Fraud (from 14th century Latin) n – deceit, trickery, intentional perversion of truth in order to induce another to part with something of value or to surrender legal rights: and art of deceiving or misrepresenting; imposter, cheat, one who is not who that person pretends to be: something that is not what it appears to be
Hoax (probable contraction of hocus, circa 1796) n – an act intended to trick or dupe: something accepted or established by fraud or fabrication; v – to trick into believing or accepting as genuine something false and often preposterous
Swindle (from Old English, coined circa 1782, “to vanish”) v – to take money or property by fraud or deceit.
“Great Hoaxes, Swindles, Scandals, Cons, Stings and Scams” Joyce Madison, 1992
COVID-19: Lockdowns on... Buy New $16.99 (as of 03:38 EDT - Details) Frauds often have powerful counter-narratives. When Wirecard went straight from a DAX-30 €12bn capitalisation to insolvency in June, we wondered not only why it had taken so long for the auditor to seek confirmation of cash balances but why so many investors had been hoodwinked for so long by its empty claims to have been a legitimate player at the epicentre of the digital payments industry. We had also long been inclined to believe that $4bn FTSE-100 member NMC Healthcare’s management had been siphoning off shareholders’ assets (and that the same was true of its smaller sister “fintech” company Finablr), but were bemused to see continued institutional demand for insider share placings and belief in faked takeover rumours, right up until the declared insolvency in March. Whilst we think there is plentiful potential for further stock-market flops it is time to consider whether these serious corporate failings have now been dwarfed by the unnecessary damage caused by the “science” behind lockdown and the current parallel focus on a vaccine as the sole long-term COVID solution1.
Part 1 – The hocus “science” behind lockdown
When lockdown was imposed, we were told we were facing a second Spanish flu pandemic (thought to have killed up to 50 million people2); that hospitals would be overrun and there would be 500,000 deaths in the UK alone3. This was a powerful and emotive narrative, but it was never true4. Governments and an obedient media focused exclusively on Imperial College’s now discredited doomsday scenario built on a hypothetical, badly coded model5, ignoring its author’s history of failed doomsday predictions6 and the different views of other scientists7.
Brainwashing: Its Hist... Best Price: $19.04 Buy New $9.98 (as of 05:25 EDT - Details) Alternative evidence-based (i.e. theories based on facts) population samples already existed: the most prominent being the Diamond Princess Cruise Ship; which at the end of February accounted for over half of all confirmed infections outside of China8. “Cruise ships are like an ideal experiment of a closed population”, according to Stamford Professor of Medicine John Ioannidis. “You know exactly who is there and at risk and you can measure everyone” 9.
Quarantined for over a month after a virus outbreak, the entire cruise ship ‘closed population’ of 3,711 passengers and crew, with an average age of 58, were repeatedly tested. There were 705 cases (19% total infection rate) and six deaths (a Case Fatality Rate of just 1%) by the end of March (eventually 14 in total10). This compared to 116 deaths that would have been predicted by the Imperial model11).
Over half of the cruise ship cases were asymptomatic12, at a time when the official “science” behind the lockdown, Prof. Neil Ferguson (UK), dismissed the lack of any evidence for a high proportion of cases so mild that they had no symptoms13 and Dr Anthony Fauci (US) had written in the New England Journal of Medicine that in the event of a high proportion of asymptomatic cases, the COVID mortality rate would ultimately be “akin to a severe seasonal influenza”14 (a statement which he now at least seems to have clearly forgotten in his enthusiasm for a vaccine solution). Great Hoaxes, Swindles... Best Price: $10.00 Buy New $45.63 (as of 03:38 EDT - Details)
The cruise ship deaths were exclusively amongst an over 70’s age cohort15. Although the Diamond Princess sample size was small it remains the earliest and most accurate predictor of mortality, infection and asymptomatic cases16. Extrapolating this data to the wider, younger population would logically lead to downward revision on the mortality risk and upwards revisions to the level of asymptomatic cases. COVID outbreaks aboard naval ships with younger populations confirmed this: only 1 death and 3 hospitalised cases out of 1,156 infections on the USS Theodore Roosevelt17; zero deaths out of 1,046 confirmed cases on the Charles de Gaulle18. Even in ships which could not carry out effective social distancing the virus mortality rate, whilst a serious public health risk, was certainly not the “Spanish flu”.
As more testing was carried out across population samples (and not just on the patients hospitalised) studies came to the same conclusion: the rate of infection was higher than thought with more harmless cases19 and therefore the ultimate mortality risk was much lower than originally claimed20. Despite this empirical evidence and the contrarian opinions of other expert epidemiologists which have since proven to be much more accurate21, the Imperial College virus narrative of “the worst pandemic in 100 years22”(Fig. 1) did not change: governments, the media23 and the official “science” doubled down on the “dialogue of doom”24. Ferguson then broke his own lockdown in a tryst with his married lover and justified it by claiming he had antibody immunity25 (which given what we now know about decaying antibodies may not have been correct).26
Fig 1. COVID-19 mortality in perspective27
The population mortality risk of the virus was initially estimated at 3.8% by the WHO28 which had arrived at this number simply by dividing the number of Chinese deaths by the number of confirmed cases, ignoring the fact that only a small proportion of likely infected people had actually been tested; that asymptomatic cases were likely to be significantly underrepresented in testing and that the more serious cases were likely highly correlated to serious symptoms. This basic statistical error of simply dividing deaths by reported infections not only exaggerated the severity of the risk but led directly to policy error on hospital capacity and care home deaths.