According to the World Health Organization, more than 600,000 people died from malaria in 2010, with a majority of those deaths occurring among African children. The infection is caused by the parasite Plasmodium falciparum and is transmitted via mosquito bites. As you might imagine, where there are more mosquitos—and fewer dollars for public health programs—there is more malaria.
But thanks to increased prevention and control measures, malaria mortality rates fell between 2000 and 2010 by more than 25% globally and by 33% in Africa, according to the WHO.
Before joining the College of Computer and Information Science, research assistant professor Nathan Eagle lived in Kenya for two years working as a Fulbright Professor at the University of Nairobi. Along with his wife Caroline Buckee, a computational epidemiologist at the Harvard School of Public Health, Eagle saw first-hand how malaria can effect the lives of people living in high-risk communities.
“Even if you eradicate malaria in a particular region of Kenya,” said Eagle, “the question is, will that region be able to stay eradicated?” The answer, he said, lies partly in human mobility data. After all, no one stays in one place anymore and traveling between regions of varying malaria rates could undermine the efforts of a single region.
In an article to be published in the journal Science on Friday, Eagle, Buckee and a team of epidemiologists and data scientists report an analytical method that could inform future public health programs in developing more effective eradication strategies.
Every day, millions of Kenyan mobile phone subscribers—just like everyone else in the world—leave traces of their whereabouts when local cell towers transmit their calls. Eagle and his team decided to “take this data and use it in a way that improves the lives of the people who are generating it.”
If a subscriber’s phone connects to a cell tower in Nairobi one day and then to a cell tower near Lake Victoria the next, the researchers infer that this individual traveled between the two places over the last twenty-four hours (it being rather unlikely that the phone would make that journey on its own). The team looked at these mobility traces for nearly 15 million subscribers and used them to generate a global snapshot of travel among the entire population.
By coupling this information with epidemiological “prevalence data,” stakeholders can get a more comprehensive picture of malaria transmission across the country.
“We first qualified the aggregate population behavior,” said Eagle, “looking at the nation as a whole.” They then zoned in on particular regions, separating them based on environmental characteristics that determine the likelihood of disease transmission. Two factors will inform whether a subscriber’s travel is likely to move the parasite, in addition to the person, from one place to another: the characteristics of their home and those of the place they are visiting.
“How do you make sure that if you do get rid of malaria in one particular village that you’ll have a high probability that it stays eradicated?” asked Eagle. This work provides a tool for policy makers to decide whether complete eradication efforts in a single region make sense economically.
“While they can be very confident that they can eradicate it in the short term, if they don’t take [mobility] into account,” he explained, “they may be wasting their money.”
While some regions of Kenya are so isolated from both malaria and travel that it is unlikely they’ll be reinfected after complete eradication, most of the nation might be better served through national eradication approach rather than a local one, said Eagle.