“It’s really a 100-year thing,” Nathan Wolfe said. It was 2006, and Wolfe, then a 36-year-old virologist with an unruly nest of curly hair, was sitting across a table from me at a bustling restaurant in Yaoundé, the capital of Cameroon. An epidemiology professor at UCLA, he had been living in West Africa for six years, establishing a research center to identify and study viruses as they crossed over from wild animals into humans.
That night Wolfe told me he was forming a network of research outposts around the globe, in hot spots where potentially devastating viruses were poised to make the jump: Cameroon, where HIV likely passed from chimpanzees into local hunters; the Democratic Republic of Congo, which had seen human outbreaks of monkeypox; Malaysia, home to a 1998 emergence of the Nipah virus; and China, where SARS-CoV had crossed over, likely from bats, in 2002. Wolfe’s hope was that by understanding what he called the “viral chatter” of such places, it would be possible not only to react more quickly to outbreaks but to forecast their arrival and stop them before they spread. The “100-year thing” he was thinking about was a global pandemic, and how history would judge humanity’s efforts to prepare for it. His biggest fear, he said, was a virus unknown to human immune defenses starting a human-to-human transmission chain that would encircle the globe.
As we knocked back Cameroonian beers and talked between sets of a local band, he admitted his project could fail. “It could be that we look at this and it’s stochastic—you can’t predict it,” he said. “Or, it could be that we are on the edge of a paradigm shift.” The ultimate question, Wolfe added, was “Will people look back and say you did a good job responding to epidemics, but you didn’t do anything to prevent them?” The 100-year notion so captivated me that I used it as the last line of a story I wrote in 2007, in this magazine.
Thirteen years later, as the SARS-CoV-2 virus burned across the globe this March, it appeared that the 100-year judgment had arrived. We’d failed both at preventing the exact danger Wolfe had warned us about and at responding when it emerged. He wasn’t the only pandemic Cassandra, of course. Not even close. Scientists, journalists, and public health experts had sounded the alarm for decades, filling journals, government reports, and popular books with their pleas. There were conferences, commissions, hearings, exercises, consortiums. Every few years another near-miss epidemic emerged that cried out for long-term preparation.
But Wolfe was the Cassandra I’d known, and I couldn’t help wondering what it felt like to be living through the pandemic you predicted. We had corresponded a few times since 2007, and I’d followed his career sporadically as he started a company called Metabiota. As best I could gather, he had transferred his original idea of a disease surveillance network into a kind of epidemiological data company.
I dug up his email and wrote to him. “It must be a strange sensation,” I said, “to have been terribly right about something you didn’t want to be right about.”
When he called me the next afternoon, the US had just passed 4,000 cases of Covid-19, and Wolfe sounded beleaguered. “Right now I’m a little bit—what’s the right word for it—overwhelmed,” he said. But he seemed decidedly unenthusiastic about discussing his own prescience. “I’m not interested in Monday morning quarterbacking,” he said. “If you are the person who says the sky is falling and it falls, you definitely feel like saying ‘Why didn’t people listen to me?’ But there are a lot of people saying the sky is falling about other things, and it doesn’t.”
Nor was he particularly interested in casting blame—in offering an I-told-you-so from the intrepid virus hunter. “Plenty of people can speak to that,” he said. “It’s like Good Vibrations: I don’t want to play that anymore. I have a new record.” Now 49, Wolfe had traded the Cameroonian jungle for the conference rooms of Silicon Valley. When I saw him on Zoom, his shoulder-length locks were gone, and his quarantine beard was shot through with gray. But he had the same glow of enthusiasm I remembered. His new preoccupation, he told me, was pandemic insurance.
I’ll confess this didn’t immediately pique my interest. The word insurance evokes in me feelings of tedium and loathing. Like many Americans, my personal interface with the industry has, let’s just say, been less than positive. But then Wolfe began to explain the unexpected direction his career had taken. After years of thinking about epidemics in terms of the symptomatic and the dead, he’d begun considering their economic ramifications. A global pandemic, and the steps we would take to stop it, would mean business closings, layoffs, and mass unemployment. Preparing to face an outbreak, he’d come to believe, required anticipating those impacts.
This was where insurance came in, specifically a kind of pandemic insurance policy—for businesses, and perhaps even for countries—that would pay out as soon as an epidemic reached a certain threshold. In 2015, Metabiota had partnered with German reinsurance giant Munich Re and American insurance brokerage Marsh to develop and sell a policy specifically to guard large businesses against pandemics—to stanch the financial losses and keep them afloat. They’d launched it in mid-2018, a year and a half before the first Covid-19 cases appeared in China.
