There’s an intense, often ugly debate over the response to the COVID-19 pandemic. In broad brushstrokes, some contend that the lower-than-predicted death toll proves the models forecasting massive fatalities were needlessly sensational. Some even suggest they were deliberately so, to scare politicians, including President Donald Trump, into taking drastic and unnecessary action.
A few conspiracy theorists – so-called “coronavirus truthers” – see more sinister motives at play. But, as is always the case, these cranks and grifters are best ignored, so I won’t shine a light on them here.
The serious debate centers on whether the initial models were always exaggerated, or whether the response to them is driving down fatalities more effectively than epidemiologists predicted. For what it’s worth, my opinion is “both.”
The first error is assuming that the scientists at the Institute for Health Metrics and Evaluation, Imperial College London, National Institutes of Health and Centers for Disease Control and Prevention were acting in bad faith. Just because a cashier makes a math error when giving you change doesn’t mean he’s trying to steal from you. And just because these models weren’t perfectly oracular doesn’t mean anyone was lying.
It’s almost surely the case that the models were wrong to one degree or another for the simple reason that any model is only as good as the data fed into it. With imperfect information – partly thanks to the outrageous dishonesty of the Chinese government and the grave missteps of the World Health Organization – it was inevitable that the models would never be more than best guesses. We’re far from out of the woods, but the fact that “only” some 60,000 Americans may die instead of 240,000 seems like something to celebrate, not an excuse to scapegoat officials who scrambled to save lives.