A future app may help you avoid getting the flu by informing you of your local risk on a given day.
(© Dmytro Flisak/Adobe)
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.
Donna Shirley pictured at her home in Tulsa, with a model of the Sojourner rover she was in charge of that explored Mars.
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"Periodically, I'll hear somebody say they got into the space program because of me, and that makes me feel really good," Shirley told Leaps.org. "I look at the mission control area, and there are a lot of women in there. I'm quite pleased I was able to break the glass ceiling."
Her $25-million, 25-pound microrover – powered by solar energy and designed to get rock samples and test soil chemistry for evidence of life – was named after Sojourner Truth, a 19th-century Black abolitionist and women's rights activist. Unlike Mars Pathfinder, Shirley didn't have to travel more than 131 million miles to reach her goal, but her path to scientific fame as a woman sometimes resembled an asteroid field.
The Sojourner Rover in 1997 on Mars.NASA/JPLAs a high-IQ tomboy growing up in Wynnewood, Oklahoma (pop. 2,300), Shirley yearned to escape. She decided to become an engineer at age 10 and took flying lessons at 15. Her extraterrestrial aspirations were fueled by Ray Bradbury's The Martian Chronicles and Arthur C. Clarke's The Sands of Mars. Yet when she entered the University of Oklahoma (OU) in 1958, her freshman academic advisor initially told her: "Girls can't be engineers." She ignored him.
Years later, Shirley would combat such archaic thinking, succeeding at JPL with her creative, collaborative management style. "If you look at the literature, you'll find that teams that are either led by or heavily involved with women do better than strictly male teams," she noted.
However, her career trajectory stalled at OU. Burned out by her course load and distracted by a broken engagement to marry a fellow student, she switched her major to professional writing. After graduation, she applied her aeronautical background as a McDonnell Aircraft technical writer, but her boss, she says, harassed her and she faced gender-based hostility from male co-workers.
Returning to OU, Shirley finished off her engineering degree and became a JPL aerodynamist in 1966 after answering an ad in the St. Louis Post-Dispatch. At first, she was the only female engineer among the research center's 2,000-odd engineers. She wore many hats, from designing planetary atmospheric entry vehicles to picking the launch date of November 4, 1973 for Mariner 10's mission to Venus and Mercury.
By the mid-1980's, she was managing teams that focused on robotics and Mars, delivering creative solutions when NASA budget cuts loomed. In 1989, the same year the Sojourner microrover concept was born, President George H.W. Bush announced his Space Exploration Initiative, including plans for a human mission to Mars by 2019.
That target, of course, wasn't attained, despite huge advances in technology and our understanding of the Martian environment. Today, Shirley believes humans could land on Mars by 2030. She became the founding director of the Science Fiction Museum and Hall of Fame in Seattle in 2004 after leaving NASA, and to this day, she enjoys checking out pop culture portrayals of Mars landings – even if they're not always accurate.
After the novel The Martian was published in 2011, which later was adapted into the hit film starring Matt Damon, Shirley phoned author Andy Weir: "You've got a major mistake in here. It says there's a storm that tries to blow the rocket over. But actually, the Mars atmosphere is so thin, it would never blow a rocket over!"
Fearlessly speaking her mind and seeking the stars helped Donna Shirley make history. However, a 2019 Washington Post story noted: "Women make up only about a third of NASA's workforce. They comprise just 28 percent of senior executive leadership positions and are only 16 percent of senior scientific employees." Whether it's traveling to Mars or trending toward gender equality, we've still got a long way to go.