My sense of tedium evaporated. As Wolfe and I were talking, a total economic lockdown was in place, with millions of jobs disappearing by the week and lines at food pantries stretching by the hour. And here he was saying that they had come up with a kind of financial vaccine for exactly this scenario, released not long before the worst pandemic in a century. It wouldn’t stop the virus, of course, but it could help alleviate some of the misery that flowed from it.
How must those CEOs feel, I wondered aloud, who had the foresight to buy the world’s first pandemic business insurance? What a story they would have to tell.
There was just one problem. “By and large we failed,” Wolfe said. “Not because we didn’t do the models well. We enabled the first business-disruption insurance for pandemics. But nobody bought it.”
I was so stunned I called him up a few days later to ask him again. Did he mean literally nobody bought it?
“As far as I know, nobody bought the policy,” he said.
It was a life insurance quandary that first got Gunther Kraut thinking about pandemics, nearly a decade ago. A mathematician by training, Kraut was working at Munich Re, one of the world’s largest reinsurers. As it sounds, reinsurance is the business of insuring insurers. The local and national insurance companies that you and I buy life or auto coverage from—the Geicos and Allstates of the world—need their own protection against rare but catastrophic events that might create enough claims to bankrupt them. Reinsurance companies provide that backstop on insurance for everything from homes and infrastructure projects to business losses and individual lives. Reinsurance is a staggeringly lucrative endeavor: Munich Re had $56 billion in revenue and $3 billion in profit last year. The market is large enough that its perennial competitor, Swiss Re, took in $49 billion itself.
Kraut, sandy-haired and still slightly boyish-looking at 39, grew up near Munich, where the eponymous company has dominated the economic landscape since its founding in 1880. He talks about the intricacies of underwriting with a friendly patience that implies he has done so countless times before, none of which have dimmed his passion. He gravitated toward math at university, and, he told me, “it’s hard to study mathematics in Munich without ever learning about the existence of reinsurance companies.” After completing his PhD in risk management and insurance at Ludwig-Maximilians University, he took a job as a quantitative analyst in Munich Re’s life insurance division in 2007. “Reinsurance is sometimes called the business of a hundred professions,” he said. “Because you don’t just have mathematicians and lawyers and businessmen. You have former mining engineers. You have former captains who steered ships across the ocean. You have art experts who are specialized in art insurance. It is, if you like, always close to life. Admittedly with a little bit of this negative view on it.”
Munich Re—a company built to absorb the risk of others—had a risk problem of its own: namely, the possibility of a global pandemic. Insurance is essentially the business of quantifying risk and then smoothing it out. But for a worldwide outbreak, the math in its life insurance portfolio looked worrying even to Kraut and his colleagues, who spent their careers pondering the darkest risks. In late 2011, Kraut’s team decided to try to do something about it.
“Let’s take the example of Munich and car insurance,” Kraut told me. “That’s a very, very stable business.” A local company might insure tens of thousands of cars, each with a certain probability of having a small accident. “You can predict very well how much money you will have to pay on the claim settlements, and hence how much premium you will need to collect,” he said. But let’s say that one year there is a freakishly large hailstorm in Bavaria, damaging half the cars in the portfolio. The resulting claims could be an extinction-level event for an insurance company. Such storms may occur statistically only once every three decades—a one-in-30-year event, in risk parlance—but every car insurance company would have to keep enough cash on hand to pay out on claims on half its cars, just in case. “That’s a lot of money you need to put aside for something that happens very rarely,” Kraut said.
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Now consider an auto insurer in Paris with the same problem: a fleet of cars, a predictable number of accidents, the threat of a one-in-30-year hailstorm event. Herein lies the mathematical advantage of reinsurance. If Munich Re pledges to cover both companies against freakish hailstorms, “what we can assume with a high chance is that there will be hailstorms in Paris, there will be hailstorms in Munich, but most likely they will not happen in the same year,” Kraut said. That means Munich Re can set aside less money to prepare for a rare event. Even better: The more car insurers that Munich Re adds to its portfolio, in more geographical regions, the more it can convert a rare and expensive risk into a predictable and cheaper one for itself. In insurance it’s called diversification. “The more that you can spread the risk, the better for making it insurable,” Kraut said. “That’s why reinsurance companies are global companies.”
