The Failed Experiment of Covid Lockdowns
Six months into the Covid-19 pandemic, the U.S. has now carried out two large-scale experiments in public health—first, in March and April, the lockdown of the economy to arrest the spread of the virus, and second, since mid-April, the reopening of the economy. The results are in.
Counterintuitive though it may be, statistical analysis shows that locking down the economy didn’t contain the disease’s spread and reopening it didn’t unleash a second wave of infections.
Considering that lockdowns are economically costly and create well-documented long-term public-health consequences beyond Covid, imposing them appears to have been a large policy error. At the beginning, when little was known, officials acted in ways they thought prudent.
But now evidence proves that lockdowns were an expensive treatment with serious side effects and no benefit to society.
TrendMacro, my analytics firm, tallied the cumulative number of reported cases of Covid-19 in each state and the District of Columbia as a percentage of population, based on data from state and local health departments aggregated by the Covid Tracking Project.
We then compared that with the timing and intensity of the lockdown in each jurisdiction. That is measured not by the mandates put in place by government officials, but rather by observing what people in each jurisdiction actually did, along with their baseline behavior before the lockdowns. This is captured in highly detailed anonymized cellphone tracking data provided by Google and others and tabulated by the University of Maryland’s Transportation Institute into a “Social Distancing Index.”
Measuring from the start of the year to each state’s point of maximum lockdown—which range from April 5 to April 18—it turns out that lockdowns correlated with a greater spread of the virus. States with longer, stricter lockdowns also had larger Covid outbreaks. The five places with the harshest lockdowns—the District of Columbia, New York, Michigan, New Jersey and Massachusetts—had the heaviest caseloads.
It could be that strict lockdowns were imposed as a response to already severe outbreaks. But the surprising negative correlation, while statistically weak, persists even when excluding states with the heaviest caseloads. And it makes no difference if the analysis includes other potential explanatory factors such as population density, age, ethnicity, prevalence of nursing homes, general health or temperature. The only factor that seems to make a demonstrable difference is the intensity of mass-transit use.
We ran the experiment a second time to observe the effects on caseloads of the reopening that began in mid-April. We used the same methodology, but started from each state’s peak of lockdown and extended to July 31. Confirming the first experiment, there was a tendency (though fairly weak) for states that opened up the most to have the lightest caseloads.
The states that had the big summer flare-ups in the so-called “Sunbelt second wave”—Arizona, California, Florida and Texas—are by no means the most opened up, politicized headlines notwithstanding.
The lesson is not that lockdowns made the spread of Covid-19 worse—although the raw evidence might suggest that—but that lockdowns probably didn’t help, and opening up didn’t hurt. This defies common sense. In theory, the spread of an infectious disease ought to be controllable by quarantine. Evidently not in practice, though we are aware of no researcher who understands why not.
We’re not the only researchers to have discovered this statistical relationship. We first published a version of these findings in April, around the same time similar findings appeared in these pages. In July, a publication of the Lancet published research that found similar results looking across countries rather than U.S. states.
“A longer time prior to implementation of any lockdown was associated with a lower number of detected cases,” the study concludes.
Those findings have now been enhanced by sophisticated measures of actual social distancing, and data from the reopening phase.
There are experimental controls that all this research lacks. There are no observable instances in which there were either total lockdowns or no lockdowns at all. But there’s no escaping the evidence that, at minimum, heavy lockdowns were no more effective than light ones, and that opening up a lot was no more harmful than opening up a little.
So where’s the science that would justify the heavy lockdowns many public-health officials are still demanding?
Read more at www.wsj.com
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Alder
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Re one of the interesting questions here- spread does not drop with lockdown. or even the other way.
Perhaps the spread, as reported and measured, increases as the severity of the lockdown increases.
Reason / mechanism-
The authorities are concerned, they impose lockdown, they increase testing.
But, the test used, pcr, does not detect the virus, it is not random, it is subject to operator and reporting biases so the result is statistically what the operators understand what the authorities, the bosses, want to get.
When government reads that the people will not accept any more they announce a drop in cases, and that is the data that then comes in. No instruction is given, it does not need to be given.
You may say that this is a cynical explanation, but I have experience in the public sector, and have been in the design of questionnaires to get answers desired. I know that if you as an advisor or analyst give ‘the wrong’ answer to a question you are thanked, but your work and the result disappear. The ‘right’ answer gets immediate attention.
If what I see about this pcr test is correct, it is the perfect tool for government.
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