A Data-Driven Approach to Robust Predictions of Food Insecurity Crises

Abstract

Globally, over 800 million people are food insecure. Current methods for identifying food insecurity crises are not based on statistical models and fail to systematically incorporate readily available data on prices, weather, and demographics. As a result, policymakers cannot rapidly identify food insecure populations, hampering responses to mitigate hunger. We develop a replicable, near real-time model incorporating spatially and temporally granular market data, remotely-sensed rainfall and geographic data, and demographic characteristics. We train the model on 2010-2011 data from Malawi and forecast 2013 food security. Our model correctly identifies the food security status of 77% of the most food insecure village clusters in 2013 while the prevailing approach fails to correctly classify any of these village clusters. Our results show the power of modeling food insecurity to provide early warning and suggest model-driven approaches could dramatically improve food insecurity responses.