Quotation Of The Day: Governor Kristi Noem's Favorite Scientist Edition

Yesterday, Governor Noem sent out the following tweet.

No.

People should have the freedom to wear masks if it makes them feel safe, but the science on masks is very mixed.

The media should ask scientists like Stanford's John Ioannidis and Scott Atlas whether we've gone too far on masks for everyone at all times. https://t.co/F90ir6uzf0

— Governor Kristi Noem (@govkristinoem) July 9, 2020

Via Seth Tupper and The South Dakota Standard, this Wired article about John Ioannidis. 

First, the background.

Let’s face it, the field of epidemiology hasn’t so far covered itself in glory with regard to the coronavirus crisis. The field was for the most part fatefully slow to recognize that a pandemic was aborning; later on, it produced a stream of conflicting and sometimes wildly off-the-mark assessments of infection and mortality rates, and where they might be heading. 

 But even in this fast-paced and sloppy context, Ioannidis’ study is seen as standing out. Not just for its methodological weaknesses but for the apparent wrongness of its main conclusions—and the risk that these could have a harmful influence on public health recommendations. In a nutshell, Ioannidis and his study coauthors tested about 3,300 residents of California’s Santa Clara County for antibodies to the new coronavirus. The results, according to Ioannidis, imply that the disease isn’t nearly as deadly as believed. “Based on what we’re seeing now, the fatality of the virus is more or less the same as influenza, about 0.1 percent,” he says. “Most of the earlier data was completely bogus.”

The study, posted as a preprint on April 17, has been pilloried nonstop. Critics noted problems in the way subjects were recruited, potential defects in the antibody test, and apparent mistakes in the statistical analysis. Ioannidis might have received a pass if his involvement went no further than being listed among the suspect study’s 17 coauthors. But he’d already needled colleagues with an essay that he wrote in March, calling the response to Covid-19 “a once-in-a-century evidence fiasco”; and now, again, he took to the airwaves to hawk these new results as evidence that stay-at-home measures are misguided.

Now, the money quote:

Other epidemiologists’ assessment of Ioannidis’ claim, that staying at home will likely kill far more people than Covid-19, might best be summed up the way physics giant Wolfgang Pauli is said to have dismissed the lesser work of a colleague: It’s not even wrong. To be promoted to wrong, the Ioannidis position would have to be based on data and analysis that scientists could argue over. Even allowing his 0.1 percent fatality rate for the disease—which most epidemiologists think is way too low, but not beyond-the-realm-of-possibility low—there is almost no data to go on for the likely cost in human life of the lockdown. We know Covid-19 is killing tens of thousands of people, and that staying at home is slowing the spread; but we know virtually nothing about the number deaths caused by staying at home. As such, what Ioannidis is promoting simply isn’t science, says Loren Lipworth, a Vanderbilt University epidemiologist. “It’s impossible to do that risk-benefit analysis,” she says. “It’s just relying on anecdote and common sense.” In other words, Ioannidis is pitting his gut against the collective data-driven wisdom and analysis of medicine and public health.