Machine learning techniques can be successfully deployed to better identify food insecurity outbreaks across the world long before they take place, according to a new study.

Why it matters: The timely disbursement of humanitarian aid can be a matter of life or death during a food crisis. How we gather information and when we respond can make all the difference.

What they found: A new study in Science Advances, the journal of the American Association for the Advancement of Science, used deep learning to extract relevant text from more than 11 million news articles focused on food-insecure countries and published between 1980 and 2020.

The study analyzed how journalists reported about food insecurity and causes associated with it.

Researchers found that between 2009 and 2020, and across 21 countries, news indicators "substantially" improved the Integrated Phase Classification (IPC) predictions of food insecurity, which are available at a district level — or particular area of a country — up to 12 months ahead of time.