Scientists Are Building an “AccuWeather” for Germs to Predict Your Risk of Getting the Flu

Scientists Are Building an “AccuWeather” for Germs to Predict Your Risk of Getting the Flu

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.

Sarah Scoles
Sarah Scoles is a freelance science journalist based in Denver. She is a contributing writer at Wired, a contributing editor at Popular Science, and the author of the book Making Contact: Jill Tarter and the Search for Extraterrestrial Intelligence.
Why we should put insects on the menu

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David Waltner-Toews

I walked through the Dong Makkhai forest-products market, just outside of Vientiane, the laid-back capital of the Lao Peoples Democratic Republic or Lao PDR. Piled on rough display tables were varieties of six-legged wildlife–grasshoppers, small white crickets, house crickets, mole crickets, wasps, wasp eggs and larvae, dragonflies, and dung beetles. Some were roasted or fried, but in a few cases, still alive and scrabbling at the bottom of deep plastic bowls. I crunched on some fried crickets and larvae.

One stall offered Giant Asian hornets, both babies and adults. I suppressed my inner squirm and, in the interests of world food security and equity, accepted an offer of the soft, velvety larva; they were smooth on the tongue and of a pleasantly cool, buttery-custard consistency. Because the seller had already given me a free sample, I felt obliged to buy a chunk of the nest with larvae and some dead adults, which the seller mixed with kaffir lime leaves.

The year was 2016 and I was in Lao PDR because Veterinarians without Borders/Vétérinaires sans Frontières-Canada had initiated a project on small-scale cricket farming. The intent was to organize and encourage rural women to grow crickets as a source of supplementary protein and sell them at the market for cash. As a veterinary epidemiologist, I had been trained to exterminate disease spreading insects—Lyme disease-carrying ticks, kissing bugs that carry American Sleeping Sickness and mosquitoes carrying malaria, West Nile and Zika. Now, as part of a global wave promoting insects as a sustainable food source, I was being asked to view arthropods as micro-livestock, and devise management methods to keep them alive and healthy. It was a bit of a mind-bender.

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David Waltner-Toews
David Waltner-Toews is a veterinary epidemiologist and author of more than twenty books of poetry, fiction, and science. His most recent books are On Pandemics: deadly diseases from bubonic plague to coronavirus (Greystone Books, 2020); Eat the Beetles: an exploration into our conflicted relationship with insects (ECW Press, 2017) and The Origin of Feces: what excrement tells us about evolution, ecology and a sustainable society (ECW Press, 2013).
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In this episode of Making Sense of Science, my guest is Raina Plowright, a leading researcher when it comes to how and why viruses sometimes jump from bats to humans.

Pete Hudson

For this podcast episode, my guest is Raina Plowright, one of the world’s leading researchers when it comes to how and why viruses sometimes jump from bats to humans. The intuition may be that bats are the bad guys in this situation, but the real culprits are more likely humans and ways that we intrude on nature.

Plowright is a Cornell Atkinson Scholar and professor at Cornell in the Department of Public and Ecosystem Health in the College of Veterinary Medicine. Read her full bio here. For a shorter (and lightly edited) version of this conversation, you can check out my Q&A interview with Plowright in the single-issue magazine, One Health / One Planet, published earlier this month by Leaps.org in collaboration with the Aspen Institute and the Science Philanthropy Alliance.

In the episode, Plowright tells me about her global research team that is busy studying the complex chain of events in between viruses originating in bats and humans getting infected with those viruses. She’s collecting samples from bats in Asia, Africa and Australia, which sounds challenging enough, but now consider the diligence required to parse out 1400 different bat species.

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Matt Fuchs
Matt Fuchs is the host of the Making Sense of Science podcast and served previously as the editor-in-chief of Leaps.org. He writes as a contributor to the Washington Post, and his articles have also appeared in the New York Times, WIRED, Nautilus Magazine, Fortune Magazine and TIME Magazine. Follow him @fuchswriter.