First, in mid-March, as the pandemic was making its way to America, Ioannidis wrote an
article for STAT News where he argued that we should avoid rushing into big decisions like country-wide lockdowns without what he called “reliable data” on the virus. The most memorable part of the article was his prediction — on the basis of his analysis of the cursed cruise ship Diamond Princess — that around 10,000 people in the US would die from COVID-19 — a number that, he said, “is buried within the noise of the estimate of deaths from ‘influenza-like illness’”. As US deaths have just hit 125,000, I don’t need to emphasise how wrong that prediction was.
So far, so fair enough: everyone makes bad predictions sometimes. But some weeks later, it emerged that Ioannidis had helped co-author the infamous Santa Clara County
study, where Stanford researchers estimated that the number of people who had been infected with the coronavirus was considerably higher than had been previously supposed. The message was that the “infection fatality rate” of the virus (the proportion of people who, once infected, die from the disease), must be very low, since the death rate had to be divided across a much larger number of infections. The study became extremely popular in anti-lockdown quarters and in the Right-wing populist
media. The virus is hardly a threat, they argued — lift the lockdown now!
But the study had serious problems. When you do a study of the prevalence of a virus, your sample needs to be as random as possible. Here, though, the researchers had
recruited participants using Facebook and via email, emphasising that they could get a test if they signed up to the study. In this way, it’s probable that they recruited disproportionate numbers of people who were worried they were (or had been) infected, and who thus wanted a test. If so, the study was fundamentally broken, with an artificially-high COVID infection rate that didn’t represent the real population level of the virus (there were also
other issues relating to the false-positive rate of the test they used