COVID-19 has proved to be a crisis not only for public health but for public policy. As credentialed experts, media commentators, and elected officials have insisted that ordinary men and women heed “the science,” the statistical models cited by scientists to predict the spread of contagion and justify the lockdown of the national economy have proven to be far off-base.
Gov. Andrew Cuomo of New York complained this week about the “guessing business” experts had presented to him dressed up as scientific fact: “All the early national experts [said]: Here’s my projection model. Here’s my projection model,” Cuomo said. “They were all wrong. They were all wrong.”
Neil Ferguson of Imperial College London, whose computer modeling of the coronavirus predicted up to 2.2 million U.S. deaths. He has since resigned.
A computer model produced by statisticians at Imperial College London had an outsized effect on government policy, predicting up to 2.2 million American deaths from the new coronavirus and as many as 9.6 million people requiring hospitalization. Instead, emergency rooms and hospital beds in all but the few hardest hit cities remained empty; rather than being overwhelmed by cases, many doctors and nurses found themselves out of work.
As the staggering social and economic costs of shutdown have become painfully clear, the failure of the models to accurately anticipate what would happen is raising questions about their use to justify life-altering public policies.
If computer models projecting the near-term future of an epidemic were so wrong, what does that mean for the far more complicated computer models predicting the far-off future of the entire planet?
