by Jon Sanders
Director of the Center for Food, Power, and Life, Research Editor, John Locke Foundation
Paul F. Cwik, the BB&T Professor of Economics and Finance at the University of Mount Olive, and his colleague Abir Mandal, an assistant professor of economics, writes about bad data leading to bad results for North Carolina’s economy. Some snippets:
Medical workers needed to know if the patient in front of them was a risk to others and with a limited supply of tests (especially in March 2020) tests were restricted only to those who were symptomatic. The nonserious and asymptomatic cases were left out. Thus, the data that we were collecting was skewed from the very beginning. This sort of error is called sample selection bias. …
The rates of infection were unknown at the beginning. But estimates could have been roughly “ballparked” using the lab-derived figures for rates of infection and the empirical multiplier used each year by the CDC to estimate the annual flu load from confirmed cases. Policy makers, who were mostly led by a team of health experts, chose not to pause and do so. Therefore, the projected death rates are likely to be too high by a factor of 50 to 100 times, as now evidenced by the serology tests on the general population which test for COVID-19 antibodies.
The overall result was massively inaccurate projections and apocalyptic scenarios. The number of infected people was projected from biased data. Using the number of people infected as the base, the projections of the number of ventilators needed and resulting deaths were grossly exaggerated. A statistician could have helped matters, in our opinion, by highlighting the dangers of conflating the case fatality rate with the overall mortality rate. The unfortunate result was that flawed models, which predicted between 500,000 deaths with social distancing completely implemented, and 2.2 million deaths if nothing were done in the United States, were touted as scientific truth.
Cwik and Mandal then examine how actual, data-driven science could inform policies rather than one-size-fits-none lockdowns. As they show, there’s no evidence that lockdowns even work. There is plenty of evidence that lockdowns take people’s rights.
The largest consequence of this statistical illiteracy on the part of American policy makers is that we have essentially destroyed our economy. The irony is that antibodies and herd immunity, either via infection and recovery or gained through a vaccine, are the key to defeating the virus. Keeping ourselves locked up in isolation from each other would not really save lives because the virus is here to stay. Isolation and quarantining are only prolonging our misery.
What is the alternate scenario? This, and it’s devastating to consider in hindsight:
If statewide lockdown measures were not put in place, and instead we chose to protect the most vulnerable, the virus would spread throughout the population, harmlessly for most, while generating antibodies and herd immunity.
Rather than the state’s response being actually data-driven, what we’ve seen increasingly is something far more dangerous:
Does it make sense to quarantine the people who are in their prime working age range? When we more closely examine the governor’s executive orders, we see that restaurants can open but not bars. Day camps are allowed to open, but not playgrounds. Salons can open but not gyms. For all the calls for data and science, Governor Cooper seems to have regressed to whimsy.
Whimsy, by definition, is capricious, odd or fanciful. Those are not the descriptors of hard science.