Research page
International Relations / Political Science

Modelling Early Risk Indicators to Anticipate Malnutrition (MERIAM)

Projet Leads: Action Against Hunger, Shanon Doocy,  David Backer, Ravi Bhavnani
Timeline: 201 7- 2020
Keywords: Early Warning, Risk, Malnutrition, Conflict
Funding Organisation: DIFID
Partners: Action Against Hunger (ACF), the University of Maryland (UMD), the Graduate Institute of International and Development Studies (IHEID) and Johns Hopkins University (JHU) from the MERIAM consortium


It is no coincidence that fragile and conflict-affected states have some of the highest rates of hunger, child undernutrition and child mortality in the world today (UNICEF 2011). Despite progress in strengthening early warning systems for food insecurity, current approaches to detect declines in nutritional status still tend to be ‘late’ warning systems, reliant upon indicators such as the prevalence of moderate and severe acute malnutrition, which are only able to detect a nutrition crisis after it has already begun. Therefore, a shift to preventative actions will require a shift in the way we conceptualize nutrition security, forecast nutrition-related vulnerabilities at a relatively local level, identify the causal factors driving nutritional deterioration, and design nutrition-sensitive services that mitigate the impact of shocks on households and communities. While previous efforts to predict increases in nutritional risk have been limited by the quality, availability and frequency of data collection, especially at sub-national levels, the on-going data revolution provides a compelling stimulus to avoid the past challenges to accelerate reductions in undernutrition and build nutritional resilience to shocks in fragile contexts. The central aim of the Modelling Early Risk Indicators to Anticipate Malnutrition (MERIAM) project is to do just that – to identify, test and scale up cost-effective means to improve the prediction and monitoring of undernutrition in difficult contexts, in such a way that it enables an effective response to manage and mitigate nutritional risk.

Modelling Early Risk Indicators to Anticipate Malnutrition


Click the button below to access the project page by AAC