Nathan Eagle, an adjunct assistant professor at HSPH, was one of the first people to mine unstructured data from businesses with an eye to improving public health in the world’s poorest nations. A self-described engineer and “not much of an academic” (despite having held professorships at numerous institutions including MIT), much of his work has focused on innovative uses of cell-phone data. Drawn by the explosive growth of the mobile market in Africa, he moved in 2007 to a rural village on the Kenyan coast and began searching for ways to improve the lives of the people there. Within months, realizing that he would be more effective sharing his skills with others, he began teaching mobile-application development to students in the University of Nairobi’s computer-science department.
While there, he began working with the Kenyan ministry of health on a blood-bank monitoring system. The plan was to recruit nurses across the country to text the current blood-supply levels in their local hospitals to a central database. “We built this beautiful visualization to let the guys at the centralized blood banks in Kenya see in real time what the blood levels were in these rural hospitals,” he explains, “and more importantly, where the blood was needed.” In the first week, it was a giant success, as the nurses texted in the data and central monitors logged in every hour to see where they should replenish the blood supply. “But in the second week, half the nurses stopped texting in the data, and within about a month virtually no nurses were participating anymore.”
Eagle shares this tale of failure because the episode was a valuable learning experience. “The technical implementation was bulletproof,” he says. “It failed because of a fundamental lack of insight on my part…that had to do with the price of a text message. What I failed to appreciate was that an SMS represents a fairly substantial fraction of a rural nurse’s day wage. By asking them to send that text message we were asking them to essentially take a pay cut.”
Fortunately, Eagle was in a position to save the program. Because he was already working with most of the mobile operators in East Africa, he had access to their billing systems. The addition of a simple script let him credit the rural nurses with a small denomination of prepaid air time, about 10 cents’ worth—enough to cover the cost of the SMS “plus about a penny to say thank you in exchange for a properly formatted text message. Virtually every rural nurse reengaged,” he reports, and the program became a “relatively successful endeavor”—leading him to believe that cell phones could “really make an impact” on public health in developing nations, where there is a dearth of data and almost no capacity for disease surveillance.
Eagle’s next project, based in Rwanda, was more ambitious, and it also provided a lesson in one of the pitfalls of working with big data: that it is possible to find correlations in very large linked datasets without understanding causation. Working with mobile-phone records (which include the time and location of every call), he began creating models of people’s daily and weekly commuting patterns, termed their “radius of generation.” He began to notice patterns. Abruptly, people in a particular village would stop moving as much; he hypothesized that these patterns might indicate the onset of a communicable disease like the flu. Working with the Rwandan ministry of health, he compared the data on cholera outbreaks to his radius of generation data. Once linked, the two datasets proved startlingly powerful; the radius of generation in a village dropped two full weeks before a cholera outbreak. “We could even predict the magnitude of the outbreak based on the amount of decrease in the radius of generation,” he recalls. “I had built something that was performing in this unbelievable way.”
And in fact it was unbelievable. He tells this story as a “good example of why engineers like myself shouldn’t be doing epidemiology in isolation—and why I ended up joining the School of Public Health rather than staying within a physical-science department.” The model was not predicting cholera outbreaks, but pinpointing floods. “When a village floods and roads wash away, suddenly the radius of generation decreases,” he explains. “And it also makes the village more susceptible in the short term to a cholera outbreak. Ultimately, all this analysis with supercomputers was identifying where there was flooding—data that, frankly, you can get in a lot of other ways.”
Despite this setback, Eagle saw what was missing. If he could couple the data he had from the ministry of health and the mobile operators with on-the-ground reports of what was happening, then he would have a powerful tool for remote disease surveillance. “It opened my eyes to the fact that big data alone can’t solve this type of problem. We had petabytes* of data and yet we were building models that were fundamentally flawed because we didn’t have real insight about what was happening” in remote villages. Eagle has now built a platform that enables him to survey individuals in such countries by paying them small denominations of airtime (as with the Kenyan nurses) in exchange for answering questions: are they experiencing flu-like symptoms, sleeping under bednets, or taking anti-malarials? This ability to gather and link self-reported information to larger datasets has proven a powerful tool—and the survey technology has become a successful commercial entity named Jana, of which Eagle is co-founder and CEO.
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