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.
Conner Curran, now 10 years old, can walk more than two miles after gene therapy treatment for his Duchenne's muscular dystrophy.
That the medical world is on the cusp of a successful treatment for a crippling and deadly disease is the culmination of more than 35 years of work by Dr. Jude Samulski, a professor of pharmacology at the University of North Carolina School of Medicine in Chapel Hill. More recently, he's become a leading gene therapy entrepreneur.
But Samulski likens this breakthrough to the frustrations of solving a Rubik's cube. "Just because one side is now all the color yellow does not mean that it is completely aligned," he says.
Although Conner's life and future have dramatically improved, he's not cured. The gene therapy tamed but did not extinguish his disorder: Conner is now suffering from the equivalent of Becker's muscular dystrophy, a milder form of the disease with symptoms that appear later in life and progress more slowly. Moreover, the loss of muscle cells Conner suffered prior to the treatment is permanent.
"It will take more time and more innovations," Samulski says of finding an even more effective gene therapy for muscular dystrophy.
Conner's family is still overjoyed with the results. "Jude's grit and determination gave Conner a chance at a new life, one that was not in his cards before gene therapy," says his mother Jessica Curran. She adds that "Conner is more confident than before and enjoys life, even though he has limitations, if compared to his brothers or peers."
Conner Curran holding a football post gene therapy treatment.
Courtesy of the Curran family
For now, the use of gene therapy as a treatment for diseases and disorders remains relatively isolated. On paper at least, progress appears glacially slow. In 2018, there were four FDA-approved gene therapies (excluding those reliant on bone marrow/stem cell transplants or implants). Today, there are 10. One therapy is solely for the cosmetic purpose of reducing facial lines and folds.
Nevertheless, experts in the space believe gene therapy is poised to expand dramatically.
"Certainly in the next three to five years you will see dozens of gene therapies and cell therapies be approved," says Dr. Pavan Cheruvu, who is CEO of Sio Gene Therapies in New York. The company is developing treatments for Parkinson's disease and Tay-Sachs, among other diseases.
Cheruvu's conclusion is supported by NEWDIGS, a think tank at the Massachusetts Institute of Technology that keeps tabs on gene therapy developments. NEWDIGS predicts there will be at least 60 gene therapies approved for use in the U.S. by the end of the decade. That number could be closer to 100 if Chinese researchers and biotech ventures decide the American market is a good fit for the therapies they develop.
"We are watching something of a conditional evolution, like a dot-com, or cellphones that were sizes of shoeboxes that have now matured to the size of wafers. Our space will follow along very similarly."
Dr. Carsten Brunn, a chemist by training and CEO of Selecta Biosciences outside of Boston, is developing ways to reduce the immune responses in patients who receive gene therapy. He observes that there are more than 300 therapies in development and thousands of clinical trials underway. "It's definitely an exciting time in the field," he says.
That's a far cry from the environment of little more than a decade ago. Research and investment in gene therapy had been brought low for years after the death of teenager Jesse Gelsinger in 1999 while he had been enrolled in a clinical trial to treat a liver disease. Gene therapy was a completely novel concept back then, and his death created existential questions about whether it was a proper pathway to pursue. Cheruvu, a cardiologist, calls the years after Gelsinger's death an "ice age" for gene therapy.
However, those dark years eventually yielded to a thaw. And while there have been some recent stumbles, they are considered part of the trial-and-error that has often accompanied medical research as opposed to an ominous "stop" sign.
The deaths of three patients last year receiving gene therapy for myotubular myopathy – a degenerative disease that causes severe muscle weakness – promptly ended the clinical trial in which they were enrolled. However, the incident caused few ripples beyond that. Researchers linked the deaths to dosage sizes that caused liver toxicity, as opposed to the gene therapy itself being an automatic death sentence; younger patients who received lower doses due to a less advanced disease state experienced improvements.
The gene sequencing and editing that helped create vaccines for COVID-19 in record time also bolstered the argument for more investment in research and development. Cheruvu notes that the field has usually been the domain of investors with significant expertise in the field; these days, more money is flowing in from generalists.
The Challenges Ahead
What will be the next step in gene therapy's evolution? Many of Samulski's earliest innovations came in the laboratory, for example. Then that led to him forming a company called AskBio in collaboration with the Muscular Dystrophy Association. AskBio sold its gene therapy to Pfizer five years ago to assure that enough could be manufactured for stage 3 clinical trials and eventually reach the market.
Cheruvu suggests that many future gene therapy innovations will be the result of what he calls "congruent innovation." That means publicly funded laboratories and privately funded companies might develop treatments separately or in collaboration. Or, university scientists may depend on private ventures to solve one of gene therapy's most vexing issues: producing enough finished material to test and treat on a large scale. "Manufacturing is a real bottleneck right now," Brunn says.
The alternative is referred to in the sector as the "valley of death": a lab has found a promising treatment, but is not far enough along in development to submit an investigational new drug application with the FDA. The promise withers away as a result. But the new abundance of venture capital for gene therapy has made this scenario less of an issue for private firms, some of which have received hundreds of millions of dollars in funding.
There are also numerous clinical challenges. Many gene therapies use what are known as adeno-associated virus vectors (AAVs) to deliver treatments. They are hollowed-out husks of viruses that can cause a variety of mostly mild maladies ranging from colds to pink eye. They are modified to deliver the genetic material used in the therapy. Most of these vectors trigger an antibody reaction that limits treatments to a single does or a handful of smaller ones. That can limit the potential progress for patients – an issue referred to as treatment "durability."
Although vectors from animals such as horses trigger far less of an antibody reaction in patients -- and there has been significant work done on using artificial vectors -- both are likely years away from being used on a large scale. "For the foreseeable future, AAV is the delivery system of choice," Brunn says.
Also, there will likely be demand for concurrent gene therapies that can lead to a complete cure – not only halting the progress of Duchenne's in kids like Conner Curran, but regenerating their lost muscle cells, perhaps through some form of stem cell therapy or another treatment that has yet to be devised.
Nevertheless, Samulski believes demand for imperfect treatments will be high – particularly with a disease such as muscular dystrophy, where many patients are mere months from spending the remainder of their lives in wheelchairs. But Samulski believes those therapies will also inevitably evolve into something far more effective.
"We are watching something of a conditional evolution, like a dot-com, or cellphones that were sizes of shoeboxes that have now matured to the size of wafers," he says. "Our space will follow along very similarly."
Jessica Curran will remain forever grateful for what her son has received: "Jude gave us new hope. He gave us something that is priceless – a chance to watch Conner grow up and live out his own dreams."
