Cell-Phone Data Might Help Predict Ebola’s Spread

As reported by MIT Technology Review: A West African mobile carrier has given researchers access to data gleaned from cell phones in Senegal, providing a window into regional population movements that could help predict the spread of Ebola. The current outbreak is so far known to have killed at least 1,350 people, mainly in Liberia, Guinea, and Sierra Leone.

The model created using the data is not meant to lead to travel restrictions, but rather to offer clues about where to focus preventive measures and health care. Indeed, efforts to restrict people’s movements, such as Senegal’s decision to close its border with Guinea this week, remain extremely controversial.

Orange Telecom made “an exceptional authorization in support of Ebola control efforts,” according to Flowminder, the Swedish nonprofit that analyzed the data. “If there are outbreaks in other countries, this might tell what places connected to the outbreak location might be at increased risk of new outbreaks,” says Linus Bengtsson, a medical doctor and cofounder of Flowminder, which builds models of population movements using cell-phone data and other sources.

The data from Senegal was gathered in 2013 from 150,000 phones before being anonymized and aggregated. This information had already been given to a number of researchers as part of a data analysis challenge planned for 2015, and the carrier chose to authorize its release to Flowminder as well to help meet the Ebola crisis.

The new model helped Flowminder build a picture of the overall travel patterns of people across West Africa. In addition to using data from Senegal, researchers used an earlier data set from Ivory Coast, which Orange had released two years ago as part of a similar conference (see “Released: A Trove of Data-Mining Research from Phones” and “African Bus Routes Redrawn Using Cell-Phone Data”). The model also includes data about population movements from more conventional sources, including surveys.

Separately, HealthMap, a team based at Boston Children’s Hospital, has produced an animation of the epidemic’s spread since March, based on records of when and where people died of the disease.

Bengtsson cautions that the model is essentially a first draft, and that it’s based on historical movements, so it does not take into account how people may have changed their behavior in response to the recent crisis. Ideally, he adds, it would include real-time data. But “in countries that already have epidemics,” he says, “this is the best estimate we can do of what mobility will look like. This can give the sense of the radius people tend to travel around.”

Ebola is transmissible via bodily fluids during an incubation period of between two and 21 days, during which victims may not know they are infected. That makes it particularly important to know where people are going and where they’ve been.

Mobile phones—which are ubiquitous even in poor countries—can play a key role. All cell phones “ping” nearby towers with a unique ID number to announce their presence. In this way, mobile carriers amass huge databases containing fine-grained information on population movements and social patterns.

The application to public health is compelling. Caroline Buckee, a Harvard epidemiologist who also worked with Flowminder to develop the West African model, has demonstrated how such data can show where people have gone after leaving a hot spot, suggesting where a disease cluster will crop up next (see “35 Innovators under 35: Caroline Buckee” and “Big Data from Cheap Phones”).

Last year Buckee demonstrated how cell-phone data could aid in fighting malaria by revealing where to focus mosquito eradication efforts. Previously, researchers trying to model mobility relied on techniques like counting heads at bus stations and asking sick people where they’d been traveling.

There’s no indication thus far that health officials are using the Flowminder model, which was released Wednesday. While public health agencies are interested in the topic, Bengtsson says that agencies such as the World Health Organization didn’t ask the researchers to develop the model or work with them to do so.

Emmanuel Letouzé, cofounder and director of Data-Pop Alliance, which is working on similar projects, says the approach holds promise. “If mobile carriers provide all the data at a very granular level, the value you can extract is huge,” says Letouzé, a visiting scholar at MIT’s Media Lab. Nevertheless, he says, “the privacy concerns are even more salient.” That is because such data reveal detailed social and business connections and location information, which can often be linked back to individuals.