High-frequency monitoring enables machine learning–based forecasting of acute child malnutrition for early warning

Susana Constenla-Villoslada, Yanyan Liu, Linden McBride, Clinton Ouma, Nelson Mutanda and Christopher Barrett
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Susana Constenla-Villoslada: b Markets, Trade, and Institutions, International Food Policy Research Institute , Washington , DC 20005
Yanyan Liu: c Charles H. Dyson School of Applied Economics and Management, Cornell University , Ithaca , NY 14853
Linden McBride: d Center for Economic Studies, U.S. Census Bureau, Suitland, MD 20746. Any opinions and conclusions expressed herein are those of the authors and do not reflect the views of the U.S. Census Bureau
Clinton Ouma: e National Drought Management Authority , Nairobi 00200 , Kenya
Nelson Mutanda: e National Drought Management Authority , Nairobi 00200 , Kenya

Proceedings of the National Academy of Sciences, 2025, vol. 122, issue 23, e2416161122

Abstract:

The number of acutely food insecure people worldwide has doubled since 2017, increasing demand for early warning systems (EWS) that can predict food emergencies. Advances in computational methods, and the growing availability of near-real time remote sensing data, suggest that big data approaches might help meet this need. But such models have thus far exhibited low predictive skill with respect to subpopulation-level acute malnutrition indicators. We explore whether updating training data with high frequency monitoring of the predictand can help improve machine learning models’ predictive performance with respect to child acute malnutrition by directly learning the dynamic determinants of rapidly evolving acute malnutrition crises. We combine supervised machine learning methods and remotely sensed feature sets with time series child anthropometric data from EWS’ sentinel sites to generate accurate forecasts of acute malnutrition at operationally meaningful time horizons. These advances can enhance intertemporal and geographic targeting of humanitarian response to impending food emergencies that otherwise have unacceptably high case fatality rates.

Keywords: food security; food crises; humanitarian response; nonstationarity; resilience (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:nas:journl:v:122:y:2025:p:e2416161122

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