The math applies to other insurance “perils,” as they’re known—earthquakes, floods, wildfires. And ordinary deaths, most of the time. But therein lay the problem for Kraut, who was partly responsible for making sure the company’s life insurance division didn’t shoulder unsustainable risks. Local disease outbreaks were like the hailstorms of life insurance: rare and devastating regional events that could be counted on to happen at different times in different locales. “Now you quickly see the problem with insuring pandemic risk, because a pandemic is by definition a global event,” Kraut said. Imagine a hailstorm spreading from town to town, across the globe, in a cataclysmic chain: “The whole concept of global diversification doesn’t work out anymore.” An outbreak on the scale of the 1918 flu—50 million dead worldwide—might be a one-in-500-year risk, an event way out on the tail of a probability curve. But a pandemic at that scale, or even one considerably smaller, could not only overwhelm life insurance companies but Munich Re too.
To tackle Munich Re’s exposure, Kraut’s team began attempting to quantify and price this incredibly remote, unpredictable risk. If they managed to do that, they would then need to sell part of that risk—to find someone willing to insure the reinsurer. “No one really had tried to do a transaction at a one-in-500-year return period,” Kraut said. His boss gave it a 50–50 chance of success.
But over the course of two years, the group gradually built up a list of potential buyers. It turned out that there were a few large institutional investors looking to diversify their own portfolios, and a little bit of pandemic risk was just the thing. Munich Re would provide them with annual payments, year after year. In the rare event of a pandemic, they would have to cover Munich Re’s losses. One interested class of investor—if a macabre one—was pension funds, which typically grapple with something called longevity risk: the chance that people will live longer than expected. “It’s not really good terminology to call it a ‘risk,’ ” Kraut said. “It’s a good thing, technically! But if people live a lot longer than expected, then a pension fund needs to pay out a lot more pensions than they originally calculated.” A deadly pandemic that takes the lives of pensioners, to put it in the most clinical terms, means fewer years of pension payouts, canceling some of the longevity risk. Should no pandemic arise, they would pocket payments from Munich Re. By 2013, Kraut and his team had put together enough investors—starting with a large Australian pension fund—to take some of the pandemic problem off of Munich Re’s books. But he soon encountered an unexpected hitch: The mechanisms written to trigger the deal relied on a series of “pandemic phases” monitored by the World Health Organization. (Phase 1: Virus is circulating in animals. Phase 2: Reports of human infection. Phase 3: Human-to-human transmission. And so on up to Phase 6: Sustained outbreaks in multiple regions.) Sometime in 2013, however, the WHO abandoned this system for a less specific four phases. Kraut suddenly needed some other organization to delineate the stages of epidemics reliably enough to write into an insurance policy. And he needed someone to monitor epidemics closely, to know when they hit agreed upon triggers—illnesses, deaths, spread. “But you can’t just hire the WHO,” he said.
In studying up on the world of epidemiology, Kraut happened to have picked up a book called The Viral Storm. It was written by Nathan Wolfe. Part memoir, part prescription, the book laid out a vision for how to counter the threat that novel viruses represent to humans. Kraut looked up Wolfe and saw that he’d formed a company. “I thought, oh, maybe these guys actually can do it,” he said. He sent an email to firstname.lastname@example.org. “Hello, have you ever heard of a reinsurance company? I might have a good reason to talk to you.”
As it happened, Wolfe was already thinking about the business shocks of pandemics when Kraut’s email arrived in Metabiota’s inbox in 2013. By this time, Wolfe’s public profile as an Indiana Jones-like virus hunter had been well established. He’d been featured on CNN and had given the obligatory TED talks. He’d walked away from his tenured position at UCLA, moved to San Francisco, and founded Metabiota. Wolfe leveraged his academic work into the private sector, using the data from his network of research stations to conduct disease surveillance for clients. For years, the company subsisted largely on government contracts, including more than $20 million from the Department of Defense and aid agencies involved in managing epidemic outbreaks. Metabiota also partnered with the foreign assistance agency USAID on a project called Predict, helping to build a database cataloging viruses in their animal reservoirs and forecasting which ones might jump to humans. “There was some success,” Wolfe told me. “Some money was put into prediction and prevention. Not enough, obviously.”
As Wolfe started to appear on stages alongside business leaders, he became convinced that the commercial sector had seriously underestimated epidemic risk. In 2010 he sat on a panel at Davos called “Prepare for a Pandemic.” In advance of the talk, the organizers circulated a survey showing that while 60 percent of CEOs believed the threat of a global outbreak was real, only 20 percent had an emergency plan in place. That same year he’d been invited to a cruise industry conference. He’d tried, without luck, to convince executives that Metabiota could help them avoid the havoc of an epidemic. “I felt like nobody was paying attention to it,” he said.
