A Data-Driven Approach for Early Detection of Food Insecurity in Yemen's Humanitarian Crisis
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Washington, DC: World Bank
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The Republic of Yemen is enduring the
world's most severe protracted humanitarian crisis,
compounded by conflict, economic collapse, and natural
disasters. Current food insecurity assessments rely on
expert evaluation of evidence with limited temporal
frequency and foresight. This paper introduces a data-driven
methodology for the early detection and diagnosis of food
security emergencies. The approach optimizes for simplicity
and transparency, and pairs quantitative indicators with
data-driven optimal thresholds to generate early warnings of
impending food security emergencies. Historical validation
demonstrates that warnings can be reliably issued before
sharp deterioration in food security occurs, using only a
few critical indicators that capture inflation, conflict,
and agricultural productivity shocks. These indicators
signal deterioration most accurately at five months of lead
time. The paper concludes that simple data-driven approaches
show a strong capability to generate reliable food security
warnings in Yemen, highlighting their potential to
complement existing assessments and enhance lead time for
effective intervention.
Palabras clave
AGRICULTURE AND FOOD SECURITY, CRISIS, EARLY WARNING SYSTEMS, FOOD PRICE ANALYSIS, VULNERABILITY, ECONOMIC MONITORING, ZERO HUNGER, SDG 2