NRPLUS MEMBER ARTICLE T echnology adoption is often described by the “hype cycle,” which starts with a rapid rise toward the “peak of inflated expectations,” plummets to the “trough of disillusionment,” and then rises gradually along the “slope of enlightenment” before ultimately arriving at the “plateau of productivity” — when we finally figure out how to use the technology effectively. That cycle seems as apt a description as any for the biomedical science around COVID.
When the virus first arrived on our shores, there seemed to be a rush of hope, a sense that, as Matt Damon’s marooned astronaut phrased it in The Martian, we were prepared to “science the sh** out of this.” Epidemiologists, data scientists, academic researchers, big drug companies, and scrappy startups — everyone, it seemed, turned their attention to this emerging problem. You could be forgiven for assuming that scientists had the problem in their sights and were well on the way to victory.
Flash-forward a month or so and a very different picture emerges. If the earliest days were captivated by what science seemed poised to deliver, the most recent stretch — which seems to have taken us well into the trough of despair — has reminded us, day after day, in explainer after explainer, just how little we actually know.
How many people in the U.S. have been infected by SARS-CoV-2, the virus that causes COVID-19? We don’t know.
What’s the fatality rate of COVID-19? We don’t know.
How many Americans are immune to SARS-CoV-2? We don’t know.
Is immunity likely to be enduring, or is it likely to fade out over a year, as is the case with some similar viruses? We don’t know.
Given the mutation rate of similar viruses, are we even sure an individual vaccine is likely to prove durably effective? We don’t know.
As we collectively dig deeper into what’s really known about COVID, the more we recognize just how fragile the foundation is — a point some seasoned experts such as Stanford’s John Ioannidis have earnestly highlighted from the outset.
Nassim Taleb, author of The Black Swan, frequently reminds us that some data aren’t necessarily better than no data. Testifying before Congress in 2009, in the wake of the financial crisis and the flawed models that contributed to it, he highlighted the harm of “sterile” information, emphasizing its potential to lead to dangerous overconfidence.
Conversely, the most hopeful aspect of our current ignorance may be the intrinsic value of recognizing and acknowledging it.
The need to find a solid place to stand on is a common challenge in science, something researchers often discover when they descend on an emerging discipline or a new area. The moment is typically characterized by exciting but preliminary publications in top-tier journals, research that often fails to stand the test of time.
I saw this firsthand as a postdoc, when I joined the lab of an exceptional developmental biologist who established his reputation understanding the molecular mechanisms of frog development. After two of his children were diagnosed with Type 1 diabetes, he made the radical decision to refocus his efforts on human stem cells, hoping to develop approaches to replace the insulin-producing cells destroyed in this disease. He was inspired to make this transition by a large body of promising research that had accumulated, data that seemed to suggest rapid progress in this area. He was excited to bring his talents to this field and to help drive it forward.
As a world-class scientist, he took early steps that involved reproducing existing research, so his lab had a starting point on which to build. But as he did this, he discovered, time and again, that the breathless scientific literature was riddled with mistakes, errors, and oversights. For example, one paper claimed to have coaxed cells into producing insulin, yet it turned out, on careful review, that the insulin was likely already present in the culture media used to grow the cells. To finally arrive at a solid foundation, extensive scientific excavation was required.
This feels like exactly what we’re experiencing with COVID: a recognition that, in contrast to our earliest hopes, we know far less about the virus than we originally thought. But we also now have the potential to move forward intelligently.
Progress will require robust data. This means not only the capability for widespread testing of both the virus (Are you currently infected?) and immunity to the virus (Do you have antibodies against the virus?) but also the ability to integrate and analyze such testing at a national level. This is an urgent need, which multiple recent task forces have insisted on. It’s also a responsibility that the federal government has not seemed eager to assume.
We are now slowly climbing up the slope of enlightenment, but the speed of this journey will be driven by our ability to gather, integrate, analyze, and act on comprehensive, high-quality data. Imperfect knowledge about an emerging virus is understandable, and even expected. But failing to move aggressively, as a nation, to remediate this — that would be unforgivable.