Along with Sunetra Gupta and Carl Henegan, John Ioannidis is one of the best-credentialed opponents of the broad set of lockdown policies put in place by many countries since the advent of the pandemic.

A professor at the Stanford University School of Medicine, Ioannidis's 2005 paper 'Why Most Published Research Findings Are False' is regarded as a seminal contribution to the recognition of the replication crisis in scientific research.

Ioannidis' immediate contribution to the Covid-19 debate came in mid-March at around the time national lockdowns in parts of Europe and North America were coming into force. In STAT, he wrote that the data supporting lockdown measures were unreliable, suggesting that the case fatality rate would be between 0.05% to 1%, possibly lower than seasonal influenza.

In that article, Ioannidis suggested that:

"If we assume that case fatality rate among individuals infected by SARS-CoV-2 is 0.3% in the general population — a mid-range guess from my Diamond Princess analysis — and that 1% of the U.S. population gets infected (about 3.3 million people), this would translate to about 10,000 deaths. This sounds like a huge number, but it is buried within the noise of the estimate of deaths from “influenza-like illness.” If we had not known about a new virus out there, and had not checked individuals with PCR tests, the number of total deaths due to “influenza-like illness” would not seem unusual this year. At most, we might have casually noted that flu this season seems to be a bit worse than average."

Like Sunetra Gupta, Ioannidis wanted a large-scale understanding of the true infection rate through widespread testing:

"Some worry that the 68 deaths from Covid-19 in the U.S. as of March 16 will increase exponentially to 680, 6,800, 68,000, 680,000 … along with similar catastrophic patterns around the globe. Is that a realistic scenario, or bad science fiction? How can we tell at what point such a curve might stop?

"The most valuable piece of information for answering those questions would be to know the current prevalence of the infection in a random sample of a population and to repeat this exercise at regular time intervals to estimate the incidence of new infections. Sadly, that’s information we don’t have." (March 2020)

Ioannidis' next contribution came in April, when he published a paper with colleagues suggesting that in Santa Clara, infections were 50 to 85 times higher than cases, and as such the fatality rate was 0.12-0.2%. But the sample was not randomly selected. Andrew Gelman, a professor of statistics and political science at Columbia University, said:

"I think the authors of the above-linked paper owe us all an apology. We wasted time and effort discussing this paper whose main selling point was some numbers that were essentially the product of a statistical error.

"I’m serious about the apology. Everyone makes mistakes. I don’t think they authors need to apologize just because they screwed up. I think they need to apologize because these were avoidable screw-ups. They’re the kind of screw-ups that happen if you want to leap out with an exciting finding and you don’t look too carefully at what you might have done wrong." (April 2020)

Before its publication, two of the other authors of the study suggested that the true mortality rate could be as low as 0.01%, at a time when Italy had already recorded a higher death rate, even when compared to its entire population.

A BuzzFeed News investigation subsequently revealed that the study was funded in part by David Neeleman, JetBlue Airways founder and critic of lockdown policies, a fact which was not disclosed at the time of publication.

Neeleman told the reporters that he had been in touch with the researchers while the study was conducted. Ioannidis told BuzzFeed News that he was not personally aware that Neeleman had funded the study.

In early April Ioannidis was also quoted on CNN and in the Washington Post offering a Covid-19 death total for the United States:

"If I were to make an informed estimate based on the limited testing data we have, I would say that COVID-19 will result in fewer than 40,000 deaths this season in the USA" (April 2020)

A month later, around twice that many people were recorded as Covid-19 deaths in the United States. Currently, the cumulative total sits at around 10 times that level, though it is unclear what period Ioannidis meant precisely by "this season."

A video of Ioannidis speaking to documentary filmmakers about Covid was removed by YouTube on the grounds that it violated the site's policies on misinformation.

In May, Ioannidis followed up with another paper which expressed the risk of dying from Covid-19 as a proportion of the risk of death from driving:

"The COVID-19 death risk in people <65 years old during the period of fatalities from the epidemic was equivalent to the death risk from driving between 13 and 101 miles per day for 11 countries and 6 states, and was higher (equivalent to the death risk from driving 143-668 miles per day) for 6 other states and the UK. People <65 years old without underlying predisposing conditions accounted for only 0.7-2.6% of all COVID-19 deaths (data available from France, Italy, Netherlands, Sweden, Georgia, and New York City)." (May 2020)

The conclusions came under criticism since the data Ioannidis used was for the risk of driving was based on the population which was by definition traveling by car, whereas the risk of dying of Covid-19 was judged against the whole population, when only a limited share had already had the disease, while the pandemic was still ongoing.

Ioannidis is still an active participant to the debate, publishing a paper in January 2021 which found no evidence that lockdowns contibuted to falling caseloads. The study has been criticised for using small sample sizes, using partial data, and failing to properly account for the obvious correlation between lockdown policies and growing cases — since lockdowns were progressively tightened in an effort to stem growth in cases, but could only operate with a lag. So the argument of the paper is like arguing that "People in hospital are more likely to have heart disease; therefore hospitals cause heart disease."

Page added on 19 January 2021