Then Gunther Kraut’s email arrived. Kraut and Wolfe met up at a conference in Munich and began riffing. Soon Metabiota was providing disease monitoring for Munich Re’s life insurance division.
Kraut, however, had an even more ambitious idea in mind. What if, instead of simply hedging its own life insurance business in the case of a pandemic, Munich Re could use the same concept to insure other businesses against them? Business interruption insurance, the policies that protect companies against income losses from disasters like fires or hurricanes, often explicitly excluded disease. (And when it didn’t, insurers could still use the ambiguity to deny claims.) The risk was thought to be too large, too unpredictable to quantify. But Munich Re had already proven it could cover its own life insurance risk in pandemics, and now it had a partner in Metabiota that specialized in seemingly unpredictable outbreaks. What if they could create and sell a business interruption insurance policy that covered epidemics, starting with acutely vulnerable industries like travel and hospitality? They could then pass on the payout risk from those policies to the same types of investors who had bought their life risk. “There is a bit of financial alchemy to the whole thing,” Wolfe told me later. “You really are creating something from nothing.”
At the same time, Wolfe had been working to operate Metabiota more like a technology company. In 2015, he hired Nita Madhav, an epidemiologist who’d spent 10 years modeling catastrophes at a company called AIR Worldwide, one of a handful of firms the insurance industry relies on to compute extreme risks. (Munich Re, in fact, had worked with AIR epidemiological models in its life insurance calculations.) Madhav’s mandate at Metabiota was to build the industry’s most comprehensive pandemic model. Her team, which eventually grew to include data scientists, epidemiologists, programmers, actuaries, and social scientists, began by painstakingly gathering historical data on thousands of major disease outbreaks dating back to the 1918 flu. Her colleagues had recently created what they called the Epidemic Preparedness Index, an assessment of 188 countries’ capacity to respond to outbreaks. Together, the two efforts informed an infectious disease model and software platform. A user could begin with a set of parameters around a hypothetical virus—its geographic origin point, how easily it was transmitted, its virulence—and then run scenarios exploring how the disease spread around the world. The goal was a model that could, for example, help a manufacturer understand how a disease might impact its supply chain or a drug company plan for how a treatment would need to be distributed.
As sophisticated as Metabiota’s system was, however, it would need to be even more refined to incorporate into an insurance policy. The model would need to capture something much more difficult to quantify than historical deaths and medical stockpiles: fear. The economic consequences of a scourge, the historical data showed, were as much a result of society’s response as they were to the virus itself.
The group started building what became known as the Sentiment Index. Ben Oppenheim, head of the product team and a political scientist, had studied the work of Paul Slovic, a University of Oregon psychology professor who studied how human beings perceive and respond to risk. Inspired by Slovic’s data-driven approach, they gathered their own information from around the world on how much various symptoms frightened people. To validate their measures, they also began tracking and studying how media coverage evolved around different types of outbreaks. Scarier diseases tended to generate more news stories.
In 2015 the Zika virus outbreak superscript text arrived and crystallized the reality that fear was a critical variable in understanding the economics of outbreaks. A mosquito-borne disease with no vaccine or treatment, Zika almost never killed its victims, but in pregnant women it could lead to a rare and terrifying birth defect called microcephaly. After decades of low-level outbreaks, the disease suddenly surfaced in Brazil and raged northward, causing billions of dollars in tourism losses across South and Central America. Even two years later, Oppenheim, whose wife was then pregnant, canceled a trip to a conference in Bogotá, despite the fact that his own company’s research told him the risk of Zika-carrying mosquitoes at the city’s altitude was negligible. “I remember thinking, we have to solve this,” he said of the question of how to model fear. “Because if a pretty rational person with access to a lot of data is making an emotional decision, imagine this magnified in a pandemic.”
The Sentiment Index was built to be, as Oppenheim put it, “a catalog of dread.” For any given pathogen, it could spit out a score from 0 to 100 according to how frightening the public would find it. That number could then be used to help calculate the possible financial losses from an epidemic, everything from empty hotels to postponed mining projects. Madhav and her team, along with Wolfe and Oppenheim, also researched the broader economic consequences of disease outbreaks, measured in the “cost per death prevented” incurred by societal interventions. “Measures that decreased person-to-person contact, including social distancing, quarantine, and school closures, had the greatest cost per death prevented, most likely because of the amount of economic disruption caused by those measures,” they wrote in a 2018 paper.
