News You Can Use—To Better Predict Food Crisis Outbreaks

A team of researchers has developed a machine learning model that draws from the contents of news articles to effectively predict locations that face risks of food insecurity. The model, which could be used to help prioritize the allocation of emergency food assistance across vulnerable regions, marks an improvement over existing measurements. “Our approach could drastically improve the prediction of food crisis outbreaks up to 12 months ahead of time using both real-time news streams and a predictive model that is simple to interpret,” says Samuel Fraiberger, a visiting researcher at New York University’s Courant Institute of Mathematical Sciences, a data scientist at the World Bank, and an author of the study, which appears in the journal Science Advances. “Traditional measurements of food insecurity risk factors, such as conflict severity indices or changes in food prices, are often incomplete, delayed, or outdated,” adds Lakshminarayanan Subramanian, a professor at the Courant Institute and one of the paper’s authors. “Our approach takes advantage of the fact that risk factors triggering a food crisis are mentioned in the news prior to being observable with traditional measurements.”