It’s been awhile, is there a good COVID review article you can recommend to your readers?
Yes! We agree, as this pandemic continues on, it is easy to be overwhelmed, and even confused, by the amount of information coming at us every day. Of course, part of the goal of our “CV🦠News” newsletter is to filter and parse your mental inbox. However, on April 29th, The Atlantic published a really excellent review of the Virus, the Disease, the Research, the Experts, the Messaging, the Information, the Numbers, and lastly, the Narrative. I recommend that you read the full article here. However, here is a bit of a summary to get you started (thank you again Ed Yong and his team at The Atlantic for this piece):
Much about the pandemic is maddeningly unclear. Why do some people get really sick, but others do not? Are the models too optimistic or too pessimistic? Exactly how transmissible and deadly is the virus? How many people have actually been infected? How long must social restrictions go on for? Why are so many questions still unanswered? The confusion partly arises from the pandemic’s scale and pace.
There isn’t just one coronavirus. Besides SARS-CoV-2, six others are known to infect humans—four are mild and common, causing a third of colds, while two are rare but severe, causing MERS and the original SARS. But scientists have also identified about 500 other coronaviruses among China’s many bat species.
SARS-CoV-2 is the virus. COVID-19 is the disease that it causes. The two aren’t the same. The disease arises from a combination of the virus and the person it infects, and the society that person belongs to. Some people who become infected never show any symptoms; others become so ill that they need ventilators. The virus might vary little around the world, but the disease varies a lot. This explains why some of the most important stats about the coronavirus have been hard to pin down. Estimates of its case-fatality rate (CFR)—the proportion of diagnosed people who die—have ranged from 0.1 to 15 percent. It’s frustrating to not have a firm number, but also unrealistic to expect one.
The CFR’s denominator—total cases—depends on how thoroughly a country tests its population. Its numerator—total deaths—depends on the spread of ages within that population, the prevalence of preexisting illnesses, how far people live from hospitals, and how well staffed or well equipped those hospitals are.
Human beings are notorious for our desire to see patterns,” says Vinay Prasad, a hematologist and an oncologist at Oregon Health and Science University. “Put that in a situation of fear, uncertainty, and hype, and it’s not surprising that there’s almost a folk medicine emerging.”
“Clinicians are under tremendous stress, which affects our ability to process information,” says Zoë McLaren, a health-policy professor at the University of Maryland at Baltimore County. “‘Is this actually working, or does it seem to be working because I want it to work and I feel powerless?’”
Since the pandemic began, scientists have published more than 7,500 papers on COVID-19. But despite this deluge, “we haven’t seen a lot of huge plot twists,” says Carl Bergstrom, an epidemiologist and a sociologist of science at the University of Washington. This is how science actually works. It’s less the parade of decisive blockbuster discoveries that the press often portrays, and more a slow, erratic stumble toward ever less uncertainty. That said, the precise magnitude of the virus’s fatality rate is a matter of academic debate. The reality of what it can do to hospitals is not.
Related, the scientific discussion of the Santa Clara study might seem ferocious to an outsider, but it is fairly typical for academia. Yet such debates might once have played out over months. Now they are occurring over days—and in full public view. “People from partisan media outlets find this stuff and use a single study as a cudgel to beat the other side,” Bergstrom says. “The climate-change people are used to it, but we epidemiologists are not.”
Like many COVID-19 studies, the Santa Clara one was uploaded as a preprint—a paper that hasn’t yet run the peer-review gauntlet. Preprints also allow questionable work to directly enter public discourse. For example, the editor at the Journal of Virology, says that she and her colleagues have been flooded with submitted papers, most of which are so obviously poor that they haven’t even been sent out for review. “They shouldn’t be published anywhere,” she says, “and then they end up [on a preprint site].” Some people are genuinely trying to help, but there’s also a huge amount of opportunism.”
Expertise is not just about knowledge, but also about the capacity to spot errors. We hunger for information, but lack the know-how to evaluate it or the sources that provide it. In a pandemic, the strongest attractor of trust shouldn’t be confidence, but the recognition of one’s limits, the tendency to point at expertise beyond one’s own, and the willingness to work as part of a whole.
The instinct to be calm and measured is laudable—until it isn’t. If officials—and journalists—are clear about uncertainties from the start, the public can better hang new information onto an existing framework, and understand when shifting evidence leads to new policy. “We go seeking fresher and fresher information, and end up consuming unvetted misinformation that’s spreading rapidly,” Bergstrom says. Historically, people would have struggled to find enough information. Now people struggle because they’re finding too much.
It does not help that online information channels are heavily personalized and politicized, governed by algorithms that reward certain and extreme claims over correct but nuanced ones. On Twitter, false information spreads further than true information, and at six times the speed.
When looking at case counts, remember this: Those numbers do not show how many people have been infected on any given day. They reflect the number of tests that were done (which is still insufficient), the lag in reporting results from those tests (which can be long), and the proportion of tests that are incorrectly negative (which seems high)
COVID-19 deaths are counted based either on a positive diagnostic test for the coronavirus or on clinical judgment. Flu deaths are estimated through a model that looks at hospitalizations and death certificates, and accounts for the possibility that many deaths are due to flu but aren’t coded as such. If flu deaths were counted like COVID-19 deaths, the number would be substantially lower. This doesn’t mean we’re overestimating the flu. It does mean we are probably underestimating COVID-19.
And statistics is important. One antibody test claims to correctly identify people with those antibodies 93.8 percent of the time. By contrast, it identifies phantom antibodies in 4.4 percent of people who don’t have them. That false-positive rate sounds acceptably low. It’s not. Let’s assume 5 percent of the U.S. has been infected so far. Among 1,000 people, the test would correctly identify antibodies in 47 of the 50 people who had them. But it would also wrongly spot antibodies in 42 of the 950 people without them. The number of true positives and false positives would be almost equal. In this scenario, if you were told you had coronavirus antibodies, your odds of actually having them would be little better than a coin toss.
The numbers still matter; they’re just messy and hard to interpret, especially in the moment. “I think people underestimate how difficult it is to measure things,” Natalie Dean, a statistician at the University of Florida says. “For us who work in public health, measuring things is, like, 80 percent of the problem.”
If measuring the present is hard, predicting the future is even harder. “There are two lessons one can learn from an averted disaster,” Zeynep Tufekci, a sociologist at the University of North Carolina says. “One is: That was exaggerated. The other is: That was close.”
The coronavirus not only co-opts our cells, but exploits our cognitive biases. Humans construct stories to wrangle meaning from uncertainty and purpose from chaos. We crave simple narratives, but the pandemic offers none. The facile dichotomy between saving either lives or the economy belies the broad agreement between epidemiologists and economists that the U.S. shouldn’t reopen prematurely. The lionization of health-care workers and grocery-store employees ignores the risks they are being asked to shoulder and the protective equipment they aren’t being given
And the desire to name an antagonist, be it the Chinese Communist Party or Donald Trump, disregards the many aspects of 21st-century life that made the pandemic possible: humanity’s relentless expansion into wild spaces; soaring levels of air travel; chronic underfunding of public health; a just-in-time economy that runs on fragile supply chains; health-care systems that yoke medical care to employment; social networks that rapidly spread misinformation; the devaluation of expertise; the marginalization of the elderly; and centuries of structural racism that impoverished the health of minorities and indigenous groups. It may be easier to believe that the coronavirus was deliberately unleashed than to accept the harsher truth that we built a world that was prone to it, but not ready for it.