MERIAM Project

Modeling Early Risk Indicators to Anticipate Malnutrition (MERIAM)

Project duration:

2017-2020

Project Consortium:

Action Against Hunger (US)
University of Maryland (US)
Graduate Institute Geneva (CH)
Johns Hopkins University (US)

Website:

Coming Soon

Project Description:

The project is funded through a grant of the Department for International Development of the United Kingdom (DFID). Its central aim is 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. The cross-disciplinary research team that forms the MERIAM consortium is comprised of experts from several pertinent fields and drawn from organizations and institutions that are world leaders in nutrition-related practice and research.

The project specifically aims to produce techniques and tools that are suited to strengthen early warning systems by both forecasting an increased risk of undernutrition and identifying the key drivers of that risk, and, generating scenarios that demonstrate how the timing and type of services provided may affect the impact of a shock on communities, including consideration of the cost-implications of the response for the outcome achieved. The project therefore concentrates on the early (‘leading’) indicators of undernutrition, leveraging a wide variety of existing and accessible data to rigorously capture its causal factors and dynamically model its fluctuation in contexts where this information is most urgently required.

Complementary types of quantitative methodologies will be employed to take account of the data landscape and to appropriately reflect the intricacies of potential contributing mechanisms. First, spatio-temporal econometrics will be used to identify empirical relationships, paying close attention to geographic (e.g., local conditions) and time-series properties (e.g., seasonality), endogeneity (i.e., reciprocal causality), and shocks (e.g., conflict, drought, spikes in food prices). Second, computational modeling will be used to explore undernutrition as an emergent phenomenon and to trace explanations for its sources. Resulting simulations will supply a versatile means for analysis of “what-if” counterfactuals relevant to policy and practice (e.g. undernutrition fluctuation in response to shocks, seasonal patterns and variations, early vs. late humanitarian response, etc.) and for linking micro-level attributes and behaviours, meso-level context, and emergent macro-level outcomes.

The initial geographical focus of the project is on Africa—Kenya, Somalia, Uganda and Niger, in particular. This allows to fully harness both the available data for these country contexts as well as existing operations of Action Against Hunger in those countries. With the project focused primarily on the process and applications of forecasting rather than on concrete findings of risk, it is expected to derive insights relevant for malnutrition early warning though that are valid beyond the specific country contexts studied.