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

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cg.authorship.typesCGIAR and advanced research institute
cg.authorship.typesCGIAR and developing country institute
cg.authorship.typesCGIAR single centre
cg.contributor.affiliationUniversity of California
cg.contributor.affiliationInternational Food Policy Research Institute
cg.contributor.affiliationCornell University
cg.contributor.affiliationUnited States Census Bureau
cg.contributor.affiliationNational Drought Management Authority, Kenya
cg.contributor.donorUnited States Agency for International Development
cg.contributor.donorEuropean Commission
cg.contributor.programAcceleratorBetter Diets and Nutrition
cg.contributor.programAcceleratorFood Frontiers and Security
cg.contributor.programAcceleratorPolicy Innovations
cg.creator.identifierSusana Constenla-Villoslada: 0000-0001-7736-8725
cg.creator.identifierYanyan Liu: 0000-0001-7553-2464
cg.howPublishedFormally Published
cg.identifier.dataurlhttps://github.com/susanaconstenla/Constenla_Villoslada_et_al_2025_PNAS
cg.identifier.doihttps://doi.org/10.1073/pnas.2416161122
cg.identifier.projectIFPRI - Markets, Trade, and Institutions Unit
cg.identifier.projectIFPRI - Feed the Future
cg.identifier.projectIFPRI - Food Security Portal
cg.identifier.publicationRankA Plus
cg.isijournalISI Journal
cg.issn0027-8424
cg.issue23
cg.journalProceedings of the National Academy of Sciences of the United States of America (PNAS)
cg.reviewStatusPeer Review
cg.subject.impactAreaNutrition, health and food security
cg.volume122
dc.contributor.authorConstenla-Villoslada, Susana
dc.contributor.authorLiu, Yanyan
dc.contributor.authorMcBride, Linden
dc.contributor.authorOuma, Clinton
dc.contributor.authorMutanda, Nelson
dc.contributor.authorBarrett, Christopher B.
dc.date.accessioned2025-06-12T15:02:20Z
dc.date.available2025-06-12T15:02:20Z
dc.identifier.urihttps://hdl.handle.net/10568/175080
dc.titleHigh-frequency monitoring enables machine learning–based forecasting of acute child malnutrition for early warningen
dcterms.abstractThe 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.en
dcterms.accessRightsOpen Access
dcterms.audienceAcademics
dcterms.bibliographicCitationConstenla-Villoslada, Susana; Liu, Yanyan; McBride, Linden; Ouma, Clinton; Mutanda, Nelson; and Barrett, Christopher B. 2025. High-frequency monitoring enables machine learning–based forecasting of acute child malnutrition for early warning. Proceedings of the National Academy of Sciences of the United States of America (PNAS) 122(23): e2416161122. https://doi.org/10.1073/pnas.2416161122
dcterms.issued2025-06-06
dcterms.languageen
dcterms.licenseCC-BY-NC-ND-4.0
dcterms.publisherNational Academy of Sciences
dcterms.subjectmonitoring
dcterms.subjectmachine learning
dcterms.subjectchildren
dcterms.subjectmalnutrition
dcterms.subjectfood security
dcterms.subjectearly warning systems
dcterms.typeJournal Article

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