I was not surprised to see Republican senators try to take down Dr. Anthony Fauci last week. But I am disappointed.
When the first cases of Covid-19 were discovered in a hospital in Wuhan, China, I had already studied the U.S. public health response to seven epidemics and pandemics. The political theater and shoot-the-messenger tactics deployed in Wednesday’s hearing are nothing new. But the current focus on public health officials’ errors during a time of crisis and ever-shifting information isn’t about improving readiness for the next pandemic. It’s about assigning blame in a black-and-white binary, and that’s a mistake.
Skepticism is essential in the process of separating fact from fiction. We have more confidence in results that have survived such pressure testing.
Imagine if, when the next novel infectious disease threatens our health, we celebrate learning instead of punishing it? If we anticipate learning and are ready for it, that could go a long way toward improving our experiences in the next pandemic. But for this to happen, we need to come to terms with the messy reality of learning in the sciences. One can hope that scientists, as they gather data during a pandemic driven by a novel infectious disease, will be able to reduce uncertainties in a linear and predictable fashion. But that is not the norm.
I am a social scientist. When I discover something new and present my findings to my colleagues, the most likely result is that they will tell me all the ways they think I might be wrong. Skepticism is essential in the process of separating fact from fiction. We have more confidence in results that have survived such pressure testing.
Experts at the Centers for Disease Control and Prevention during the pandemic received just this type of skepticism from other experts. And yet, much of the political commentary surrounding these normal and essential criticisms cited the evidence of controversy as proof the CDC was flailing.
This approach treats science as though the discovery of new facts happens like a treasure hunt. As if everyone is looking for a treasure and, when someone finds it, everyone can immediately agree on what has been found and who found it.
But science does not work this way. Take the discovery of HIV. Dr. Luc Montaignier found the virus under an electron microscope in February 1983. His claim was debated for more than a year and finally settled, affirmatively, by Dr. Robert Gallo and colleagues in 1984. This example shows something that is true across the sciences. New discoveries that eventually become accepted as scientific facts can be treated with skepticism for months or years. Not part of this example, but equally true in the sciences, is that things accepted as facts can lose that standing based on new discoveries.
Climate scientists would almost certainly never argue their experience in the public eye has been a picnic. But they have had one big advantage over those trying to guide the response to a pandemic driven by a novel infectious disease. Climate scientists had decades — as opposed to only weeks or days — to conduct research before they were asked to explain to the public what they knew. Consider: Climate scientists did not always expect warming to result from burning fossil fuels. Until the 1950s, the prevailing wisdom was that the oceans would absorb excess carbon dioxide so there would be minimal, if any, warming. Roger Revelle and Hans Seuss, who together discovered the error, both received awards for their research.
Monday-morning quarterbacking is easy. Try advising countries on how to manage a growing pandemic while the ground is moving beneath your feet.
When a novel infectious disease emerges, most early cases go undetected. There are no tests with which to make a positive diagnosis. Some of the suspected cases turn out to have other causes. People who are infected are overlooked. Data collected at the outset is patchy and error-prone. As the public health community pivots to emergency response footing, data collection becomes more systematic. Even so, new data can rapidly overturn early assumptions. The case definition for Covid-19 was updated almost weekly during the first 10 weeks of the pandemic.
Monday-morning quarterbacking is easy. Try advising countries on how to manage a growing pandemic while the ground is moving beneath your feet.
As the U.S. performance during the Covid-19 pandemic is reviewed, even those who are not engaging in political grandstanding tend to reduce the effort to tallying the times experts were right vs. the times they were wrong. Even if the reversals are few in number, they are barely tolerated. Usually examinations are given conto people after they have had time to learn and master the material. What is the right metric to use for public health experts who are trying to inform us when the answers are still being discovered?
One of the most important public goods we can have during a pandemic is the willingness of public health experts to admit when they were wrong and share what they are learning. Things will go markedly worse if, during the next pandemic, our public health experts are less forthcoming.
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