University Park, PA
Satellite images of nighttime lights, which normally are 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, an assistant professor of biology at Penn State, found that the new technique accurately indicates fluctuations in population density — and thus the corresponding risk of epidemic — that can elude current methods of monitoring outbreaks.
The research, reported in the current issue of the journal Science, is expected to help medical professionals to 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 to correlate seasonal population fluctuations with the onset of measles epidemics during the country’s dry season, roughly from September to May. Because many pathogens that cause epidemics flourish in areas where the population density is the greatest, satellite imagery showing brighter areas — indicating greater numbers of people — then can be used to pinpoint disease hot spots. 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 Maradi, Zinder, and Niger’s capital, Niamey.
In many agriculturally dependent nations, such as Niger, people migrate from rural to urban areas after the growing season, explained Nita Bharti, a postdoctoral researcher at Princeton University and the first-listed author of the research 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, the night lights are an important source of data for Africa and Asia, especially, where other sources of data are sometimes absent.
The team found that measles cases were most prevalent when a city’s lighted area was largest and brightest. “We found that seasonal brightness for all three cities changed similarly,” Ferrari said. “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 in providing medical treatment.
For example, in Niamey, measles cases were recorded daily for three districts, or communes, during the 2003-to-2004 dry season. 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 population density, as determined by light brightness, already had started to decline in the north of the city.
“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 said.
Bharti added that the team’s new method is not limited to understanding measles. “Think about malaria or meningitis,” she said. “These diseases are geographically specific, for the most part, to areas where this would be a useful technique. These are places that are not so industrialized that they always will be saturated with brightness and where there may be some level of agricultural dependence so that there are detectable labor migrations.”
The researchers also are exploring the use of nighttime lights with other large-scale population-tracking methods such as the monitoring of mobile-phone usage.
“When used alone, both population-tracking methods have their shortcomings,” Bharti said. “Nighttime-lights imagery is susceptible to weather conditions, while mobile-phone usage data are biased in the portion of the population it can represent.” Bharti and her co-authors hope that when nighttime imagery is combined with other techniques, the measures will be complementary.
In addition, the team members hope to explore uses for nighttime satellite data outside of epidemiology, such as tracking population displacement and mass migration during a war or following a natural disaster.
“We now have a technique that allows us to observe and measure changes in population density,” Bharti said. “This short-term use of nighttime-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.”
In addition to Ferrari and Bharti, other authors of the study include Andrew Tatem of the University of Florida; Rebecca Grais of Epicentre, a non-profit research facility located in France; Ali Djibo of the Nigerian Ministry of Health; and Bryan Grenfell of Princeton University. The research was supported by the Bill and Melinda Gates Foundation.
[box type=”shadow”]This article originally published at Penn State Live. For more information, contact Ferrari at 814-865-6080 or firstname.lastname@example.org, or Barbara Kennedy, Penn State Science PIO, at 814-863-4682 or email@example.com. Photo via Flickr.[/box]