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.
Tiny, tough “water bears” may help bring new vaccines and medicines to sub-Saharan Africa

Tardigrades can completely dehydrate and later rehydrate themselves, a survival trick that scientists are harnessing to preserve medicines in hot temperatures.

Adobe Stock

Microscopic tardigrades, widely considered to be some of the toughest animals on earth, can survive for decades without oxygen or water and are thought to have lived through a crash-landing on the moon. Also known as water bears, they survive by fully dehydrating and later rehydrating themselves – a feat only a few animals can accomplish. Now scientists are harnessing tardigrades’ talents to make medicines that can be dried and stored at ambient temperatures and later rehydrated for use—instead of being kept refrigerated or frozen.

Many biologics—pharmaceutical products made by using living cells or synthesized from biological sources—require refrigeration, which isn’t always available in many remote locales or places with unreliable electricity. These products include mRNA and other vaccines, monoclonal antibodies and immuno-therapies for cancer, rheumatoid arthritis and other conditions. Cooling is also needed for medicines for blood clotting disorders like hemophilia and for trauma patients.

Keep Reading Keep Reading
Gail Dutton
Gail Dutton has covered the biopharmaceutical industry as a journalist for the past three decades. She focuses on the intersection of business and science, and has written extensively for GEN – Genetic Engineering & Biotechnology News, Life Science Leader, The Scientist and BioSpace. Her articles also have appeared in Popular Science, Forbes, Entrepreneur and other publications.
Man Who Got the First Fecal Transplant to Cure Melanoma Shares His Experience

Jamie Rettinger with his now fiance Amie Purnel-Davis, who helped him through the clinical trial.

Photo courtesy of Jamie Rettinger

Jamie Rettinger was still in his thirties when he first noticed a tiny streak of brown running through the thumbnail of his right hand. It slowly grew wider and the skin underneath began to deteriorate before he went to a local dermatologist in 2013. The doctor thought it was a wart and tried scooping it out, treating the affected area for three years before finally removing the nail bed and sending it off to a pathology lab for analysis.

"I have some bad news for you; what we removed was a five-millimeter melanoma, a cancerous tumor that often spreads," Jamie recalls being told on his return visit. "I'd never heard of cancer coming through a thumbnail," he says. None of his doctors had ever mentioned it either. "I just thought I was being treated for a wart." But nothing was healing and it continued to bleed.

A few months later a surgeon amputated the top half of his thumb. Lymph node biopsy tested negative for spread of the cancer and when the bandages finally came off, Jamie thought his medical issues were resolved.

Keep Reading Keep Reading
Bob Roehr
Bob Roehr is a biomedical journalist based in Washington, DC. Over the last twenty-five years he has written extensively for The BMJ, Scientific American, PNAS, Proto, and myriad other publications. He is primarily interested in HIV, infectious disease, immunology, and how growing knowledge of the microbiome is changing our understanding of health and disease. He is working on a book about the ways the body can at least partially control HIV and how that has influenced (or not) the search for a treatment and cure.