- BDU IR Home
- →
- College of Science
- →
- Statistics
- →
- Dissertations
- →
- View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.
| dc.contributor.author | Mesfin, Mulu | |
| dc.date.accessioned | 2026-09-25T12:42:00Z | |
| dc.date.available | 2026-09-25T12:42:00Z | |
| dc.date.issued | 2026-07 | |
| dc.identifier.uri | http://ir.bdu.edu.et/handle/123456789/17397 | |
| dc.description.abstract | Food insecurity remains a major humanitarian and development challenge in African countries. Understanding its distribution, underlying determinants, and future trajectories is essential for strengthening early-warning systems and targeted interven tions. Therefore, this study examined the spatio-temporal dynamics and determinants of severe food insecurity across 52 African countries during 2015–2021 and projected its future trajectories. The dataset was sourced and compiled from food and agricul tural organization, world bank, malaria atlas project, and Ethiopian statistical service. Inference was carried out using integrated nested Laplace approximation algorithm with Gaussian Markov random fields. The analyses employed different methodolog ical approaches including spatial and spatio-temporal clustering analytical tools, ma chine learning models, spatial models, spatio-temporal models, spatio-temporal vary ing coefficient model and deep learning architectures. Among the models considered in each methodological class, extreme gradient boost, geoadditive mixed model, spatio temporal type I interaction model, and spatio-temporal graph convolutional network outperformed the other models within their respective classes. The results showed that malaria incidence (β = 0.01, SD = 0.003), climate change (β = 0.65, SD = 0.31), livestock production ( β = −0.05 , SD = 0.02), and investment inflow (β = −0.03, SD = 0.01) exhibited relatively consistent and statistically significant linear fixed ef fects across countries and over time at 95% credible intervals, whereas unemployment, greenhouse gas emissions, gross domestic product per capita, dietary energy supply, and dietary protein supply demonstrated statistically significant spatial and temporal hetero geneity in both the magnitude and, in some instances, the direction of their effects. The findings also showed that severe food insecurity was significantly spatially dependent (τ 2 θ = 2705.77) and temporally correlated (τ 2 α = 10.75) across the continent. In addition, the space-time interactions (τ 2 δ = 29,438.77) contributed additional variability. The find ings also revealed substantial and persistent disparities across African countries over time. Throughout the study period, South Sudan, Central Africa Republic, Republic of the Congo, Malawi, Guinea, Cameroon, and Somalia experienced higher severe food insecurity risks compared to other countries. The southern, eastern and central subre gions exhibited strong spatial dependence in severe food insecurity. In addition, across much of the study period, Kenya, Ethiopia, Niger, and Burkina Faso frequently expe rienced unusual country-year-specific shocks in severe food insecurity crises compared to other countries. Data-driven forecasts further suggested that severe food insecurity is expected to intensify in Africa. In particular, severe deterioration was projected in South Sudan, the Central African Republic, the Republic of Congo, Malawi, Guinea, Somalia, viii Ethiopia, Kenya, Democratic Republic of Congo, Angola, Namibia, Niger and Nigeria. These findings indicated the coexistence of cross-country shared structural influences and country-specific factors in shaping severe food insecurity across Africa. Policy makers should prioritize targeted and context-specific interventions in high-risk areas, strengthen national multi-sectoral and regional coordination, and emphasize proactive and anticipatory strategies, including early warning systems, to support progress toward achieving sustainable development goal 2 (Zero Hunger) across Africa. | en_US |
| dc.language.iso | en_US | en_US |
| dc.subject | Statistics | en_US |
| dc.title | Statio-Temporal Modeling and Deep Learning–Based Forecasting Of Severe Food Insecurity In Africa | en_US |
| dc.type | Dissartation | en_US |
