ORIGINAL: Tech Review
By David Talbot
April 23, 2013
Collecting and analyzing information from simple cell phones can provide surprising insights into how people move about and behave—and even help us understand the spread of diseases.
WHY IT MATTERS
Poor countries lack data-gathering infrastructure; phone data can provide it.
Breakthrough
Creating disease–fighting tools with cell-phone mobility data.
Key Players
• Caroline Buckee, Harvard University
• William Hoffman, World Economic Forum
• Alex Pentland, MIT
• Andy Tatem, University of Southampton
At a computer in her office at the Harvard School of Public Health in Boston, epidemiologist Caroline Buckee points to a dot on a map of Kenya’s western highlands, representing one of the nation’s thousands of cell-phone towers. In the fight against malaria, Buckee explains, the data transmitted from this tower near the town of Kericho has been epidemiological gold.
When she and her colleagues studied the data, she found that people making calls or sending text messages originating at the Kericho tower were making 16 times more trips away from the area than the regional average. What’s more, they were three times more likely to visit a region northeast of Lake Victoria that records from the health ministry identified as a malaria hot spot. The tower’s signal radius thus covered a significant waypoint for transmission of malaria, which can jump from human to human via mosquitoes. Satellite images revealed the likely culprit: a busy tea plantation that was probably full of migrant workers. The implication was clear, Buckee says. “There will be a ton of infected [people] there.”
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| Caroline Buckee |
This work is now feeding into a new set of predictive models she is building. They show, for example, that even though malaria cases were seen at the tea plantation, taking steps to control malaria there would have less effect on the disease’s spread than concentrating those efforts at the source: Lake Victoria. That region has long been understood as a major center of malaria, but what hasn’t been available before is detailed information about the patterns of human travel there: how many people are coming and going, when they’re arriving and departing, which specific places they’re coming to, and which of those destinations attract the most people traveling on to new places.
Caroline Buckee, a Harvard epidemiologist, is using detailed data on population movements—gleaned from mobile phones— to build precise new tools for fighting the spread of malaria.
Existing efforts to gather that kind of travel data are spotty at best; sometimes public-health workers literally count people at transportation hubs, Buckee says, or nurses in far-flung clinics ask newly diagnosed malaria victims where they’ve been recently. “At many border crossings in Africa, they keep little slips of paper—but the slips get lost, and nobody keeps track,” she says. “We have abstractions and general models on travel patterns but haven’t been able to do this properly—ever.”
The data mining will help inform the design of new measures that are likely to include cheap, targeted campaigns of text messages—for example, warning visitors entering the Kericho tower’s signal zone to use bed netting. And it will help officials choose where to focus mosquito control efforts in the malarial areas. “You don’t want to be spraying every puddle for mosquito larvae all the time. But if you know there is a ton of importation from a certain spot, you want to increase your control program at that spot,” Buckee says. “And now I can pinpoint where the importation of a disease is especially important.”
Buckee’s most recent study, published last year in
Science and based on records from 15 million Kenyan phones, is a result of a collaboration with her husband,
Nathan Eagle, who has been working to make sense of cell-phone data for more than a decade. In the mid-2000s, after getting attention for his work mining data from the phones of volunteers at MIT, Eagle started to get calls from mobile carriers asking for insight into questions like why customers canceled their phone plans. Eagle began working with them. And when the couple spent 18 months in Africa starting in 2006—Buckee was doing work on the
genetics of the malaria parasite—he studied call data for various purposes, trying to understand phenomena like ethnic divisions in Nairobi slums and the spread of cholera in
Rwanda. Buckee’s results show what might be possible when the technology is turned on public-health problems. “
This demonstrated ‘Yeah, we can really provide not just insight, but actually something that is actionable,’” says Eagle, now CEO of
Jana, which runs mobile-phone surveys in the developing world. “This really does work.”
“This is the future of epidemiology. If we are to eradicate malaria, this is how we will do it.”
That demonstration suggests how such data might be harnessed to build tools that health-care workers, governments, and others can use to detect and monitor epidemics, manage disasters, and optimize transportation systems. Already, similar efforts are being directed toward goals as varied as understanding commuting patterns around Paris and managing festival crowds in Belgium. But mining phone records could be particularly useful in poor regions, where there’s often little or no other data-gathering infrastructure. “We are just at the start of using this data for these purposes,” says Vincent Blondel, a professor of applied mathematics at the University of Louvain in Belgium and a leading researcher on data gleaned from cell phones. “The exponential adoption of mobile phones in low-income settings—and the new willingness of some carriers to release data—will lead to new technological tools that could change everything.”
Blank Slate
The world’s six billion mobile phones generate huge amounts of data—including location tracking and information on commercial activity, search history, and links in social networks. Innumerable efforts to mine the data in different ways are under way in research and business organizations around the world. And of those six billion phones, five billion are in developing countries. Many of them are cheap phones that can do little besides make calls and send text messages. But all such activity can be tracked back to cell-phone towers, providing a rough way to trace a person’s movements. Throw in the spread of mobile payment technology for simple commerce and you have the raw material for insights not only into epidemiology but into employment trends, social tensions, poverty, transportation, and economic activity.
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| This map, a product of cell-phone data analytics, shows the most important sources of malaria infections (darker shades)—taking into account the potential for further transmission caused by human travel—as well as the major destinations of people exposed to the disease (lighter shades). It can be used to determine where best to focus warnings and mosquito control techniques. |