In a rare act of bipartisanship, Congress passed a law to deal with this potential problem. Physicians are now required to make a diagnosis before prescribing Dormigen. A doctor has to at least make some conjecture as to why your chest is covered with open sores. Then you get your gel caps. Yes, physicians still cut corners, offering vague, incomplete, and often inaccurate diagnoses (e.g., “tropical disease”), as I would if I were an overworked general practitioner with twelve and a half minutes to spend with each patient. Still, some information is better than none.
The same law requires that if a physician prescribes Dormigen without a firm diagnosis—if he or she really cannot identify the underlying illness—the symptoms have to be entered into a federal database. Doctors hate it; so do the anti-government folks. Big Brother now knows if you have a rash on your penis. Like many federal regulations, this one is imperfectly observed and poorly enforced. Nonetheless, on that snowy March evening in Natick, Dr. Helen Spellings typed a few vague lines into the Dormigen Prescription Without Diagnosis (DP-WoD) database at the end of her shift. The salient key words of the undiagnosed illness were “healthy male,” “nonresponsive fever” and “flu-like symptoms.”
In Tampa, Florida, at about the same time that Dr. Spellings was typing those vague symptoms into the DP-WoD database, Tom Elliott, a management consultant from San Francisco, was spiking a fever as he prepared to give a presentation to the board of directors of a large auto parts manufacturer. Elliott did not seek medical attention, as far as we know. Our best guess is that his fever was around 102 as he rushed through his PowerPoint slides. He mentioned in passing at the beginning of his presentation that he was not feeling well. He later sent a text to his daughter saying he felt miserable—so bad that he was not sure he could tolerate the five-hour flight home. Elliott went back to his room at the Marriott Courtyard, presumably to rest. A hotel employee found him unconscious that afternoon when she went to clean the room. By the time Elliott was transported to a hospital, his major organs had shut down. He died less than an hour later. Tom Elliott was forty-three and healthy. As far as we know, he was the first fatality of the Outbreak.
Elliott’s death, and what little could be pieced together of his symptoms, were entered in a different federal database, the Fatal Infectious Disease Surveillance Instrument (FIDSI). This database has been around longer; doctors take it much more seriously. Public health officials pay attention when otherwise healthy people die from unknown causes. On the same day that Tom Elliott died in a Florida hotel room, six other people died with similar symptoms: five middle-aged men and a female lacrosse player at the University of Vermont. But to spot that pattern, someone has to be looking for it.
10.
“WHAT DO YOU MAKE OF THIS?” TATIANA BOROVSKY ASKED her supervisor.
She had walked down the hall at the CDC on a Monday morning clutching a handful of papers and some rough statistical analysis she had done earlier that morning. The public would eventually know her simply as “Tatiana” after stories described her as the “discoverer” of the Outbreak. I suppose it was technically true, in the sense that Tatiana Borovsky was the first person to notice a statistical anomaly, the mathematical manifestation of an epidemic. If I were to be less generous, I would point out that the first person to discover it is raining is not necessarily a genius. The data, like raindrops, tend to fall on your head.
Tatiana was tall—nearly six feet—and exotic-looking. One has to wonder if she would have received as much attention if she had been a balding, middle-aged man, like her supervisor. She had an intriguing backstory, including family that hailed from the Balkans and Syria. (Her provenance tended to shift, depending on the news story.) Tatiana had long legs, long jet-black hair, and exotic if not necessarily beautiful features. As you may remember from some of the news stories, she also had a proclivity for skinny-dipping while on holiday at various destinations on the Black Sea. One lesson from the Outbreak is that if you are lanky, reasonably good-looking, and prone to post topless photos on the Internet, you are likely to garner undue attention. Tatiana walked down the hall on that Monday morning with data showing an increase in unexplained deaths that was two standard deviations above the norm. If one does a Google search of “Tatiana Borovsky and two standard deviations” the topless photos come up, along with a succinct explanation of what a standard deviation is, using breasts as a teaching tool. One standard deviation is significantly larger (or smaller) than normal. Two standard deviations is very unusual—as the photos illustrate. Very clever, I suppose. (To be clear, I never visited this site while using my government computer.)
