A new machine learning model that draws from the contents of news articles can 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, the researchers say.

“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 Science Advances study.

“Traditional measurements of food insecurity risk factors, such as conflict severity indices or changes in food prices, are often incomplete, delayed, or outdated,” says coauthor Lakshminarayanan Subramanian, a professor at the Courant Institute. “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.”

Food insecurity threatens the lives of hundreds of millions of people around the world. According to the Food and Agriculture Organization of the United Nations, the number of undernourished increased from 624 million people in 2014 to 688 million in 2019.

The authors write that conditions have deteriorated since then due to the COVID-19 pandemic, climate change, and armed conflicts—in 2021, between 702 and 828 million people worldwide faced hunger. Moreover, severe food insecurity increased both globally and in every region in 2021.

Despite the acute and widespread nature of this affliction, current methods to detect future food crises rely on risk measures that are insufficient, hindering efforts to address them.

Read the full article about food insecurity and machine learning by James Devitt at Futurity.