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.
Sharing land and other resources among farmers isn’t new. But research shows it may be increasingly relevant in a time of climatic upheaval.
Sharing land and other resources among farmers isn’t new. But research shows it may be increasingly relevant in a time of climatic upheaval, potentially influencing “farmers to adopt environmentally friendly practices and agricultural innovation,” according to a 2021 paper in the Journal of Economic Surveys. Farmers might share knowledge about reducing pesticide use, says Heather Frambach, a supply chain consultant who works with farmers in California and elsewhere. As a group they may better qualify for grants to monitor soil and water quality.
Most research around such practices applies to cooperatives, whose owner-members equally share governance and profits. But a collective like Carman Ranch’s — spearheaded by fourth-generation rancher Cory Carman, who purchases beef from eight other ranchers to sell under one “regeneratively” certified brand — shows when producers band together, they can achieve eco-benefits that would be elusive if they worked alone.
Vitamins and minerals in soil pass into plants through their roots, then into cattle as they graze, then back around as the cows walk around pooping.
Carman knows from experience. Taking over her family's land in 2003, she started selling grass-fed beef “because I really wanted to figure out how to not participate in the feedlot world, to have a healthier product. I didn't know how we were going to survive,” she says. Part of her land sits on a degraded portion of Zumwalt Prairie replete with invasive grasses; working to restore it, she thought, “What good does it do to kill myself trying to make this ranch more functional? If you want to make a difference, change has to be more than single entrepreneurs on single pieces of land. It has to happen at a community level.” The seeds of her collective were sown.
Raising 100 percent grass-fed beef requires land that’s got something for cows to graze in every season — which most collective members can’t access individually. So, they move cattle around their various parcels. It’s practical, but it also restores nutrient flows “to the way they used to move, from lowlands and canyons during the winter to higher-up places as the weather gets hot,” Carman says. Meaning, vitamins and minerals in soil pass into plants through their roots, then into cattle as they graze, then back around as the cows walk around pooping.
Cory Carman sells grass-fed beef, which requires land that’s got something for cows to graze in every season.
Courtesy Cory Carman
Each collective member has individual ecological goals: Carman brought in pigs to root out invasive grasses and help natives flourish. Probert also heads a more conventional grain-finished beef collective with 100 members, and their combined 6.5 million ranchland acres were eligible for a grant supporting climate-friendly practices, which compels them to improve soil and water health and biodiversity and make their product “as environmentally friendly as possible,” Probert says. The Washington veg farmer reduced tilling and pesticide use thanks to the ecoservices of visiting cows. Similarly, a conventional hay farmer near Carman has reduced his reliance on fertilizer by letting cattle graze the cover crops he plants on 80 acres.
Additionally, the collective must meet the regenerative standards promised on their label — another way in which they work together to achieve ecological goals. Says David LeZaks, formerly a senior fellow at finance-focused ecology nonprofit Croatan Institute, it’s hard for individual farmers to access monetary assistance. “But it's easier to get financing flowing when you increase the scale with cooperatives or collectives,” he says. “This supports producers in ways that can lead to better outcomes on the landscape.”
New, smaller scale farmers might gain the most from collective and cooperative models.
For example, it can help them minimize waste by using more of an animal, something our frugal ancestors excelled at. Small-scale beef producers normally throw out hides; Thousand Hills’ 50 regenerative beef producers together have enough to sell to Timberland to make carbon-neutral leather. In another example, working collectively resulted in the support of more diverse farms: Meadowlark Community Mill in Wisconsin went from working with one wheat grower, to sourcing from several organic wheat growers marketing flour under one premium brand.
Another example shows how these collaborations can foster greater equity, among other benefits: The Federation of Southern Cooperatives has a mission to support Black farmers as they build community health. It owns several hundred forest acres in Alabama, where it teaches members to steward their own forest land and use it to grow food — one member coop raises goats to graze forest debris and produce milk. Adding the combined acres of member forest land to the Federation’s, the group qualified for a federal conservation grant that will keep this resource available for food production, and community environmental and mental health benefits. “That's the value-add of the collective land-owner structure,” says Dãnia Davy, director of land retention and advocacy.
New, smaller scale farmers might gain the most from collective and cooperative models, says Jordan Treakle, national program coordinator of the National Family Farm Coalition (NFFC). Many of them enter farming specifically to raise healthy food in healthy ways — with organic production, or livestock for soil fertility. With land, equipment and labor prohibitively expensive, farming collectively allows shared costs and risk that buy farmers the time necessary to “build soil fertility and become competitive” in the marketplace, Treakle says. Just keeping them in business is an eco-win; when small farms fail, they tend to get sold for development or absorbed into less-diversified operations, so the effects of their success can “reverberate through the entire local economy.”
