Last month, I wrote an article for Planetsave about astrophysicists’ proposal to search for E.T. civilizations by optical telescope detection of urban lights on candidate exoplanets. Well, a similar concept can be put to a more Earth-bound use: tracking from space the spread of diseases — like measles outbreaks — based upon the spread and concentration of nighttime lights.
Such disease outbreaks tend to occur in more densely populated areas, but it’s often difficult to determine an accurate population count in order to develop models that can predict how and where an epidemic will spread.
In a recent Princeton University study (published in Science, Dec. 9) researchers utilized night time, satellite images of three of the largest cities in Niger, West Africa and were able to correlate the increase in night time lights to seasonal population growth between September and May, which is Niger’s dry season. The dry season also coincides with measles epidemics.
The images, captured over a four year period between 2000 and 2004, by a Defense Dept. satellite*, were later compared to Ministry of Health Niger) records from that same period. The Princeton team discovered that outbreaks of measles were most prevalent when a given city’s lighted area was largest and brightest.
Many regions in Africa (and Asia) have large migratory populations, with seasonal influxes to cities at certain times of the year (usually following harvest season). As the population density of urban areas increases, so too does the risk of epidemics.
How does the new space-based technique compare to other disease-tracking techniques?
But traditional methods of monitoring outbreaks (such as housing density gauges) often miss these fluctuations in population density. These satellite images can detect where people are clustering by capturing the expansion and brightness of lighted areas. The research team found that this technique accurately reflects these fluctuation in density.
From 1995 to 2004, measles outbreaks in Niger (C, light-shaded area) spiked in the dry season. Satellite imagery of nighttime lights suggests that changes in population density contributed to the epidemics, rather than weather-related factors such as the rainy season (dark-shaded area). The researchers studied measles and nighttime-lights data from 2000 to 2004, the years in which the two datasets overlap. Measles cases in the three cities studied (D) followed the same pattern as the nighttime brightness (E) the researchers observed for each city, with transmission rates and brightness highest during the September to May dry season and lowest during the mid-year rainy season (shaded area on both data sets). Image by Science/AAAS Such migratory populations are notoriously difficult to track and this makes developing an intervention strategy — such as a large-scale vaccination program — extremely challenging. Key to determining where an outbreak is likely to occur (and thus where to initiate a vaccination program) is knowing where a population is expanding.
Lead author Nita Bharti, a researcher in the university’s Department of Ecology and Evolutionary Biology and the Woodrow Wilson School of Public and International Affairs, stated in a press release:
“Once you establish the patterns of epidemics, you can adjust your intervention strategy. 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.”
What about other diseases besides measles?
Regions in Africa and Asia have vast areas of rural land, typically, in which there are only a few scattered, industrialized areas. As populations move over these regions to find work, other diseases tend to follow.
Bharti continues:
“This method isn’t limited to understanding measles — think about malaria or meningitis. 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.”
The Future of Epidemiology
Some prior research had postulated that disease outbreaks were caused by environmental changes, such as decreased/increased rainfall. Earlier research by Grenfell and Bharti had made two important findings: 1] that measles epidemics only occur during Niger’s dry season, and 2] that the severity of an outbreak was related to an area’s population.
But, until they utilized this night time satellite-imaging technique, they had no way of linking the two findings together to indicate a stronger, causal relationship.
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 three-dimensional 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. Image Credit: Science/AAAS The results of the study showed clearly that seasonal brightness for all three cities (Maradi, Zinder and the capital, Niamey) changed similarly: brightness was below average for each city during the rainy season (when a high degree of agricultural activity occurs outside industrial areas) , then rose to above average during the dry season as people migrated back to more urban areas. And, measles transmission rates followed the same pattern — low in the rainy season, high in the dry.
Outside researchers have been calling this use of night-light, satellite imagery “path-breaking” and extol its significant advantages over more common methods. Other researchers applaud the discovery of a clear relationship between disease outbreaks and shifts in population density — apart from being a clever, proxy method for gauging population density by itself.
The method has one significant short-coming: it is hampered by prevailing weather conditions (although images can be readied for analysis in as little as 48 hours). But the researchers hope to combine it with other methods such as tracking cell phone usage (which has its own limitations on it own) to make more accurate predictions, and of course, save lives in the process.
The research was supported by the Bill and Melinda Gates Foundation.
In addition to Bharti, the research team included: 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.
* Defense Meteorological Satellite Program’s Operational Linescan System, operated by the U.S. Department of Defense.
For more information on this research, checkout: Nighttime images help track disease from the sky
