PRINCETON (US) — Researchers are using satellite images of nighttime lights to keep tabs on disease hotspots in developing nations.
By revealing the population boom that often coincides with seasonal epidemics, the images can indicate where people are clustered by capturing expansion and increasing brightness of lighted areas. The technique accurately indicates fluctuations in population density—and thus the risk of epidemic—that can elude current methods of monitoring outbreaks.
A team from Princeton University used nighttime images of the three largest cities in the West African nation of Niger to correlate seasonal population growth 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 used to obtain night-light data, were compared to records from Niger’s Ministry of Health of measles cases from the same years. Measles cases were most prevalent when a city’s lighted area was largest and brightest.
People in nations such as Niger commonly migrate from rural to urban areas after the growing season, explains Nita Bharti, postdoctoral researcher in ecology and evolutionary biology and public and international affairs. As people gather in cities during the dry-season months when agricultural work is unavailable, these urban centers frequently become host to outbreaks of crowd-dependent diseases such as measles and meningitis.
Migratory populations are notoriously difficult to track, Bharti says, which can amplify the difficulty and complexity of carrying out large-scale vaccinations. Changes in nighttime lights clearly indicates where and when a population is expanding and where an epidemic will most likely occur.
“Once you establish the patterns of epidemics, you can adjust your intervention strategy,” Bharti says. “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 method isn’t limited to understanding measles—think about malaria or meningitis,” she says. “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 will always be saturated with brightness and where there may be some level of agricultural dependence so that there are detectable labor migrations.”
Tracking migratory populations
Use of city-scale nighttime-lights imagery to examine the spread of disease is “pathbreaking” and offers significant advantages over more common techniques, says Deborah Balk, a professor at the City University of New York.
Images of nighttime lights have typically been used to study urbanization and economic development, as well as physical-science questions, says Balk, who is familiar with the project but had no role in it. In this case, Bharti and colleagues used the nighttime brightness data to illustrate seasonal population swings, information that other types of satellite data such as images of housing density can’t detect.
“Temporary and seasonal migrants are very hard to measure,” she says. “The night lights are an important source of data for Africa and Asia, especially, where data is sometimes absent or quite poor.”
Responses to epidemics are more complicated in areas with migratory or unstable populations, according to Pej Rohani, a University of Michigan professor who studies infectious disease ecology and evolution, who says he is unaware of any other application of nighttime imagery to epidemics.
“If you’re thinking about a city with hundreds of thousands or millions of people, how can you know at any one time how many people are in the city, which is why these kind of proxy measures are clever and useful,” says Rohani, who also had no role in the research.
Beyond providing a unique method to gauge population density, the Princeton-led project also is notable for the unusually clear relationship it shows between outbreaks and shifts in population density in the first place.
“Traditionally, we’ve been having to make inferences about what determines the patterns of seasonality we see in disease outbreaks,” Rohani says. “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 difficulty of the project and the fact that night-lights data are largely associated with long-term studies of stable populations could explain why nighttime satellite images have not previously been used to gather information about short-term events such as epidemics.
“Nighttime imagery is used as a tool to look at stable populations, which is the opposite of what we used it for,” Bharti says. “Setting up this latest project was very labor-intensive. The idea of applying nighttime-lights data in this way is somewhat unconventional, so there was no previous research for us to work from.”
Follow the lights
The work stems from a longtime effort in the lab of Bryan Grenfell, professor of ecology and evolutionary biology and public affairs, to understand seasonal measles epidemics in Niger. In 2010, Bharti and colleagues published a paper reporting that measles epidemics in Niger only occur during the dry season and that an outbreak’s severity is related to an area’s population.
Thus, the researchers concluded, these events are likely the result of population shifts, rather than environmental factors such as rainfall. But without an accurate method for measuring population movement and changes in density, they couldn’t test their hypothesis.
The new project reported in Science is intended to provide such a method. The researchers selected nighttime images clear of excess light pollution and obscuring weather from several hundred photos captured between 2000 and 2004 by the Defense Meteorological Satellite Program’s Operational Linescan System, operated by the U.S. Department of Defense. Those images were compared to records from Niger’s Ministry of Health of weekly measles outbreaks during the same years in the country’s three largest cities: Maradi, Zinder, and the capital, Niamey.
Seasonal brightness for all three cities changed similarly, the researchers report. Brightness was below average for each city during the agriculturally busy rainy season, then rose to above average as people packed urban areas during the dry season. Measles transmission rates followed the same pattern—low in the rainy season, high in the dry.
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. In Niamey, measles cases were recorded daily for three districts, or communes, during the 2003-04 dry season. Brightness and measles infection both peaked early in the first and second communes in February and March of 2004.
A two-week mass-vaccination campaign was launched in March and April, but population density, as determined by light brightness, had already started to decline in communes 1 and 2, meaning that because the vaccination was not synchronized with population-density increases in communes 1 and 2, large numbers of people in those districts may have left the city without receiving the measles vaccine.
Under similar circumstances, measurements of population density determined by nighttime imagery—which can be ready to analyze within 48 hours of the satellite collecting the data—could be used to help coordinate preventative and reactive treatment with periods when the most people are arriving or are present in a certain area.
The technique could become important in predicting the peak of measles outbreaks in other susceptible countries, but might also apply to other diseases that, like measles, are driven by population density more than any other factor.
“This is probably the most careful dissection of an epidemic of measles in any setting I’m aware of—it’s very careful work that provides a mechanistic explanation for the progression of measles in a large population,” Rohani says. “It also shows promise for understanding seasonality in places like Niger for other directly transmitted infectious diseases like meningococcal infections or pertussis, or maybe influenza.”
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 methods have their shortcomings, Bharti says. Nighttime lights imagery is susceptible to weather conditions, while mobile-phone usage data is biased in the portion of the population it can represent. She and her co-authors hope that when nighttime imagery is combined with other techniques, the measures will be complementary.
The team is also looking at uses for nighttime satellite data outside of epidemiology that also involve mass migration, such as tracking population displacement 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 says. “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. ”
Researchers from Penn State, University of Florida, Epicentre, the Paris-based research branch of Doctors Without Borders; and the Niger Ministry of Health also contributed to the study that was supported by the Bill and Melinda Gates Foundation.
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