Attachments
Abstract
The Republic of Yemen is enduring the world’s most severe protracted humanitarian crisis, compounded by conflict, economic collapse, and natural disasters. Current food inse- curity assessments rely on expert evaluation of evidence with limited temporal frequency and foresight. This paper intro- duces a data-driven methodology for the early detection and diagnosis of food security emergencies. The approach opti- mizes 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.