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In the first part of his investigation, Nafeez Ahmed looks at the serious flaws of scientific fatalism and inaccurate modelling.
On Thursday 12 March, the day after UK Health Minister Nadine Dorries tested positive for the Coronavirus, I accused the British Government of making flawed decisions which had endangered the public by accelerating the spread of infection, making the deaths of many vulnerable elderly and ill people inevitable.
At that time, the number of confirmed Coronavirus cases was 596. By 22 March, this had shot up to 5,682 cases and continues to rise – with new confirmed cases now roughly doubling every two to three days.
My back-of-the-envelope projections suggested that the Government’s refusal to attempt to slow and curtail the spread of the virus could lead to as many as 458,752 deaths – possibly as high as 1.6 million in an absolute worst-case ‘do nothing’ scenario.
From Complacency to Shut-Down
At that time, the Government’s abject failure to do anything beyond suggesting that British citizens should wash their hands and sing happy birthday twice while doing so was allowing the virus to spread exponentially.
But, on Monday 16 March, a scientific model published by Imperial College’s COVID-19 Response Team shocked the Government into rapidly changing course.
The study confirmed what many scientists around the world had already been saying, but this time with an epidemiological model. The team had found that the Government’s previous course of action was on course to massively exceed “the surge limits for both general ward and ICU [intensive care unit] beds… [by] at least eight-fold under the more optimistic scenario for critical care requirements”.
Even in a best-case scenario of all patients receiving treatment, the model projected some 250,000 deaths in the UK and some 1.1–1.2 million deaths in the US.
Whereas the Government’s approach up until then had recommended mild measures encouraging the self-isolation of symptomatic individuals, this new research prompted a rapid U-turn – in both the administrations of Boris Johnson and Donald Trump (the paper was also sent to the White House where it triggered the belated emergency response from the Federal Government). The U-turn has included more stringent social distancing strategies including bans on large gatherings, the quarantining of the elderly and, eventually, a decision to close schools and universities.
However, the Imperial College study did not stop there. Its “epidemic suppression” strategy – designed to “reverse” the growth of the outbreak, rather than simply slow it down – recommended that social distancing policies applied across the entire population would now need to be in place for potentially as long as 18 months or more – until a vaccine becomes available.
This offered a much-needed reality check on the disastrous strategy previously adopted by the UK Government. But many questions remained.
How could the scientific teams advising the Government have failed so dramatically to understand the scale of the crisis that was coming until only “a few days ago”, to quote the paper? Why did the Government previously adopt a strategy which would continue to allow the Coronavirus to spread through the population in a way that would inevitably cause a certain level of fatalities?
Why did the Imperial College team underestimate the escalating burden on the NHS despite a flurry of published data from Italy prompting scientists around the world to warn that the UK Government’s inaction was setting the country’s healthcare services up for catastrophe (“previous planning estimates assumed half the demand now estimated,” according to the paper)? Why did the modellers predict that virus “transmission will quickly rebound” if restrictions are relaxed?
The answer is simple: they were making the wrong assumptions.
Unscientific Fatalism
Any model is only as good as the assumptions that go into it and the empirical data it crunches. To get models to work, the assumptions need to be founded as much as possible on what is known. This also means ensuring that the data used to formulate assumptions is based on an appreciation of how multiple complex systems actually interact.
A scientific analysis by the New England Complex Systems Institute at New York University has found the Imperial College model to be deeply flawed. Although the authors praise the Imperial study for recognising the disastrous consequences of the Government’s previous “mitigation” approach, they identify a number of inexplicable failings.
One of the most egregious is the stubborn belief that the outbreak of the virus cannot be stopped. This belief can be traced partly to how the Government interpreted a range of modelling results produced by British scientists advising it, as part of its Scientific Advisory Group for Emergencies (SAGE).
Overlooking the Power of Tests and Tracing
Several models produced by SAGE scientists indicate that using contact tracing to contain the outbreak would be extremely difficult without sufficient speed, but they still emphasised that identifying people infected and isolating them systematically on a mass scale would at least help to reduce or control the epidemic.
Independent scientists who have reviewed the SAGE corpus say that it reveals a fatally incompetent scientific process. According to Professor Devi Sridhar, chair of global public health at the University of Edinburgh’s Medical School, the SAGE corpus inadvertently reveals why the UK Government “got it wrong… SAGE analysis was overcomplicated, too academic, relied on incomplete data, overlooked testing and health service capacity. Why didn’t we go fast down the path of test, isolate, trace while delaying spread for health services to prepare?”
According to the New York University complex systems scientists – Dr Chen Shen, Professor Nassim Nicholas Taleb and Dr Yaneer Bar Yam – early assumptions that the outbreak might inevitably grow beyond control in a way that would mute the point of mass testing and tracing, was completely unfounded. Dr Chen and his co-authors point out that the Imperial College scientists, along with their other colleagues and SAGE, have consistently overlooked data from China and elsewhere.
“They ignore standard contact tracing allowing isolation of infected prior to symptoms,” they state. “They also ignore door-to-door monitoring to identify cases with symptoms… They ignore the possibility of superspreader events in gatherings by not including the fat tail distribution of contagion in their model. This leads them to deny the importance of banning them, which has been shown to be incorrect, including in South Korea.”
The ‘fat tail’ refers to the existence of small probabilities leading to a much higher probability of extreme outcomes.
South Korea’s pursuit of the method of mass contact tracing has allowed the country to demonstrably contain and reduce the rate of infections without resorting to more draconian lockdowns.
Assuming an Inevitable Disease Rebound
The Ne