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.
Jessica Fitzjohn, a postdoctoral fellow at the University of Canterbury, demonstrates the novel breast cancer screening device.
Researchers at the University of Canterbury and startup Tiro Medical in Christchurch, New Zealand are hoping their new device—which doesn’t involve any radiation or compression of the breasts—could increase the accuracy of breast cancer screening, broaden access and encourage more women to get checked. They’re digging into clues from the way buildings move in an earthquake to help detect more cases of this disease.
Earthquake engineering inspires new breast cancer screening tech
What’s underneath a surface affects how it vibrates. Earthquake engineers look at the vibrations of swaying buildings to identify the underlying soil and tissue properties. “As the vibration wave travels, it reflects the stiffness of the material between that wave and the surface,” says Geoff Chase, professor of engineering at the University of Canterbury in Christchurch, New Zealand.
Chase is applying this same concept to breasts. Analyzing the surface motion of the breast as it vibrates could reveal the stiffness of the tissues underneath. Regions of high stiffness could point to cancer, given that cancerous breast tissue can be up to 20 times stiffer than normal tissue. “If in essence every woman’s breast is soft soil, then if you have some granite rocks in there, we’re going to see that on the surface,” explains Chase.
The earthquake-inspired device exceeds the 87 percent sensitivity of a 3D mammogram.
That notion underpins a new breast screening device, the brainchild of Chase. Women lie face down, with their breast being screened inside a circular hole and the nipple resting on a small disc called an actuator. The actuator moves up and down, between one and two millimeters, so there’s a small vibration, “almost like having your phone vibrate on your nipple,” says Jessica Fitzjohn, a postdoctoral fellow at the University of Canterbury who collaborated on the device design with Chase.
Cameras surrounding the device take photos of the breast surface motion as it vibrates. The photos are fed into image processing algorithms that convert them into data points. Then, diagnostic algorithms analyze those data points to find any differences in the breast tissue. “We’re looking for that stiffness contrast which could indicate a tumor,” Fitzjohn says.
A nascent yet promising technology
The device has been tested in a clinical trial of 14 women: one with healthy breasts and 13 with a tumor in one breast. The cohort was small but diverse, varying in age, breast volume and tumor size.
Results from the trial yielded a sensitivity rate, or the likelihood of correctly detecting breast cancer, of 85 percent. Meanwhile, the device’s specificity rate, or the probability of diagnosing healthy breasts, was 77 percent. By combining and optimizing certain diagnostic algorithms, the device reached between 92 and 100 percent sensitivity and between 80 and 86 percent specificity, which is comparable to the latest 3D mammogram technology. Called tomosynthesis, these 3D mammograms take a number of sharper, clearer and more detailed 3D images compared to the single 2D image of a conventional mammogram, and have a specificity score of 92 percent. Although the earthquake-inspired device’s specificity is lower, it exceeds the 87 percent sensitivity of a 3D mammogram.
The team hopes that cameras with better resolution can help improve the numbers. And with a limited amount of data in the first trial, the researchers are looking into funding for another clinical trial to validate their results on a larger cohort size.
Additionally, during the trial, the device correctly identified one woman’s breast as healthy, while her prior mammogram gave a false positive. The device correctly identified it as being healthy tissue. It was also able to capture the tiniest tumor at 7 millimeters—around a third of an inch or half as long as an aspirin tablet.
Diagnostic findings from the device are immediate.
When using the earthquake-inspired device, women lie face down, with their breast being screened inside circular holes.
University of Canterbury.
But more testing is needed to “prove the device’s ability to pick up small breast cancers less than 10 to 15 millimeters in size, as we know that finding cancers when they are small is the best way of improving outcomes,” says Richard Annand, a radiologist at Pacific Radiology in New Zealand. He explains that mammography already detects most precancerous lesions, so if the device will only be able to find large masses or lumps it won’t be particularly useful. While not directly involved in administering the clinical trial for the device, Annand was a director at the time for Canterbury Breastcare, where the trial occurred.
Meanwhile, Monique Gary, a breast surgical oncologist and medical director of the Grand View Health Cancer program in Pennsylvania, U.S., is excited to see new technologies advancing breast cancer screening and early detection. But she notes that the device may be challenging for “patients who are unable to lay prone, such as pregnant women as well as those who are differently abled, and this machine might exclude them.” She adds that it would also be interesting to explore how breast implants would impact the device’s vibrational frequency.
Diagnostic findings from the device are immediate, with the results available “before you put your clothes back on,” Chase says. The absence of any radiation is another benefit, though Annand considers it a minor edge “as we know the radiation dose used in mammography is minimal, and the advantages of having a mammogram far outweigh the potential risk of radiation.”
The researchers also conducted a separate ergonomic trial with 40 women to assess the device’s comfort, safety and ease of use. Angela was part of that trial and described the experience as “easy, quick, painless and required no manual intervention from an operator.” And if a person is uncomfortable being topless or having their breasts touched by someone else, “this type of device would make them more comfortable and less exposed,” she says.
While mammograms remain “the ‘gold standard’ in breast imaging, particularly screening, physicians need an option that can be used in combination with mammography.
Fitzjohn acknowledges that “at the moment, it’s quite a crude prototype—it’s just a block that you lie on.” The team prioritized function over form initially, but they’re now planning a few design improvements, including more cushioning for the breasts and the surface where the women lie on.
While mammograms remains “the ‘gold standard’ in breast imaging, particularly screening, physicians need an option that is good at excluding breast cancer when used in combination with mammography, has good availability, is easy to use and is affordable. There is the possibility that the device could fill this role,” Annand says.
Indeed, the researchers envision their new breast screening device as complementary to mammograms—a prescreening tool that could make breast cancer checks widely available. As the device is portable and doesn’t require specialized knowledge to operate, it can be used in clinics, pop-up screening facilities and rural communities. “If it was easily accessible, particularly as part of a checkup with a [general practitioner] or done in a practice the patient is familiar with, it may encourage more women to access this service,” Angela says. For those who find regular mammograms uncomfortable or can’t afford them, the earthquake-inspired device may be an option—and an even better one.
Broadening access could prompt more women to go for screenings, particularly younger women at higher risk of getting breast cancer because of a family history of the disease or specific gene mutations. “If we can provide an option for them then we can catch those cancers earlier,” Fitzjohn syas. “By taking screening to people, we’re increasing patient-centric care.”
With the team aiming to lower the device’s cost to somewhere between five and eight times less than mammography equipment, it would also be valuable for low-to-middle-income nations that are challenged to afford the infrastructure for mammograms or may not have enough skilled radiologists.
For Fitzjohn, the ultimate goal is to “increase equity in breast screening and catch cancer early so we have better outcomes for women who are diagnosed with breast cancer.”