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
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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.
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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).
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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.
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