Satellite and drone images used to predict how a tropical disease spreads: Study
Researchers have used satellite images, drone photos, and even Google Earth to identify communities most at risk for contracting schistosomiasis -- a parasitic disease that is second only to malaria in its global health impact.
The researchers, including those from the University of Washington (UW) in the US, used rigorous field sampling and aerial images to precisely map communities that are at greatest risk for schistosomiasis, and discovered clues in the environment that can help identify transmission hotspots for the disease.
The study, published in the journal PNAS, noted that more than 200 million people across the world have schistosomiasis -- which is treatable but has been difficult to eliminate from some regions of the world.
The researchers said that the worms that cause the disease -- schistosomes -- grow within freshwater snails, where they multiply and are released into the waters of rivers, lakes, and streams.
They added that the worms infect people by penetrating their skin when they swim, bathe, or wade.
The diseases, they said, causes bloody urine and stool, abdominal pain, and can damage the liver, spleen, intestines, lungs, and bladder.
The study noted that the infection can stunt growth in children, and impair their cognitive development.
"The ecological side of the problem is what's holding us back from schistosomiasis control and elimination -- and now ecologists are stepping in and filling that gap," said Chelsea Wood, lead author of the study from UW.
Wood and her team worked across more than 30 sites in northwestern Senegal in Africa, where villages used a local river and lake for everything from bathing and swimming to washing dishes and clothes.
The researchers said that the location in Senegal was the epicenter of the largest schistosomiasis outbreak ever recorded, in the mid-1980s.
The researchers counted and mapped the distribution of snails across each site over two years.
They found through their fieldwork that the snails were present in the river in patchy and inconsistent distributions over time.
Snails may be found in one location, then completely absent three months later, they said.
"Counting snails is not an easy undertaking, and it also produces data that are not as useful as the data you can get from a drone," Wood said.
The researchers then noted that targeting aggregations of snails for removal might not be an efficient way to reduce the transmission of the disease.
They shifted their focus to the habitat where snails lived.
They found that the snails thrive in unrooted, floating vegetation visible in images from satellites and drones.
The researchers used models to evaluate which factors best predicted schistosomiasis transmission using all the data they gathered about each site such as snail density, village size, and location.
They found that the total area of a water access point, and the area of floating vegetation were the two best indicators that human infection would occur nearby.
The study noted that these habitat features are all easy to measure in drone or satellite imagery.
"This is a game-changer for developing-country public health agencies, because it will make it possible for them to efficiently find the villages that need their help the most," said Wood.
According to Wood, once researchers know the association between snail presence and particular habitat features, they can use drone and satellite imagery to detect those habitat features.
She said that this would cut the time needed to evaluate the risk of schistosomiasis infection down to a fraction of what it would be if one were just looking at snails.
"Now we can take these aerial images from season to season and have an idea of how the pathogenic landscape changes in time and space. This can give us a better idea of infection rates," said study co-author Giulio De Leo from Stanford University.
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