On March 27, as the U.S. topped 100,000 confirmed cases of COVID-19, Donald Trump stood at the lectern of the White House press-briefing room and was asked what he’d say about the pandemic to a child. Amid a meandering answer, Trump remarked, “You can call it a germ, you can call it a flu, you can call it a virus. You know, you can call it many different names. I’m not sure anybody even knows what it is.”

That was neither the most consequential statement from the White House, nor the most egregious. But it was perhaps the most ironic. In a pandemic characterized by extreme uncertainty, one of the few things experts know for sure is the identity of the pathogen responsible: a virus called SARS-CoV-2 that is closely related to the original SARS virus. Both are members of the coronavirus family, which is entirely distinct from the family that includes influenza viruses. Scientists know the shape of proteins on the new coronavirus’s surface down to the position of individual atoms. Give me two hours, and I can do a dramatic reading of its entire genome.

But much else about the pandemic is still 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. Worldwide, at least 3.1 million people have been infected in less than four months. Economies have nose-dived. Societies have paused. In most people’s living memory, no crisis has caused so much upheaval so broadly and so quickly. “We’ve never faced a pandemic like this before, so we don’t know what is likely to happen or what would have happened,” says Zoë McLaren, a health-policy professor at the University of Maryland at Baltimore County. “That makes it even more difficult in terms of the uncertainty.”

But beyond its vast scope and sui generis nature, there are other reasons the pandemic continues to be so befuddling—a slew of forces scientific and societal, epidemiological and epistemological. What follows is an analysis of those forces, and a guide to making sense of a problem that is now too big for any one person to fully comprehend.

I. The Virus

Because coronavirus wasn’t part of the popular lexicon until SARS-CoV-2 ran amok this year, earlier instances of the term are readily misconstrued. When people learned about a meeting in which global leaders role-played through a fictional coronavirus pandemic, some wrongly argued that the actual pandemic had been planned. When people noticed mentions of “human coronavirus” on old cleaning products, some wrongly assumed that manufacturers had somehow received advance warning.

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. “There will be many more—I think it’s safe to say tens of thousands,” says Peter Daszak of the EcoHealth Alliance, who has led that work. Laboratory experiments show that some of these new viruses could potentially infect humans. SARS-CoV-2 likely came from a bat, too.

It seems unlikely that a random bat virus should somehow jump into a susceptible human. But when you consider millions of people, in regular contact with millions of bats, which carry tens of thousands of new viruses, vanishingly improbable events become probable ones. In 2015, Daszak’s team found that 3 percent of people from four Chinese villages that are close to bat caves had antibodies that indicated a previous encounter with SARS-like coronaviruses. “Bats fly out every night over their houses. Some of them shelter from rain in caves, or collect guano for fertilizer,” Daszak says. “If you extrapolate up to the rural population, across the region where the bats that carry these viruses live, you’re talking 1 [million] to 7 million people a year exposed.” Most of these infections likely go nowhere. It takes just one to trigger an epidemic.

Once that happens, uncertainties abound as scientists race to characterize the new pathogen. That task is always hard, but especially so when the pathogen is a coronavirus. “They’re very hard to work with; they don’t grow very well in cell cultures; and it’s been hard to get funding,” says Vineet Menachery of the University of Texas Medical Branch. He is one of just a few dozen virologists in the world who specialize in coronaviruses, which have attracted comparatively little attention compared with more prominent threats like flu. The field swelled slightly after the SARS epidemic of 2003, but then shrunk as interest and funding dwindled. “It wasn’t ’til MERS came along [in 2012] that I even thought I could have an academic career on coronaviruses,” Menachery says.

The tight group of coronavirologists is now racing to make up for years of absent research—a tall order in the middle of a pandemic. “We’re working as hard as possible,” says Lisa Gralinski, a virologist at the University of North Carolina. “Our space is so intermingled that we can’t socially distance among ourselves much.”

One small mercy, she notes, is that SARS-CoV-2 isn’t changing dramatically. Scientists are tracking its evolution in real time, and despite some hype about the existence of different strains, the virologists I’ve spoken with largely feel that the virus is changing at a steady and predictable pace. There are no signs of “an alarming mutation we need to be worried about,” Gralinski says. For now, the world is facing just one threat. But that threat can manifest in many ways.

II. The Disease

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. Early Chinese data suggested that severe and fatal illness occurs mostly in the elderly, but in the U.S. (and especially in the South), many middle-aged adults have been hospitalized, perhaps because they are more likely to have other chronic illnesses. 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. “Folks are talking about CFR as this unchangeable quantity, and that is not how it works,” says Maia Majumder, an epidemiologist at Harvard Medical School and Boston Children’s Hospital.

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. These factors vary among countries, states, and cities, and the CFR will, too. (Majumder and her colleagues are now building tools for predicting regional CFRs, so local leaders can determine which regions are most vulnerable.)

