Last month, I reported on academic Wilfred Reilly’s analysis of lockdowns in the US in response to the Chinese virus pandemic. Reilly’s data showed that lockdowns provided no benefit at all in controlling infections or deaths. Others, such as John Ioannidis and colleagues, have reported similar findings.
With the benefit of a few weeks’ hindsight and in response to criticisms, Reilly has revisited the data and found…exactly the same result.
Lockdowns do not work.
Once again, I find limited – if any – evidence for the efficacy of lockdowns. My data set is available for anyone to request it via my Twitter[…]
The most basic response I received was that this could all change in a fortnight. The lockdowns simply needed more time to succeed. This argument has turned out to be false.
Once again, Reilly’s data shows a per-state average of 2,882 cases-per-million for lockdown states (excluding New York as an outlier). Social distancing states (with no lockdowns) show an average of 1,704 cases-per-million.
Lockdown states recorded a mean of 147 per-million; 126 per-million if New York is excluded. Non-lockdown states recorded just 34 per-million. Less than one third that of lockdown states.
Interestingly, I observed a strong, significant and meaningful correlation between increasing temperature and decreasing Covid-19 caseload[…]with all other variables adjusted for in the model, each one-degree increase in mean temperature correlated with a 2,065-unit decrease in Covid-19 cases and a 169-unit decrease in Covid deaths.
Repeating his analysis over a period of weeks has enabled Reilly to find something even more telling: Deaths in lockdown states have continued to outnumber non-lockdown states by about 66%, but their death rates have increased faster, too. Deaths in lockdown states have increased by an average of roughly 65% in just the last two weeks, compared to 55% for non-lockdown states.
This gap in new, post-lockdown deaths per million people once again suggests that the lockdowns are not working.
This should be a powerful argument for adopting social distancing. While social-distancing measures – like wearing a light medical mask or washing one’s hands 11 times a day – might be annoying, the practical impact of country-wide lockdowns has been utterly devastating. Unemployment in the US is approaching (if not surpassing) Great Depression levels. Thirty million Americans have filed jobless claims since March. Almost eight million small- to medium-sized businesses are at risk of closing permanently.
Reilly points out that the original argument for lockdowns – ‘flattening the curve’ – said nothing about preventing infections or deaths; it explicitly assumed that the same number of people would become infected and die. The argument was that “flattening the curve” would prevent hospitals being swamped.
Yet that hasn’t happened. Especially in non-lockdown states. Emergency field hospitals have been dismantled without ever treating a single patient. Hospitals in non-lockdown states are laying off staff, after cancelling elective surgeries in order to prepare for a wave of infections that never happened.
Now that we know the hospital system has not been swamped, there is arguably no reason whatsoever to destroy our economies simply to experience roughly the same number of infections later rather than sooner[…]
No single set of numbers can be perfect, but it is becoming increasingly apparent that numbers, not emotions, must guide the debate about how best to respond to Covid-19. And the numbers just discussed, human and economic, do not make the case for lockdowns.
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