New work maps a region’s nutrient landscape
Micronutrient deficiencies afflict more than two billion people worldwide, including 340 million children.
Diagnosing deficiencies early enough for effective treatment requires expensive, time-consuming blood draws and laboratory tests.
Computer scientist Elizabeth Bondi and her colleagues at Harvard University used publicly available satellite data and artificial intelligence to reliably pinpoint geographical areas where populations are at high risk of micronutrient deficiencies.
Existing AI systems can use satellite data to predict localized food security issues, but they typically rely on directly observable features.
Their work shows that combining data such as vegetation cover, weather and water presence can suggest where populations will lack iron, vitamin B12 or vitamin A. The team examined raw satellite measurements and consulted with local public health officials, then used AI to sift through the data and pinpoint key features.
They used real-world biomarker data to train and test their AI program.
Predictions of regional-level micronutrient deficiency in populations outside the training data sets met, and sometimes exceeded, the accuracy of estimates based on surveys administered by local public health officials.
"This is a novel contribution that highlights AI's potential to advance public health," says Emory University epidemiologist Christine Ekenga, who was not involved with the study.
Collecting health data in low-resource settings can be difficult because of cost and infrastructure constraints, she adds, and "The authors have validated a method that can overcome these challenges."
The researchers aim to develop a software application that extends this analysis to other countries that have public satellite data.