“Probably just noise,” Tatiana’s supervisor said as he perused the data. “But those are big numbers.” He had a small, windowless office with pleasant but artless photos of his second wife and their blended family. He most likely did not take photos of himself swimming naked, and if he did, no one would want to see them. “Check all the databases,” he told her. “See if anything turns up in the OECD numbers.”
This was basic epidemiological detective work. You look for patterns. The more data you have, the easier it is to spot them. Suppose six people driving the same kind of pickup truck die in traffic accidents in six different states. A police officer at each accident scene will investigate the crash and fill out the paperwork. Why would any one of those officers—just one person examining one crash site—suspect that the brakes on that particular pickup truck were faulty? Each report gets filed away. Maybe it ends up in a state database, one piece of a jigsaw puzzle. No one can do the puzzle without seeing the other pieces. But when a mid-level bureaucrat at the National Highway Traffic Safety Administration—Tatiana without the legs and exotic looks—gets a weekly report showing a cluster of fatal crashes involving a T-370 pickup, she is going to put down her coffee and walk that report down the hall. Someone is eventually going to take a look at the brakes on the T-370.
Tatiana did that. The rhetoric about her as “the hero” and “the Syrian savior” was not merely overblown; it was ridiculous. She had only one basic responsibility, which was to gather reports and look for anomalies. She was not a paper pusher, more of a paper catcher. The oddity she spotted was 107 unexplained deaths from flu-like symptoms in the previous week. All were otherwise healthy people. Most were under age fifty, including eight college students. Even in the era of Dormigen, there are periodic spikes in unexplained deaths. Most of them turn out to be statistical noise, but as Tatiana’s supervisor rightly observed, 107 was a curiously high number for unexplained deaths among healthy people. Each of those deaths was its own tragedy; together they may have been the pieces of a more interesting puzzle. Or maybe not.
Tatiana plodded back to her cubicle and did three things as a matter of protocol.† First, she sent a blanket query to the public health entities in the other thirty-three OECD countries asking if they were observing a similar trend. (The Organization for Economic Cooperation and Development—OECD—is a consortium of the world’s most developed countries that share data and cooperate on assorted things.) That is the fastest and easiest way to spot a global epidemic. True, any nasty pandemic is more likely to originate in Liberia than in France, but there are no meaningful data in the Liberias of the world, so the best we can do is spot a trend when it begins to appear in places where you can drink the water.
Second, Tatiana requested follow-up data for each of the 107 deaths. There would be autopsy results for all of them because the cause of death was unknown. That is the law in most states, even if it is often skirted. She also requested the medical histories and treatment files for each case. This is trickier territory given the privacy issues, but doctors and hospitals typically comply if the identifying information can be stripped from the files. The sad irony is that the CDC—our first responder for anything that looks contagious—does not care at all about personal information. At the same time families and friends are reminiscing at funerals about intimate details—hobbies and weddings and crazy college stories—the CDC
wants none of that information. The puzzle pieces do not need names, let alone golf handicaps or boyfriends.
Last, Tatiana accessed a different database to check for any unexplained increase in the prescription of Dormigen. If there were an epidemic afoot, particularly a flu epidemic, most patients would be treated successfully with Dormigen. They would stop by the doctor’s office after a day or two of feeling miserable, or on the way home from work in the middle of the day. Neither doctor nor patient would ever know—or care—how bad things might have been in the absence of Dormigen. Still, there should be a record for each of those cases, even if the last thing a beleaguered physician wants to do at the end of the day is fill out extra paperwork. More puzzle pieces.
