February 26, 2021, 7:43

The “experts” don’t know everything. They can’t.

The “experts” don’t know everything. They can’t.

We need to adjust our expectations of what scientists know — and even what they can know.

Democrats are standing up for science once again, this time against those on the right, like President Donald Trump and the Fox News hosts who’ve peddled misinformation about the coronavirus and downplayed its risks.

“Follow the science, listen to the experts, do what they tell you,” Joe Biden exhorted Trump during an April 5 appearance on ABC’s This Week.

House Speaker Nancy Pelosi on Tuesday said that “Americans must ignore lies and start to listen to scientists and other respected professionals in order to protect ourselves and our loved ones.”

There’s no question that listening to experts and respected professionals is a better idea than listening to patent falsehoods. But it’s important to recognize that the partisan clash over the science of coronavirus is not the same as that over issues like climate change and air pollution.

Scientists have barely begun the work of understanding this virus, so they don’t have decades of research and data to rely on to answer our questions. Already we’ve seen America’s public health experts flip-flop on wearing face masks, while the most prominent epidemiological model has gone from predicting between 100,000 and 200,000 deaths in the US to forecasting a bit less than 70,000 — a horrific toll, but one that’s outside the confidence interval of the previous forecast.

This is not a reason to dismiss experts, but a way to illustrate the nature of scientific advancement: It takes time. Scientists spend years gathering the data and information necessary to produce increasingly better answers to complex questions. We need to make choices about policy and our individual lives right now, but we’re watching science play out in real time. The answers will get better, but not as fast as we want them to.

And while expertise is important, turning “the experts” and “the science” into false gods could create a backlash cycle of unrealistic expectations and dashed hopes.

We need to value scientists and listen to experts, but part of listening means understanding that right now, what they’re saying is that they do not have all the answers.

There are a ton of “known unknowns” about coronavirus

Two weeks ago, the country was gripped with fear about potential shortages of ventilators in coronavirus hot spots. The idea was that it would be the best treatment for severe cases, but that if a given hospital had limited supply, the death rate might soar due to the lack of treatment capacity. But on April 8, Sharon Begley reported for Stat News that doctors were beginning to think that ventilators were being overused for Covid-19 patients.

On April 14, Jim Dwyer wrote in the New York Times about the new ventilator skepticism, explaining that Covid-19 patients with low blood oxygen levels seem to have unexpectedly strong ability to retain consciousness and that reorienting them into prone positions to facilitate breathing is now a preferred treatment technique. He noted that special massage tables designed for pregnant women — a commodity that, like ventilators, there is a somewhat limited supply of — seem to be especially useful for this. The turnabout from “national ventilator shortage crisis” to “maybe ventilators aren’t useful and we need pregnancy massage tables” is stunning. It reflects the reality that doctors have very limited clinical experience with this disease.

And the coronavirus pandemic is littered with this sort of situation in which the state of our knowledge is simply not very good.

  • Lots of doctors (not just Donald Trump) believe that hydroxychloroquine is an effective treatment option, but there are no proper clinical trials on this.
  • Another drug called remdesivir has seen some promising results but also lacks any really good trials.
  • The Trump administration has moved to make antibody tests that could detect Covid-19 immunity free, but scientists don’t actually know how long acquired immunity will last.
  • Indeed, some researchers think they have found cases of recovered Covid-19 patients quickly becoming reinfected. The consensus among most scientists is that these are testing errors rather than genuine reinfection, but the fact that there are questions about the accuracy of the tests is grounds for further uncertainty.
  • One New York hospital did something unusual and tested every woman delivering a baby between March 22 and April 4 for the novel coronavirus whether or not she had symptoms. They found seven infected women with no symptoms for every one who had symptoms, a much higher rate than earlier consensus estimates.
  • If it’s true that there are many more asymptomatic cases than we realize, that could be good news because the virus is less lethal than we thought. It could also make case-tracking and isolation extremely difficult. But just as experts aren’t sure how many asymptomatic infections there are, they also aren’t entirely sure how much asymptomatic people can actually spread the virus.
  • Experts seem very leery of giving people false hope that summer weather will halt the spread of coronavirus, but a National Academies of Sciences, Engineering, and Medicine report on the subject does say that lab experiments suggest high temperatures de-activate the virus. Separately, it’s true as a matter of physics that the ambient temperature and humidity level impacts how far airborne water droplets can spread. We just don’t have really clear knowledge of how those factors all interplay in part because the relative importance of contaminated surfaces versus droplets in spreading the disease is not perfectly understood.
  • Last but by no means least, now that Western conventional wisdom has swung around to the idea that Asian countries had it right and people should be encouraged to wear masks, there is a new South Korean study arguing that masks don’t work after all.

This is not to knock the experts, but simply to say that listening to the experts means really listening to what they have to say, which is that they are not sure exactly how the virus spreads and under which conditions. They are not sure how many people are getting infected. They are not sure how best to treat the most severely ill people. And they are not sure how robust immunity will be among those previously infected.

All of which is to say that, of course, model-builders can’t give us good conditional forecasts about the trajectory of a disease when underlying building blocks of scientific understanding aren’t very strong.

Science requires patience

The underlying difficulty is that SARS-CoV-2 is a genuinely new subject for study.

Every Ebola outbreak is alarming because of how deadly the disease is, and the policy work around containing the outbreaks is difficult. But scientists have been studying the Ebola virus for decades, and if you ask them questions about it, they can give you precise answers. But that wasn’t the case right in the middle of the original Ebola outbreak. And at times, we’ve been much worse off. AIDS was first clinically identified in June 1981, but HIV, the virus that causes AIDS, wasn’t identified until 1983. Modern science, thankfully, is able to proceed at a much faster pace than that.

But so many key questions about the pandemic can’t be answered in a lab alone. We want to know about how the virus interacts with human beings, both from the standpoint of transmission and the progress of the disease. Conducting the kind of experiments that would provide really clear evidence on these questions is inherently time-consuming. Policymakers need to make decisions under conditions of intense uncertainty.

That’s hard. And the scientists themselves are inevitably going to change their minds as they learn more. That should let us improve the quality of our decision-making over time. But it’s going to require a degree of patience, not just in terms of waiting for answers but being tolerant of errors and reversals and problems in expert judgment rather than a crash-and-burn cycle where we look to experts as seers and get disillusioned when they turn out to be mistaken.

Source: vox.com

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