As the US economy reopens amid a deadly pandemic, a dire question looms. Let’s weigh the risks—and do the math.
By then, the Sentiment Index had been tested against Metabiota’s database of historical pandemics, and Munich Re began incorporating it into a business interruption policy. Gunther Kraut’s group was then operating as a stand-alone unit called Epidemic Risk Solutions, with groups in Singapore, Munich, and London. The promise, for both companies, was enormous. Metabiota had raised $30 million via venture funding in 2015, partly on the idea that providing the technology behind pandemic coverage could be a growth business. There was, after all, only so much a government agency might pay Metabiota for disease surveillance; the universe of large businesses that could suffer losses from a major pandemic, however, was nearly limitless. Munich Re had a chance to create an entirely new segment of the insurance market, for a risk that existed in literally every part of the globe.
To Wolfe, the product felt like an elegant solution to the inaction he’d seen for years, as whole industries lacked the tools to prepare for the peril that an inevitable pandemic represented, even if they understood the risk. Insurance would provide a mechanism whereby the financial risks that companies faced—shuttered locations, vanishing customers—would be shouldered by investors eager to accept it in exchange for a regular premium.
Munich Re wasn’t the only company looking for a bit of financial alchemy. The US insurance firm Marsh had been grappling with the same question for its customers. Like Oppenheim at Metabiota, Christian Ryan had personal reasons to be struck by the financial consequences of the Zika outbreak. “My father was a hotelier down in Brazil,” said Ryan, the head of Marsh’s hospitality, sports, and gaming division. When the disease began spreading in 2016, his dad lost a significant amount of his business and eventually sold the hotel for a fraction of the price he once could have gotten. “It just showed how fragile hospitality was. Because it’s predicated on people continuing to show up and feel safe and feel secure.”
Ryan and his colleagues went looking for someone who might have modeled out the risk, and like Munich Re, they ended up on Metabiota’s doorstep. Soon Marsh had formed a three-way partnership with Wolfe’s company and Munich Re. Marsh would sell the insurance under the name PathogenRX. (Munich Re set up similar sales relationships in other parts of the world.) The policies would be tailored for each company, but most would contain what’s called a parametric solution: a preset amount of coverage that could automatically pay out when the epidemic reached certain thresholds, giving companies an infusion of cash without the delays of filing a claim.
The marketing materials for the policy now read like a letter from 2020. To the airline and hospitality industries, they warned that “these outbreaks have had widespread impact on personal and business travel.” For sports teams and leagues, they cautioned, “individuals must be able to participate in and attend events without fear for their safety and health. Pandemic outbreaks can disrupt public confidence, and in turn, make or break many companies.”
But selling the insurance meant first persuading risk managers and chief risk officers—the figures responsible for insurance coverage at large corporations—that pandemics were a risk worth hedging. Then the risk managers would need to persuade their bosses—the CFOs and CEOs—to pony up for a new expense that wasn’t going to help the company’s quarterly bottom line.
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Oftentimes Munich Re and Marsh would bring someone from Metabiota along to client meetings to drive home the existential risks. Jaclyn Guerrero, Metabiota’s associate product director, told me that for a meeting with one major hospitality conglomerate, she used the company’s data on average hotel bookings and ancillary income to show the executives what the losses could look like. Her analysis made it clear that the shock from a severe monthslong SARS-like pandemic could erase between $300 million and $800 million from the company’s annual bottom line. The chief risk officer “really believed that this was something worth protecting against,” she said. But the company passed on buying the policy. “A lot of times in these conversations,” she said, “clients will say, ‘OK, we understand why this could potentially be such an impact. But we haven’t seen an event like this in 100 years. Why do we need to care about it now?’”
Marsh and Munich Re both knew they were fighting an uphill battle. “Insurance is sold, not bought,” the industry saying goes, and pandemic insurance would be both novel and quite expensive—potentially millions of dollars on top of what the company was paying for insurance. No CFO was eager to be the first among their competitors to take on a significant new cost.
“They all recognized that it was a risk, but I think at the end of the day it was a business decision,” Ryan said. “We had a lot of clients say, ‘Not now, but let’s think about it next year, and I can plan and budget for it.’ Well, next year is right now, and unfortunately Covid-19 happened this year.”
