COVID-19 represents the first time in the history of pandemics that we confined healthy populations. While the ancients did not understand the mechanisms of infectious disease—they knew nothing of viruses and bacteria—they nevertheless figured out many ways to mitigate the spread of contagion during epidemics. These time-tested measures ranged from quarantining symptomatic patients to enlisting those with natural immunity, who had recovered from the illness, to care for the sick.
From the lepers in the Old Testament to the plague of Justinian in Ancient Rome to the 1918 Spanish flu pandemic, lockdowns were never part of conventional public health measures. The concept of lockdowns arose in part from a public health apparatus that had become militarized over the previous two decades. We now routinely hear of “countermeasures,” but doctors and nurses never use that word, which is a term of spycraft and soldiering.
In 1968, while an estimated one to four million people died in the H3N2 influenza pandemic, businesses and schools stayed open and large events were never cancelled. Until 2020 we had not previously locked down entire populations, because that strategy does not work. In 2020 we had zero empirical evidence that lockdowns would save lives, only flawed mathematical models whose predications were not just slightly off, but wildly exaggerated by orders of magnitude.
The piece did not give enough credit to Medieval society, which sometimes locked the gates of walled cities or closed borders during epidemics, but never ordered people to stay in their homes, never stopped people from plying their trade, and never isolated asymptomatic individuals from others in the community.
No, Mr. McNeil, lockdowns were not a Medieval throwback but a wholly modern invention. In March of 2020, pandemic lockdowns were an entirely de novo experiment, untested on human populations.
Although these measures were unprecedented, there was virtually no public conversation or debate about lockdown policies. Wise solutions to vexing policy questions always involve prudential judgments that no single epidemiological model can provide.
Our politicians abdicated responsibility by hiding behind “The Science” or “The Experts,” as though these trademarked phrases conjured a single monolithic table of all-encompassing data. They should have considered the various complex risks and harms—not to mention a thousand other imponderables—of decisions like lockdowns or mask mandates.
This term “lockdown” originated not in medicine or public health but the penal system. Prisons go into lockdown to restore order when prisoners riot. When the most tightly controlled and surveilled environment on the planet erupts into chaos, order is restored by asserting swift and complete control of the entire prison population by force. Only strictly surveilled confinement can keep the dangerous and unruly population in check. Prisoners cannot be permitted to riot; inmates cannot run the asylum.
In February of 2020 our society believed that chaos was coming, and we embraced the idea that this penal solution was the right, indeed the only sensible, response. Lockdowns met remarkably little resistance when initially implemented. “Fifteen days to flatten the curve” seemed reasonable to most people. One after another in rapid succession, governors ordered us to stay at home.
We readily obeyed. To refuse, we were told, was to recklessly court death. Any small pockets of resistance were swiftly stigmatized. As one journalist described it, “Appeals to science were weaponized to enforce conformity, and the media portrayed anti-lockdown protesters as backwards, astroturfed white nationalists bent on endangering the public.” Who wanted to be classed in that camp?
Reports about COVID had already mesmerized the world for a few months leading up to lockdowns. We stayed glued to screens, watching case counts rise as we tracked coronavirus deaths in foreign countries. Not yet seeing cases in the United States and UK, we relied for guidance on mathematical modeling.
Because we were primed for panic, the model chosen was not one of the many sober statistical predictions, but the terrifying numbers published by Neil Ferguson’s group at the Imperial College London, which predicted 40 million deaths in 2020. We conveniently ignored Ferguson’s dismal track record of wildly overestimated predictions in prior epidemics, and sidelined critics like the legendary biostatistician John Ioannidis of Stanford, who warned that the Imperial College model was grounded in seriously faulty assumptions.
No matter—this time, surely, Ferguson’s dire prophecies would be vindicated. As it turned out, the model was proven more wildly wrong than any of the other leading models on offer. The Imperial College model predicted that if it did not lock down, Sweden would have 80,000 deaths by the end of June.
It remained one of the few countries that did not lock down and had 20,000 deaths, even using methods resulting in overcounting. Ferguson’s model was testable and was clearly proven wrong, but that fact did nothing to shift our trajectory.