The variability of COVID-19 is also perplexing doctors. The disease seems to wreak havoc not only on lungs and airways, but also on hearts, blood vessels, kidneys, guts, and nervous systems. It’s not clear if the virus is directly attacking these organs, if the damage stems from a bodywide overreaction of the immune system, if other organs are suffering from the side effects of treatments, or if they are failing due to prolonged stays on ventilators.

Past coronavirus epidemics offer limited clues because they were so contained: Worldwide, only 10,600 or so people were ever diagnosed with SARS or MERS combined, which is less than the number of COVID-19 cases from Staten Island. “For new diseases, we don’t see 100 to 200 patients a week; it usually takes a whole career,” says Megan Coffee, an infectious-disease doctor at NYU Langone Health. And “if you see enough cases of other diseases, you’ll see unusual things.” During the flu pandemic of 2009, for example, doctors also documented heart, kidney, and neurological problems. “Is COVID-19 fundamentally different to other diseases, or is it just that you have a lot of cases at once?” asks Vinay Prasad, a hematologist and an oncologist at Oregon Health and Science University.

Prasad’s concern is that COVID-19 has developed a clinical mystique—a perception that it is so unusual, it demands radically new approaches. “Human beings are notorious for our desire to see patterns,” he says. “Put that in a situation of fear, uncertainty, and hype, and it’s not surprising that there’s almost a folk medicine emerging.” Already, there are intense debates about giving patients blood thinners because so many seem to experience blood clots, or whether ventilators might do more harm than good. These issues may be important, and when facing new diseases, doctors must be responsive and creative. But they must also be rigorous. “Clinicians are under tremendous stress, which affects our ability to process information,” McLaren says. “‘Is this actually working, or does it seem to be working because I want it to work and I feel powerless?’”

Consider hydroxychloroquine—the antimalarial drug that’s been repeatedly touted by the White House and conservative pundits as a COVID-19 “game changer.” The French studies that first suggested that the drug could treat COVID-19 were severely flawed, abandoning standard elements of solid science like randomly assigning patients to receive treatments or placebos, or including a control group to confirm if the drug offers benefits above normal medical care. The lead scientist behind those studies has railed against the “dictatorship of the methodologists,” as if randomization or controls were inconveniences that one should rebel against, rather than the backbone of effective medicine.

Larger (but still preliminary) studies from the U.S., France, and China have cast doubt on hydroxychloroquine’s effectiveness, and because it can cause heart problems, the National Institutes of Health has recommended against using it outside clinical trials. Those trials will offer clearer answers by the summer, and the drug may yet prove beneficial. For now, doctors are routinely prescribing it without knowing if it works or, crucially, if it does more harm than good. Meanwhile, people with lupus and rheumatoid arthritis, who actually need hydroxychloroquine, can’t get it. It is not the case that every new study contributes to our understanding of COVID-19. Sloppy ones are a net negative, adding to the already considerable uncertainty by offering the illusion of confidence where none exists.

III. The Research

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. The most important, he says, was the realization that people can spread the virus before showing symptoms. But even that insight was slow to dawn. A flawed German study hinted at it in early February, but scientific opinion shifted only after many lines of evidence emerged, including case reports, models showing that most infections are undocumented, and studies indicating that viral levels peak as symptoms appear.

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. “Our understanding oscillates at first, but converges on an answer,” says Natalie Dean, a statistician at the University of Florida. “That’s the normal scientific process, but it looks jarring to people who aren’t used to it.”

For example, Stanford University researchers recently made headlines after testing 3,330 volunteers from Santa Clara County for antibodies against the new coronavirus. The team concluded that 2.5 to 4.2 percent of people have already been infected—a proportion much higher than the official count suggests. This, the authors claimed, means that the virus is less deadly than suspected, and that severe lockdowns may be overreactions—views they had previously espoused in opinion pieces. But other scientists, including statisticians, virologists, and disease ecologists, have criticized the study’s methods and the team’s conclusions.

One could write a long article assessing the Santa Clara study alone, but that would defeat the point: that individual pieces of research are extremely unlikely to single-handedly upend what we know about COVID-19. About 30 similar “serosurveys” have now been released. These and others to come could collectively reveal how many Americans have been infected. Even then, they would have to be weighed against other evidence, including accounts from doctors and nurses in New York or Lombardy, Italy, which clearly show that SARS-CoV-2 can crush health-care systems. 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.

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. Epidemiologists who are used to interacting with only their peers are racking up followers on Twitter. They have suddenly been thrust into political disputes. “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.”

In an earlier era, issues with the Santa Clara study would have been addressed during peer review—the process in which scientific work is assessed by other researchers before being published in a journal. But like many COVID-19 studies, this one was uploaded as a preprint—a paper that hasn’t yet run the peer-review gauntlet. Preprints allow scientists to share data quickly, and speed is vital in a pandemic: Several important studies were uploaded and discussed a full month before being published.

