@article{3532994af8ba46c78a6de32c012b11f0,
title = "A data-driven approach improves food insecurity crisis prediction",
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. These problems delay 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 83 to 99% of the most food insecure village clusters in 2013, depending on the food security measure, while the prevailing approach correctly identifies between 0 and 10%. Our results show the power of modeling food insecurity to provide early warning and suggest model-driven approaches could dramatically improve food insecurity crisis response.",
keywords = "Crisis, Early warning, Famine, Food insecurity, Prediction, Sub-Saharan Africa",
author = "Lentz, {E. C.} and H. Michelson and K. Baylis and Y. Zhou",
note = "Funding Information: This work was supported by the Policy Research Institute at the Lyndon B Johnson School of Public Affairs, University of Texas at Austin ; the National Science Foundation Award# 1520683 (HAZARD Sees); and National Institute of Food and Agriculture Hatch Project# 1014548. Funding Information: Hina Acharya, Richard Barad, Gary Eilerts, Christopher Hillbruner, Alison McGuignan, Angela Hamann, Nour Nourey, Caleb Rudow, Anne Speca, and Guyu Ye helped identify and compile data. Chris Barrett, Molly Brown, Peter Christensen, Jason Cons, Christopher Hillbruner, Seppe Kuehn, Daniel Maxwell, Sonja Perakis, Silke Pietzsch, Joanna Upton, two anonymous reviewers, and participants at the University of Illinois at Urbana-Champaign International Policy and Development seminar and Agricultural and Applied Economics Association 2017 and 2018 annual meetings provided valuable feedback. This work was supported by the Policy Research Institute at the Lyndon B Johnson School of Public Affairs, University of Texas at Austin; the National Science Foundation Award# 1520683 (HAZARD Sees); and National Institute of Food and Agriculture Hatch Project# 1014548. EL and HM and KB designed and performed research; KB and YZ analyzed data; all wrote the paper. Senior authorship is shared among EL, HM and KB. EL, HM, KB, and YZ declare no conflicts of interest.",
year = "2019",
month = oct,
doi = "10.1016/j.worlddev.2019.06.008",
language = "English (US)",
volume = "122",
pages = "399--409",
journal = "World Development",
issn = "1873-5991",
publisher = "Elsevier BV",
}