Fine-grained prediction of food insecurity using news streams

Anticipating the outbreak of a food crisis is crucial to efficiently allocate emergency relief and reduce human suffering. However, existing food insecurity early warning systems rely on risk measures that are often delayed, outdated, or incomplete. Here, we leverage recent advances in deep learning...

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Bibliographic Details
Published inarXiv.org
Main Authors Balashankar, Ananth, Subramanian, Lakshminarayanan, Fraiberger, Samuel P
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 17.11.2021
Subjects
Decision making
Deep learning
Disaster relief
Early warning systems
Emergency communications systems
Food
Machine learning
News
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Summary:Anticipating the outbreak of a food crisis is crucial to efficiently allocate emergency relief and reduce human suffering. However, existing food insecurity early warning systems rely on risk measures that are often delayed, outdated, or incomplete. Here, we leverage recent advances in deep learning to extract high-frequency precursors to food crises from the text of a large corpus of news articles about fragile states published between 1980 and 2020. Our text features are causally grounded, interpretable, validated by existing data, and allow us to predict 32% more food crises than existing models up to three months ahead of time at the district level across 15 fragile states. These results could have profound implications on how humanitarian aid gets allocated and open new avenues for machine learning to improve decision making in data-scarce environments.
ISSN:2331-8422