Should We Trust the Experts?

At the beginning of March, we began acquiring newspapers to use for packing materials when we moved. The Wall Street Journal was offering 12 weeks for $12, which is both ridiculous and a reminder of where print news is these days. It is a considerable increase sometime in May, at which point I’ll have to decide how much it is worth to be Wall Street Journal subscriber.

Needless to say, this has been an interesting time to browse the newspaper each day. Over the weekend, there were several interesting articles (I may circle back to this one), but one stuck out as especially relevant (assuming all news if it’s fit to print should be somewhat relevant).

The article in question, “The Bearer of Good Coronavirus News” is an interview with Stanford scientist John Ioannidis. As you might tell from the title, he is a minority report who is optimistic that “COVID-19 is far less deadly than modelers were assuming.”

This raises several questions, the first of which is “Should we trust the experts?”

In this case, Ioannidis is certainly an expert. He is a professor in Stanford’s School of Medicine. His among the top 100 most quoted scientists in peer-reviewed articles. He’s published more than 1,000 papers. He has wide ranging expertise, much of which is relevant to understanding the nature of a pandemic.

A follow-up question is in order, namely, “which experts should we pay attention to, and what happens when they disagree?” In Ioannidis’ case, his most significant expertise is that many of his published papers are meta-analyses of other studies. In other words, he is an expert in evaluating scientific studies, particularly in terms of method and bias.

In one of his meta-analyses, he mapped 235 biases across science. As he notes, “the biggest cluster is biases that are trying to generate significant, spectacular, fascinating, extraordinary results.” When it comes to something like projections for a pandemic, “Early results tend to be inflated. Claims for significance tend to be exaggerated.”

We have seen this play out recently, both with the Imperial College London report, as well as the IHME models that somehow get the past as well as the future wrong (e.g. their numbers for Florida’s peak death day is off by over 30). As a side note here, friends don’t let friends care about the IHME models because they have shown they lack expertise in projecting how COVID-19 will unfold.

But, back to the Imperial College, which if you remember, was probably the first major report/model that got people’s attention about the dangers of COVID-19 (or potential dangers). However, note Ioannidis’ analysis:

“They used inputs that were completely off in some of their calculation,” he says. “If data are limited or flawed, their errors are being propagated through the model. . . . So if you have a small error, and you exponentiate that error, the magnitude of the final error in the prediction or whatever can be astronomical.”

“I love models,” he adds. “I do a lot of mathematical modeling myself. But I think we need to recognize that they’re very, very low in terms of how much weight we can place on them and how much we can trust them. . . . They can give you a very first kind of mathematical justification to a gut feeling, but beyond that point, depending on models for evidence, I think it’s a very bad recipe.”

The haunting line is “They can give you a very first kind of mathematical justification to a gut feeling.” In other words, when experts offer their models, it is not pristine unadulterated scientific data. Instead, it is data run through the biases of the particular scientist.

The meat of the article though is when he discusses the media’s role, and hints at how we can move forward:

“We have some evidence that bad news, negative news [stories], are more attractive than positive news—they lead to more clicks, they lead to people being more engaged. And of course we know that fake news travels faster than true news. So in the current environment, unfortunately, we have generated a very heavily panic-driven, horror-driven, death-reality-show type of situation.”

The news is filled with stories of healthy young people who die of coronavirus. But Dr. Ioannidis recently published a paper with his wife, Despina Contopoulos-Ioannidis, an infectious-disease specialist at Stanford, that showed this to be a classic man-bites-dog story. The couple found that people under 65 without underlying conditions accounted for only 0.7% of coronavirus deaths in Italy and 1.8% in New York City.

“Compared to almost any other cause of disease that I can think of, it’s really sparing young people. I’m not saying that the lives of 80-year-olds do not have value—they do,” he says. “But there’s far, far, far more . . . young people who commit suicide.” If the panic and attendant disruption continue, he says, “we will see many young people committing suicide . . . just because we are spreading horror stories with Covid-19. There’s far, far more young people who get cancer and will not be treated, because again, they will not go to the hospital to get treated because of Covid-19. There’s far, far more people whose mental health will collapse.”

He argues that public officials need to weigh these factors when making public-health decisions, and more hard data from antibody and other studies will help. “I think that we should just take everything that we know, put it on the table, and try to see, OK, what’s the next step, and see what happens when we take the next step. I think this sort of data-driven feedback will be the best. So you start opening, you start opening your schools. You can see what happens,” he says. “We need to be open minded, we need to just be calm, allow for some error, it’s unavoidable. We started knowing nothing. We know a lot now, but we still don’t know everything.”

We need to listen to the experts, but we need experts like this. Qualified to discuss the subject at hand, but aware of how bias can cloud conclusions, and aware of the limits of knowledge. Further, when experts disagree, we should realize it is often not a matter of facts but a matter of perspectives and how those facts are interpreted for significance.

We need to listen to experts who are using data and not projections. No one’s projections have been correct yet. But, we now have a 4-6 weeks of data on how COVID-19 progressed in the US. And looking at the data, in most places, lockdowns don’t save many lives.

We can start re-opening things and see how it goes. To some, that may sound dangerous or even reckless. But, when you live in a county that has seen almost 11,000 people apply for unemployment in one week but only 8 deaths from COVID-19, it seems it is more reckless to livelihoods and human flourishing to keep everything closed.

Ultimately, it’s not a choice between saving lives or putting people at risk. Everyone is at risk to one degree or another, and let’s be frank, everyone is going to die one way or the other. I’m not saying that in a cavalier way. I’m saying that because staying home doesn’t save lives. It prolongs some lives and it makes others worse. It is causing mental and emotional problems that will lead to death by suicide as livelihoods are ruined. That may be hard to quantify in the short term, but when you destroy people’s hope, you can often destroy their souls and lives in the process.

When we look at our situation, we long for control and dominion, but we can’t have it. There is no God’s eye perspective on this situation. Experts know more than most people, but they are fallible and open to mistakes and bias. But, we don’t have to pick between blindly listening to the experts or making it up as we go. We can practice critical thinking. We can dig past click-bait headlines and read print newspapers. We can evaluate different sources, and form our own informed opinions instead of just reguritating the prevailing narrative that supports our political bias (stay home = left, open it up = right). And, we can tell Chrissy Teigen that while these may be uncertain times, we will get through this together.