Predicting Food Crises 2020, Dataset for reproducing working paper results - Afghanistan, Burkina Faso, Ethiopia, Guatemala, Haiti, Kenya, Mali, Mozambique, Mauritania, Malawi, Niger, Nigeria, Sudan, Somalia, South Sudan, ...

Abstract
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Globally, more than 130 million people are estimated to be in food crisis. These humanitarian disasters are associated with severe impacts on livelihoods that can reverse years of development gains. The existing outlooks of crisis-affected populations rely on expert assessment of evidence and are limited in their temporal frequency and ability to look beyond several months. This paper presents a statistical foresting approach to predict the outbreak of food crises with sufficient lead time for preventive action. Different use cases are explored related to possible alternative targeting policies and the levels at which finance is typically
unlocked. The results indicate that, particularly at longer forecasting horizons, the statistical predictions compare favorably to expert-based outlooks. The paper concludes that statistical models demonstrate good ability to detect future outbreaks of food crises and that using statistical forecasting approaches may help increase lead time for action.

Geographic coverage
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Afghanistan, Burkina Faso, Chad, Democratic Republic of Congo, Ethiopia, Guatemala, Haiti, Kenya, Malawi, Mali, Mauritania, Mozambique, Niger, Nigeria, Somalia, South Sudan, Sudan, Uganda, Yemen, Zambia, Zimbabwe

摘要
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全球范围内，预计有超过一亿三千万人正处于粮食危机之中。此类人道主义灾难对生计的严重影响可能逆转多年的发展成果。现有的粮食危机受影响人群的预测依赖于专家对证据的评估，且在时间频率上有限，且难以超越数月的预测范围。本文提出了一种统计森林方法，以在预防行动有足够提前量的情况下预测粮食危机的爆发。探讨了与可能的替代目标政策和通常解锁资金的层级相关的不同用例。结果表明，特别是在较长的预测时域内，统计预测与基于专家的预测相比具有竞争优势。本文得出结论，统计模型显示出良好的能力来检测未来粮食危机的爆发，并且采用统计预测方法可能有助于增加行动的提前量。
地理覆盖范围
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阿富汗、布基纳法索、乍得、刚果民主共和国、埃塞俄比亚、危地马拉、海地、肯尼亚、马拉维、马里、毛里塔尼亚、莫桑比克、尼日尔、尼日利亚、索马里、南苏丹、苏丹、乌干达、也门、赞比亚、津巴布韦