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Filling survey gaps in food security monitoring with spatio-temporal additive Gaussian process models

Year: 2026

Type: article

Abstract: This repository implements a Bayesian spatio-temporal additive Gaussian process model for forecasting food insecurity (Food Consumption Score) at the sub-national level in Nigeria and Chad, benchmarked against a Bayesian Ridge with BYM2 spatial effect, a Multi-Layer Perceptron, and XGBoost. The GP model is estimated via Stan/CmdStanR, with R scripts for data preparation, cross-validation, and diagnostics, and Python scripts for the MLP, XGBoost. Model outputs, figures, and metric tables used in the paper are included under output/. The underlying WFP survey data are not included and are available upon reasonable request.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.24104811

Authors: Ishida, Sahoko, Panero, Francesca, Leygonie, Rebecca, Kopp, Emma

Institutions: Sapienza University of Rome, Université Paris Dauphine-PSL

Language: English

Topic: Gaussian Processes and Bayesian Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7225732530

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