A Data-Driven Approach for Early Detection of Food Insecurity in Yemen's Humanitarian Crisis

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.