When, along with applied systems scientist Dr Joe Norman, we first reacted to coronavirus on 25 January with the publication of an academic note urging caution, the virus had reportedly infected fewer than 2,000 people worldwide and fewer than 60 people were dead. That number need not have been so high.
At the time of writing, the numbers are 351,000 and 15,000 respectively. Our research did not use any complicated model with a vast number of variables, no more than someone watching an avalanche heading in their direction calls for complicated statistical models to see if they need to get out of the way.
We called for a simple exercise of the precautionary principle in a domain where it mattered: interconnected complex systems have some attributes that allow some things to cascade out of control, delivering extreme outcomes. Enact robust measures that would have been, at the time, of small cost: constrain mobility. Immediately. Later, we invoked a rapid investment in preparedness: tests, hospital capacity, means to treat patients. Just in
The error in the UK is on two levels. Modelling and policymaking.
First, at the modelling level, the government relied at all stages on epidemiological models that were designed to show us roughly what happens when a preselected set of actions are made, and not what we should make happen, and how.
The modellers use hypotheses/assumptions, which they then feed into models, and use to draw conclusions and make policy recommendations. Critically, they do not produce an error rate. What if these assumptions are wrong? Have they been tested? The answer is often no. For academic papers, this is fine. Flawed theories can provoke discussion. Risk management – like wisdom – requires robustness in models.
But if we base our pandemic response plans on flawed academic models, people die. And they will.
This was the case with the disastrous “herd immunity” thesis. The idea behind herd immunity was that the outbreak would stop if enough people got sick and gained immunity. Once a critical mass of young people gained immunity, so the epidemiological modellers told us, vulnerable populations (old and sick people) would be protected. Of course, this idea was nothing more than a dressed-up version of the “just do nothing” approach.
Individuals and scientists around the world immediately pointed out the obvious flaws: there’s no way to ensure only young people get infected; you need 60-70% of the population to be infected and recover to have a shot at herd immunity, and there aren’t that many young and healthy people in the UK, or anywhere. Moreover, many young people have severe cases of the disease, overloading healthcare systems, and a not-so-small number of them die. It is not a free ride.