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.
Biochemist Longxing Cao is working with colleagues at the University of Washington on promising research to disable infectious coronavirus in a person's nose.
This may sounds like science fiction, but several research groups are already working on such trickster coronavirus drugs, with some candidates close to clinical trials and possibly even becoming available late this year. The teams began working on them when the pandemic arrived, and continued in lockdown.
When the pandemic first hit and the state of California issued a lockdown order on March 16, postdoctoral researchers Anum and Jeff Glasgow found themselves stuck at home with nothing to do. The two scientists who study bioengineering felt that they were well equipped to research molecular ways of disabling coronavirus's cell-penetrating spike protein, but they could no longer come to their labs at the University of California San Francisco.
"We were upset that no one put us in the game," says Anum Glasgow. "We have a lot of experience between us doing these types of projects so we wanted to contribute." But they still had computers so they began modeling the potential virus-disabling proteins in silico using Robetta, special software for designing and modeling protein structures, developed and maintained by University of Washington biochemist David Baker and his lab.
"We saw some imperfections in that lock and key and we created something better. We made a 10 times tighter adhesive."
The SARS-CoV-2 virus that causes Covid-19 uses its surface spike protein to bind on to a specific receptor on human cells called ACE2. Unfortunately for humans, the spike protein's molecular shape fits the ACE2 receptor like a well-cut key, making it very successful at breaking into our cells. But if one could design a molecular ACE2-mimic to "trick" the virus into latching onto it instead, the virus would no longer be able to enter cells. Scientists call such mimics receptor traps or inhibitors, or blockers. "It would block the adhesive part of the virus that binds to human cells," explains Jim Wells, professor of pharmaceutical chemistry at UCSF, whose lab took part in designing the ACE2-receptor mimic, working with the Glasgows and other colleagues.
The idea of disabling infectious or inflammatory agents by tricking them into binding to the targets' molecular look-alikes is something scientists have tried with other diseases. The anti-inflammatory drugs commonly used to treat autoimmune conditions, including rheumatoid arthritis, Crohn's disease and ulcerative colitis, rely on conceptually similar molecular mechanisms. Called TNF blockers, these drugs block the activity of the inflammatory cytokines, molecules that promote inflammation. "One of the biggest selling drugs in the world is a receptor trap," says Jeff Glasgow. "It acts as a receptor decoy. There's a TNF receptor that traps the cytokine."
In the recent past, scientists also pondered a similar look-alike approach to treating urinary tract infections, which are often caused by a pathogenic strain of Escherichia coli. An E. coli bacterium resembles a squid with protruding filaments equipped with proteins that can change their shape to form hooks, used to hang onto specific sugar molecules called ligands, which are present on the surface of the epithelial cells lining the urinary tract.
A recent study found that a sugar-like compound that's structurally similar to that ligand can play a similar trick on the E. Coli. When administered in in sufficient amounts, the compound hooks the bacteria on, which is then excreted out of the body with urine. The "trickster" method had been also tried against the HIV virus, but it wasn't very effective because HIV has a high mutation rate and multiple ways of entering human cells.
But the coronavirus spike protein's shape is more stable. And while it has a strong affinity for the ACE2 receptors, its natural binding to these receptors isn't perfect, which allowed the UCSF researchers to design a mimic with a better grip. "We saw some imperfections in that lock and key and we created something better," says Wells. "We made a 10 times tighter adhesive." The team demonstrated that their traps neutralized SARS-CoV-2 in lab experiments and published their study in the Proceedings of the National Academy of Sciences.
Baker, who is the director of the Institute for Protein Design at the University of Washington, was also devising ACE2 look-alikes with his team. Only unlike the UCSF team, they didn't perfect the virus-receptor lock and key combo, but instead designed their mimics from scratch. Using Robetta, they digitally modeled over two million proteins, zeroed-in on over 100,000 potential candidates and identified a handful with a strong promise of blocking SARS-CoV-2, testing them against the virus in human cells. Their design of the miniprotein inhibitors was published in the journal Science.
Biochemist David Baker, pictured in his lab at the University of Washington.
UW
The concept of the ACE2 receptor mimics is somewhat similar to the antibody plasma, but better, the teams explain. Antibodies don't always coat all of the virus's spike proteins and sometimes don't bind perfectly. By contrast, the ACE2 mimics directly compete with the virus's entry mechanism. ACE2 mimics are also easier and cheaper to make, researchers say.
Antibodies, which are long protein chains, must be grown inside mammalian cells, which is a slow and costly process. As drugs, antibody cocktails must be kept refrigerated. On the contrary, proteins that mimic ACE2 receptors are smaller and can be produced by bacteria easily and inexpensively. Designed to specs, these proteins don't need refrigeration and are easy to store. "We designed them to be very stable," says Baker. "Our computation design tries to come up with the stable proteins that have the desired functions."
That stability may allow the team to create an inhaler drug rather than an intravenous one, which would be another advantage over the antibody plasma, given via an IV. The team envisions people spraying the miniprotein solution into their nose, creating a protecting coating that would disable the inhaled virus. "The infection starts from your respiratory system, from your nose," explains Longxing Cao, the study's co-author—so a nasal spray would be a natural way to administer it. "So that you can have it like a layer, similar to a mask."
As the virus evolves, new variants are arising. But the teams think that their ACE2 protein mimics should work on the new variants too for several reasons. "Since the new SARS-CoV-2 variants still use ACE2 for their cell entry, they will likely still be susceptible to ACE2-based traps," Glasgow says.
Cao explains that their approach should work too because most of the mutations happen outside the ACE2 binding region. Plus, they are building multiple binders that can bind to an array of the coronavirus variants. "Our binder can still bind with most of the variants and we are trying to make one protein that could inhibit all the future escape variants," he says.
Baker and Cao hope that their miniproteins may be available to patients later this year. But besides getting the medicine out to patients, this approach will allow researchers to test the computer-modeled mimics end-to-end with an unprecedented speed. That would give humans a leg up in future pandemics or zoonotic disease outbreaks, which remain an increasingly pressing threat due to human activity and climate change.
"That's what we are focused on right now—understanding what we have learned from this pandemic to improve our design methods," says Baker. "So that we should be able to obtain binders like these very quickly when a new pandemic threat is identified."