Neil Ferguson and his Imperial College COVID-19 Response Team colleagues scared the world silly in mid-March. They announced they ran a sophisticated epidemiological model which said Doom was on the way.
They said that if we did nothing to stop the coronavirus, that “In total, in an unmitigated epidemic, we would predict approximately 510,000 deaths in GB [Great Britain] and 2.2 million in the US, not accounting for the potential negative effects of health systems being overwhelmed on mortality.”
If there were negative effects, and if we did nothing, the totals would soar even higher. To avoid this apocalypse the authors recommended two possible strategies.
They were: “(a) mitigation, which focuses on slowing but not necessarily stopping epidemic spread–reducing peak healthcare demand while protecting those most at risk of severe disease from infection, and (b) suppression, which aims to reverse epidemic growth, reducing case numbers to low levels and maintaining that situation indefinitely.”
Relying on their model, they insisted that “optimal mitigation policies…might reduce peak healthcare demand by 2/3 and deaths by half. However, the resulting mitigated epidemic would still likely result in hundreds of thousands of deaths and health systems…being overwhelmed many times over”. By mitigation they meant such things as “combining home isolation of suspect cases, home quarantine of those living in the same household as suspect cases, and social distancing of the elderly and others at most risk of severe disease.”
The team went on to suggest suppression rather than mitigation. Suppression meant a “combination of social distancing of the entire population, home isolation of cases and household quarantine of their family members”, “school and university closures,” and other similar policies, many of which were adopted. Authorities would need to enforce suppression, they insisted, for “potentially 18 months or more,” until a vaccine could be discovered.
Even if the range of measures they suggested were to be adopted, and “In the most effective mitigation strategy examined, which leads to a single, relatively short epidemic…[and] even if all patients were able to be treated, we predict there would still be in the order of 250,000 deaths in GB, and 1.1-1.2 million in the US.”
On the other hand, the model also suggested that if a full suite of rigorous suppression strategies were implemented and enforced very early on, deaths in Great Britain (GB) could be anywhere from 5,600 to 48,000, depending on the disease transmissibility and ICU use. They said numbers would be proportional for the US, but did not quote any figures.
Reported deaths of this writing in the whole United Kingdom are 11,329.
The model itself is from a class of epidemiological tools, used by Imperial College and organizations like the Institute for Health Metrics and Evaluation, that mathematically represent several aspects of human behavior, the supply and use of hospital resources, virulence and theoretical transmissibility of diseases, and the like. These models are hideously complex. But then so is the subject, or subjects, being modeled.
Human behavior in practice is so difficult to predict that nobody has yet claimed to have mastered it. If somebody has, think of the killing he could make in the stock market. Yet these models claim to foretell what a country of 66.7 million souls would do under the circumstances of a novel pandemic under a range of posited government behavioral suppression tactics.
Besides the expertise of the model creators, what evidence had we to believe the Imperial College model?
Again, their model predicted that if government did nothing, about 510,000 would die in Great Britain (GB) and 2.2 million in the US. These are staggering numbers. There are about 66.7 million people in the UK and 328.2 million in the US.
The model predicted that deaths per day per 100,000 population would peak at about 21 in GB (around June 1st) and 17 in the US (around June 20th).
These figures translate into about 14,000 deaths per day in GB and 56,000 per day in the US at the peak. Per day! These numbers are astounding, and it’s shocking anybody swallowed them. Perhaps those that did, did so because the predictions were presented in terms of so-many deaths per 100,000, and not in plain numbers.
There is, of course, no way to measure whether the model would have got these morbid predictions right. This is because both countries suppressed and mitigated in various ways. Just how much can be debated.
The business-as-usual projections are so far removed from experience, though, that anyone not invested in the model should have doubted it instantly. Here’s why.
The Spanish flu of 1918 was a horrific event. It befell a world fresh from a global war, one with poor medical care, aspirin poisoning, shortages of every kind. Between 17 and 58 million were killed worldwide.
The CDC estimated that about 675,000 Americans died, when the population in the US was about 106 million. This makes 637 per 100,000 dead of Spanish flu in the US.
Imperial college predicted 670 per 100,000 would die of coronavirus.
When the COVID-19 Response team constructed their model on 16 March there were only “6,470 deaths confirmed worldwide” and 97 in the US. Yet, somehow, in the presence of modern medicine and these low figures, coronavirus was predicted to be deadlier than the Spanish flu.
This is idiotic. The model should have been strapped to a ventilator, not used to form the basis of global policy.
To support this site and its wholly independent host using credit card or PayPal (in any amount) click here