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Background <p>High infant mortality rate (IMR) is a critical public health indicator. Understanding its trajectory is vital for policymaking, particularly in Somalia, where rates remain elevated. This study aimed to model and forecast Somalia’s IMR to identify the most accurate method, providing data-driven insights for public health strategy.</p> Objectives <p>The study aimed to formulate time series models, propose hybrid models, and rigorously compare their performance in forecasting IMR.</p> Methods <p>This quantitative time-series analysis utilized annual World Bank data for Somalia from 1950 to 2022. Three models were developed: Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN), and a hybrid ARIMA-ANN model. Performance was evaluated using Root Mean Square Error (RMSE) and Symmetric Mean Absolute Percentage Error (sMAPE) on a test set spanning 2013 to 2022.</p> Results <p>Upon achieving stationarity via first-order differencing, the ARIMA (1, 1, 3) model proved most effective. It demonstrated superior accuracy with an RMSE of 0.85, significantly outperforming the ANN (RMSE 1.04) and hybrid models (RMSE 3.01). Validated ARIMA forecasts project a continued, gradual decline in IMR from 59.02 deaths per 1,000 live births in 2023 to 40.37 in 2032.</p> Conclusion <p>The ARIMA model offers the most reliable framework for forecasting Somalia’s infant mortality rate based on historical data.</p>

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Tracking progress towards sustainable development goal 3.2 in Somalia using time series models: a comparative forecasting analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月3日

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  • Khadar Mowlid Abdi,
  • Abdirisak Mohamed Moumin,
  • Hibo Abdilahi Omer,
  • Saralees Nadarajah,
  • Abdisalam Hassan Muse

摘要

Background

High infant mortality rate (IMR) is a critical public health indicator. Understanding its trajectory is vital for policymaking, particularly in Somalia, where rates remain elevated. This study aimed to model and forecast Somalia’s IMR to identify the most accurate method, providing data-driven insights for public health strategy.

Objectives

The study aimed to formulate time series models, propose hybrid models, and rigorously compare their performance in forecasting IMR.

Methods

This quantitative time-series analysis utilized annual World Bank data for Somalia from 1950 to 2022. Three models were developed: Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN), and a hybrid ARIMA-ANN model. Performance was evaluated using Root Mean Square Error (RMSE) and Symmetric Mean Absolute Percentage Error (sMAPE) on a test set spanning 2013 to 2022.

Results

Upon achieving stationarity via first-order differencing, the ARIMA (1, 1, 3) model proved most effective. It demonstrated superior accuracy with an RMSE of 0.85, significantly outperforming the ANN (RMSE 1.04) and hybrid models (RMSE 3.01). Validated ARIMA forecasts project a continued, gradual decline in IMR from 59.02 deaths per 1,000 live births in 2023 to 40.37 in 2032.

Conclusion

The ARIMA model offers the most reliable framework for forecasting Somalia’s infant mortality rate based on historical data.