On December 31, 2019, Nita Madhav was in Portland, Oregon, attending a cousin’s wedding. That summer, after four years leading the infectious disease data science team, she’d taken over as CEO of Metabiota. Now she was enjoying a holiday away from the stress of running a 60-plus-employee company. Her extended family had traveled from around the US and beyond to celebrate the wedding and count down the last moments of 2019 together. But that morning, before the ceremony, Madhav began getting texts from Oppenheim telling her about a cluster of unusual pneumonia-like infections in Wuhan, China. The company’s early detection system, which included an algorithm for parsing and highlighting news stories about outbreaks, was flagging Wuhan as a potential hot spot. The team typically looked at hundreds of media reports a week and approached new ones cautiously. At the reception, Madhav messaged with Oppenheim and wondered: If it was respiratory, could the source be more like H7N9, the avian flu? A coronavirus like SARS-CoV?
The next day, she checked in with her staff, who would need to quickly marshal enough data to project where the outbreak could land. “We were just trying to see what we could find out,” she said. “We weren’t yet in the all-hands-on-deck mode. By the third week in January, we certainly were.”
As the human and economic devastation multiplied in tandem across the globe, Metabiota’s employees suddenly found themselves living inside their own model’s projections. Just two years earlier, the company had run a large set of scenarios forecasting the consequences of a novel coronavirus spreading around the globe. “I guess part of what I’m struggling with emotionally is that it’s almost like we’ve been attacked by a cliché,” Oppenheim told me later. “No one can predict the exact timing and location and dynamics, but the broad contours are a story that people have walked through specifically before.”
At the same time Metabiota was watching the nightmare that its models had anticipated unfold, Gunther Kraut was in Singapore facing a different problem. Where Munich Re’s epidemic solutions division had been struggling to get traction with potential customers, now, in early January, buyers were banging at the door. “That’s just the nature of human psychology,” he said. “Whenever a catastrophe arrives, people immediately want insurance for that catastrophe.” The virus was still confined to China and Kraut faced a grim calculation: Should the company write business interruption policies that would cover SARS-CoV-2, outside of Asia? “You clearly have the human tragedy,” he said. “On the other hand you are in charge of the business unit.” But there were too many warning signs—too much risk for Munich Re. It would have been like selling fire insurance for a house already in flames. Kraut made the decision not to sell.
In a sense, Munich Re had dodged a bullet: Had the company succeeded at selling pandemic protection to corporate giants starting 19 months before, it would have collected almost no premiums and now be paying out on every single one. Kraut acknowledged as much, but offered that if insurers never pay out, “then you lose the reason of existence.”
By March, Metabiota had closed its offices in downtown San Francisco, and its employees joined the legions of new remote workers. “It is painful to see loss of livelihoods, insecurity, fear,” Oppenheim said, “when potentially we would have had tools to prevent that.”
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On the afternoon of April 10, as the worldwide death toll crossed 100,000, the data science and product teams gathered on a Zoom call to discuss a new Covid-19 scenario tool. The goal was to help an international aid agency concerned about the possible trajectories for developing countries. Metabiota’s models are built for long-term understanding rather than real-time analysis, but as clients turned to them for information, they scrambled to adapt. With home and office life now fully merged—“Was Ben going to join this one?” Madhav asked. “No, I think he’s on childcare,” came the response—everyone turned off their video to save bandwidth for the screenshare. One data scientist kicked off the call by showing a rough version of the new tool, paging through alternately disheartening and terrifying graphs illustrating the best- and worst-case results for 16 countries, depending on how the virus was contained. The former showed hundreds of thousands of additional deaths from late March onward. In the latter, reflecting a total breakdown in containment, the deaths reached into the tens of millions.
Nicole Stephenson, Metabiota’s director of infectious disease modeling, then pulled up a data set the company had obtained, capturing an individual country’s epidemic controls: travel restrictions, school closures, border closures, limits on public gatherings. It was the kind of data they could later feed into their disease-spread model. “We’re trying to figure out a way to rank countries in their proactiveness,” Stephenson reported. The group discussed which parameters to quantify to feed into the system and tossed out ideas on what was missing. They needed data on food security, one suggested, since it could impact the feasibility of national lockdowns. Another had a line on some data about Covid-19’s comorbidity with HIV—a critical concern in some African countries.
“Are we tracking which countries have put into place economic stimulus packages?” Madhav asked. “And which countries are seeking out relief or aid?”
“Some of this is captured in this data set,” Stephenson said. “But it’s very qualitative.”
That would be the next step: figuring how to convert thousands of rows of words into quantifiable measures that the model could use for calculations—and ultimately show the client how bad things could get. “Everybody’s got some fun data to play with over the weekend,” Stephenson said. “I know that’s what I’ll be doing.”
