How dozens of men across Alaska (and their dogs) teamed up to save one town from a deadly outbreak
During the winter of 1924, Curtis Welch – the only doctor in Nome, a remote fishing town in northwest Alaska – started noticing something strange. More and more, the children of Nome were coming to his office with sore throats.
Initially, Welch dismissed the cases as tonsillitis or some run-of-the-mill virus – but when more kids started getting sick, with some even dying, he grew alarmed. It wasn’t until early 1925, after a three-year-old boy died just two weeks after becoming ill, that Welch realized that his worst suspicions were true. The boy – and dozens of other children in town – were infected with diphtheria.
A DEADLY BACTERIA
Diphtheria is nearly nonexistent and almost unheard of in industrialized countries today. But less than a century ago, diphtheria was a household name – one that struck fear in the heart of every parent, as it was extremely contagious and particularly deadly for children.
Diphtheria – a bacterial infection – is an ugly disease. When it strikes, the bacteria eats away at the healthy tissues in a patient’s respiratory tract, leaving behind a thick, gray membrane of dead tissue that covers the patient's nose, throat, and tonsils. Not only does this membrane make it very difficult for the patient to breathe and swallow, but as the bacteria spreads through the bloodstream, it causes serious harm to the heart and kidneys. It sometimes also results in nerve damage and paralysis. Even with treatment, diphtheria kills around 10 percent of people it infects. Young children, as well as adults over the age of 60, are especially at risk.
Welch didn’t suspect diphtheria at first. He knew the illness was incredibly contagious and reasoned that many more people would be sick – specifically, the family members of the children who had died – if there truly was an outbreak. Nevertheless, the symptoms, along with the growing number of deaths, were unmistakable. By 1925 Welch knew for certain that diphtheria had come to Nome.
In desperation, Welch tried treating an infected seven-year-old girl with some expired antitoxin – but she died just a few hours after he administered it.
AN INACCESSIBLE CURE
A vaccine for diphtheria wouldn’t be widely available until the mid-1930s and early 1940s – so an outbreak of the disease meant that each of the 10,000 inhabitants of Nome were all at serious risk.
One option was to use something called an antitoxin – a serum consisting of anti-diphtheria antibodies – to treat the patients. However, the town’s reserve of diphtheria antitoxin had expired. Welch had ordered a replacement shipment of antitoxin the previous summer – but the shipping port that was set to deliver the serum had been closed due to ice, and no new antitoxin would arrive before spring of 1925. In desperation, Welch tried treating an infected seven-year-old girl with some expired antitoxin – but she died just a few hours after he administered it.
Welch radioed for help to all the major towns in Alaska as well as the US Public Health Service in Washington, DC. His telegram read: An outbreak of diphtheria is almost inevitable here. I am in urgent need of one million units of diphtheria antitoxin. Mail is the only form of transportation.
FOUR-LEGGED HEROES
When the Alaskan Board of Health learned about the outbreak, the men rushed to devise a plan to get antitoxin to Nome. Dropping the serum in by airplane was impossible, as the available planes were unsuitable for flying during Alaska’s severe winter weather, where temperatures were routinely as cold as -50 degrees Fahrenheit.
In late January 1925, roughly 30,000 units of antitoxin were located in an Anchorage hospital and immediately delivered by train to a nearby city, Nenana, en route to Nome. Nenana was the furthest city that was reachable by rail – but unfortunately it was still more than 600 miles outside of Nome, with no transportation to make the delivery. Meanwhile, Welch had confirmed 20 total cases of diphtheria, with dozens more at high risk. Diphtheria was known for wiping out entire communities, and the entire town of Nome was in danger of suffering the same fate.
