Satellite images of nighttime lights, normally used to detect population centers, also can help keep tabs on diseases in developing nations, according to new research. An international research team that includes Matthew Ferrari, assistant professor of biology at Penn State, found that the composited images accurately indicate fluctuations in population density—and thus the corresponding risk of epidemic—that can elude current methods of monitoring outbreaks.
A single satellite image of southwest Niger’s nighttime lights at the beginning of the dry season. Niger’s national borders are outlined in blue (see inset), and the districts are outlined in gray. The bright pixels (shown in white), indicate relatively high population density. The brightest point in Niger is Niamey, the largest city and the capital, shown in the center of this image. During the dry season in Niger, population density increases in the cities.
The research, reported in a recent issue of the journal Science, is expected to help medical professionals synchronize vaccination strategies with increases in population density.
Ferrari and his team used nighttime images of the three largest cities in the West African nation of Niger—Maradi, Zinder, and the capital, Niamey—to correlate seasonal population fluctuations with the onset of measles epidemics during the country’s dry season, roughly from September to May. The images, taken between 2000 and 2004 by a U.S. Department of Defense satellite, were compared to records from Niger’s Ministry of Health of weekly measles outbreaks during the same years in the three cities.
In many agriculturally dependent nations such as Niger, people migrate from rural to urban areas after the growing season, explains Nita Bharti, a postdoctoral researcher at Princeton University and lead author on the Science paper. As people gather in cities during the dry-season months when agricultural work is unavailable, these urban centers frequently become hosts to outbreaks of crowd-dependent diseases such as measles. Because temporary and seasonal migrations are very hard to measure directly (as opposed to rough estimates), the night lights are an important source of data, especially for Africa and Asia where other sources of data are sometimes absent.
“We found that seasonal brightness for all three cities changed similarly,” Ferrari says. “Brightness was below average for Maradi, Zinder, and Niamey during the agriculturally busy rainy season, then rose to above average as people moved to urban areas during the dry season. Measles transmission rates followed the same pattern—low in the rainy season, high in the dry season.“ The team members also found that the relationship between brightness and measles transmission appeared even clearer at the local level, as did the potential value of the researchers’ technique for planning medical intervention.
For example, in Niamey, both brightness and measles infection peaked early in the northern districts in February and March of 2004. A two-week mass-vaccination campaign was launched in March and April of 2004, but by then population density, as determined by light brightness, had already started to decline. If public health officials had had the satellite data at the time, they could have hastened intervention, the researchers suggest.
Nita Bharti, Princeton University
Researchers analyzed the seasonal changes in nighttime-light brightness in Niger's largest cities. The height of the peaks represents the brightness of nighttime lights over the course of one year. Niamey is the tallest peak at far left.
“Ultimately, the goal is to use this research to design better preventative-vaccination programs and more efficient responsive vaccination strategies when outbreaks do occur,” Ferrari says.
Bharti added that the team’s new method could also be used for understanding malaria and meningitis, other diseases which are largely limited to areas that are not heavily industrialized and where there are detectable labor migrations that are difficult to measure precisely. In addition, the researchers are exploring the use of nighttime lights with other large-scale population-tracking methods such as the monitoring of mobile-phone usage. They hope, too, to find uses for brightness data in tracking population displacement and mass migration due to war or natural disaster.
“We now have a technique that allows us to observe and measure changes in population density,” Bharti says. “We’re excited about the potential this method has for other important global-health issues.”
Matthew Ferrari, Ph.D., is assistant professor of biology, firstname.lastname@example.org. Nita Bharti, a postdoctoral researcher at Princeton, received her Ph.D. in biology from Penn State in 2009.