“Nobody bought the policy.” I couldn’t stop thinking about what Wolfe had told me, back when I reconnected with him in March. It wasn’t quite nobody, as it turned out. Kraut told me that one company in the health care industry in the US had bought some level of pandemic protection, although the insurer that sold it had later quit selling the policies due to lack of interest. For confidentiality reasons, Kraut wouldn’t say who the end client was or whether it had received payment.
There are some large corporate insurance policies that do cover disease-related losses, such as event cancellation coverage; both Munich Re and Swiss Re announced that they potentially faced hundreds of millions of dollars in claims connected to suspension of the Olympics and other events. In April, news surfaced that the Wimbledon tennis tournament was set to collect $140 million from an insurance policy in which it had demanded a pandemic protection clause 17 years earlier—after the SARS outbreak in 2003. And even as late as February, when the virus was already worldwide news, hedge fund manager Bill Ackman managed to find a taker on a $27 million investment bet that the virus could crash the stock market. It was essentially an insurance policy for his portfolio. When he cashed it in to the tune of $2.6 billion in March, after going on TV and warning of the potential devastation the virus could cause, he felt the need to take to Twitter and defend himself against accusations of profiting off human misery.
But the existence of a few prescient exceptions only served to underscore the question of why no one else had heeded the warnings. The failures are massive, almost incomprehensible. (Among them is the fact that, in September 2019, the Trump administration canceled funding for Predict, the USAID disease surveillance program that had been working to identify dangerous viruses—including work with the Wuhan Institute of Virology in China.) But after weeks of asking the question, I realized that at least part of the answer was already there, in that first conversation I’d had with Wolfe in March. After all, I’d written about him more than a decade before. I had heard the warnings directly from him, listened to him describe the hundreds of thousands of unknown mammalian viruses that lurked out in the biosphere. I’d hiked through the jungles where HIV likely made its jump to humans. And then I’d come home, written my story, and largely forgotten about the pandemic he’d predicted.
“I just don’t think our brains are particularly well suited to sorting out these kinds of risks, particularly ones that are infrequent,” he told me recently. Companies are led by humans who suffer from the same failures of sustained imagination as the rest of us—unable to truly internalize the one-in-100-year disaster until it arrives on our doorsteps. “It will be a defining event for all humans who have lived through this, including my 3- and 5-year-old children,” Wolfe said. “But still, everybody is going to go back to their jobs, and people will wonder whether the risk is really that great again.” Researchers who study epidemics even have a term for the phenomenon: the cycle of panic and neglect.
But now, as we swing wildly through the panic end of the pendulum—justified panic, as hundreds of thousands die and the international economy collapses—there’s no longer a need to explain to airlines or hotel chains or sports franchises how even a small amount of pandemic insurance might help them. Gunther Kraut and his group find themselves deluged with hundreds of requests for the business interruption policies on the next outbreak. Now their challenge is volume, taking a policy meant to be customized for each client and converting it into a commodity that can be sold to many of them at once.
“The demand for insurance arises at particular moments, often in response to dramatic crises that demonstrate human vulnerability,” the Princeton historian Harold James has written. In 1666, after the Great Fire of London destroyed a third of the city, the modern fire insurance business was born. A financial crisis in the 1830s prompted the development of the US life insurance market. In 1906, the San Francisco earthquake became the greatest payout, relative to premiums, in Munich Re’s history and reshaped natural disaster preparedness forever. Hurricane Andrew, Hurricane Katrina, 9/11: Each has shifted how our society thinks about risk and the money we set aside to try to prepare for it. Climate change is doing so again.
Without a doubt, insurance will factor into thinking about the economic consequences of pandemics going forward. Already several prominent US restaurants have sued to try to force the issuers of their current business interruption policies to cover coronavirus losses. (Where policies don’t specifically include or exclude disease, insurers have just denied any Covid-related claims from small businesses, leaving them with no relief.) Some in the insurance industry speculate that banks may now make business loans in some industries, like travel and hospitality, contingent on having epidemic insurance. Or governments may simply mandate such coverage. In any case, the demand for disease-based insurance may quickly outstrip even the reinsurers’ and other investors’ ability to cover the policies.
National governments may end up the ultimate pandemic reinsurers, stepping in to prop up the insurance market, as the US did after 9/11 with the 2002 Terrorism Risk Insurance Act. By late May, there were already multiple proposals in Congress do just that. “I think it’s very fair to think 9/11 is to terrorism as Covid-19 is to epidemic risk,” Wolfe said.
