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.
Miniature lab-grown models of human organs have major advantages over standard animal testing.
But he wasn't the only one to follow this train of thought. In the year since the pandemic began, many researchers have been using organoids to study how the coronavirus infects human cells, and find potential treatments. Beumer's pivot represents a remarkable and fast-emerging paradigm shift in how drugs and diseases will be studied in the coming decades. With future pandemics likely to be more frequent and deadlier, such a shift is necessary to reduce the average clinical development time of 5.9 years for antiviral agents.
Part of that shift means developing models that replicate human biology in the lab. Animal models, which are the current standard in biomedical research, fail to do so—96% of drugs that pass animal testing, for example, fail to make it to market. Injecting potentially toxic drugs into living creatures, before eventually slaughtering them, also raises ethical concerns for some. Organoids, on the other hand, respond to infectious diseases, or potential treatments, in a way that is relevant to humans, in addition to being slaughter-free.
Human intestinal organoids infected with SARS-CoV-2 (white).
Credit: Joep Beumer/Clevers group/Hubrecht Institute
Urgency Sparked Momentum
Though brain organoids were previously used to study the Zika virus during the 2015-16 epidemic, it wasn't until COVID-19 that the field really started to change. "The organoid field has advanced a lot in the last year. The speed at which it happened is crazy," says Shuibing Chen, an associate professor at Weill Cornell Medicine in New York. She adds that many federal and private funding agencies have now seen the benefits of organoids, and are starting to appreciate their potential in the biomedical field.
Last summer, the Organo-Strat (OS) network—a German network that uses human organoid models to study COVID-19's effects—received 3.2 million euros in funding from the German government. "When the pandemic started, we became aware that we didn't have the right models to immediately investigate the effects of the virus," says Andreas Hocke, professor of infectious diseases at the Charité Universitätsmedizin in Berlin, Germany, and coordinator of the OS network. Hocke explained that while the World Health Organization's animal models showed an "overlap of symptoms'' with humans, there was "no clear reflection" of the same disease.
"The network functions as a way of connecting organoid experts with infectious disease experts across Germany," Hocke continues. "Having organoid models on demand means we can understand how a virus infects human cells from the first moment it's isolated." Overall, OS aims to create infrastructure that could be applied to future pandemics. There are 28 sub-projects involved in the network, covering a wide assortment of individual organoids.
Cost, however, remains an obstacle to scaling up, says Chen. She says there is also a limit to what we can learn from organoids, given that they only represent a single organ. "We can add drugs to organoids to see how the cells respond, but these tests don't tell us anything about drug metabolism, for example," she explains.
A Related "Leaps" in Progress
One way to solve this issue is to use an organ-on-a-chip system. These are miniature chips containing a variety of human cells, as well as small channels along which functions like blood or air flow can be recreated. This allows scientists to perform more complex experiments, like studying drug metabolism, while producing results that are relevant to humans.
An organ-on-a-chip system.
Credit: Fraunhofer IGB
Such systems are also able to elicit an immune response. The FDA has even entered into an agreement with Wyss Institute spinoff Emulate to use their lung-on-a-chip system to test COVID-19 vaccines. Representing multiple organs in one system is also possible. Berlin-based TissUse are aiming to make a so-called 'human on a chip' system commercially available. But TissUse senior scientist Ilka Maschmeyer warns that there is a limit to how far the technology can go. "The system will not think or feel, so it wouldn't be possible to test for illnesses affecting these abilities," she says.
Some challenges also remain in the usability of organs-on-a-chip. "Specialized training is required to use them as they are so complex," says Peter Loskill, assistant professor and head of the organ-on-a-chip group at the University of Tübingen, Germany. Hocke agrees with this. "Cell culture scientists would easily understand how to use organoids in a lab, but when using a chip, you need additional biotechnology knowledge," he says.
One major advantage of both technologies is the possibility of personalized medicine: Cells can be taken from a patient and put onto a chip, for example, to test their individual response to a treatment. Loskill also says there are other uses outside of the biomedical field, such as cosmetic and chemical testing.
"Although these technologies offer a lot of possibilities, they need time to develop," Loskill continues. He stresses, however, that it's not just the technology that needs to change. "There's a lot of conservative thinking in biomedical research that says this is how we've always done things. To really study human biology means approaching research questions in a completely new way."
Even so, he thinks that the pandemic marked a shift in people's thinking—no one cared how the results were found, as long as it was done quickly. But Loskill adds that it's important to balance promise, potential, and expectations when it comes to these new models. "Maybe in 15 years' time we will have a limited number of animal models in comparison to now, but the timescale depends on many factors," he says.
Beumer, now a post-doc, was eventually allowed to return to the lab to develop his coronavirus model, and found working on it to be an eye-opening experience. He saw first-hand how his research could have an impact on something that was affecting the entire human race, as well as the pressure that comes with studying potential treatments. Though he doesn't see a future for himself in infectious diseases, he hopes to stick with organoids. "I've now gotten really excited about the prospect of using organoids for drug discovery," he says.
The coronavirus pandemic has slowed society down in many respects, but it has flung biomedical research into the future—from mRNA vaccines to healthcare models based on human biology. It may be difficult to fully eradicate animal models, but over the coming years, organoids and organs-on-a-chip may become the standard for the sake of efficacy -- and ethics.