Preprints also allow questionable work to directly enter public discourse, but that problem is not unique to them. The first flawed paper on hydroxychloroquine and COVID-19 was published in a peer-reviewed journal, whose editor in chief is one of the study’s co-authors. Another journal published a paper claiming that the new coronavirus probably originated in pangolins, after most virologists had considered and dismissed that idea.

Meanwhile, scientists are poring over preprints in open online spaces: The Santa Clara study may not have been formally peer-reviewed, but it has very much been reviewed by peers. It is easier than ever for journalists to assess how new research is being received, but only some are presenting these debates to their audience. Others are not. Some are even reporting on press-releasedresearch that hasn’t been uploaded as a preprint. “The rules for reporting on preprints shouldn’t be any different from reporting on journal articles,” the journalist Ivan Oransky told the media watchdog Health News Review. “Everything needs to be scrutinized beyond belief.”

Such scrutiny will become ever more necessary as the pandemic wears on. Julie Pfeiffer of UT Southwestern, who is an 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 come from nonscientists who have cobbled together a poor mathematical model; others come from actual virologists who have suddenly pivoted to studying coronaviruses and “are submitting work they never normally would in a rush to be first,” Pfeiffer says. “Some people are genuinely trying to help, but there’s also a huge amount of opportunism.”

IV. The Experts

Last month, the legal scholar Richard Epstein claimed that “the current organized panic in the United States does not seem justified” and that as the pandemic continued, “good news is more likely than bad.” His piece was widely circulated in conservative circles and the Trump administration. When asked about his lack of epidemiological training in an interview with The New Yorker’s Isaac Chotiner, Epstein responded, “One of the things you get as a lawyer is a skill of cross-examination. I spent an enormous amount of time over my career teaching medical people about some of this stuff.” His essay initially speculated that 500 Americans would die from COVID-19. He later updated that estimate to 5,000. So far, the death toll stands at 58,000, and is still rising.

Many other non-epidemiologists seem to have similarly accrued expertise in the field. The military historian Victor Davis Hanson proffered the widely shared idea that the coronavirus has been spreading in California since last fall—a claim disproved by genetic studies showing that the earliest U.S. case likely arrived in January. During a White House meeting, the economist Peter Navarro reportedly pointed to a pile of hydroxychloroquine studies and said, “That’s science, not anecdote” to Anthony Fauci, who has worked in public health for five decades and directs the National Institute of Allergy and Infectious Diseases. The Silicon Valley technologist Aaron Ginn self-published an article on Medium called “Evidence Over Hysteria—COVID-19” that was viewed millions of times before being debunked by Bergstrom and taken down.

Expertise is not just about knowledge, but also about the capacity to spot errors. Ginn couldn’t see them in his own work; Bergstrom could. The rest of us are more likely to fall in the former group than the latter. We hunger for information, but lack the know-how to evaluate it or the sources that provide it. “This is the epistemological crisis of the moment: There’s a lot of expertise around, but fewer tools than ever to distinguish it from everything else,” says Zeynep Tufekci, a sociologist at the University of North Carolina and an Atlanticcontributing writer. “Pure credentialism doesn’t always work. People have self-published a lot of terrible pieces on Medium, but some of the best early ones that explained stuff to laypeople were from tech guys.”

Bergstrom agrees that experts shouldn’t be dismissive gatekeepers. “There’s a lot of talent out there, and we need all hands on deck,” he says. For example, David Yu, a hockey analyst, created a tool that shows how predictions from the most influential COVID-19 model in the U.S. have changed over time. “Looking at that thing for, like, an hour helped me see things I hadn’t seen for three weeks,” Bergstrom says.

A lack of expertise becomes problematic when it’s combined with extreme overconfidence, and with society’s tendency to reward projected confidence over humility. “When scientists offer caveats instead of absolutes,” Gralinski says, “that uncertainty we’re trained to acknowledge makes it sound like no one knows what’s going on, and creates opportunities for people who present as skeptics.” Science itself isn’t free from that dynamic, either. Through flawed mechanisms like the Nobel Prize, the scientific world elevates individuals for work that is usually done by teams, and perpetuates the myth of the lone genius. Through attention, the media reward voices that are outspoken but not necessarily correct. Those voices are disproportionately male.

The idea that there are no experts is overly glib. The issue is that modern expertise tends to be deep, but narrow. Even within epidemiology, someone who studies infectious diseases knows more about epidemics than, say, someone who studies nutrition. But pandemics demand both depth and breadth of expertise. To work out if widespread testing is crucial for controlling the pandemic, listen to public-health experts; to work out if widespread testing is possible, listen to supply-chain experts. To determine if antibody tests can tell people if they’re immune to the coronavirus, listen to immunologists; to determine if such testing is actually a good idea, listen to ethicists, anthropologists, and historians of science. No one knows it all, and those who claim to should not be trusted.

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. “One signature a lot of these armchair epidemiologists have is a grand solution to everything,” Bergstrom says. “Usually we only see that coming from enormous research teams from the best schools, or someone’s basement.”

V. The Messaging

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VI. The Information

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VII. The Numbers

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VIII. The Narrative