From a certain angle, it will always appear ghoulish for insurers to capitalize on the risk of misery. Insurance triggers are themselves inherently cold, emotionless calculations—a number of sick or dead, or a level of fear on a Sentiment Index. Both Metabiota and Munich Re have explored the possibility that countries themselves, particularly in the developing world, could be insured against epidemics and pandemics. But one pandemic-insurance-like product on the market, a $425 million “pandemic bond” set up by the World Bank in consultation with Munich Re and Swiss Re, has been heavily criticized for failing to pay out quickly enough. While the bond did eventually deliver the part that covered coronaviruses in April, the World Bank was accused of making the triggers needlessly complex and then dawdling while bodies were piling up.
Epidemics are inherently chaotic, as Metabiota itself experienced during the 2014 Ebola outbreak in West Africa, which killed 11,000 people in six countries. A 2016 Associated Press investigation detailed accusations that the company’s laboratory in Sierra Leone had mishandled testing samples and underplayed the epidemic’s potential scope. “We’re not a response organization,” Wolfe told me recently, by way of explanation. “But the government was our partner and it was an emergency, so we stepped up to respond. Everybody makes mistakes in those kinds of environments, and we were not free of mistakes.”
Even if and when pandemic insurance policies become widespread, they aren’t a panacea for the kind of economic ruin we are currently living through. One only has to look to the 2008 mortgage crisis to see how financial alchemy can go wrong. There will be small businesses priced out of coverage, insurers who exploit every loophole to avoid claims, and corporate executives who enrich themselves and not their workers when they do receive payments. But if SARS-CoV-2 has shown anything, it’s that we need every preventive weapon in the arsenal. Even a marginal amount of pandemic insurance could have meant fewer layoffs, diluting the economic pain. “Right now, taxpayers are going to soak up 100 percent of the risk,” Wolfe said of the coronavirus impacts. As of late May, the US economic bailout alone amounted to $2 trillion and counting. Pandemic insurance would shift at least some of that burden onto investors who’d willingly taken on the risk. “How much risk will the private sector be able to take? I’m an optimist on that. More than it’s currently taking. I don’t think anybody would say it’s not at least 5 to 10 percent,” Wolfe said. Five percent of the bailout would amount to $100 billion lifted off the books of the taxpayers and onto investors who had gambled on the risk.
In the central square in Munich sits a clock tower atop the town hall, completed in 1908. One of the city’s most popular tourist attractions, the tower’s edifice includes a pair of famous glockenspiels, mechanical-musical dioramas depicting scenes from the region’s past. At designated hours, small figurines spin in time with the chiming bells. One portrays the lavish wedding of a Bavarian duke. The other reenacts the “dance of the barrel makers,” celebrating the end of a 16th-century plague. Local lore has it that in 1517, the barrel makers took to the streets, dancing to convince the population that the plague had subsided and normal life could resume.
Gunther Kraut often found himself recounting his hometown legend over the years, as he tried to distill the mathematics of pandemic risk into some digestible reality. A one-in-500-year disease event wasn’t some abstract concept, he would tell people. It was something that had reshaped our societies in the past and would do so again. And whatever level of truth one ascribed to the glockenspiel’s legend, 1517 was just about 500 years ago. The plague would come again, and someone would have to be the barrel makers, bringing everyone back out into the sunlight.
The remoteness of the risk is always the hardest part to get our heads around. Our past moments of calm or our current nightmare, like the last coin flip or turn of the roulette wheel, tell us nothing about when the next one might arrive. One in 500 years isn’t a prophesy, just a probability. If anything, as Wolfe pointed out when I first met him over a decade ago, global warming, urbanization, and destruction of species habitats are only accelerating the speed at which the next pandemic may arrive.
“I have a long-term view of this, and this is not the last one,” Wolfe said. “It’s a bad one.” He paused. “It’s a bad one. I don’t think there’s any more ‘ifs’: This will fundamentally change the future. It is not impossible that over the course of the next 50 years, humanity has an event which is substantially worse than this event, and people at that point look back and say, ‘As terrible as Covid-19 was, if we had not had it, the consequences would have been so much more dramatic.’” Even amid the pandemic he had predicted, Wolfe said he still considered himself an optimist. “You want to honor the devastation that this virus is going to have on families, on livelihoods. But in the grand scheme of history, it may also be seen as a very costly inoculum against future events. I believe that the world has no choice but to respond in such a forceful way that will make humanity safer.”
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