While great strides have been made in reducing measles outbreaks in the U.S., for many remote communities around the world, the disease is still a very real concern. The WHO estimates there were more than 170,000 measles cases worldwide in 2017, the majority of which occurred in underserved areas with a lack of access to healthcare. To complicate matters, it’s especially hard to prevent outbreaks in areas where seasonal population migrations influence the amount of health care resources needed.
But as Nita Bharti has found, the answer to providing accessible health care in these areas might lie in satellite imagery.
As part of research with Penn State’s Center for Infectious Disease Dynamics and the Huck Institutes of the Life Sciences, Bharti has discovered a way to use satellite imagery to monitor changes in population sizes that can be difficult to track, particularly seasonal migration patterns. With this tool, healthcare officials can better prepare underserved areas with necessary resources, ensuring they’re well equipped to treat and prevent outbreaks of infectious diseases like measles.
Each year in the Sahel, a vast band of grasslands just south of the Sahara Desert, seasonal farmers and their families move from farms to cities when the long, dry season begins. Many travel long distances to squeeze into already crowded districts, finding spaces in extended family compounds or temporary sites on the city’s edges. In places like Niamey, capital of the West African nation of Niger, the dry season also brings measles. When the rainy season begins and people return to farms, measles cases drop off abruptly.
Researchers suspected the outbreaks were related to fluxes in population size as migrating families moved into cities. Measles, after all, is highly infectious; it flourishes under crowded conditions. But with no good way to track the changing population in a densely populated place like Niamey, they had little chance to test their hypothesis.
Bharti began working on this problem as a postdoctoral student and continued advancing the research as part of Penn State’s Center for Infectious Disease Dynamics. Bharti and geographer Andrew Tatem devised a novel solution. They used satellite images of nighttime lights, a data source that had been previously used to create composite images over large periods of time in order to study urbanization and economic development but had never been analyzed across shorter time scales or applied to predicting disease outbreaks.
When large numbers of migrants moved into Niamey, they reasoned, the city would appear both brighter and larger in satellite imagery taken at night, reflecting the increased number of fires and electric lights associated with a swollen population. When seasonal migrants left the city to return to agricultural areas, the nighttime images of the city would dim to reflect a reduced population size. By comparing satellite images over time, they were able to estimate population changes, and then correlate those changes with public-health records of measles outbreaks.
