A team led by Princeton University researchers found that satellite images of nighttime lights can be used to pinpoint disease hotspots in developing nations by revealing the boom in population density that typically coincides with seasonal epidemics. The researchers correlated increases in brightness in three cities in Niger with the onset of seasonal measles epidemics. Measles cases in Niger spike in the September to May dry season as people migrate to urban areas from the agricultural countryside. A 3D rendering shows the amount of brightness for urban areas in Niger over the course of an average year, with the height of each spike representing total brightness (the color gradient is for emphasis). The three tallest spikes indicate the cities the researchers studied: from left, Niamey, Niger’s capital and largest city; Maradi; and Zinder. Imag: Science/AAAS |
Normally used to spot where people live, satellite images of nighttime
lights can help keep tabs on the diseases festering among them, too, according
to new research.
Princeton University-led researchers report in Science that nighttime-lights imagery presents a new tool for
pinpointing disease hotspots in developing nations by revealing the population
boom that typically coincides with seasonal epidemics. In urban areas with
migratory populations, the images can indicate where people are clustering by
capturing the expansion and increasing brightness of lighted areas. The
researchers found the technique accurately indicates fluctuations in population
density—and thus the risk of epidemic—that can elude current methods of
monitoring outbreaks.
The team 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. The team found that
measles cases were most prevalent when a city’s lighted area was largest and
brightest.
Lead author Nita Bharti, a Princeton postdoctoral researcher in the Department
of Ecology and Evolutionary Biology and the Woodrow Wilson School of Public and
International Affairs, explains that people in nations such as Niger commonly
migrate from rural to urban areas after the growing season. 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.
She and her co-authors found, however, that monitoring changes in nighttime
lights clearly indicates where and when a population is expanding and where an
epidemic would 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,” Bharti 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.”
Bharti, who works in the laboratory of co-author Bryan Grenfell, a
Princeton professor of ecology and evolutionary biology and public affairs,
also worked with second author Andrew Tatem, a geography professor at the University
of Florida; Matthew Ferrari, a biology professor at Pennsylvania State
University; Rebecca Grais, an epidemiologist with Epicentre, the Paris-based
research branch of Doctors Without Borders; and Ali Djibo of the Niger Ministry
of Health.
The trouble with tracking migratory populations
Deborah Balk, a professor at the City University of New York and associate
director of the university’s Institute for Demographic Research, said the researchers’
use of city-scale nighttime-lights imagery to examine the spread of disease is
“pathbreaking” and offers significant advantages over more common
techniques.
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
her collaborators use the nighttime brightness data to illustrate seasonal
population swings, information that other types of satellite data such as
images of housing density cannot detect, she says.
“Temporary and seasonal migrants are very hard to measure,” Balk
says. “The night lights are an important source of data for Africa and Asia, especially, where data is sometimes absent or quite
poor.”
Pej Rohani, a University
of Michigan professor who
studies infectious disease ecology and evolution, says that responses to
epidemics are more complicated in areas with migratory or unstable populations.
Rohani said that he was 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 is familiar with the research but also had no role in it.
Beyond providing a unique method to gauge population density, Rohani says
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, Bharti says.
“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, follow the crowd
The work stems from a longtime effort in Grenfell’s laboratory to understand
seasonal measles epidemics in Niger.
In 2010, Bharti published a paper with Djibo,
Grenfell, Grais and lead author Ferrari, as well as Penn State entomology
professor Ottar Bjornstad, 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 could not test their hypothesis, Bharti says.
The 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, the Princeton-led team found. This means 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, Bharti says.
Under similar circumstances, the researchers write, 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.
Rohani says that 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,” he 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 says. Bharti and her co-authors hope that when nighttime imagery
is combined with other techniques, the measures will be complementary.
In addition, Bharti says, the team is 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.”
