By Enza Ferreri
We’ve shown in a previous post how in the UK the Covid-19 growth factor had already declined before the introduction of lockdown on 24 March 2020.
As we’ll see, this event is not limited to the UK, but applies generally and globally.
That’s because the reproduction number of infections (how many new infections are generated by 1 infected person) spontaneously decreases over time, as the number of vulnerable uninfected people falls.
Plus, when in the early 2020 the news of an epidemic began to circulate, most people started adopting measures like washing hands more thoroughly more often and avoiding too close contact with others. Lockdowns, which amount to quarantining entire healthy populations, are a historic first, an extreme measure never taken before.
The spontaneous decline of the disease reproduction number, combined with anti-Covid measures that people spontaneously adopted out of prudence but without further constrictions, were sufficient to produce the reduction of contagion before the lockdowns.
The idea of the necessity of lockdowns was only in the statistical models which had highly overestimated the real numbers and severity of Covid-19, as we now know, when they were created at the beginning of the epidemic.
As The Price of Panic: How the Tyranny of Experts Turned a Pandemic into a Catastrophe , an American book written by a biologist, a statistician and a philosopher, clearly expresses:
We’ll say more about models later. For now, suffice it to say that when dealing with something as complex as a pandemic, such models are, at best, educated guesses—always wrong in the details, but sometimes helpful in showing what we don’t know. At worst they’re bundles of prejudices wrapped in pretentious academic packaging.
Alas, the coronavirus pandemic featured more of the latter than the former. No one should doubt that the main models, and government officials who trusted them, played an oversized role in creating the panic.
We don’t think forecasters are stupid or evil. Nor do we think public health advisors want to harm people. The problem came when the press, public health advisors, and political leaders all accepted these models uncritically and relied on them in their reporting to the public and in their public policy decisions. These forecasts should have been treated for what they were — one-sided conjectures from people focused on a narrow part of a multi-part problem.
The model eventually adopted by the World Health Organization, unfortunately, was the one elaborated by Imperial College London, a strange choice, given the record of disastrous mistakes this institution’s Head of Epidemiology Department Neil Ferguson had accumulated in both human and animal epidemiology in the past.
The Imperial College generated a cliff-edge graph, shown below in a BBC reproduction:
The above graph was produced at the beginning of Covid-19 epidemic.
Now we know that things have gone very differently. The truth reflects the quote from the book earlier on: this kind of mathematical models are over-simplified “one-sided conjectures from people focused on a narrow part of a multi-part problem”.
Here is how things developed at the time of the UK lockdown introduction:
This graph comes from a study by Simon Wood, Chair of Computational Statistics at the School of Mathematics, University of Edinburgh, UK, and was updated on June 18, 2021, which poses the question “were infections already in decline before the UK lockdowns?”.
This is the answer:
A Bayesian inverse problem approach applied to UK data on first wave Covid-19 deaths and the disease duration distribution suggests that fatal infections were in decline before full UK lockdown (24 March 2020), and that fatal infections in Sweden started to decline only a day or two later… Similar patterns appear to have occurred in the subsequent two lockdowns. Estimates from the main UK Covid statistical surveillance surveys, available since original publication, support these results. [Emphasis added]
In the next part we’ll look at countries where lockdown was not imposed, which is a sort of “natural experiment”, in which yes-lockdown countries are the experimental group and no-lockdown countries are the control group.