The bureaucratic gears turn slowly. Tatiana left for a short vacation in the Bahamas with her fiancé. Many of the European equivalents of the CDC were closed for assorted holidays (and, to be honest, they do not work at light speed even when they are open). The files were eventually shuffled and filed and analyzed, after which there was a clear pattern. Six of the OECD countries showed an anomalous rise in flu-like deaths among young, healthy adults. The spike in unexplained Dormigen prescriptions, both in the U.S. and in those six OECD countries, was also pronounced. The numbers were not huge, but they were significant. More reports were prepared. Meetings were held. Information was shared across countries—the usual administrative protocol. No one was particularly alarmed. The pattern was noteworthy but relatively modest in the grand scheme of things.
That changed in week three. Tatiana, back from the Bahamas, sent an e-mail to the whole Contagious Disease Working Group: “Hey All, I just compiled the new data on global unexplained deaths, and there is something going on here. The numbers are still relatively small, but they are increasing at a rapid rate. Can we get together before the end of the day?”
In CDC-speak, that means “Holy shit!” Nobody in public health likes to see anything “increasing at a rapid rate.” Epidemics do not meander along at fifty-five miles per hour. When a sick person has the potential to infect ten or twenty-five others, every sporting event or crowded subway car or day-care center becomes the disease equivalent of a cluster bomb. That was how forty million people died in the Spanish flu pandemic. There were three more deaths at the University of Vermont, all athletes. The media did not have access to the broader data, which was showing a rise in unexplained deaths across the country, but when three otherwise healthy college students die in the same place from an ambiguous cause, people notice. Google News blasted a segment on the “mysterious deaths” at the University of Vermont. CNN sent a camera crew to campus to interview alarmed students, thereby generating more alarm. The Internet was abuzz with theories.
The CDC Director asked Ron Justman, the head of Tropical Diseases, to lead a working group to coordinate our response to whatever was going on, this anomalous spike. There was no reason to believe it was a tropical disease, but Justman was senior and relatively competent. More important, he had some free time, having just rotated off of a task force working on an outbreak of dengue fever in Hawaii. The NIH sent over a handful of research scientists. Yes, there was a CNN camera crew at the University of Vermont, but in our world (though I was not yet involved) this felt like more of the same: another statistical aberration that would likely run its course.
The first meeting of the still-unnamed working group reflected this sense of business as usual. While the group waited for a conference room, the junior staffers discussed a new sushi restaurant in downtown Baltimore. “Do we really need another sushi restaurant?” one of them asked.
“It’s totally different,” a CDC staffer weighed in. “More creative stuff, a fusion thing. Pricey, though.”
“A good date-night spot?”
“Perfect. It’s in an old warehouse. Very cool space. If you sit upstairs you get a view of the harbor.”
The conference room door opened and another team filed out, carrying papers and water bottles. They left behind a tray of semi-stale bagels and muffins. Justman’s working group filed in, six or seven of them, with Justman taking a seat near the end of the small conference table. The bagels and muffins remained untouched until one of the NIH guys cut them into quarters, at which point they all disappeared rapidly, even the raisin bagels.
“Okay, what do we have?” Justman asked. He was forty or fifty (or maybe thirty-five or fifty-five), with a full head of unruly reddish hair and a matching mustache. The mustache was impressive; he could have passed for a state trooper if you gave him a decent haircut, the black boots, the uniform, and a cool hat. Instead, he was wearing a short-sleeve no-iron dress shirt with a breast pocket full of pens. One of the junior staffers turned on the projector, which illuminated a screen on the opposite wall, but he could not get the laptop computer to recognize his encrypted data pod. “Just walk me through it,” Justman told him. “I don’t need to see slides.”
The junior staffer summarized the patterns that had emerged in the previous week: the increase in unexplained deaths; the corresponding spike in Dormigen prescriptions; similar anomalies in a few other developed countries; the cluster of deaths at the University of Vermont. “What are we seeing in the autopsies?” Justman asked.
