By Jeffrey A. Tucker
Fifteen years ago, writers schooled in computer science began to imagine various totalitarian schemes for pandemic control.
Experienced public health officials in 2006 warned this would lead to disaster. Donald A. Henderson et. al., for example, went through the whole list of possible restrictions, shooting them down one by one.
Still, a decade and a half later, governments all over the world tried lockdowns anyway. And sure enough, since April 2020, scholars have observed these lockdown policies haven’t worked. The politicians preached, the cops enforced, citizens shamed each other, and businesses and schools did their best to comply with all the strictures. But the virus kept going with seeming disregard for all these antics.
Neither oceans of sanitizer, nor towers of plexiglass, nor covered mouths and noses, nor crowd avoidance, nor the seeming magic of six feet of distance, nor even mandated injections caused the virus to go away or otherwise be suppressed.
Policy versus Outcomes
Restrictions aren’t associated with any set of virus mitigation goals. Forty studies have shown no connection between the policy (egregious violations of human liberty) and the intended outcomes (diminishing the overall disease impact of the pathogen).
You can forget about “causal inference” here because there is an absence of correlation between policy and outcomes at all. You can do a deeper dive and find 400 studies showing that the impositions of basic freedoms didn’t achieve the intended result but instead produced terrible public health outcomes.
The two years of the hell into which hundreds of governments simultaneously plunged the globe achieved nothing but economic, social, and cultural destruction. Very obviously, this realization is shocking and suggests a crying need for a reassessment of the power and influence of the people who did this.
This reassessment is happening now, all over the world.
Media Ignored Evidence
A major frustration for those of us who have denounced lockdowns (which go by many names and take many forms) is that these studies haven’t exactly rocked the headlines. Indeed, they have been buried for the better part of two years.
Among the ignored studies was a December 2020 examination of light and voluntary measures (discouraging large gatherings, isolating the sick, generally being careful) versus heavy and forced measures. This article, by Eran Bendavid et al. observes some effects on the spread from light measures but nothing statistically significant from heavy measures, such as stay-at-home (or shelter-in-place) orders.
The most recent meta-analysis from Johns Hopkins University (JHU) by Jonas Herby of the Center for Political Studies in Copenhagen, Denmark, Lars Jonung of Lund University, and Steve Hanke of JHU seems to have achieved some measure of media attention. It focuses in particular on the effects of heavy interventions on mortality, finding little to no relationship between policies and severe disease outcomes.
The attention given to this meta-analysis seems to have annoyed the small cabal of academics who still defend lockdowns.
Among the comments were those of the University of Oxford’s Seth Flaxman, a major figure in this realm, who isn’t trained in biological science or medicine but computer science with a specialization in machine learning. And yet it has been his work that has most often been cited in defense of the idea that lockdowns achieved some good.
In opposition to the JHU study, Flaxman writes: “Smoking causes cancer, the earth is round, and ordering people to stay at home (the correct definition of lockdown) decreases disease transmission. None of this is controversial among scientists. A study purporting to prove the opposite is almost certain to be fundamentally flawed.”
See how this rhetoric works? If you question his claim, you are not a scientist; you are denying the science! To say that this isn’t controversial is ridiculous since such policies had never before been attempted on this scale. Such a policy isn’t at all like an established causal claim (smoking increases cancer risk) nor a mere empirical observation (the earth is round). It’s subject to verification.
Turned Pandemic into a Catastrophe
It isn’t possible to order everyone to stay home, not even for a day or two. The groceries must get to the store or be delivered to homes and apartments. People must staff the hospitals. The electrical plants still need staff. Cops still must be on the beat. There is literally no option available to “shut down” society in real life versus in computer models.
In the end, what is the point of the stay-home orders? For a widespread virus such as this one, everyone will eventually meet the virus anyway. Only once the winter wave of 2021 finally swept the Zoom class did we start to see a shift in media messaging that there is no shame in sickness, and perhaps we need to start relaxing these restrictions.
The dogma that ordering people to stay home reduces the spread comes not from evidence but from Flaxman-style modeling plus a remarkable capacity to ignore reality.
Lockdown policies are easily marketed to political players who might get a power rush from the exercise. But, in the end, Henderson’s prediction was correct: These interventions turned a manageable pandemic into a catastrophe.
It’s a sure bet, however, that lockdown proponents will be in denial at least for another decade.
Jeffrey A. Tucker (email@example.com) is the founder and president of the Brownstone Institute. A version of this article has was published by Epoch Times on February 13 and the Brownstone Institute. Reprinted with permission.