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.
Repository name | URI |
---|---|
Reproducible Research Repository | https://reproducibility.worldbank.org |
Paper exhibits were reproduced on a computer with the following specifications:
• OS: Windows 11 Enterprise
• Processor: 11th Gen Intel(R) Core(TM) i5-1145G7 @ 2.60GHz 1.50 GHz
• Memory available: 15.7 GB
• Software version:
– Python 3.10
– Power BI 2.103.881.0 64-bit (March 2022
Runtime: 3 hours
Users need to download data from the dataset "Armed Conflict Location & Event Data Project (ACLED)", the Yemen Baseline Assessment and Rapid Displacement Indicators of the IOM, gain access to the dataset "High Resolution Population Density Maps from Facebook for Yemen", and recreate the input file "yemen_adm2_population.csv" to be able to run the code. Instructions for all of these are included in the README of the reproducibility package.
Most data used is public and included in the reproducibility package. Seven datasets are public but cannot be redistributed in the reproducibility package. One dataset is restricted and requires contacting the data owners for access. The README contains information on accessing all datasets.
Author | Affiliation | |
---|---|---|
Steve Penson | World Bank | spenson@worldbank.org |
Mathijs Lomme | World Bank, ACAPS | mathijs.lomme@acaps.org |
Bo Pieter Johannes Andree | World Bank | bandree@worldbank.org |
Zacharey Austin Carmichael | World Bank | zcarmichael@worldbank.org |
Alemu Manni | World Bank | alemu.Manni@fao.org |
Sudeep Shrestha | ACAPS | sds@acaps.org |
2024-06
Location | Code |
---|---|
Yemen | YEM |
The materials in the reproducibility packages are distributed as they were prepared by the staff of the International Bank for Reconstruction and Development/The World Bank. The findings, interpretations, and conclusions expressed in this event do not necessarily reflect the views of the World Bank, the Executive Directors of the World Bank, or the governments they represent. The World Bank does not guarantee the accuracy of the materials included in the reproducibility package.
Name | URI |
---|---|
Modified BSD3 | https://opensource.org/license/bsd-3-clause/ |
Name | Affiliation | |
---|---|---|
Steve Penson | World Bank | spenson@worldbank.org |
Reproducibility WBG | World Bank | reproducibility@worldbank.org |
Name | Abbreviation | Affiliation | Role |
---|---|---|---|
Reproducibility WBG | DIME | World Bank - Development Impact Department | Verification and preparation of metadata |
2024-06-24
1