Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Key Predictors and Geographic Variation of Food Security and Nutrition in Africa: A Spatio-temporal Model-Based Study

Version 1 : Received: 7 July 2023 / Approved: 7 July 2023 / Online: 10 July 2023 (09:53:15 CEST)

How to cite: BOFA, A.; Zewotir, T. Key Predictors and Geographic Variation of Food Security and Nutrition in Africa: A Spatio-temporal Model-Based Study. Preprints 2023, 2023070525. https://doi.org/10.20944/preprints202307.0525.v1 BOFA, A.; Zewotir, T. Key Predictors and Geographic Variation of Food Security and Nutrition in Africa: A Spatio-temporal Model-Based Study. Preprints 2023, 2023070525. https://doi.org/10.20944/preprints202307.0525.v1

Abstract

There is voluminous literature on Food Security in Africa. This study explicitly considers the spatio-temporal factors in addition to the usual FAO-based metrics in modeling and understanding the dynamics of food security and nutrition across the African continent. To better understand the complex trajectory and burden of food insecurity and nutrition in Africa, it is crucial to consider space-time factors when modeling and interpreting food security. The spatio-temporal anova model was found to be superior(employing statistical criteria) to the other there models from the spatio-temporal interaction domain models. The results of the study suggest that dietary supply adequacy, food stability, and consumption status are positively associated with severe food security, while average food supply and environmental factors have negative effects on Food Security and Nutrition. The findings also indicate that severe food insecurity and malnutrition are spatially and temporally correlated across the African continent. Spatio-temporal modeling and spatial mapping are essential components of a comprehensive practice to reduce the burden of severe food insecurity. likewise, any planning and intervention to improve the average food supply and environment to promote sustainable development should be regional instead of one size fit all.

Keywords

Autoregressive process; Bayesian Poisson model; principal component analysis (PCA); spatial conditional autoregressive; Sustainable Development Goals

Subject

Public Health and Healthcare, Other

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