Scientists Are Building an “AccuWeather” for Germs to Predict Your Risk of Getting the Flu
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A future app may help you avoid getting the flu by informing you of your local risk on a given day.
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.
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Kira Peikoff was the editor-in-chief of Leaps.org from 2017 to 2021. As a journalist, her work has appeared in The New York Times, Newsweek, Nautilus, Popular Mechanics, The New York Academy of Sciences, and other outlets. She is also the author of four suspense novels that explore controversial issues arising from scientific innovation: Living Proof, No Time to Die, Die Again Tomorrow, and Mother Knows Best. Peikoff holds a B.A. in Journalism from New York University and an M.S. in Bioethics from Columbia University. She lives in New Jersey with her husband and two young sons. Follow her on Twitter @KiraPeikoff.
George Papanicolaou (1883-1962), Greek-born American physician developed a simple cytological test for cervical cancer in 1928.
For decades, women around the world have made the annual pilgrimage to their doctor for the dreaded but potentially life-saving Papanicolaou test, a gynecological exam to screen for cervical cancer named for Georgios Papanicolaou, the Greek immigrant who developed it.
The Pap smear, as it is commonly known, is credited for reducing cervical cancer mortality by 70% since the 1960s; the American Cancer Society (ACS) still ranks the Pap as the most successful screening test for preventing serious malignancies. Nonetheless, the agency, as well as other medical panels, including the US Preventive Services Task Force and the American College of Obstetrics and Gynecology are making a strong push to replace the Pap with the more sensitive high-risk HPV screening test for the human papillomavirus virus, which causes nearly all cases of cervical cancer.
So, how was the Pap developed and how did it become the gold standard of cervical cancer detection for more than 60 years?
Born on May 13, 1883, on the island of Euboea, Greece, Georgios Papanicolaou attended the University of Athens where he majored in music and the humanities before earning his medical degree in 1904 and PhD from the University of Munich six years later. In Europe, Papanicolaou was an assistant military surgeon during the Balkan War, a psychologist for an expedition of the Oceanographic Institute of Monaco and a caregiver for leprosy patients.
When he and his wife, Andromache Mavroyenous (Mary), arrived at Ellis Island on October 19, 1913, the young couple had scarcely more than the $250 minimum required to immigrate, spoke no English and had no job prospects. They worked a series of menial jobs--department store sales clerk, rug salesman, newspaper clerk, restaurant violinist--before Papanicolaou landed a position as an anatomy assistant at Cornell University and Mary was hired as his lab assistant, an arrangement that would last for the next 50 years.
Papanikolaou would later say the discovery "was one of the greatest thrills I ever experienced during my scientific career."
In his early research, Papanikolaou used guinea pigs to prove that gender is determined by the X and Y chromosomes. Using a pediatric nasal speculum, he collected and microscopically examined vaginal secretions of guinea pigs, which revealed distinct cell changes connected to the menstrual cycle. He moved on to study reproductive patterns in humans, beginning with his faithful wife, Mary, who not only endured his almost-daily cervical exams for decades, but also recruited friends as early research participants.
Writing in the medical journal Growth in 1920, the scientist outlined his theory that a microscopic smear of vaginal fluid could detect the presence of cancer cells in the uterus. Papanikolaou would later say the discovery "was one of the greatest thrills I ever experienced during my scientific career."
At this time, cervical cancer was the number one cancer killer of American women but physicians were skeptical of these new findings. They continued to rely on biopsy and curettage to diagnose and treat the disease until Papanicolaou's discovery was published in American Journal of Obstetrics and Gynecology. An inexpensive, easy-to-perform test that could detect cervical cancer, precancerous dysplasia and other cytological diseases was a sea change. Between 1975 and 2001, the cervical cancer rate was cut in half.
Papanicolaou became Emeritus Professor at Cornell University Medical College and received numerous awards, including the Albert Lasker Award for Clinical Medical Research and the Medal of Honor from the American Cancer Society. His image was featured on the Greek currency and the US Post Office issued a commemorative stamp in his honor. But international acclaim didn't lead to a more relaxed schedule. The researcher continued to work seven days a week and refused to take vacations.
After nearly 50 years, Papanicolaou left Cornell to head and develop the Cancer Institute of Miami. He died of a heart attack on February 19, 1962, just three months after his arrival. Mary continued to work in the renamed Papanicolaou Cancer Research Institute until her death 20 years later.
The annual pap smear was originally tied to renewing a birth control prescription. Canada began recommending Pap exams every three years in 1978. The United States followed suit in 2012, noting that it takes many years for cervical cancer to develop. In September 2020, the American Cancer Society recommended delaying the first gynecological pelvic exam until age 25 and replacing the Pap test completely with the more accurate human papillomavirus (HPV) test every five years as the technology becomes more widely available.
Not everyone agrees that it's time to do away with this proven screening method, though. The incidence rate of cervical cancer among Hispanic women is 28% higher than for white women, and Black women are more likely to die of cervical cancer than any other racial or ethnicities.
Whether the Pap is administered every year, every three years or not at all, Papanicolaou will always be known as the medical hero who saved countless women who would otherwise have succumbed to cervical cancer.