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Tracking progress towards sustainable development goal 3.2 in Somalia using time series models: a comparative
Khadar Mowlid Abdi1,2, Abdirisak Mohamed Moumin3, Hibo Abdilahi Omer4
1Research and Innovation Center, Amoud University, Borama, 25263, Somalia. khadar.mowlid@amoud.edu.so.
Conflict and Health
|June 4, 2026
Summary
The Autoregressive Integrated Moving Average (ARIMA) model accurately forecasts Somalia's infant mortality rate (IMR). Projections show a continued decline in IMR, offering crucial data for public health strategies.
Area of Science:
- Public Health
- Time Series Analysis
- Econometrics
Background:
- High infant mortality rate (IMR) is a critical public health indicator.
- Somalia faces elevated IMR, necessitating accurate forecasting for effective policymaking.
- Understanding IMR trends is vital for public health strategy development.
Purpose of the Study:
- To model and forecast Somalia's infant mortality rate (IMR).
- To identify the most accurate time series forecasting method for IMR.
- To provide data-driven insights for public health interventions.
Main Methods:
- Quantitative time-series analysis using World Bank data (1950-2022).
- Development and comparison of Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN), and hybrid ARIMA-ANN models.
- Performance evaluation using Root Mean Square Error (RMSE) and Symmetric Mean Absolute Percentage Error (sMAPE).
Main Results:
- The ARIMA (1, 1, 3) model demonstrated superior accuracy (RMSE 0.85) compared to ANN (RMSE 1.04) and hybrid models (RMSE 3.01).
- Achieved stationarity through first-order differencing was crucial for ARIMA model effectiveness.
- Forecasts project a gradual decline in IMR from 59.02 in 2023 to 40.37 deaths per 1,000 live births by 2032.
Conclusions:
- The ARIMA model provides the most reliable framework for forecasting Somalia's infant mortality rate.
- Historical data analysis supports the ARIMA model's predictive power.
- Findings offer valuable insights for targeted public health interventions in Somalia.
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