It was Mark Summer, the Board of Health superintendent, who suggested something unorthodox: Using a relay team of sled-racing dogs to deliver the antitoxin serum from Nenana to Nome. The Board quickly voted to accept Summer’s idea and set up a plan: The thousands of units of antitoxin serum would be passed along from team to team at different towns along the mail route from Nenana to Nome. When it reached a town called Nulato, a famed dogsled racer named Leonhard Seppala and his experienced team of huskies would take the serum more than 90 miles over the ice of Norton Sound, the longest and most treacherous part of the journey. Past the sound, the serum would change hands several times more before arriving in Nome.
Between January 27 and 31, the serum passed through roughly a dozen drivers and their dog sled teams, each of them carrying the serum between 20 and 50 miles to the next destination. Though each leg of the trip took less than a day, the sub-zero temperatures – sometimes as low as -85 degrees – meant that every driver and dog risked their lives. When the first driver, Bill Shannon, arrived at his checkpoint in Tolovana on January 28th, his nose was black with frostbite, and three of his dogs had died. The driver who relieved Bill Shannon, named Edgar Kalland, needed the owner of a local roadhouse to pour hot water over his hands to free them from the sled’s metal handlebar. Two more dogs from another relay team died before the serum was passed to Seppala at a town called Ungalik.
THE FINAL STRETCHES
Seppala and his team raced across the ice of the Norton Sound in the dead of night on January 31, with wind chill temperatures nearing an astonishing -90 degrees. The team traveled 84 miles in a single day before stopping to rest – and once rested, they set off again in the middle of the night through a raging winter storm. The team made it across the ice, as well as a 5,000-foot ascent up Little McKinley Mountain, to pass the serum to another driver in record time. The serum was now just 78 miles from Nome, and the death toll in town had reached 28.
The serum reached Gunnar Kaasen and his team of dogs on February 1st. Balto, Kaasen’s lead dog, guided the team heroically through a winter storm that was so severe Kaasen later reported not being able to see the dogs that were just a few feet ahead of him.
Visibility was so poor, in fact, that Kaasen ran his sled two miles past the relay point before noticing – and not wanting to lose a minute, he decided to forge on ahead rather than doubling back to deliver the serum to another driver. As they continued through the storm, the hurricane-force winds ripped past Kaasen’s sled at one point and toppled the sled – and the serum – overboard. The cylinder containing the antitoxin was left buried in the snow – and Kaasen tore off his gloves and dug through the tundra to locate it. Though it resulted in a bad case of frostbite, Kaasen eventually found the cylinder and kept driving.
Kaasen arrived at the next relay point on February 2nd, hours ahead of schedule. When he got there, however, he found the relay driver of the next team asleep. Kaasen took a risk and decided not to wake him, fearing that time would be wasted with the next driver readying his team. Kaasen, Balto, and the rest of the team forged on, driving another 25 miles before finally reaching Nome just before six in the morning. Eyewitnesses described Kaasen pulling up to the town’s bank and stumbling to the front of the sled. There, he collapsed in exhaustion, telling onlookers that Balto was “a damn fine dog.”
A LIVING LEGACY
Just a few hours after Balto’s heroic arrival in Nome, the serum had been thawed and was ready to administer to the patients with diphtheria. Amazingly, the relay team managed to complete the entire journey in just 127 hours – a world record at the time – without one serum vial damaged or destroyed. The serum shipment that arrived by dogsled – along with additional serum deliveries that followed in the next several weeks – were successful in stopping the outbreak in its tracks.
Balto and several other dogs – including Togo, the lead dog on Seppala’s team – were celebrated as local heroes after the race. Balto died in 1933, while the last of the human serum runners died in 1999 – but their legacy lives on: In early 2021, an all-female team of healthcare workers made the news by braving the Alaskan winter to deliver COVID-19 vaccines to people in rural North Alaska, traveling by bobsled and snowmobile – a heroic journey, and one that would have been unthinkable had Balto, Togo, and the 1925 sled runners not first paved the way.
The Case for an Outright Ban on Facial Recognition Technology
[Editor's Note: This essay is in response to our current Big Question, which we posed to experts with different perspectives: "Do you think the use of facial recognition technology by the police or government should be banned? If so, why? If not, what limits, if any, should be placed on its use?"]
