Call it researching disease in the cloud or just plain illuminating, but Princeton researchers today published a paper in Science that demonstrates how aerial night images can be used to monitor the spread of epidemic in the developing world by tracking light density.

That’s a mouthful, but the concept is really pretty straightforward. In developing nations with migratory populations, there are parts of the year when everyone tends to meet up, creating localized, seasonal population booms. Those booms, with a large number of people in a concentrated area, are a hotspot for the spread of disease. Unfortunately for health agencies, tracking migratory populations has never been an easy task, which means epidemic can get out of hand quickly.

The Princeton team, led by postdoc researcher Nita Bharti, used night time images of Niger’s three largest cities, Zinder and the capital, Niamey—to show that increased local populations were correlated with increased light output at night. The images, taken by a Defense Department satellite from 2000 to 2004, were compared to records of measles cases from Niger’s Ministry of Health from the same time period. Bharti’s team found that the rate of measles cases was highest when a city’s lighted area was largest and brightest.

“Once you establish the patterns of epidemics, you can adjust your intervention strategy,” Bharti said. “We turned to this technique because there is really no other way to get any idea of how populations are changing in a place like Niger. That’s true throughout most of sub-Saharan Africa and a lot of other places in the world.”

This 3D rendering shows light values in Niger averaged over a year. The three tallest spikes are from the cities studied. From left to right: Niamey, Maradi and Zinder. (Image by Science/AAAS)

It truly is a novel solution, and should be a boon for health organizations who often struggle to effectively implement targeted vaccination programs. By being able to track when scattered groups are most bunched up, health agencies can also be sure that they get the most bang for their buck.

“Traditionally, we’ve been having to make inferences about what determines the patterns of seasonality we see in disease outbreaks,” Pej Rohani, a University of Michigan professor who studies infectious disease ecology and who did not participate in the study, said. “The beauty of this study is that they were able to dissect with great precision how the presence of susceptible individuals in the population correlates with and determines the growth rate of the epidemic.”

The study coincides with other research aimed at sorting out just how to track large populations that are always on the move, like efforts to monitor cell phone usage patterns. Neither technique is perfect—the night lights method requires clear weather for quality imagery, and anything involving cell phones requires that people own and use them—but combined they offer a wealth of new opportunities for gathering data on the movements of transient populations, especially in Africa and Asia where data-driven aid solutions are hard to come by.

“We now have a technique that allows us to observe and measure changes in population density,” Bharti said. “This short-term use of night time lights data could apply to a number of different situations beyond seasonal migrations and infectious diseases, such as humanitarian and disaster aid. We’re excited about the potential this method has for other important global health issues.”

It’s certainly not a cure-all, as there still is the whole issue of actually getting aid to the ground, and vaccination programs are never easy or without controversy. But the research (which, rather unsurprisingly, was supported by the Bill and Melinda Gates Foundation) is an excellent example of the fact that humanitarian organizations are increasingly looking to develop data-driven solutions. That should mean less waste and more effective services, which are both very good things.