ISI Foundation, Via Chisola 5, 10126, Turin, Italy.
Department of Sociology and Social Research, University of Trento, Via Verdi, 26, 38122, Trento, Italy.
Sci Rep. 2023 Mar 16;13(1):2793. doi: 10.1038/s41598-023-29700-y.
Food insecurity, defined as the lack of physical or economic access to safe, nutritious and sufficient food, remains one of the main challenges included in the 2030 Agenda for Sustainable Development. Near real-time data on the food insecurity situation collected by international organizations such as the World Food Programme can be crucial to monitor and forecast time trends of insufficient food consumption levels in countries at risk. Here, using food consumption observations in combination with secondary data on conflict, extreme weather events and economic shocks, we build a forecasting model based on gradient boosted regression trees to create predictions on the evolution of insufficient food consumption trends up to 30 days in to the future in 6 countries (Burkina Faso, Cameroon, Mali, Nigeria, Syria and Yemen). Results show that the number of available historical observations is a key element for the forecasting model performance. Among the 6 countries studied in this work, for those with the longest food insecurity time series, that is Syria and Yemen, the proposed forecasting model allows to forecast the prevalence of people with insufficient food consumption up to 30 days into the future with higher accuracy than a naive approach based on the last measured prevalence only. The framework developed in this work could provide decision makers with a tool to assess how the food insecurity situation will evolve in the near future in countries at risk. Results clearly point to the added value of continuous near real-time data collection at sub-national level.
食物不安全是指缺乏获取安全、营养和充足食物的物质或经济手段，这仍是可持续发展目标 2030 议程中包含的主要挑战之一。世界粮食计划署等国际组织实时收集的食物不安全情况数据对于监测和预测处于风险中的国家粮食摄入不足水平的时间趋势至关重要。在这里，我们结合冲突、极端天气事件和经济冲击的二手数据，利用食物消费观察数据，基于梯度提升回归树构建了一个预测模型，用以对 6 个国家（布基纳法索、喀麦隆、马里、尼日利亚、叙利亚和也门）未来 30 天内食物摄入不足趋势的演变进行预测。结果表明，历史观测数据的数量是预测模型性能的关键因素。在所研究的 6 个国家中，对于那些拥有最长食物不安全时间序列的国家，如叙利亚和也门，所提出的预测模型可以比仅基于最后一次测量的流行率的简单方法更准确地预测未来 30 天内食物摄入不足的人群比例。本工作中开发的框架可以为决策者提供一种工具，以评估处于风险中的国家在不久的将来食物不安全情况将如何演变。结果清楚地表明，在国家以下各级持续进行近乎实时的数据收集具有附加价值。
