Forecasting Under-5 Mortality Rate in Somalia to 2030: a comparative analysis of univariate and multivariate ARIMAX models

Background Accurate forecasting of the Under-5 Mortality Rate (U5MR) is essential for monitoring health progress and informing policy interventions in conflict-affected regions. In Somalia, achieving the Sustainable Development Goal (SDG) 3.2 target of reducing child mortality to at least 25 deaths per 1,000 live births by 2030 remains a formidable challenge due to persistent socio-political instability and economic shocks. Methods Utilizing a two-phase analytical approach, the study first evaluated 11 time-series models (6 single and 5 hybrid) based on historical U5MR data from 1960 to 2023. In the second phase, a multivariate ARIMAX framework (1989–2023) was implemented to incorporate exogenous drivers, including total conflict-related fatalities, GDP per capita, and immunization coverage. Model performance was validated using MAPE, sMAPE, and Theil’s U statistics, supplemented by rigorous diagnostic tests such as Shapiro–Wilk and Breusch–Pagan. Results The univariate analysis identified the TBATS and hybrid ARIMA–TBATS models as the superior forecasting tools, with the latter selected for its robustness in handling Somalia’s high data volatility. The multivariate ARIMAX model revealed that conflict fatalities are a highly significant predictor of mortality ( p 0.001 ), while GDP per capita serves as a significant negative determinant ( p = 0.0128 ). The ARIMAX model projects a U5MR of 86.29 deaths per 1,000 live births by 2030, while trend-based univariate models project a plateau at 109.78. Both projections indicate that Somalia remains significantly off-track to meet the SDG 3.2 target. Conclusion The findings underscore a concerning stagnation in child mortality reduction. Achieving international benchmarks in Somalia requires a dual focus on intensifying health interventions and ensuring national security stability. These data-driven insights offer critical evidence for policymakers to design resilient strategies for child survival in the region.