Jeffrey A. Tucker
Recall that the Covid fiasco went into overdrive when Neil Ferguson of Imperial College London generated a wildly incorrect estimate of the fatality rate of the virus from China. He had two forecasts, one without lockdowns (death everywhere) and one with (not terrible). The idea was to inspire the replication of the CCP’s extreme methods of people control in the West.
That model, first shared in classified realms, flipped the narrative. Once select advisors – Deborah Birx and Anthony Fauci among them – presented it to Trump, he went from opposing lockdowns to getting in front of the seemingly inevitable.
Before long, every Gates-funded NGO was pushing more such models that proved the point. Masses of people observed the models as if they were an accurate reflection of reality. Major media reported on them daily.
As the fiasco dragged on, so did data fakery. The PCR tests were generating false positives, giving the impression of an unfolding calamity even though medically significant infections were highly limited. Infections and even exposures were redefined as cases, for the first time in epidemiological history.Then came the subsidized “deaths from Covid” that clearly generated waves of misclassification that underscore the overestimation of the fatality rate.
It’s awesome and terrifying once you add it all up. Bad models and bad data created a killer pandemic of uncertain gravity that was later supposedly solved by shots tested with bad data and whose efficacy was further demonstrated by awful models and data.
There is surely a lesson here. And yet the romance with bad models and bad data is not entirely over.
There is evidence that a very similar scenario unfolded with regard to the claim that Iran was constructing a nuclear weapon, resulting in a hellfire of bombs and death in both Iran and Israel.
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