Scientists Are Building an “AccuWeather” for Germs to Predict Your Risk of Getting the Flu
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.
In May 2022, Californian biotech Ultima Genomics announced that its UG 100 platform was capable of sequencing an entire human genome for just $100, a landmark moment in the history of the field. The announcement was particularly remarkable because few had previously heard of the company, a relative unknown in an industry long dominated by global giant Illumina which controls about 80 percent of the world’s sequencing market.
Ultima’s secret was to completely revamp many technical aspects of the way Illumina have traditionally deciphered DNA. The process usually involves first splitting the double helix DNA structure into single strands, then breaking these strands into short fragments which are laid out on a glass surface called a flow cell. When this flow cell is loaded into the sequencing machine, color-coded tags are attached to each individual base letter. A laser scans the bases individually while a camera simultaneously records the color associated with them, a process which is repeated until every single fragment has been sequenced.
Instead, Ultima has found a series of shortcuts to slash the cost and boost efficiency. “Ultima Genomics has developed a fundamentally new sequencing architecture designed to scale beyond conventional approaches,” says Josh Lauer, Ultima’s chief commercial officer.
This ‘new architecture’ is a series of subtle but highly impactful tweaks to the sequencing process ranging from replacing the costly flow cell with a silicon wafer which is both cheaper and allows more DNA to be read at once, to utilizing machine learning to convert optical data into usable information.
To put $100 genome in perspective, back in 2012 the cost of sequencing a single genome was around $10,000, a price tag which dropped to $1,000 a few years later. Before Ultima’s announcement, the cost of sequencing an individual genome was around $600.
Several studies have found that nearly 12 percent of healthy people who have their genome sequenced, then discover they have a variant pointing to a heightened risk of developing a disease that can be monitored, treated or prevented.
While Ultima’s new machine is not widely available yet, Illumina’s response has been rapid. Last month the company unveiled the NovaSeq X series, which it describes as its fastest most cost-efficient sequencing platform yet, capable of sequencing genomes at $200, with further price cuts likely to follow.
But what will the rapidly tumbling cost of sequencing actually mean for medicine? “Well to start with, obviously it’s going to mean more people getting their genome sequenced,” says Michael Snyder, professor of genetics at Stanford University. “It'll be a lot more accessible to people.”
At the moment sequencing is mainly limited to certain cancer patients where it is used to inform treatment options, and individuals with undiagnosed illnesses. In the past, initiatives such as SeqFirst have attempted further widen access to genome sequencing based on growing amounts of research illustrating the potential benefits of the technology in healthcare. Several studies have found that nearly 12 percent of healthy people who have their genome sequenced, then discover they have a variant pointing to a heightened risk of developing a disease that can be monitored, treated or prevented.
“While whole genome sequencing is not yet widely used in the U.S., it has started to come into pediatric critical care settings such as newborn intensive care units,” says Professor Michael Bamshad, who heads the genetic medicine division in the University of Washington’s pediatrics department. “It is also being used more often in outpatient clinical genetics services, particularly when conventional testing fails to identify explanatory variants.”
But the cost of sequencing itself is only one part of the price tag. The subsequent clinical interpretation and genetic counselling services often come to several thousand dollars, a cost which insurers are not always willing to pay.
As a result, while Bamshad and others hope that the arrival of the $100 genome will create new opportunities to use genetic testing in innovative ways, the most immediate benefits are likely to come in the realm of research.
Bigger Data
There are numerous ways in which cheaper sequencing is likely to advance scientific research, for example the ability to collect data on much larger patient groups. This will be a major boon to scientists working on complex heterogeneous diseases such as schizophrenia or depression where there are many genes involved which all exert subtle effects, as well as substantial variance across the patient population. Bigger studies could help scientists identify subgroups of patients where the disease appears to be driven by similar gene variants, who can then be more precisely targeted with specific drugs.
If insurers can figure out the economics, Snyder even foresees a future where at a certain age, all of us can qualify for annual sequencing of our blood cells to search for early signs of cancer or the potential onset of other diseases like type 2 diabetes.
David Curtis, a genetics professor at University College London, says that scientists studying these illnesses have previously been forced to rely on genome-wide association studies which are limited because they only identify common gene variants. “We might see a significant increase in the number of large association studies using sequence data,” he says. “It would be far preferable to use this because it provides information about rare, potentially functional variants.”
Cheaper sequencing will also aid researchers working on diseases which have traditionally been underfunded. Bamshad cites cystic fibrosis, a condition which affects around 40,000 children and adults in the U.S., as one particularly pertinent example.
“Funds for gene discovery for rare diseases are very limited,” he says. “We’re one of three sites that did whole genome sequencing on 5,500 people with cystic fibrosis, but our statistical power is limited. A $100 genome would make it much more feasible to sequence everyone in the U.S. with cystic fibrosis and make it more likely that we discover novel risk factors and pathways influencing clinical outcomes.”
For progressive diseases that are more common like cancer and type 2 diabetes, as well as neurodegenerative conditions like multiple sclerosis and ALS, geneticists will be able to go even further and afford to sequence individual tumor cells or neurons at different time points. This will enable them to analyze how individual DNA modifications like methylation, change as the disease develops.
