The fickle ‘science’ of lockdowns
‘Follow the science” has been the battle cry of lockdown supporters since the Covid-19 pandemic began. Yet before March 2020, the mainstream scientific community, including the World Health Organization, strongly opposed lockdowns and similar measures against infectious disease.
That judgment came from historical analysis of pandemics and an awareness that societywide restrictions have severe socioeconomic costs and almost entirely speculative benefits. Our pandemic response, premised on lockdowns and closely related “non-pharmaceutical interventions,” or NPIs, represented an unprecedented and unjustified shift in scientific opinion from where it stood a few months before the discovery of Covid-19.
In March 2019 WHO held a conference in Hong Kong to consider NPI measures against pandemic influenza. The WHO team evaluated a quarantine proposal—“home confinement of non-ill contacts of a person with proven or suspected influenza”—less indiscriminate than the Covid lockdowns. They called attention to the paucity of data to support this policy, noting that “most of the currently available evidence on the effectiveness of quarantine on influenza control was drawn from simulation studies, which have a low strength of evidence.” The WHO team declared that large-scale home quarantine was “not recommended because there is no obvious rationale for this measure.” …
A Johns Hopkins team reached similar conclusions in 2006: “No historical observations or scientific studies” could be found to support the effectiveness of large-scale quarantine. The scientists concluded that “the negative consequences of large-scale quarantine are so extreme . . . that this mitigation measure should be eliminated from serious consideration.” They rejected the modeling approach for relying too heavily on its own assumptions—circular reasoning that confuses a model’s predictions with observed reality.
Even at the outset of Covid-19, the unwisdom of lockdowns guided mainstream epidemiology. When the Wuhan region of China imposed harsh restrictions on Jan. 23, 2020, Anthony Fauci questioned the move. “That’s something that I don’t think we could possibly do in the United States, I can’t imagine shutting down New York or Los Angeles,” Dr. Fauci told CNN. He likely had the scientific literature in mind when he advised that “historically, when you shut things down, it doesn’t have a major effect.”
What caused the scientific community to abandon its aversion to lockdowns? The empirical evidence didn’t change. Rather, the lockdown strategy originated from the same sources the WHO had heavily deprecated in its 2019 report: speculative and untested epidemiological models.
The most influential model came from Imperial College London. In April 2020, the journal Nature credited the Imperial team led by Neil Ferguson for developing one of the main computer simulations “driving the world’s response to Covid-19.” The New York Times described it as the report that “jarred the U.S. and the U.K. to action.”
