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.
Obesity is a risk factor for worse outcomes for a variety of medical conditions ranging from cancer to Covid-19. Most experts attribute it simply to underlying low-grade inflammation and added weight that make breathing more difficult.
Now researchers have found a more direct reason: SARS-CoV-2, the virus that causes Covid-19, can infect adipocytes, more commonly known as fat cells, and macrophages, immune cells that are part of the broader matrix of cells that support fat tissue. Stanford University researchers Catherine Blish and Tracey McLaughlin are senior authors of the study.
Most of us think of fat as the spare tire that can accumulate around the middle as we age, but fat also is present closer to most internal organs. McLaughlin's research has focused on epicardial fat, “which sits right on top of the heart with no physical barrier at all,” she says. So if that fat got infected and inflamed, it might directly affect the heart.” That could help explain cardiovascular problems associated with Covid-19 infections.
Looking at tissue taken from autopsy, there was evidence of SARS-CoV-2 virus inside the fat cells as well as surrounding inflammation. In fat cells and immune cells harvested from health humans, infection in the laboratory drove "an inflammatory response, particularly in the macrophages…They secreted proteins that are typically seen in a cytokine storm” where the immune response runs amok with potential life-threatening consequences. This suggests to McLaughlin “that there could be a regional and even a systemic inflammatory response following infection in fat.”
It is easy to see how the airborne SARS-CoV-2 virus infects the nose and lungs, but how does it get into fat tissue? That is a mystery and the source of ample speculation.
The macrophages studied by McLaughlin and Blish were spewing out inflammatory proteins, While the the virus within them was replicating, the new viral particles were not able to replicate within those cells. It was a different story in the fat cells. “When [the virus] gets into the fat cells, it not only replicates, it's a productive infection, which means the resulting viral particles can infect another cell,” including microphages, McLaughlin explains. It seems to be a symbiotic tango of the virus between the two cell types that keeps the cycle going.
It is easy to see how the airborne SARS-CoV-2 virus infects the nose and lungs, but how does it get into fat tissue? That is a mystery and the source of ample speculation.
Macrophages are mobile; they engulf and carry invading pathogens to lymphoid tissue in the lymph nodes, tonsils and elsewhere in the body to alert T cells of the immune system to the pathogen. Perhaps some of them also carry the virus through the bloodstream to more distant tissue.
ACE2 receptors are the means by which SARS-CoV-2 latches on to and enters most cells. They are not thought to be common on fat cells, so initially most researchers thought it unlikely they would become infected.
However, while some cell receptors always sit on the surface of the cell, other receptors are expressed on the surface only under certain conditions. Philipp Scherer, a professor of internal medicine and director of the Touchstone Diabetes Center at the University of Texas Southwestern Medical Center, suggests that, in people who have obesity, “There might be higher levels of dysfunctional [fat cells] that facilitate entry of the virus,” either through transiently expressed ACE2 or other receptors. Inflammatory proteins generated by macrophages might contribute to this process.
Another hypothesis is that viral RNA might be smuggled into fat cells as cargo in small bits of material called extracellular vesicles, or EVs, that can travel between cells. Other researchers have shown that when EVs express ACE2 receptors, they can act as decoys for SARS-CoV-2, where the virus binds to them rather than a cell. These scientists are working to create drugs that mimic this decoy effect as an approach to therapy.
Do fat cells play a role in Long Covid? “Fat cells are a great place to hide. You have all the energy you need and fat cells turn over very slowly; they have a half-life of ten years,” says Scherer. Observational studies suggest that acute Covid-19 can trigger the onset of diabetes especially in people who are overweight, and that patients taking medicines to regulate their diabetes “were actually quite protective” against acute Covid-19. Scherer has funding to study the risks and benefits of those drugs in animal models of Long Covid.
McLaughlin says there are two areas of potential concern with fat tissue and Long Covid. One is that this tissue might serve as a “big reservoir where the virus continues to replicate and is sent out” to other parts of the body. The second is that inflammation due to infected fat cells and macrophages can result in fibrosis or scar tissue forming around organs, inhibiting their function. Once scar tissue forms, the tissue damage becomes more difficult to repair.
Current Covid-19 treatments work by stopping the virus from entering cells through the ACE2 receptor, so they likely would have no effect on virus that uses a different mechanism. That means another approach will have to be developed to complement the treatments we already have. So the best advice McLaughlin can offer today is to keep current on vaccinations and boosters and lose weight to reduce the risk associated with obesity.
Air pollution can lead to lung cancer. The connection suggests new ways to stop cancer in its tracks.
Forget taking a deep breath. Around the world, 99 percent of people breathe air polluted to unsafe levels, according to data from the World Health Organization. Activities such as burning fossil fuels release greenhouse gases that contribute to air pollution, which could lead to heart disease, stroke, asthma, emphysema, and some types of cancer.
“The burden of disease attributable to air pollution is now estimated to be on a par with other major global health risks such as unhealthy diet and tobacco smoking, and air pollution is now recognized as the single biggest environmental threat to human health,” wrote the authors of a 2021 WHO report.
