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.
Jamie Rettinger with his now fiance Amie Purnel-Davis, who helped him through the clinical trial.
Melanoma is the deadliest form of skin cancer. About 85,000 people are diagnosed with it each year in the U.S. and more than 8,000 die of the cancer when it spreads to other parts of the body, according to the Centers for Disease Control and Prevention (CDC).
There are two peaks in diagnosis of melanoma; one is in younger women ages 30-40 and often is tied to past use of tanning beds; the second is older men 60+ and is related to outdoor activity from farming to sports. Light-skinned people have a twenty-times greater risk of melanoma than do people with dark skin.
"When I graduated from medical school, in 2005, melanoma was a death sentence" --Diwakar Davar.
Jamie had a follow up PET scan about six months after his surgery. A suspicious spot on his lung led to a biopsy that came back positive for melanoma. The cancer had spread. Treatment with a monoclonal antibody (nivolumab/Opdivo®) didn't prove effective and he was referred to the UPMC Hillman Cancer Center in Pittsburgh, a four-hour drive from his home in western Ohio.
An alternative monoclonal antibody treatment brought on such bad side effects, diarrhea as often as 15 times a day, that it took more than a week of hospitalization to stabilize his condition. The only options left were experimental approaches in clinical trials.
Early research
"When I graduated from medical school, in 2005, melanoma was a death sentence" with a cure rate in the single digits, says Diwakar Davar, 39, an oncologist at UPMC Hillman Cancer Center who specializes in skin cancer. That began to change in 2010 with introduction of the first immunotherapies, monoclonal antibodies, to treat cancer. The antibodies attach to PD-1, a receptor on the surface of T cells of the immune system and on cancer cells. Antibody treatment boosted the melanoma cure rate to about 30 percent. The search was on to understand why some people responded to these drugs and others did not.
At the same time, there was a growing understanding of the role that bacteria in the gut, the gut microbiome, plays in helping to train and maintain the function of the body's various immune cells. Perhaps the bacteria also plays a role in shaping the immune response to cancer therapy.
One clue came from genetically identical mice. Animals ordered from different suppliers sometimes responded differently to the experiments being performed. That difference was traced to different compositions of their gut microbiome; transferring the microbiome from one animal to another in a process known as fecal transplant (FMT) could change their responses to disease or treatment.
When researchers looked at humans, they found that the patients who responded well to immunotherapies had a gut microbiome that looked like healthy normal folks, but patients who didn't respond had missing or reduced strains of bacteria.
Davar and his team knew that FMT had a very successful cure rate in treating the gut dysbiosis of Clostridioides difficile, a persistant intestinal infection, and they wondered if a fecal transplant from a patient who had responded well to cancer immunotherapy treatment might improve the cure rate of patients who did not originally respond to immunotherapies for melanoma.
The ABCDE of melanoma detection
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Clinical trial
"It was pretty weird, I was totally blasted away. Who had thought of this?" Jamie first thought when the hypothesis was explained to him. But Davar's explanation that the procedure might restore some of the beneficial bacterial his gut was lacking, convinced him to try. He quickly signed on in October 2018 to be the first person in the clinical trial.
Fecal donations go through the same safety procedures of screening for and inactivating diseases that are used in processing blood donations to make them safe for transfusion. The procedure itself uses a standard hollow colonoscope designed to screen for colon cancer and remove polyps. The transplant is inserted through the center of the flexible tube.
Most patients are sedated for procedures that use a colonoscope but Jamie doesn't respond to those drugs: "You can't knock me out. I was watching them on the TV going up my own butt. It was kind of unreal at that point," he says. "There were about twelve people in there watching because no one had seen this done before."
A test two weeks after the procedure showed that the FMT had engrafted and the once-missing bacteria were thriving in his gut. More importantly, his body was responding to another monoclonal antibody (pembrolizumab/Keytruda®) and signs of melanoma began to shrink. Every three months he made the four-hour drive from home to Pittsburgh for six rounds of treatment with the antibody drug.
"We were very, very lucky that the first patient had a great response," says Davar. "It allowed us to believe that even though we failed with the next six, we were on the right track. We just needed to tweak the [fecal] cocktail a little better" and enroll patients in the study who had less aggressive tumor growth and were likely to live long enough to complete the extensive rounds of therapy. Six of 15 patients responded positively in the pilot clinical trial that was published in the journal Science.
Davar believes they are beginning to understand the biological mechanisms of why some patients initially do not respond to immunotherapy but later can with a FMT. It is tied to the background level of inflammation produced by the interaction between the microbiome and the immune system. That paper is not yet published.
Surviving cancer
It has been almost a year since the last in his series of cancer treatments and Jamie has no measurable disease. He is cautiously optimistic that his cancer is not simply in remission but is gone for good. "I'm still scared every time I get my scans, because you don't know whether it is going to come back or not. And to realize that it is something that is totally out of my control."
"It was hard for me to regain trust" after being misdiagnosed and mistreated by several doctors he says. But his experience at Hillman helped to restore that trust "because they were interested in me, not just fixing the problem."
He is grateful for the support provided by family and friends over the last eight years. After a pause and a sigh, the ruggedly built 47-year-old says, "If everyone else was dead in my family, I probably wouldn't have been able to do it."
"I never hesitated to ask a question and I never hesitated to get a second opinion." But Jamie acknowledges the experience has made him more aware of the need for regular preventive medical care and a primary care physician. That person might have caught his melanoma at an earlier stage when it was easier to treat.
Davar continues to work on clinical studies to optimize this treatment approach. Perhaps down the road, screening the microbiome will be standard for melanoma and other cancers prior to using immunotherapies, and the FMT will be as simple as swallowing a handful of freeze-dried capsules off the shelf rather than through a colonoscopy. Earlier this year, the Food and Drug Administration approved the first oral fecal microbiota product for C. difficile, hopefully paving the way for more.
An older version of this hit article was first published on May 18, 2021