In a surprise appearance at the tail end of Amazon's much-hyped annual product event last month, CEO Jeff Bezos casually told reporters that his company is writing its own facial recognition legislation.
The use of computer algorithms to analyze massive databases of footage and photographs could render human privacy extinct.
It seems that when you're the wealthiest human alive, there's nothing strange about your company––the largest in the world profiting from the spread of face surveillance technology––writing the rules that govern it.
But if lawmakers and advocates fall into Silicon Valley's trap of "regulating" facial recognition and other forms of invasive biometric surveillance, that's exactly what will happen.
Industry-friendly regulations won't fix the dangers inherent in widespread use of face scanning software, whether it's deployed by governments or for commercial purposes. The use of this technology in public places and for surveillance purposes should be banned outright, and its use by private companies and individuals should be severely restricted. As artificial intelligence expert Luke Stark wrote, it's dangerous enough that it should be outlawed for "almost all practical purposes."
Like biological or nuclear weapons, facial recognition poses such a profound threat to the future of humanity and our basic rights that any potential benefits are far outweighed by the inevitable harms.
We live in cities and towns with an exponentially growing number of always-on cameras, installed in everything from cars to children's toys to Amazon's police-friendly doorbells. The use of computer algorithms to analyze massive databases of footage and photographs could render human privacy extinct. It's a world where nearly everything we do, everywhere we go, everyone we associate with, and everything we buy — or look at and even think of buying — is recorded and can be tracked and analyzed at a mass scale for unimaginably awful purposes.
Biometric tracking enables the automated and pervasive monitoring of an entire population. There's ample evidence that this type of dragnet mass data collection and analysis is not useful for public safety, but it's perfect for oppression and social control.
Law enforcement defenders of facial recognition often state that the technology simply lets them do what they would be doing anyway: compare footage or photos against mug shots, drivers licenses, or other databases, but faster. And they're not wrong. But the speed and automation enabled by artificial intelligence-powered surveillance fundamentally changes the impact of that surveillance on our society. Being able to do something exponentially faster, and using significantly less human and financial resources, alters the nature of that thing. The Fourth Amendment becomes meaningless in a world where private companies record everything we do and provide governments with easy tools to request and analyze footage from a growing, privately owned, panopticon.
Tech giants like Microsoft and Amazon insist that facial recognition will be a lucrative boon for humanity, as long as there are proper safeguards in place. This disingenuous call for regulation is straight out of the same lobbying playbook that telecom companies have used to attack net neutrality and Silicon Valley has used to scuttle meaningful data privacy legislation. Companies are calling for regulation because they want their corporate lawyers and lobbyists to help write the rules of the road, to ensure those rules are friendly to their business models. They're trying to skip the debate about what role, if any, technology this uniquely dangerous should play in a free and open society. They want to rush ahead to the discussion about how we roll it out.
We need spaces that are free from government and societal intrusion in order to advance as a civilization.
Facial recognition is spreading very quickly. But backlash is growing too. Several cities have already banned government entities, including police and schools, from using biometric surveillance. Others have local ordinances in the works, and there's state legislation brewing in Michigan, Massachusetts, Utah, and California. Meanwhile, there is growing bipartisan agreement in U.S. Congress to rein in government use of facial recognition. We've also seen significant backlash to facial recognition growing in the U.K., within the European Parliament, and in Sweden, which recently banned its use in schools following a fine under the General Data Protection Regulation (GDPR).
At least two frontrunners in the 2020 presidential campaign have backed a ban on law enforcement use of facial recognition. Many of the largest music festivals in the world responded to Fight for the Future's campaign and committed to not use facial recognition technology on music fans.
