With lockdown sceptics running for the White House and anti-vaxxers selling out arena shows, Covid denial has proved an enduring force in modern politics.
But Michael Levitt, the Nobel prizewinning Stanford University chemist who became an outspoken contrarian voice on whether the virus was as deadly as feared, says he does not regret pushing back on the scientific consensus of Covid’s likely impact. Others disagree, claiming the lockdown opposition of a Nobelist has emboldened Covid deniers and assorted conspiracy theorists.
“My view on Covid will be much closer to what actually happened than Neil Ferguson’s,” insisted Professor Levitt, referencing the Imperial College London epidemiologist whose predictions that 2.2 million could die if no preventative action was taken shocked the UK and America into lockdown in early 2020.
By contrast, Professor Levitt told Israeli prime minister Benjamin Netanyahu in March 2020 that he’d be surprised if it saw 10 deaths from Covid and tweeted to his 90,000 followers in July 2020 that Covid would be “done in four weeks with a total reported death toll below 170,000”, thanks partly to herd immunity. By the end of January 2022, the official US toll was 1.1 million, with around 7 million globally, according to the World Health Organization.
While Professor Levitt regrets that prediction, the official Covid death toll paints a misleading picture, he claims, because it fails to recognise that most Covid victims were elderly and often had pre-existing health conditions. “Lots of people reported of dying of Covid would have died anyway,” he told Times Higher Education in an interview at the Lindau Nobel Laureate Meeting this year, adding: “The angel of death came a month early for many.”
“Covid was controversial because many governments acted as if it was going to be the end of the world, or it would be comparable to the 1918 flu epidemic,” explained the South Africa-born scientist. “In fact, the early indication was it was much less likely to cause death than flu, increasing the risk of mortality by about 5 to 6 per cent,” he added.
That attitude towards the deaths of Covid victims can seem callous, even indifferent to the plight of those who died in the pandemic. Professor Levitt is, however, unapologetic, arguing that individuals constantly weigh up risk-reward calculations and should have been free to make their own decisions when Covid arrived.
“Everyone accepts there are naturally occurring deaths in any year that could maybe have been prevented but life expectancy isn’t everything. We could ban smoking, fast food and dangerous sports, and close all the pubs, if we really wanted to ensure life expectancy was much higher but we don’t,” he explained. “I’m 76, if someone told me that I couldn’t see my grandkids for two years because I might get a disease like Covid – which will affect me, but not them – I’d say: ‘Go to hell.’”
The catastrophic modelling forced governments to change their risk tolerance because millions were forecast to die without robust intervention, but Professor Levitt insisted the risk was overblown.
“Epidemiologists did everything they could not to underestimate [the death toll] but the cost of these decisions is huge. Financially, it’s about $14 trillion (£11 trillion) for the US alone – which future generations will need to pay off. And we don’t really know what the long-term consequence will be, in terms of education lost and social factors,” he continued.
For his critics, Professor Levitt’s interventions on Covid – a field outside his expertise in computational biology, which won him a share of the Nobel in chemistry in 2013 – highlight the dangers of assuming Nobelists have a great insight on scientific matters beyond their expertise. That overlooks that many of his calls were correct, he said.
“With John Ioannidis [a fellow Stanford scientist], we have written eight papers in the last year and had them accepted – many of the things we predicted went on to happen. The death rate was not as terrible as many said, and [the] jury is still out on many aspects of Covid,” he insisted.
Professor Levitt’s data has been used by some anti-vaccine activists to justify their positions, though he disputes that he has undermined the case for mass vaccinations.
“I was incredibly careful on this one and was vaccinated as soon as they were available in 2020,” he said, although he is equivocal on its overall impact. “I hope the RNA vaccines prove to be helpful but we don’t really know if they were for Covid. I’m disappointed that they didn’t stop the infections,” said Professor Levitt.
For some, Professor Levitt’s modelling contributed to the disinformation that is still plaguing science and, in doing so, sullied his legacy as a scientific great. Does that concern him?
“I don’t think that’s the case and I don’t really care,” he said, adding: “The Nobel prize is a funny thing – if you’re halfway sane, or just normal, you don’t expect to get one, so you can’t really let it influence what you say or do,” he said.