In the case of cancer, this could help scientists understand how tumors evolve to evade treatments. Within in a clinical setting, the ability to sequence not just one, but many different cells across a patient’s tumor could point to the combination of treatments which offer the best chance of eradicating the entire cancer.
“What happens at the moment with a solid tumor is you treat with one drug, and maybe 80 percent of that tumor is susceptible to that drug,” says Neil Ward, vice president and general manager in the EMEA region for genomics company PacBio. “But the other 20 percent of the tumor has already got mutations that make it resistant, which is probably why a lot of modern therapies extend life for sadly only a matter of months rather than curing, because they treat a big percentage of the tumor, but not the whole thing. So going forwards, I think that we will see genomics play a huge role in cancer treatments, through using multiple modalities to treat someone's cancer.”
If insurers can figure out the economics, Snyder even foresees a future where at a certain age, all of us can qualify for annual sequencing of our blood cells to search for early signs of cancer or the potential onset of other diseases like type 2 diabetes.
“There are companies already working on looking for cancer signatures in methylated DNA,” he says. “If it was determined that you had early stage cancer, pre-symptomatically, that could then be validated with targeted MRI, followed by surgery or chemotherapy. It makes a big difference catching cancer early. If there were signs of type 2 diabetes, you could start taking steps to mitigate your glucose rise, and possibly prevent it or at least delay the onset.”
This would already revolutionize the way we seek to prevent a whole range of illnesses, but others feel that the $100 genome could also usher in even more powerful and controversial preventative medicine schemes.
Newborn screening
In the eyes of Kári Stefánsson, the Icelandic neurologist who been a visionary for so many advances in the field of human genetics over the last 25 years, the falling cost of sequencing means it will be feasible to sequence the genomes of every baby born.
“We have recently done an analysis of genomes in Iceland and the UK Biobank, and in 4 percent of people you find mutations that lead to serious disease, that can be prevented or dealt with,” says Stefansson, CEO of deCODE genetics, a subsidiary of the pharmaceutical company Amgen. “This could transform our healthcare systems.”
As well as identifying newborns with rare diseases, this kind of genomic information could be used to compute a person’s risk score for developing chronic illnesses later in life. If for example, they have a higher than average risk of colon or breast cancer, they could be pre-emptively scheduled for annual colonoscopies or mammograms as soon as they hit adulthood.
To a limited extent, this is already happening. In the UK, Genomics England has launched the Newborn Genomes Programme, which plans to undertake whole-genome sequencing of up to 200,000 newborn babies, with the aim of enabling the early identification of rare genetic diseases.
"I have not had my own genome sequenced and I would not have wanted my parents to have agreed to this," Curtis says. "I don’t see that sequencing children for the sake of some vague, ill-defined benefits could ever be justifiable.”
However, some scientists feel that it is tricky to justify sequencing the genomes of apparently healthy babies, given the data privacy issues involved. They point out that we still know too little about the links which can be drawn between genetic information at birth, and risk of chronic illness later in life.
“I think there are very difficult ethical issues involved in sequencing children if there are no clear and immediate clinical benefits,” says Curtis. “They cannot consent to this process. I have not had my own genome sequenced and I would not have wanted my parents to have agreed to this. I don’t see that sequencing children for the sake of some vague, ill-defined benefits could ever be justifiable.”
Curtis points out that there are many inherent risks about this data being available. It may fall into the hands of insurance companies, and it could even be used by governments for surveillance purposes.
“Genetic sequence data is very useful indeed for forensic purposes. Its full potential has yet to be realized but identifying rare variants could provide a quick and easy way to find relatives of a perpetrator,” he says. “If large numbers of people had been sequenced in a healthcare system then it could be difficult for a future government to resist the temptation to use this as a resource to investigate serious crimes.”
While sequencing becoming more widely available will present difficult ethical and moral challenges, it will offer many benefits for society as a whole. Cheaper sequencing will help boost the diversity of genomic datasets which have traditionally been skewed towards individuals of white, European descent, meaning that much of the actionable medical information which has come out of these studies is not relevant to people of other ethnicities.
Ward predicts that in the coming years, the growing amount of genetic information will ultimately change the outcomes for many with rare, previously incurable illnesses.
“If you're the parent of a child that has a susceptible or a suspected rare genetic disease, their genome will get sequenced, and while sadly that doesn’t always lead to treatments, it’s building up a knowledge base so companies can spring up and target that niche of a disease,” he says. “As a result there’s a whole tidal wave of new therapies that are going to come to market over the next five years, as the genetic tools we have, mature and evolve.”
The Friday Five covers five stories in research that you may have missed this week. There are plenty of controversies and troubling ethical issues in science – and we get into many of them in our online magazine – but this news roundup focuses on scientific creativity and progress to give you a therapeutic dose of inspiration headed into the weekend.
Here is the promising research covered in this week's Friday Five:
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- How to make cities of the future less noisy
- An old diabetes drug could have a new purpose: treating an irregular heartbeat
- A new reason for mysterious stillbirths
- Making old mice younger with EVs
- No pain - or mucus - no gain
And an honorable mention this week: How treatments for depression can change the structure of the brain