There has been widespread reporting on the fact that existing facial recognition algorithms exhibit systemic racial and gender bias, and are more likely to misidentify people with darker skin, or who are not perceived by a computer to be a white man. Critics are right to highlight this algorithmic bias. Facial recognition is being used by law enforcement in cities like Detroit right now, and the racial bias baked into that software is doing harm. It's exacerbating existing forms of racial profiling and discrimination in everything from public housing to the criminal justice system.
But the companies that make facial recognition assure us this bias is a bug, not a feature, and that they can fix it. And they might be right. Face scanning algorithms for many purposes will improve over time. But facial recognition becoming more accurate doesn't make it less of a threat to human rights. This technology is dangerous when it's broken, but at a mass scale, it's even more dangerous when it works. And it will still disproportionately harm our society's most vulnerable members.
Persistent monitoring and policing of our behavior breeds conformity, benefits tyrants, and enriches elites.
We need spaces that are free from government and societal intrusion in order to advance as a civilization. If technology makes it so that laws can be enforced 100 percent of the time, there is no room to test whether those laws are just. If the U.S. government had ubiquitous facial recognition surveillance 50 years ago when homosexuality was still criminalized, would the LGBTQ rights movement ever have formed? In a world where private spaces don't exist, would people have felt safe enough to leave the closet and gather, build community, and form a movement? Freedom from surveillance is necessary for deviation from social norms as well as to dissent from authority, without which societal progress halts.
Persistent monitoring and policing of our behavior breeds conformity, benefits tyrants, and enriches elites. Drawing a line in the sand around tech-enhanced surveillance is the fundamental fight of this generation. Lining up to get our faces scanned to participate in society doesn't just threaten our privacy, it threatens our humanity, and our ability to be ourselves.
[Editor's Note: Read the opposite perspective here.]
Scientists Are Building an “AccuWeather” for Germs to Predict Your Risk of Getting the Flu
Applied mathematician Sara del Valle works at the U.S.'s foremost nuclear weapons lab: Los Alamos. Once colloquially called Atomic City, it's a hidden place 45 minutes into the mountains northwest of Santa Fe. Here, engineers developed the first atomic bomb.
Like AccuWeather, an app for disease prediction could help people alter their behavior to live better lives.
Today, Los Alamos still a small science town, though no longer a secret, nor in the business of building new bombs. Instead, it's tasked with, among other things, keeping the stockpile of nuclear weapons safe and stable: not exploding when they're not supposed to (yes, please) and exploding if someone presses that red button (please, no).
Del Valle, though, doesn't work on any of that. Los Alamos is also interested in other kinds of booms—like the explosion of a contagious disease that could take down a city. Predicting (and, ideally, preventing) such epidemics is del Valle's passion. She hopes to develop an app that's like AccuWeather for germs: It would tell you your chance of getting the flu, or dengue or Zika, in your city on a given day. And like AccuWeather, it could help people alter their behavior to live better lives, whether that means staying home on a snowy morning or washing their hands on a sickness-heavy commute.
Sara del Valle of Los Alamos is working to predict and prevent epidemics using data and machine learning.
Since the beginning of del Valle's career, she's been driven by one thing: using data and predictions to help people behave practically around pathogens. As a kid, she'd always been good at math, but when she found out she could use it to capture the tentacular spread of disease, and not just manipulate abstractions, she was hooked.
When she made her way to Los Alamos, she started looking at what people were doing during outbreaks. Using social media like Twitter, Google search data, and Wikipedia, the team started to sift for trends. Were people talking about hygiene, like hand-washing? Or about being sick? Were they Googling information about mosquitoes? Searching Wikipedia for symptoms? And how did those things correlate with the spread of disease?
It was a new, faster way to think about how pathogens propagate in the real world. Usually, there's a 10- to 14-day lag in the U.S. between when doctors tap numbers into spreadsheets and when that information becomes public. By then, the world has moved on, and so has the disease—to other villages, other victims.
