Early Warning Maps: Predicted Nutrition Severity in Fragile and Conflict-Affected Contexts

Fragile and conflict-affected regions face overlapping shocks, from displacement and market instability to escalating climate extremes, that continue to deepen food and nutrition insecurity. The combined effects of protracted conflict, economic collapse, and the breakdown of essential services have intensified humanitarian needs while restricting access to those most affected. Addressing these challenges requires integrating innovative data sources and analytical tools, such as Earth Observation (EO) products, to fill critical information gaps and support evidence-based decision-making in fragile and hard-to-reach contexts. 

On this premise, the European Space Agency’s European Resilience from Space (ERS) programme, through the Smart Connect project, supports UNICEF in developing a near-real-time, spatially detailed early warning system to monitor short-term malnutrition risk. The system produces monthly outputs in the form of Severity Nutrition Index (SNI) maps, including six consecutive one-month-ahead forecasts. Specifically, every month the SNI is calculated at the administrative level 2 for every subnational unit as a composite 0–1 score summarizing overall nutrition risk.

 

The main innovation of this work lies in the proposed risk-based methodology, that integrates large volumes of data from diverse sources to capture the key drivers and dynamics influencing nutritional conditions.

The model is organized into four thematic modules: Climate & Environmental, Socio-Economic, Conflict & Displacement, and Health & Nutrition. Each module is implemented through a multi-dimensional framework. For example, the Climate & Environmental module includes three dimensions: agriculture, livestock, and water availability. Within each dimension, the model calculates (1) a main factor representing the baseline condition, (2) an impact factor capturing stressors, (3) a temporal component reflecting the persistence of previous months, and (4) a dynamic weight that adjusts to emerging conditions. This hierarchical and modular architecture allows customized assessments across domains, ensuring coherence across diverse contexts. Moreover, its scalable design facilitates replication in other fragile settings.

 

The robustness of the approach is reflected in its use of reliable and accessible datasets, demonstrating how Earth Observation products can be effectively combined with socio-economic, conflict, health and basic nutrition data to produce simple 0–1 score at the subnational level, where higher values indicate worse conditions.

For testing and validating the result, Sudan was selected as the primary use case since recent reports are indicating that nearly half of the population is facing high levels of acute food insecurity.

For the Sudan use-case, the SNI has demonstrated its ability to highlight emerging malnutrition risk zones with sufficient lead time to inform early action and guide targeted assessments. Validation against available food security and nutrition datasets confirms its value as a relative early-warning measure, while recognizing that it is not an absolute prevalence indicator due to persistent data gaps and spatial inconsistencies. Despite these limitations, the Index offers a systematic, data-driven approach for monitoring nutrition risk in fragile and conflict-affected contexts and is designed to complement, rather than replace, existing analytical products and situation reports.