By DAVID SHAYWITZ, MD (2)
As attractive as it is to think that science exists on a distinctive, untarnished, untrammeled plane, this idealization is dangerously misleading. Science is carried out by real people, within complex social organizations. Debates about science—often civil, occasionally acrimonious—on methods and meaning are the rule.
Which is as it should be: That’s how knowledge advances.
Ignoring what scientists have to teach us about COVID-19 would be a mistake. The virus is not a “hoax.”
But it’s also a mistake to default to the idea that we must “listen to Science,” as if there’s an unambiguous perspective that all researchers share and that all scientific data are established with an equal degree of certainty. This isn’t how science views itself. So we shouldn’t view it that way, either.
Within the universe of the present pandemic, some information seems very well established—the identification of the virus responsible for the condition, for example. Other data, including some very important essential facts, aren’t as clear.
We need to recognize and acknowledge these limitations.
Some people see these limitations are reason not to trust any of the information that comes out of the scientific establishment. For them, the failure of models to perfectly predict the trajectory of the pandemic was enough. What we’ve learned, said New York Post columnist Miranda Devine, is that “computer models are unreliable when it comes to predicting the future.” Instead of relying on supposed experts and their supposed models, she says, we should instead “trust the innate common sense of the American people.”
But there are more responsible ways to understand the problems with modeling.
Respected biostatistician Ruth Etzioni, at the Fred Hutch Cancer Research Center in Seattle, recently wrote that the latest version of the Institute for Health Metrics and Evaluation (IHME) model from the University of Washington “makes me cringe.” The changes revised the death projections significantly higher, and Etzioni argues that the modelers got there by making a number of obscure changes to the model, then presented these updates as reflecting simply the consequences of reduced social distancing.