As many states and countries are beginning to emerge from lockdown, media outlets around the world continue to dwell on the numbers: the total number of cases and deaths; the R numbers and whether individual countries have brought infection rates down to acceptable levels; a nation’s total stockpile of test kits and personal protective equipment; the number of businesses kitting out with all the new protective gear; the fresh totals of the unemployed and firms going to the wall as a result of the virus.
And, largely based on these numbers, we’ve all made the automatic assumption that we’re going to suffer a second peak, and so must rush to put in place track-and-trace and immunity passports, and fast-track anything that might turn out to be the miracle drug or vaccine to deliver us from this Armageddon-like evil.
What is missing from these daily figures and most news reports is a willingness to back up a step and ask a few basic questions. Have any of those measures to control the virus actually worked? Will all our ideas for creating great swathes of immunity prevent a second peak?
And will there in fact be a second peak, so all those future measures turn out to be necessary?
Any voices of dissent to the prevailing triad of lockdown/track and trace/vaccinate haven’t gotten the front-page airing they should have, as Sherelle Jacobs recently noted in the UK’s Daily Telegraph.
There are, for instance, the quiet and persistent noises being made by mathematician and Stanford University professor of structural biology Michael Leavitt, who won a Nobel Prize for his development of multiscale models of complex chemical systems.
He and his wife split their time between the US, Israel and China (his wife researches Chinese art and curates Chinese photographers), and after hearing from Chinese friends during the initial stages of the COVID-19 outbreak in Hubei province, Leavitt started tracking the numbers. But he came to a very different view.
He discovered that while the initial rate of infection in Hubei province was increasing at first by 30 percent a day—an exponential rise that meant the entire world should have been infected within 90 days, he says—the path of the virus suddenly changed.
When Leavitt first started examining the figures on February 1, Hubei reported 1,800 new cases a day, reaching 4,700 a day six days later before plummeting “linearly.” The same happened with deaths a week later. The curve hadn’t just flattened—the peak had been reached and started rapidly crashing down.
Leavitt predicted that the situation would improve in all of China in two weeks and estimated the death rate stopping at about 3,250 deaths. By early March, he said, the viral infection would just peter out.
He was right, but no one listened to him. The world continued to be bewitched by Imperial College physicist Neil Ferguson and his doomsday reckonings of the virus infecting 81 percent of the population, with half a million deaths in the UK and 2.2 million deaths in the US unless both governments adopted total lockdown measures—a prediction based on a presumption of viral exponential growth.
Leavitt and his Stanford lab went on to analyze the figures in South Korea, Iran, Italy and New York City (and now elsewhere) and found the same pattern: an exponential peak of just two weeks before the virus slowed down and eventually burnt itself out.
Rather than one year of excess deaths, as has been calculated, Leavitt demonstrated that the figures show only one month of excess death and cases flattening out among a particular fraction of the total population.
So that begs the question: were months of full lockdown needed? A University of East Anglia study of 30 European countries concluded that while closing schools, banning mass gatherings and shutting down certain businesses in the hospitality sector (restaurants, bars and pubs and leisure centers, for example) were effective in slowing the virus, adopting wholesale lockdown measures—such as closing the whole of “non-essential” businesses, enforcing stay-at-home orders and even wearing facemasks in public—was not.
In fact, the researchers were surprised to find that stay-at-home policies actually had a positive association with cases, said Dr Julii Brainard of UEA’s Norwich Medical School. (Britain’s higher death rates, like Belgium’s, may also be related to their failure to protect the populations of nursing homes.)
Epidemiologist Carl Heneghan, director of Oxford University’s Centre for Evidence-Based Medicine, has continuously maintained that the Imperial College modeling at the heart of so much of the world’s full lockdown policies was wrong, because the incidence of the virus had actually peaked a week before lockdown was enforced.
Scientists understand that after four or five days’ incubation, deaths tend to occur two or three weeks after symptoms first appear. But in the UK, for instance, deaths began to plateau 14 days after lockdown was introduced, following the path of the pre-lockdown incidence, which was already beginning to fall.
As a JP Morgan analysis by Marko Kolanovic and Bram Kalan concluded: “Unlike rigorous testing of new drugs, lockdowns were administered with little consideration that they might not only cause economic devastation but potentially more deaths than COVID-19 itself.”
As for a second wave, the JP Morgan team analyzed individual states and European countries that had relaxed lockdown and found no increase in cases.
The problem with full-scale lockdown turns out to be the same problem with planned vaccination efforts (see page 26): the prima facie assumption of a positive effect without testing the premise, “Of course this is the right thing to do.”
The nightly horrors of fresh fatality numbers among the old and vulnerable on the evening news should not prevent us from taking a deep breath and asking a few cold, hard questions.
It may well be that this killer virus burns brightly before just fizzling out. But we will never know unless we are willing to ask.