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.
| Main Authors: | , , , , , |
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| Format: | Working Paper biblioteca |
| Language: | English en_US |
| Published: |
Washington, DC: World Bank
2024-05-09
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| Subjects: | AGRICULTURE AND FOOD SECURITY, CRISIS, EARLY WARNING SYSTEMS, FOOD PRICE ANALYSIS, VULNERABILITY, ECONOMIC MONITORING, ZERO HUNGER, SDG 2, |
| Online Access: | http://documents.worldbank.org/curated/en/099709505092462162/IDU1fd335ca917518148461a5b1154ba368027ff https://hdl.handle.net/10986/41534 |
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| Summary: | 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. |
|---|