The capricious “science” of containment

“Follow the science” has been the battle cry of lockdown supporters since the start of the Covid-19 pandemic. Yet before March 2020, the mainstream scientific community, including the World Health Organization, strongly opposed lockdowns and similar measures against infectious diseases.

This judgment came from a historical analysis of pandemics and a realization that societal restrictions have serious socio-economic costs and benefits that are almost entirely speculative. Our response to the pandemic, based on lockdowns and closely related ‘non-pharmaceutical interventions’ or NPIs, represented an unprecedented and unwarranted shift in scientific opinion from what it was a few months before the discovery of Covid -19.

In March 2019, the WHO hosted a conference in Hong Kong to review the NPI’s measures against pandemic influenza. WHO team assessed a quarantine proposal – “home confinement of non-sick contacts of a person with known or suspected influenza” – less blind than the Covid blockages. They drew attention to the lack of data to support this policy, noting that “most of the evidence currently available on the effectiveness of quarantine on influenza control has been drawn from simulation studies, which have a low probative value “. The WHO team said the large-scale home quarantine was “not recommended as there is no obvious rationale for this measure.”

September 2019 report from the Center for Health Security at Johns Hopkins University came to a similar conclusion: “In the context of a high-impact respiratory pathogen, quarantine may be the least likely NPI to be effective in controlling the spread by due to high transmissibility. This was especially true for a rapidly spreading airborne virus, such as the then unknown SARS-CoV-2.

These studies were based on historical experience. A separate WHO 2006 to study concluded that “forced isolation and quarantine are ineffective and impractical”, based on the results of the 1918 Spanish influenza pandemic. He cited the example of Edmonton, Alta., where “public meetings were banned; schools, churches, colleges, theaters and other public gathering places have been closed; and hours of operation have been restricted with no obvious impact on the outbreak. ”

Using data from a 1927 analysis of the Spanish flu in the United States, the study concluded that blockages were “not clearly effective in urban areas.” It is only in isolated rural areas, “where group contacts are less numerous”, that this strategy has become theoretically viable, but the hypothesis has not been tested. Although the study found some benefits of smaller-scale quarantines of patients and their families during the 2003 SARS outbreak, it concluded that a rapidly spreading disease, combined with “the presence of mild cases and the possibility of symptom-free transmission ”, would make these latter measures“ considerably less successful ”.

Medical historian John Barry, who wrote the Standard Account of the Spanish Flu of 1918, Okay on the ineffectiveness of containments. “Historical data clearly shows that quarantine only works if it is absolutely rigid and comprehensive,” he wrote in 2009, summarizing the results of a study of influenza epidemics at US military bases. during the World War One. Out of 120 training camps that experienced outbreaks, 99 imposed quarantines on the base and 21 did not. The case rates between the two categories of camps showed “no statistical difference”. “If a military camp cannot be quarantined successfully in wartime,” Barry concluded, “it is highly unlikely that a civilian community can be quarantined in peacetime.”

A The Johns Hopkins team came to similar conclusions in 2006: “No historical observations or scientific studies” could be found to support the effectiveness of large-scale quarantine. Scientists concluded that “the negative consequences of large-scale quarantine are so extreme. . . that this mitigation measure should be removed 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 onset of Covid-19, the lack of wisdom of blockages guided mainstream epidemiology. When the Wuhan region in China imposed severe restrictions on January 23, 2020, Anthony Fauci questioned that decision. “It’s something that I don’t think we can do in the United States, I can’t imagine closing New York or Los Angeles,” Dr. Fauci said. Recount CNN. He probably had the scientific literature in mind when he said that “historically when you close things it doesn’t have a major effect.”

What prompted the scientific community to abandon its aversion to confinements? The empirical evidence has not changed. On the contrary, the lockdown strategy comes from the same sources that the WHO heavily discounted 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 leading computer simulations “driving the global response to Covid-19”. The New York Times describe this is the report that “pushed the United States and the United Kingdom to act”.

After predicting catastrophic loss rates for an ‘unmitigated’ pandemic, Mr Ferguson’s model vowed to bring Covid-19 under increasingly severe NPI policies, resulting in event cancellations, shutdowns of schools and businesses, and ultimately blockades. Mr. Ferguson produced his model by recycling a decades-old influenza model that significantly lacked his scientific assumptions. On the one hand, it lacked a way to estimate even the viral spread in nursing homes.

Mr. Ferguson’s previous model record should have been a warning. In 2001 he predicted that mad cow disease is believed to kill up to 136,000 people in the UK, and he rebuked conservative estimates of up to 10,000. In 2018, the actual death toll was 178. His other missteps include disasters predicted for mad sheep disease, bird flu and swine flu that never went away.

We assessed Imperial’s Covid-19 performance predictions in 189 different countries on the first anniversary of their publication, March 26, 2021. Not a single country has achieved the predicted death rates of their “unmitigated spread” or even the “mitigation” model – the latter based on social distancing measures similar to what many governments have adopted. Even Mr Ferguson’s extreme ‘suppression’ model, which assumed a tight lockdown reducing public contact by 75% for more than a year, predicted more deaths than what happened in 170 of 189 countries. Imperial has predicted as many as 42,473 Covid deaths in Sweden under attenuation and 84,777 under uncontrolled spread. The country, which has refused to shut down, killed some 13,400 in the first year.

Despite the unsuccessful predictions of these models, the Imperial team rushed to study to be printed in the journal Nature in June 2020, claiming that the lockdowns had already saved 3.1 million lives. It remains the most cited pro-containment study in epidemiology, despite its premature claims and its circular dependence on its own model to arrive at this figure.

In fact, the rigor of the confinement is a poor predictor of Covid-related mortality. Our exam of the 50 U.S. states and 26 countries found no discernible pattern connecting the two – a basic expectation if lockdowns were carried out as “science” often insists.

So why have public health authorities abandoned their opposition to lockdowns? Why have they rushed to embrace untested claims of flawed epidemiological modeling? An answer appears in the 2019 Johns Hopkins study: “Some INPs, such as travel restrictions and quarantine, could be sued for social or political purposes by political leaders, rather than because of health evidence. public. “

Mr. Magness is Director of Research and Mr. Earle is a faculty member of the American Institute for Economic Research.

Covid-19 is an ever-changing virus, so rather than panicking and reimposing pandemic restrictions when new strains like Delta and Omicron are discovered, the world should learn to live with them. Image: Christopher Furlong / Getty Images Composite: Mark Kelly

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