The COVID-19 pandemic has been a testing time for the already testy academic discourse. Decisions have had to be made with partial information. Information has come in drizzles, showers and downpours. The velocity with which new information has arrived has outstripped our ability to make sense of it. On top of that, the science has been politicized in a polarized country with a polarizing president at its helm.
As the country awoke to an unprecedented economic lockdown in the middle of March, John Ioannidis, professor of epidemiology at Stanford University and one of the most cited physician scientists who practically invented “metaresearch”, questioned the lockdown and wondered if we might cause more harm than good in trying to control coronavirus. What would normally pass for skepticism in the midst of uncertainty of a novel virus became tinder in the social media outrage fire.
Ioannidis was likened to the discredited anti-vax doctor, Andrew Wakefield. His colleagues in epidemiology could barely contain their disgust, which ranged from visceral disappointment — the sort one feels when their gifted child has lost their way in college, to deep anger. He was accused of misunderstanding risk, misunderstanding statistics, and cherry picking data to prove his point.
The pushback was partly a testament to the stature of Ioannidis, whose skepticism could have weakened the resoluteness with which people complied with the lockdown. Some academics defended him, or rather defended the need for a contrarian voice like his. The conservative media lauded him.
In this pandemic, where we have learnt as much about ourselves as we have about the virus, understanding the pushback to Ioannidis is critical to understanding how academic discourse shapes public’s perception of public policy.
Saurabh Jha: On March 17, at the start of the lockdown, you wrote in STAT NEWS cautioning us against overreacting to COVID-19. You likened our response to an elephant accidentally jumping off a cliff because it was attacked by a house cat. The lockdown had just begun. What motivated you to write that editorial?
John P.A. Ioannidis: March seems a long time ago. I should explain my thinking in the early days of the COVID-19 pandemic. Like many, I saw a train approaching. Like many, I couldn’t sense the train’s precise size and speed. Many said we should be bracing for a calamity and in many ways I agreed. But I was concerned that we might inflict undue damage, what I’d call “iatrogenic harm”, controlling the pandemic.
To answer your question specifically, I wrote the piece because I felt that the touted fatality rate of COVID-19 of 3.4 % was inflated, but we had so limited data and so much uncertainty that infection fatality rate values as different as 0.05% and 1% were clearly still possible. I was pleading for better data on COVID-19 to make our response more precise and proportionate.
We now know that the infection fatality rate (IFR) is much lower than 3.4 %. I’m curious — why did you doubt this figure? At the time, the virus created havoc in Iran and Italy. Hospitals in the richest areas in Italy rationed ventilators. Was a fatality rate of 3.4 % so implausible?
Small changes in the fatality rate make a dramatic difference in the number of deaths. 3.4 % is an entirely different universe from 0.5 %. Imperial College epidemiologists, using an overall IFR of 0.9 %, assumed that if 60-80 % of the population were infected, as would happen without precaution or immunity, 2.2 million Americans would die.