The Failed Lockdown Experiment


Epidemiology is not an exact science. This is good to remember in these times when everyone is “looking to the science” for answers to the Covid pandemic.

President Trump brought in the nation’s most respected epidemiologists to advise his Corona Virus Task Force on the best way forward to respond to this pandemic. Dr. Anthony Fauci and Dr. Deborah Birx are both highly respected in their fields. They called for an immediate lockdown to “slow the spread” of the virus and to prevent overwhelming the healthcare system with viral patients that might lead to rationing of critical care.

When the spread of the virus was deemed under control, President Trump rightly called for reopening to stimulate the depressed economy. These same experts devised a plan for this reopening to avoid a resurgence of viral cases. In some parts of the country this was more successful than others. Differences in population density, the numbers of elderly versus youth, and local government policies were all blamed for this variation in virus control.

But how much did the lockdowns really slow the spread of the virus and did reopening really contribute to a resurgence of cases?  These are critical questions that should be answered because a better understanding should govern future policy.

One man says he has the answers to these questions. His name is Donald L. Luskin and he is the chief investment officer of TrendMacro, an analytics firm. Writing in The Wall Street Journal, Luskin says his firm has analyzed both the lockdown and the reopening and have come to unexpected conclusions on both. He says, “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 didn’t unleash a second wave of infections.”

He concludes that the lockdowns were economically costly and created well-documented long-term health consequences beyond Covid that were not justified based on their analysis. He acknowledges that public health officials acted in ways they thought were prudent, but evidence now proves were an expensive treatment with serious side effects and no benefit to society.

How did they come to these conclusions?

TrendMarco 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. They then compared that with the timing and intensity of the lockdown in each jurisdiction. That was 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. That was then 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.

Is this a case of which came first, the chicken or the egg? Did the lockdowns come before the heaviest caseloads or because of them? Lushkin acknowledges this question, but states the surprising negative correlation persists even when excluding states with the heaviest caseloads. Furthermore, the analysis remains the same when factoring in such variables 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.

Lushkin stands by his data and says they repeated the experiment and analysis a second time with the same conclusions. He summarizes their results in these words:

“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.”

Apparently, they are not the only researchers to see these findings. In July, a publication of 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.

Joe Biden wants to resume lockdowns “if the science calls for them.” In the light of this recent analysis, his comments are worth remembering. Just which “scientists” would he be listening to? Lushkin closes with these words of warning: “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?”                                                                                                                                                          

I began this post by saying that epidemiology was not an exact science. What more proof of that do you need?

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