Amoud University (Somali: Jaamacada Camuud) is a comprehensive public university, located in the city of Borama in Somaliland.The university started in 1998 with 66 students in two faculties (Education and Business Administration), and three teachers. It has a student population of 5,111 enrolled in 14 faculties/schools, 238 teaching staff.The first batch of medical graduates came out in June 2007 and their final exams were supervised by King's College of London, United Kingdom, which provides the curriculum and teaching assistance to the Amoud University College of Health Sciences.
Background: Access to family planning (FP) information is vital for reproductive autonomy and the Sustainable Development Goals. In Somaliland, modern contraceptive uptake remains critically low. While sociodemographic barriers are known, geospatial dimensions of information accessibility remain unexplored. This study investigates individual, community, and spatial determinants of women's exposure to FP messages.Methods: We analyzed data from the 2020 Somaliland Health and Demographic Survey among women aged 15-49. We employed multilevel mixed-effects logistic regression to assess predictors of media exposure. Additionally, spatial analysis techniques including Global Moran's I and Getis-Ord Gi* statistics were utilized to identify geographic clusters ("hotspots" and "cold spots") of exposure.Results: Only 25.28% of women reported exposure to FP messages, primarily via TV and radio; mobile dissemination was underutilized (11%). Multilevel analysis indicated that higher education (adjusted odds ratios [AOR] = 6.48), wealth, and internet usage significantly increased exposure odds. Conversely, nomadic populations (AOR = 0.38) and rural residents faced severe exclusion. Spatial analysis revealed a sharp East-West divide: significant "hotspots" clustered in western regions (Awdal, Marodijeh), while significant "cold spots" were identified in the eastern region of Sool.Conclusions: A profound "digital and developmental divide" characterizes reproductive health communication in Somaliland. Current strategies benefit urban, educated women in the west, leaving nomadic populations in an information vacuum. Policy must shift to spatially targeted interventions, utilizing community outreach in identified "cold spots" and voice-based mobile technology to reach uneducated and nomadic women.
Somalia possesses the longest coastline on the African mainland, making its fishery sector a critical pillar for national food security and economic resilience in an arid region where terrestrial agriculture is often limited. However, this vital sector faces compounding threats from climatic variability and anthropogenic environmental changes. This study fills a significant research gap by modeling the dynamic relationship between fishery production and climatic drivers using annual time-series data from 1981 to 2022. We employed the Autoregressive Distributed Lag (ARDL) bounds testing approach to estimate long- and short-run climatic elasticities, while acknowledging that this model captures statistical associations rather than direct causal physical mechanisms. The results confirm a stable long-run cointegrating relationship among the variables (F-statistic = 4.661). Long-run estimates reveal that climatic factors are significant drivers of production: a 1 This is a visual summary that serves as a pivotal entry point into the research, offering a concise overview of the study’s core findings and methodologies. Comprising simple, clear visuals, such as diagrams, flowcharts, or illustrations, it effectively communicates complex data in an accessible format. This specific graphical abstract illustrates the Somali Coastal Upwelling System as the primary driver of the Blue Economy. The workflow follows the ARDL bounds testing process using 42 years of annual data (1981–2022). The logical flow guides the reader through the positive climatic drivers—temperature and wind speed—which enhance nutrient-rich upwelling, and the negative anthropogenic threat of CO₂ emissions leading to ocean acidification. Results highlight that while temperature and wind boost productivity by 2.20
Abstract Background The fertility transition in sub-Saharan Africa remains slow; however, the drivers of high fertility in conflict-affected and resource-constrained settings, such as Somalia, remain underexplored. Objective This study analyzed the determinants of a high fertility status, defined as having five or more children born, a threshold associated with high-risk obstetric outcomes, among married women in Somalia. Methods Using data from the 2020 Somali Health and Demographic Survey (SHDS) (N = 40,402), we employed a multilevel logistic regression model. This approach was chosen to account for regional clustering and adjust for the unobserved regional-level heterogeneity inherent in the stratified sampling design. Results This study revealed a distinct socio-demographic gradient. Education acts as a powerful measure of fertility. Women with no education (AOR = 1.29; 95% CI [1.11, 1.50]) and primary education (AOR = 1.44; 95% CI [1.22, 1.70]) had significantly higher odds of high fertility than those with secondary or higher education. Delaying the age of first marriage also significantly reduced this risk. Notably, a systemic “targeting bias” was observed: women not visited by family planning workers were less likely to have high fertility (AOR = 0.77; 95% CI [0.70, 0.85]), suggesting that interventions reactively targeted large families rather than proactively reaching low-parity women. Furthermore, child loss exhibited a powerful dose-response effect, with the odds of high fertility increasing from 1.86 for one death to 8.76 for three or more deaths, supporting the “insurance effect” and replacement motives. Conclusion High fertility in Somalia is driven by educational exclusion, early marriage, and compensatory reproductive behaviors following child loss. To facilitate demographic transition, policies must prioritize female secondary education and shift family planning strategies from reactive targeting to proactive engagement with younger, low-parity women.
