Micronutrient Deficiency Prediction via Publicly Available Satellite Data


  • Elizabeth Bondi Harvard University
  • Haipeng Chen Harvard University
  • Christopher D. Golden Harvard University
  • Nikhil Behari Harvard University
  • Milind Tambe Harvard University




Public Health, Micronutrient Deficiency, Satellite Data, Small Data, Interpretability


Micronutrient deficiency (MND), which is a form of malnutrition that can have serious health consequences, is difficult to diagnose in early stages without blood draws, which are expensive and time-consuming to collect and process. It is even more difficult at a public health scale seeking to identify regions at higher risk of MND. To provide data more widely and frequently, we propose an accurate, scalable, low-cost, and interpretable regional-level MND prediction system. Specifically, our work is the first to use satellite data, such as forest cover, weather, and presence of water, to predict deficiency of micronutrients such as iron, Vitamin B12, and Vitamin A, directly from their biomarkers. We use real-world, ground truth biomarker data collected from four different regions across Madagascar for training, and demonstrate that satellite data are viable for predicting regional-level MND, surprisingly exceeding the performance of baseline predictions based only on survey responses. Our method could be broadly applied to other countries where satellite data are available, and potentially create high societal impact if these predictions are used by policy makers, public health officials, or healthcare providers.




How to Cite

Bondi, E., Chen, H., Golden, C. D., Behari, N., & Tambe, M. (2022). Micronutrient Deficiency Prediction via Publicly Available Satellite Data. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 12454-12460. https://doi.org/10.1609/aaai.v36i11.21512