Did Covid Vaccines Really Save 12 Million Lives?
Wednesday, Nov 27 2024
Modelling studies where the authors, on the basis of their subjective assumptions, put their preconfigured numbers (parameters) into their software in order to predict numbers of death (or saving of lives) through predetermined, desired values (in order to stoke fear or generate false trust) is not far removed from crystal ball gazing and palm-reading. This is because these predictive models are nothing more than sophisticated, technical versions of cruder methods used by soothsayers and fortune-tellers to deceive people and embezzle their wealth by creating fear and hope through speculations about the unseen and the future.[1]
Very sadly, people are blinded by what is claimed to be science, yet is actually superstition (as it is not based on the actual scientific method) and upon verification (taḥqīq) would likely enter into types of siḥr (of which are fortune-telling and prognostication). Instead of operating upon the base foundation of distrust of the people of disbelief, there is wholesale and often, blind acceptance of their theories and claims, and this brings both religious and worldly harms. Indeed, the entire Covid-19 Response Scam was based on computer simulations (false, outlandish predictions of deaths): See Former FDA, DOD Top Official: Pandemic Countermeasures Based on Computer Simulations Killed More People Than Covid.
The authors of the article below are Carl Heneghan[2] and Tom Jefferson[3] of the Cochrane Collaboration.
The BBC reported that AstraZeneca and Pfizer are credited with together saving more than 12 million lives in the first year of Covid vaccination. To substantiate this claim, the BBC refers to Airfinity, a ‘disease forecasting company’.
Airfinity used an Imperial College London study, which calculated that Covid vaccines saved 20 million lives between December 2020 and December 2021. Using a mathematical model, the Imperial team assumed that vaccination conferred protection against Covid infection (mRNA vaccines were estimated to have given 88 per cent protection against infection after the second dose) and the development of severe disease requiring hospital admission. The team also assumed that those who develop infection are less infectious than unvaccinated individuals.
We downloaded the supplementary data from GitHub (where academics often store their computer code) to examine the country-specific estimates and assess the plausibility of the numbers for the UK, Italy, and the USA.
In the UK, with vaccine coverage of 68 per cent, 507,200 (382,200 to 789,900) deaths were estimated to be averted because of vaccination (52 deaths prevented per 10,000 vaccinated). In Italy, the modellers predicted that, with a coverage of 73 per cent, 491,300 (444,800 to 544,400) deaths were averted (53 per 10,000 vaccinated). In the USA, with 61 per cent coverage, 1,902,000 (1,737,000 to 2,069,000) deaths were averted (44 per 10,000 vaccinated).
The Imperial team fitted a model to all-cause excess mortality, using more modelling from the Economist. They also assumed that each country would follow the same time-varying reproductive number (Rt) trend since the start of the pandemic. Despite an absence of detailed vaccination data for most countries and an assumption that the relationship between age and the infection fatality ration was the same for each country, the BBC still used the numbers.
In 2021, there were 667,479 deaths in the UK, 22,150 fewer than the 689,629 deaths in 2020. By our reckoning, the modellers want us to believe that in the absence of vaccination, there would have been 1,174,679 deaths in the UK in 2021.
Similarly, in Italy, 701,346 deaths occurred in 2021; the model predicts the number would have been 1,192,646 and 452,329 more deaths than in 2020. In the USA, a total of 3,464,231 resident deaths were registered in 2021, 80,502 more than in 2020. The modellers assumed there would have been 5,366,231 deaths in the US in 2021 if vaccination had not been implemented.
Sadly, many journalists don’t check their numbers or facts: many of the assumptions in the model are incorrect, and the estimated number of deaths averted by vaccines is implausible.
This isn’t surprising. As in medicine, models do not fit anywhere in the pathway for establishing effectiveness. Regulators don’t use them for approval, and decision-makers like the National Institute for Health and Care Excellence use economic models with reliable estimates of effect and credible costs. They do not appear in the The Centre for Evidence-Based Medicine levels of evidence or the Scottish Intercollegiate Guidelines Network system for grading evidence, as they are irrelevant to answering therapeutic questions. Clinical trials are the primary study type to determine the effectiveness of medicines or vaccinations; anything else is just a bad guess.
Footnotes
1. This is not to say that computer models and simulations cannot be informative, and useful in certain fields, they can, so long as they are based on concrete, historical data and are not riddled with assumptions. Hence, as long as hard, concrete data is used (for example historical sales data for a company) statistical probabilities can be worked out and used in the decision-making process. As for when the model requires assumptions and is designed to justify already-decided policy, then this is nothing but deception and make-believe, and the predictions almost invariably turn out to be false.2. Carl Heneghan is a clinical epidemiologist with expertise in evidence-based medicine, research methods, and evidence synthesis expertise. He has over 450 peer-reviewed publications (current H index 96) and published 110 systematic reviews. His work includes investigating the evidence for approval of drugs and devices, assessing health claims and researching common presenting conditions in the community.
3. Dr. Tom Jefferson is a Senior Associate Tutor at the University of Oxford, a former researcher at the Nordic Cochrane Centre and a former scientific coordinator for the production of HTA reports on non-pharmaceuticals for Agenas, the Italian National Agency for Regional Healthcare.
Source: Spectator
