The justification for stay-home orders, closing of “non-essential” businesses, and so on, as we have often been told, is “flattening the curve.” Sheltering in place won’t prevent the COVID-19 virus from working its way through the population, it will just do so more slowly, thus avoiding unnecessary deaths which would result from overwhelming hospitals, especially ICUs.
The policies enacted by governments are based on statistical models, and John Hinderaker writes in Powerline that as usual with models, the math is relatively simple. It is the assumptions that are critical. Hinderaker notes, for example, that in the U.K., Imperial College scientist Neil Ferguson notoriously revised his U.K. fatality projection from 500,000 to fewer than 20,000, with most of those being people who would have died this year, anyway. Ferguson said this drastic reassessment was due to the draconian stay-home order promulgated by the British government.
“I am not calling Ferguson a liar, but I would love to see the assumptions and calculations underlying his about-face,” Hinderaker writes. “Are there really numbers for Case A (500,000 fatalities) and Case B (fewer than 20,000) attributable to curve-flattening that 1) are plausible on their face, and 2) have substantial empirical support? Consider me skeptical.”
Hinderaker notes that many of the models on which public policy is based do not address measures which may lead to a reduction in infection (for example, imposing travel limits out of New York City; the increasing use of face-masks), or the success of treatments (for example, HCQ).
“Is curve flattening, via stay-home orders and business closures, really as valuable as certain modelers and many politicians allege?” Hinderaker is not so sure.