Nowcasting food insecurity on a global scale

Abstract Lack of regular physical or economic access to safe, nutritious and sufficient food is a critical issue affecting millions of people world-wide. Estimating how many and where these people are is of fundamental importance for governments and humanitarian organizations to take informed and timely decisions on relevant policies and programmes. In this study, we propose a machine learning approach to predict the prevalence of people with insufficient food consumption and of people using crisis or above crisis food-based coping when primary data is not available. Making use of a unique global data set, we show that the proposed models can explain up to 78% of the variation in insufficient food consumption and crisis or above food-based coping levels. We also show that the proposed models can be used to nowcast the food security situation in near-real time and propose a method to identify which variables are driving the changes observed in predicted trends, which is key to make predictions serviceable to decision makers.