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.
Podcast: The Friday Five - your health research roundup
The Friday Five is a new podcast series in which Leaps.org covers five breakthroughs in research over the previous week that you may have missed. There are plenty of controversies and ethical issues in science – and we get into many of them in our online magazine – but there’s also plenty to be excited about, and this news roundup is focused on inspiring scientific work to give you some momentum headed into the weekend.
Covered in this week's Friday Five:
- Puffer fish chemical for treating chronic pain
- Sleep study on the health benefits of waking up multiples times per night
- Best exercise regimens for reducing the risk of mortality aka living longer
- AI breakthrough in mapping protein structures with DeepMind
- Ultrasound stickers to see inside your body
CandyCodes could provide sweet justice against fake pills
When we swallow a pill, we hope it will work without side effects. Few of us know to worry about a growing issue facing the pharmaceutical industry: counterfeit medications. These pills, patches, and other medical products might look just like the real thing. But they’re often stuffed with fillers that dilute the medication’s potency or they’re simply substituted for lookalikes that contain none of the prescribed medication at all.
Now, bioengineer William Grover at the University of California, Riverside, may have a solution. Inspired by the tiny, multi-colored sprinkles called nonpareils that decorate baked goods and candies, Grover created CandyCodes pill coatings to prevent counterfeits.
The idea was borne out of pandemic boredom. Confined to his home, Grover was struck by the patterns of nonpareils he saw on candies, and found himself counting the number of little balls on each one. “It’s random, how they’re applied,” he says. “I wondered if it ever repeats itself or if each of these candies is unique in the entire world.” He suspected the latter, and some quick math proved his hypothesis: Given dozens of nonpareils per candy in a handful of different colors, it’s highly unlikely that the sprinklings on any two candies would be identical.
He quickly realized his finding could have practical applications: pills or capsules could be coated with similar “sprinkles,” with the manufacturer photographing each pill or capsule before selling its products. Consumers looking to weed out fakes could potentially take a photo with their cell phones and go online to compare images of their own pills to the manufacturer’s database, with the help of an algorithm that would determine their authenticity. Or, a computer could generate another type of unique identifier, such as a text-based code, tracking to the color and location of the sprinkles. This would allow for a speedier validation than a photo-based comparison, Grover says. “It could be done very quickly, in a fraction of a second.”
Researchers and manufacturers have already developed some anti-counterfeit tools, including built-in identifiers like edible papers with scannable QR codes. But such methods, while functional, can be costly to implement, Grover says.
It wouldn’t be paranoid to take such precautions. Counterfeits are a growing problem, according to Young Kim, a biomedical engineer at Purdue University who was not involved in the CandyCodes study. “There are approximately 40,000 online pharmacies that one can access via the Internet,” he says. “Only three to four percent of them are operated legally.” Purchases from online pharmacies rose dramatically during the pandemic, and Kim expects a boom in counterfeit medical products alongside it.
The FDA warns that U.S. consumers can be exposed to counterfeits through online purchases, in particular. The problem is magnified in low- to middle-income nations, where one in 10 medical products are counterfeit, according to a World Health Organization estimate. Cost doesn’t seem to be a factor, either; antimalarials and antibiotics are most often reported as counterfeits or fakes, and generic medications are swapped as often as brand-name drugs, according to the same WHO report.
Counterfeits weren’t tracked globally until 2013; since then, there have been 1,500 reports to the WHO, with actual incidences of counterfeiting likely much higher. Fake medicines have been estimated to result in costs of $200 billion each year, and are blamed for more than 72,000 pneumonia- and 116,000 malaria-related deaths.
Researchers and manufacturers have already developed some anti-counterfeit tools, including built-in identifiers like edible papers with scannable QR codes or barcodes that are stamped onto or otherwise incorporated into pills and other medical products. But such methods, while functional, can be costly to implement, Grover says.
CandyCodes could provide unique identifiers for at least 41 million pills for every person on the planet.
William Grover
“Putting universal codes on each pill and each dosage is attractive,” he says. “The challenge is, how can we do it in a way that requires as little modification to the existing manufacturing process as possible? That's where I hope CandyCodes have an edge. It's not zero modification, but I hope it is as minor a modification of the manufacturing process as possible.”
Kim calls the concept “a clever idea to introduce entropy for high-level security” even if it may not be as close to market as other emerging technologies, including some edible watermarks he’s helped develop. He points out that CandyCodes still needs to be tested for reproducibility and readability.
The possibilities are already intriguing, though. Grover’s recent research, published in Scientific Reports, predicts that unique codes could be used for at least 41 million pills for every person on the planet.
Sadly, CandyCodes’ multicolored bits probably won’t taste like candy. They must be made of non-caloric ingredients to meet the international regulatory standards that govern food dyes and colorants. But Grover hopes CandyCodes represent a simple, accessible solution to a heart-wrenching issue. “This feels like trying to track down and go after bad guys,” he says. “Someone who would pass off a medicine intended for a child or a sick person and pass it off as something effective, I can't imagine anything much more evil than that. It's fun and, and a little fulfilling to try to develop technologies that chip away at that.”