Twelve Times the Lockdowners Were Wrong


by Phillip W. Magness , The American Institute for Economic Research:

This has been a year of astonishing policy failure. We are surrounded by devastation conceived and cheered by intellectuals and their political handmaidens. The errors number in the thousands, so please consider the following little more than a first draft, a mere guide to what will surely be unearthed in the coming months and years. We trusted these people with our lives and liberties and here is what they did with that trust.

  1. Anthony Fauci says lockdowns are not possible in the United States (January 24):

When asked about the mass quarantine containment efforts underway in Wuhan, China back in January, Fauci dismissed the prospect of lockdowns ever coming to the United States:

“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, but the judgement on the part of the Chinese health authorities is that given the fact that it’s spreading throughout the provinces… it’s their judgement that this is something that in fact is going to help in containing it. Whether or not it does or does not is really open to question because historically when you shut things down it doesn’t have a major effect.”

Less than two months later, 43 of 50 US states were under lockdown – a policy advocated by Fauci himself.

  1. US government and WHO officials advise against mask use (February and March)

When mask sales spiked due to widespread individual adoption in the early weeks of the pandemic, numerous US government and WHO officials took to the airwaves to describe masks as ineffective and discourage their use.

Surgeon General Jerome Adams tweeted against masks on February 29. Anthony Fauci publicly discouraged mask use in a nationally broadcast 60 Minutes interview on March 7. At a March 30 World Health Organization briefing its Director-General supported mask use in medical settings but dissuaded the same in the general public.

By mid-summer, all had reversed course and encouraged mask-wearing in the general public as an essential tool for halting the pandemic. Fauci essentially conceded that he lied to the public in order to prevent a shortage on masks, whereas other health officials did an about-face on the scientific claims around masking.

While mainstream epidemiology literature stressed the ambiguous nature of evidence surrounding masks as recently as 2019, these scientists were suddenly certain that masks were something of a magic bullet for Covid. It turns out that both positions are likely wrong. Masks appear to have marginal effects at diminishing spread, especially in highly infectious settings and around the vulnerable. But their effectiveness at combating Covid has also been grossly exaggerated, as illustrated by the fact that mask adoption reached near-universal levels in the US by the summer with little discernible effect on the course of the pandemic.

  1. Anthony Fauci’s decimal error in estimating Covid’s fatality rates (March 11)

Fauci testified before Congress in early March where he was asked to estimate the severity of the disease in comparison to influenza. His testimony that Covid was “10 times more lethal than the seasonal flu” stoked widespread alarm and provided a major impetus for the decision to go into lockdown.

The problem, as Ronald Brown documented in an epidemiology journal article, is that Fauci based his estimates on a conflation of the Infection Fatality Rate (IFR) and Case Fatality Rate (CFR) for influenza, leading him to exaggerate the comparative danger of Covid by an order of magnitude. Fauci’s error – which he further compounded in a late February article for the New England Journal of Medicine – helped to convince Congress of the need for drastic lockdown measures, while also spreading panic in the media and general public. As of this writing Fauci has not acknowledged the magnitude of his error, nor has the journal corrected his article.

  1. “Two weeks to flatten the curve” (March 16)

The lockdowners settled on a catchy slogan in mid-March to justify their unprecedented shuttering of economic and social life around the globe: two weeks to flatten the curve. The White House Covid task force aggressively promoted this line, as did the news media and much of the epidemiology profession. The logic behind the slogan came from the ubiquitous graph showing (1) a steep caseload that would overwhelm our hospital system, or (2) a mitigated alternative that would spread the caseload out over several weeks, making it manageable.

To get to graph #2, society would need to buckle up for two weeks of shelter-in-place orders until the capacity issue could be managed. Indeed, we were told that if we did not accept this solution the hospital system would enter into catastrophic failure in only 10 days, as former DHS pandemic adviser Tom Bossert claimed in a widely-circulated interview and Washington Post column on March 11.

Two weeks came and went, then the rationale on which they were sold to the public shifted. Hospitals were no longer on the verge of being overwhelmed – indeed most hospitals nationwide remained well under capacity, with only a tiny number of exceptions in the worst-hit neighborhoods of New York City.

A US Navy hospital ship sent to relieve New York departed a month later after serving only 182 patients, and a pop-up hospital in the city’s Javits Convention Center sat mostly empty. But the lockdowns remained in place, as did the emergency orders justifying them. Two weeks became a month, which became two months, which became almost a year. We were no longer “flattening the curve” – a strategy premised on saving the hospital system from a threat than never manifested – but instead refocused on using lockdowns as a general suppression strategy against the disease itself. In short, the epidemiology profession sold us a bill of goods.

  1. Neil Ferguson predicts a “best case” US scenario of 1.1 million deaths (March 20)

The name Neil Ferguson, the lead modeler and chief spokesman for Imperial College London’s pandemic response team, has become synonymous with lockdown alarmism for good reason. Ferguson has a long track record of making grossly exaggerated predictions of catastrophic death tolls for almost every single disease that comes along, and urging aggressive policy responses to the same including lockdowns.

Covid was no different, and Ferguson assumed center stage when he released a highly influential model of the virus’s death forecasts for the US and UK. Ferguson appeared with UK Prime Minister Boris Johnson on March 16 to announce the shift toward lockdowns (with no small irony, he was coming down with Covid himself at the time and may have been the patient zero of a super-spreader event that ran through Downing Street and infected Johnson himself).

Across the Atlantic, Anthony Fauci and Deborah Birx cited Ferguson’s model as a direct justification for locking down the US. There was a problem though: Ferguson had a bad habit of dramatically hyping his own predictions to political leaders and the press. The Imperial College paper modeled a broad range of scenarios including death tolls that ranged from tens of thousands to over 2 million, but Ferguson’s public statements only stressed the latter – even though the paper itself conceded that such an extreme “worst case” scenario was highly unrealistic. A telling example came on March 20th when the New York Times’s Nicholas Kristof contacted the Imperial College modeler to ask about the most likely scenario for the United States. As Kristof related to his readers, “I asked Ferguson for his best case. “About 1.1 million deaths,” he said.”

  1. Researchers in Sweden use the Imperial College model to predict 95,000 deaths (April 10)

After Neil Ferguson’s shocking death toll predictions for the US and UK captivated policymaker attention and drove both governments into lockdown, researchers in other countries began adapting the Imperial College model to their own circumstances. Usually, these models sought to reaffirm the decisions of each country to lock down. The government of Sweden, however, had decided to buck the trend, setting the stage for a natural experiment to test the Imperial model’s performance.

In early April a team of researchers at Uppsala University adapted the Imperial model to Sweden’s population and demographics and ran its projections. Their result? If Sweden stayed the course and did not lock down, it could expect a catastrophic 96,000 deaths by early summer. The authors of the study recommended going into immediate lockdown, but since Sweden lagged behind Europe in adopting such measures they also predicted that this “best case” option would reduce deaths to “only” 30,000.

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