“Not much, really,” one of the CDC women answered. “It’s not meningitis, not measles. Doesn’t seem to be a strain of the flu. We can rule out most of the logical explanations.”
“Just unexplained deaths,” Justman said.
“Pretty much, yes.”
“And we’ve got tissue samples here?” Justman asked.
“They’re coming,” another staffer offered. “We should have a decent number to look at by the end of the week.”
Justman nodded in acknowledgment. All pretty normal stuff. A medical examiner in rural Colorado or suburban Chicago might never see a fatal case of malaria, let alone something more exotic. The NIH and CDC could do more extensive tests, with far more expertise as to what to look for. As grieving families around the country held memorial services and burials—all the more emotionally wrenching because these were healthy people struck down for no obvious reason—the tissue samples were making their way via FedEx and DHL to a nondescript federal laboratory just outside Atlanta. These samples were the disease equivalent of bullet casings and fingerprints: not always enough to find the perpetrator, but a logical place to start.
“Okay, so tell me about the University of Vermont,” Justman said. This was a “cluster,” a small group of deaths from which the working group might be able to draw some inferences. If there are three homicides in the same neighborhood on the same night, the first thing you want to figure out is what they have in common. Did the victims know each other? What did they do for a living? What were they all doing the day before they were killed? Diseases may not stalk their victim with a gun, but they each have a modus operandi, just like any other killer. If you figure out the MO, it will often lead you to the guy pulling the trigger.
“The UVM cases are kind of a head-scratcher,” one of the CDC staffers began. She was Indian-American, slightly older than the others around the table. “They were all athletes but different sports. They didn’t live together or take any of the same classes. I couldn’t find any evidence that they’d ever met.”
“It’s a small campus,” Justman said.
“True, but still nothing obvious. They weren’t sharing water bottles. They weren’t dating.”
“Food, maybe,” someone offered from around the table. “A dining hall, or the local sandwich shop.”
“Maybe,” the Indian-American woman answered, “but it doesn’t look like a foodborne outbreak. These were healthy young people. It wouldn’t explain why we are seeing a similar pattern in France and the UK. I don’t know. I’ve got nothing.”
“It probably is nothing,” said the only guy in a tie, albeit with a collar he had forgotten to button. He was the statistics guru, with no background in epidemiology or public health. He was paid twice as much as the rest of us, which we kne
w because the NIH needed approval from Congress to waive the pay scale to hire him. In the era of “big data,” every company was trying to digest terabytes of data to gain any possible advantage in the marketplace, whether it was pitching just the right vacation to divorced soccer moms in California or using online reading habits to sort the good credit risks from future deadbeats. Never mind that the NIH was using the same basic tools to save lives rather than sell shoes, the stats geeks were a hot commodity and we had to pay them a competitive salary. So we got Tie Guy (Marcus? Marc?). Word around the water cooler was that he could be smug and annoying, but it was hard to separate that from general envy of his highly publicized salary, like those college football coaches who make more than the university president. He did himself no favors by wearing a necktie in an office where the dress code consisted of matching socks, zipping one’s pants, and trying to make sure each shirt button went into its assigned hole. (Some of our brightest scientists routinely failed one or more of these sartorial challenges.)
To be fair, Tie Guy was a trenchant thinker when confronting complex challenges. I had first met him at a brown bag lunch several months earlier when he presented his findings on the impact of a proposed Chinese railroad that would connect a series of mining sites in South Africa and Zambia with a major port. He started the presentation with a pretty darn good line (as I recall it): “Copper, gold, and diamonds are going to move more efficiently on the railway. People are going to move more quickly and easily on the railway. And that means the HIV virus is going to be riding, too. I don’t know if the Chinese have named the rail line yet, but we should think about calling it the AIDS Express.” He had color-coded slides showing how the increase in mobility would change the AIDS infection rate under different scenarios, including different prices for a third-class ticket.
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