Every state and local politician who imposed lockdowns pursuant to the COVID-19 pandemic claimed their decisions were based on “the science.” In reality, they ignored the advice of leading epidemiologists, relying instead on statistical models produced by “experts” with histories of wildly inaccurate projections. The most notorious of these oracles was, of course, Neil Ferguson of Imperial College London, who claimed that coronavirus could kill 2.2 million Americans. Based on such apocalyptic predictions, combined with overcautious warnings from Beltway bureaucrats like Dr. Anthony Fauci, all but a few governors implemented economically destructive stay-at-home orders — despite the reservations of respected epidemiologists who questioned the logic of lockdowns from the beginning.
Included among the genuine experts who advised that far more information was needed before such draconian countermeasures could be scientifically justified were John P. A. Ioannidis of Stanford University, Michael T. Osterholm of the University of Minnesota, and Knut M. Wittkowski, formerly of Rockefeller University. All three pointed out flaws in the assumptions upon which officials based their lockdown orders. Ioannidis was the first to publicly question the reasoning behind these edicts. In an essay published in STAT on March 17, he wrote that the precipitous response to the pandemic was “a once-in-a-century evidence fiasco” and that decisions of monumental significance were being made without truly dependable data concerning how many people had actually been infected:
The data collected so far on how many people are infected and how the epidemic is evolving are utterly unreliable. Given the limited testing to date, some deaths and probably the vast majority of infections due to [the coronavirus] are being missed. We don’t know if we are failing to capture infections by a factor of three or 300. Three months after the outbreak emerged, most countries, including the U.S., lack the ability to test a large number of people and no countries have reliable data on the prevalence of the virus in a representative random sample of the general population.
The point Ioannidis was attempting to make involved the wildly exaggerated COVID-19 fatality rate promulgated by the World Health Organization (WHO). He declared that entity’s official 3.4 percent fatality rate meaningless because it wasn’t based on a reliable testing regimen. Testing has been disproportionately performed on patients with severe symptoms while they are still infected with the virus. Consequently, selection bias renders it impossible to draw valid conclusions about how many have been infected, which means it is also impossible to calculate a reliable fatality rate. To acquire useful infection and death rates it’s necessary to conduct random serological surveys using tests that detect both active infections as well as antibodies remaining in the systems of those who have been infected in the past.
[Interesting Read]
See Also:
(1) Anthony Fauci, Auto Mechanic
(3) Tammy Bruce blasts NYC shutdown: You can fly on a full airplane but can’t eat at a restaurant?