"We found there was a correlation between actual flu incidents in a community and the number of searches online and the number of tweets online," says del Valle. That was when she first let herself dream about a real-time forecast, not a 10-days-later backcast. Del Valle's group—computer scientists, mathematicians, statisticians, economists, public health professionals, epidemiologists, satellite analysis experts—has continued to work on the problem ever since their first Twitter parsing, in 2011.
They've had their share of outbreaks to track. Looking back at the 2009 swine flu pandemic, they saw people buying face masks and paying attention to the cleanliness of their hands. "People were talking about whether or not they needed to cancel their vacation," she says, and also whether pork products—which have nothing to do with swine flu—were safe to buy.
At the latest meeting with all the prediction groups, del Valle's flu models took first and second place.
They watched internet conversations during the measles outbreak in California. "There's a lot of online discussion about anti-vax sentiment, and people trying to convince people to vaccinate children and vice versa," she says.
Today, they work on predicting the spread of Zika, Chikungunya, and dengue fever, as well as the plain old flu. And according to the CDC, that latter effort is going well.
Since 2015, the CDC has run the Epidemic Prediction Initiative, a competition in which teams like de Valle's submit weekly predictions of how raging the flu will be in particular locations, along with other ailments occasionally. Michael Johannson is co-founder and leader of the program, which began with the Dengue Forecasting Project. Its goal, he says, was to predict when dengue cases would blow up, when previously an area just had a low-level baseline of sick people. "You'll get this massive epidemic where all of a sudden, instead of 3,000 to 4,000 cases, you have 20,000 cases," he says. "They kind of come out of nowhere."
But the "kind of" is key: The outbreaks surely come out of somewhere and, if scientists applied research and data the right way, they could forecast the upswing and perhaps dodge a bomb before it hit big-time. Questions about how big, when, and where are also key to the flu.
A big part of these projects is the CDC giving the right researchers access to the right information, and the structure to both forecast useful public-health outcomes and to compare how well the models are doing. The extra information has been great for the Los Alamos effort. "We don't have to call departments and beg for data," says del Valle.
When data isn't available, "proxies"—things like symptom searches, tweets about empty offices, satellite images showing a green, wet, mosquito-friendly landscape—are helpful: You don't have to rely on anyone's health department.
At the latest meeting with all the prediction groups, del Valle's flu models took first and second place. But del Valle wants more than weekly numbers on a government website; she wants that weather-app-inspired fortune-teller, incorporating the many diseases you could get today, standing right where you are. "That's our dream," she says.
This plot shows the the correlations between the online data stream, from Wikipedia, and various infectious diseases in different countries. The results of del Valle's predictive models are shown in brown, while the actual number of cases or illness rates are shown in blue.
(Courtesy del Valle)
The goal isn't to turn you into a germophobic agoraphobe. It's to make you more aware when you do go out. "If you know it's going to rain today, you're more likely to bring an umbrella," del Valle says. "When you go on vacation, you always look at the weather and make sure you bring the appropriate clothing. If you do the same thing for diseases, you think, 'There's Zika spreading in Sao Paulo, so maybe I should bring even more mosquito repellent and bring more long sleeves and pants.'"
They're not there yet (don't hold your breath, but do stop touching your mouth). She estimates it's at least a decade away, but advances in machine learning could accelerate that hypothetical timeline. "We're doing baby steps," says del Valle, starting with the flu in the U.S., dengue in Brazil, and other efforts in Colombia, Ecuador, and Canada. "Going from there to forecasting all diseases around the globe is a long way," she says.
But even AccuWeather started small: One man began predicting weather for a utility company, then helping ski resorts optimize their snowmaking. His influence snowballed, and now private forecasting apps, including AccuWeather's, populate phones across the planet. The company's progression hasn't been without controversy—privacy incursions, inaccuracy of long-term forecasts, fights with the government—but it has continued, for better and for worse.
Disease apps, perhaps spun out of a small, unlikely team at a nuclear-weapons lab, could grow and breed in a similar way. And both the controversies and public-health benefits that may someday spin out of them lie in the future, impossible to predict with certainty.