Frambach, the supply chain consultant, has been experimenting with what she calls “collaborative crop planning,” where she helps farmers strategize what they’ll plant as a group. “A lot of them grow based on what they hear their neighbor is going to do, and that causes really poor outcomes,” she says. “Nobody replanted cauliflower after the [atmospheric rivers in California] this year and now there's a huge shortage of cauliflower.” A group plan can avoid the under-planting that causes farmers to lose out on revenue.
It helps avoid overplanted crops, too, which small farmers might have to plow under or compost. Larger farmers, conversely, can sell surplus produce into the upcycling market — to Matriark Foods, for example, which turns it into value-add products like pasta sauce for companies like Sysco that supply institutional kitchens at colleges and hospitals. Frambach and Anna Hammond, Matriark’s CEO, want to collectivize smaller farmers so that they can sell to the likes of Matriark and “not lose an incredible amount of income,” Hammond says.
Ultimately, farming is fraught with challenges and even collectivizing doesn’t guarantee that farms will stay in business. But with agriculture accounting for almost 30 percent of greenhouse gas emissions globally, there's an “urgent” need to shift farming practices to more environmentally sustainable models, as well as a “demand in the marketplace for it,” says NFFC’s Treakle. “The growth of cooperative and collective farming can be a huge, huge boon for the ecological integrity of the system.”
How AI changes things
Sure, you would reply, but AI changes everything. With artificial intelligence, the machine itself will develop some sort of autonomy, however ill-defined. It will have a will of its own. And this will, if it reflects anything that seems human, will not be benevolent. With AI, the claim goes, the machine will somehow know what it must do to get rid of us. It will threaten us as a species.
Well, this fear is also not new. Mary Shelley wrote Frankenstein in 1818 to warn us of what science could do if it served the wrong calling. In the case of her novel, Dr. Frankenstein’s call was to win the battle against death — to reverse the course of nature. Granted, any cure of an illness interferes with the normal workings of nature, yet we are justly proud of having developed cures for our ailments, prolonging life and increasing its quality. Science can achieve nothing more noble. What messes things up is when the pursuit of good is confused with that of power. In this distorted scale, the more powerful the better. The ultimate goal is to be as powerful as gods — masters of time, of life and death.
Should countries create a World Mind Organization that controls the technologies that develop AI?
Back to AI, there is no doubt the technology will help us tremendously. We will have better medical diagnostics, better traffic control, better bridge designs, and better pedagogical animations to teach in the classroom and virtually. But we will also have better winnings in the stock market, better war strategies, and better soldiers and remote ways of killing. This grants real power to those who control the best technologies. It increases the take of the winners of wars — those fought with weapons, and those fought with money.
A story as old as civilization
The question is how to move forward. This is where things get interesting and complicated. We hear over and over again that there is an urgent need for safeguards, for controls and legislation to deal with the AI revolution. Great. But if these machines are essentially functioning in a semi-black box of self-teaching neural nets, how exactly are we going to make safeguards that are sure to remain effective? How are we to ensure that the AI, with its unlimited ability to gather data, will not come up with new ways to bypass our safeguards, the same way that people break into safes?
The second question is that of global control. As I wrote before, overseeing new technology is complex. Should countries create a World Mind Organization that controls the technologies that develop AI? If so, how do we organize this planet-wide governing board? Who should be a part of its governing structure? What mechanisms will ensure that governments and private companies do not secretly break the rules, especially when to do so would put the most advanced weapons in the hands of the rule breakers? They will need those, after all, if other actors break the rules as well.
As before, the countries with the best scientists and engineers will have a great advantage. A new international détente will emerge in the molds of the nuclear détente of the Cold War. Again, we will fear destructive technology falling into the wrong hands. This can happen easily. AI machines will not need to be built at an industrial scale, as nuclear capabilities were, and AI-based terrorism will be a force to reckon with.
So here we are, afraid of our own technology all over again.
What is missing from this picture? It continues to illustrate the same destructive pattern of greed and power that has defined so much of our civilization. The failure it shows is moral, and only we can change it. We define civilization by the accumulation of wealth, and this worldview is killing us. The project of civilization we invented has become self-cannibalizing. As long as we do not see this, and we keep on following the same route we have trodden for the past 10,000 years, it will be very hard to legislate the technology to come and to ensure such legislation is followed. Unless, of course, AI helps us become better humans, perhaps by teaching us how stupid we have been for so long. This sounds far-fetched, given who this AI will be serving. But one can always hope.
This article originally appeared on Big Think, home of the brightest minds and biggest ideas of all time.