Understanding methane emissions is crucial for climate change mitigation, given its potency as a greenhouse gas. This study offers a detailed spatiotemporal analysis of methane emissions across 54 African countries, alongside an aggregated continental baseline, from 1990 to 2023, with projections extending to 2030. Eight time-series models, including ARIMA, ETS, and neural network-based approaches (NNAR, ANN/MLP), were developed and assessed for each country to predict future emissions. The model with the lowest Symmetric Mean Absolute Percentage Error (sMAPE) for each nation was selected to generate forecasts. Historical trend analysis using the Mann–Kendall test and Theil-Sen slope indicated a statistically significant upward trend in methane emissions for the continent overall, with notable increases in Ethiopia and Chad, and significant decreases in Nigeria and Libya. Spatial analysis of historical data identified Nigeria as the highest emitter. Forecasts suggest these trends are likely to persist. To explore the spatial dynamics of future emissions, spatial autocorrelation analyses (Local Moran’s I and Getis-Ord Gi*) were conducted for each forecasted year from 2024 to 2030. The results consistently revealed statistically significant hot spots (clusters of high emissions) in North-Eastern Africa and the Horn of Africa, and cold spots (clusters of low emissions) in Southern Africa. These findings emphasize ongoing regional disparities and highlight the need for tailored geographically informed strategies for methane mitigation across the African continent.
Malaria remains a significant public health challenge in Somaliland. This study evaluates a preliminary machine learning approach—rather than a full operational system—to predict malaria outbreak years in a data-scarce environment using a limited historical dataset (2002–2021). A retrospective study was conducted using annual data. An Extreme Gradient Boosting (XGBoost) model performed binary classification of malaria incidence into ‘Outbreak’ and ‘Non-Outbreak’ years. To address the methodological constraints of the small sample size (N = 20) and mitigate the risk of overfitting, a Leave-One-Year-Out Cross-Validation (LOYOCV) strategy was employed, and results were compared against a Logistic Regression baseline. Predictor variables included temperature, rainfall, 1-year lagged rainfall, urbanization, and land-use patterns. The XGBoost model achieved an AUC of 0,880, significantly outperforming the baseline (AUC 0,710). At the optimal threshold, the model yielded a sensitivity of 0,750 and a precision of 0,600. However, the discrete staircase appearance of the resulting ROC curve reflects the model’s high sensitivity to individual data points within the small sample, indicating that these performance metrics should be interpreted with caution. While promising, these results are preliminary. The small sample size and the temporal clustering of outbreaks in the early 2000s suggest that this work serves as a proof-of-concept for data-scarce regions rather than a definitive surveillance tool. Further prospective validation with higher-resolution temporal data is required to ensure the reliability and generalizability of these associations for operational early warning.
