible, Man-made effects on climate have been ramped up with eye-watering costs for the world’s population, especially the poorest.
Who is pushing for these ‘hot’ scenarios to lead the way in policy making? The answer is the extensive ‘Green’ power blob centred around the United Nations and its Intergovernmental Panel on Climate Change.
Both bodies should be neutral, but in reality are conflicted by being materially enhanced in their roles by peddling climate fear and forcing government action which benefits exploitative and super-wealthy magnates. They see the business opportunity and appear unconcerned about the immediate and heavy cost to the general population.
In both these cases, the models served their purpose as power-givers to the politicians, bureaucrats and businesses that had the inside track and gained the subsidies and lucrative contracts. But they have been, and are, rigged and fraudulent.
I studied in a field in which computer-generated models were widely used. The saying which became a cliché amongst the academics was that all models are wrong, but some are useful.
It has turned out that the climate models and SAGE’s disease models were most useful to the charlatans and vested interests, rather than to the science to which they were nominally attached.
When retired judge Baroness Hallett gets her Covid-19 inquiry under way later this year, let us hope that a top priority is finding out who directed that deliberately pessimistic models of virus impacts to be created and used, and why they did so. We will understand a great deal more about power and money in the health scientocracy if that is done well.
Above all, we need to remember three truisms. Computer models are not science; scientific consensus is a construct used when scientists have something to sell and from which they will benefit; and following the money will tell us more about policies based on models and on consensus science than actual science ever will.
Creating the facts which fit the view is closer to what happens than any application of the scientific method.