PROFESSOR Graham Medley, co-chair of the modeling committee of the SAGE government’s scientific advisory body, uncovered the slippery practices that its members were allowed to develop which were then used by the government to control the representation of the Covid response.
Even after the persistent questions from Spectator editor Fraser Nelson were posted on Twitter, the public still hasn’t realized how meaningful Medley’s admissions were. Politicians have not admitted either that they were betrayed or, if they knew about it, that they were part of the modeling fraud.
The London School of Hygiene and Tropical Medicine, of which Medley is Professor of Infectious Disease Modeling, created outrageous scenarios (which everyone uses as predictions) on Dec. 11 of the likely effects of the Omicron virus variant on the UK. It specifies as a base case that by April 2022 there would be 25,000 more deaths without additional measures.
Investment bank JP Morgan has indicated that these scenarios assume that the Omikron variant is just as deadly as the Delta variant. In its country of origin, South Africa, the first real data strongly suggest that the Omikron variant is much milder in its effects.
When asked why Nelson was neglecting this leniency, Medley said there was no point in including her because “decision-makers are generally only interested in situations where decisions need to be made”. That is, of course, the introduction of measures.
He continued: “There is a dialogue in which the policy teams discuss with the modelers what they need for their policy”.
In other words, politics determines how the data is interpreted and used, not the other way round – this is how the scientific advisory teams have been sold to us, and most people would assume that scientific data is used.
Medley openly admitted that his disease modelers were biased by government input criteria. In other words: The model scenarios were not a neutral assessment of the probability of the disease spreading or the number of deaths.
Rather, these were deliberately pessimistic scenarios that the government and the media, both acting in their own interests, were allowed to spread as official forecasts.
It was scare tactics that the government could use against the population and that it passed off as “best science” to cover up the politicians.
The result was corrupt and costly, but the modellers gained reputation and, no doubt, money for their employers, while politicians could claim that the action came from these brilliant SAGE people, not from them.
Medley said the modelers knew about the pessimistic bias all along. The problem is, they didn’t make sure the public knew about it or that politicians were telling the truth about the models. They hid behind the fine print, in which only the initiated would admit the extreme uncertainty of the models.
Public health statistics analyst and commentator Chris Snowdon pointed to the nonsense of the latest Warwick University model which, among other wholly unlikely scenarios, showed that the number of omicron deaths in the UK was close to 3,000 a day before the end of this month would achieve. The last data point for December 31 shows 203 deaths from or with Covid-19.
If we look for more evidence of the corruption of science through the use of computer modeling, we find it in the mystery of the lack of evidence of the dire predictions of (supposedly) human-made climate change in the real world. Here the countless models have again proven to be wrong, and have been for 40 years.
Global temperatures have barely risen since 1979, although most models had predicted an increase of at least one degree Celsius.
In technical jargon, this means that the models all run hot. In other words, the models that have been built since the issue of man-made global warming became the predominant narrative for governments and stakeholders in the early 1980s have all predicted temperatures much hotter than we actually experienced. The models were wrong and excuses were found.
But policies based on supposedly unsustainable, but in reality imperceptible, human-made impacts on the climate have been stepped up at a horrific cost to the world’s population, especially the poorest.
Who is pushing for these “hot” scenarios to lead the way in policymaking? The answer is the big green “power bloc that rallies around the United Nations and its Intergovernmental Panel on Climate Change.
Both bodies should be neutral, but in reality they are in conflict, as they are materially upgraded in their role by peddling the fear of the climate and forcing government measures that benefit exploitative and super-rich magnates. They see the business opportunities and don’t seem to care about the immediate and high public costs.
In both cases, the models served as power givers to politicians, bureaucrats and companies who had the perspective and who received subsidies and lucrative contracts. But they were and are manipulated and fraudulent.
I studied in an area where computer generated models were widely used. The saying that became a cliché among academics was: All models are wrong, but some are useful.
It found that SAGE’s climate and disease models were primarily useful to the charlatans and advocates, rather than the science with which they were nominally associated.
As retired Judge Baroness Hallett embarks on her Covid-19 investigation later this year, let’s hope that one of the top priorities is figuring out who caused the deliberate creation and use of pessimistic models about the effects of viruses and why this was the case. If we can do that, we will learn a lot more about power and money in health science.
First of all, we need to remember three truisms. Computer models are not a science; scientific consensus is a construct used when scientists have something to sell and benefit from; and if one follows the money, one learns more about model-based policies and consensus science than actual science ever could.
Creating facts that fit your point of view is closer to what happens than any application of the scientific method.