One of three ‘research accelerator projects‘ starting in late 2024, the University of California-led project will improve early warning of food insecurity by integrating child malnutrition forecasts based on machine learning and anthropometric data into Kenya’s National Drought Monitoring Authority services.
The challenge
A child’s arm is measured at a malnutrition clinic in Lekwasimyen in Northern Kenya’s Turkana province. Photo: Lisa Murray / Concern Worldwide.
Kenya’s National Drought Monitoring Authority (NDMA) is a governmental organization that monitors and coordinates relief action for drought-driven hunger crises in the country’s arid and semiarid regions. Part of their monitoring activity entails the monthly collection of anthropometric data (Middle Upper Arm Circumference) from children under five, from which the prevalence of global acute malnutrition (GAM) can be derived.
Despite these monitoring efforts, the NDMA does not currently employ any GAM prevalence forecasting system, which limits their ability to trigger timely and well-dimensioned humanitarian response to avoidable acute malnutrition and death.
Our recent work has established that these high frequency anthropometric data, combined with secondary remote sensing data and machine learning methods, can forecast GAM with sufficient accuracy to be operationalized.
In this project, we will co-develop and launch an early warning tool that can be incorporated into NDMA’s operations and adapted to other settings with similar hunger crises problematic where sentinel site data exist.
More
Fuller information on project aims and activities
Download a short presentation of the project
Contact: Susana Constenla-Villoslada, University of California at Berkeley (susana_constenla@berkeley.edu)
The research described here is funded by the Jameel Observatory for Food Security Early Action. The original work to develop the underlying predictive model was funded by the United States Agency for International Development (USAID) under cooperative agreement number 7200AA18CA00014, “Innovations in Feed the Future Monitoring and Evaluation – Harnessing Big Data and Machine Learning to Feed the Future”. This work was also undertaken as part of the Food Security Portal project led by the International Food Policy Research Institute (IFPRI), with funding support provided by the European Commission (EC).