The majority of lung cancer is attributed to smoking. But as pollution levels have increased, and anti-smoking campaigns have discouraged smoking, the proportion of lung cancers diagnosed in non-smokers has grown. The CDC estimates that 10 to 20 percent of lung cancers in the U.S. currently occur in non-smokers.
The mechanism between air pollution and the development of lung cancer has been unclear, but researchers at London’s Francis Crick Institute recently made an important breakthrough in understanding the connection. Lead investigator Charles Swanton presented this research last month at a conference in Paris.
Pollution awakens mutations
The Crick Institute scientists were able to identify a new link between common air pollutants and non-small cell lung cancer (NSCLC). They focused on pollutants called particulate matter, or PM, that are 2.5 microns wide, narrower than human cells.
Most cancer diagnosed in non-smoking people is NSCLC, but this type of cancer hasn’t received the same research attention as more common lung cancers found in smokers, according to Clare Weeden, a cancer researcher at the Crick Institute and a co-author of the study.
“This is a really underserved and under-researched population that we really need to tackle, as well as lung cancers that occur in smokers,” she says. “Lung cancer is the number one cancer killer worldwide.”
In the past, some researchers believed air pollution caused mutations that led to cancer. Others believed these mutations could remain dormant without any detriment to health until pollutants or other stressors triggered them to become cancerous. Reviving the latter hypothesis that carcinogens may activate pre-existing mutations, instead of directly causing them, the Crick researchers analyzed samples from 463,679 people in the UK and parts of Asia, noting mutations and comparing changes in gene expression in mice and human cells.
“The mutation can exist in a nascent clone without causing cancer,” says Emilia Lim, a bioinformatics expert and a co-first author of the Crick study. “It is the carcinogen that promotes a conducive environment for this one little clone to grow and expand into cancer. Through our work, we were able to revive excitement for this hypothesis and bring it to light.”
The study explains a confusing pattern of lung cancer developing, particularly in women, despite a lack of environmental risk factors like smoking, secondhand smoke, or radon exposure. The culprit in these cases may have been too much PM 2.5 exposure.
Other researchers had previously identified a link between mutations in certain genes that control epidermal growth factor receptors, or EGFR mutations, and the development of NSCLC. In a 2019 study of 250 people with this type of cancer, about 32 percent had the mutation. Women are more likely to have EGFR mutations than men.
Not everyone who has the EGFR mutation will develop lung cancer. Respirologist Stephen Lam studies lung cancer at the BC Cancer Research Centre in Vancouver, Canada, but was not involved in the Crick Institute research. He says the study explains a confusing pattern of lung cancer developing, particularly in women, despite a lack of environmental risk factors like smoking, secondhand smoke, or radon exposure. The culprit in these cases may have been too much PM 2.5 exposure.
More exposure leads to inflammation and lesions
The Crick researchers found that an excess of PM 2.5 in the air sparked an inflammatory process in cells within the lung. This inflammation set the stage for NSCLC to develop in people and mice with existing EGFR mutations.
The researchers also exposed mice without EGFR mutations to PM 2.5 pollution—an experiment that couldn’t be ethically conducted in humans—to link pollution exposure to NSCLC. The mice experiments also showed that NSCLC is dose-dependent; higher levels of exposure were associated with higher number of cancerous lesions forming.
Ultimately, the study “fundamentally changed how we view lung cancer in people who have never smoked,” said Swanton in a Crick Institute press release. “Cells with cancer-causing mutations accumulate naturally as we age, but they are normally inactive. We’ve demonstrated that air pollution wakes these cells up in the lungs, encouraging them to grow and potentially form tumors.”
Preventing cancer before it begins
Targeted therapies already exist for people with EGFR mutations who’ve developed NSCLC, but they have many side effects, according to Weeden. Researchers hope that making more definitive links between pollutants and cancer could help prevent people with EGFR or other mutations from developing lung cancer in the first place.
Along those lines, as an additional component of their study presented last month, the Crick researchers were able to prevent cancer in mice that had the EGFR mutations by blocking inflammation. They used an antibody to inhibit a protein called interleukin 1 beta, which plays a key role in inflammation. Scientists could eventually use such antibodies or other therapies to make a drug treatment that people can take to stop cancer in its tracks, even if they live in highly polluted areas.
Such potential could reach beyond lung cancer; in the past, Crick and other researchers have also found associations between exposure to air pollution and mesothelioma, as well as cancers of the small intestine, lip, mouth, and throat. These links could be meaningful to a growing number of people as climate change intensifies, and with increases in air pollution from fossil fuel combustion and natural disasters like forest fires.
Plus, air pollution is just one external condition that can flip the switch of these inflammatory pathways. Identifying a link between pollution and cancer “has wide ramifications for many other environmental factors that may [play] similar roles,” Weedon says. She hopes that the Crick study and future research in this area will offer some hope for non-smokers frustrated by cancer diagnoses.