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Background <p>&#xa0;Cancer mortality is rising rapidly across the African continent, yet Somalia lacks localized national forecasting evidence due to decades of instability and the absence of a formal registry. This study aims to develop and compare high-precision time-series forecasting models to project cancer mortality trends in Somalia through 2030, providing a critical statistical baseline for proactive healthcare resource allocation.</p> Methods <p>&#xa0;Annual cancer mortality data (1980–2021) were retrieved from the Global Burden of Disease (GBD) database. A rigorous comparative analysis of 12 representative forecasting configurations was conducted, comprising six single models (including ARIMA, ETS, TBATS, and the Theta method) and six optimized hybrid ensembles integrated with Artificial Intelligence (AI) proxies like Extreme Learning Machines (ELM) and Neural Network Autoregression (NNAR). To stabilize non-stationary trends and mitigate overfitting in a limited sample context (<i>n</i> = 42), second-order differencing (<i>d</i> = 2) and a 10-year rolling-origin cross-validation (tsCV) were implemented. Model performance was benchmarked using RMSE, MAPE, Theil’s U, Willmott’s Index of Agreement (WI), and Skill Scores.</p> Results <p>&#xa0;All evaluated configurations achieved Theil’s U statistics below 1.0, indicating superior performance relative to the naive benchmark. The parsimonious&#xa0;ARIMA (2,2,1)&#xa0;model emerged as the most accurate configuration for long-term forecasting, achieving the lowest MAPE (1.127%) and RMSE (100.16) in the independent holdout set. The statistical superiority of ARIMA (2,2,1) was confirmed via the Diebold–Mariano test (<i>p</i> &lt; 0.01) against the naive benchmark, outperforming complex hybrid ensembles which showed higher error accumulation over the 8-year horizon. Projections indicate a consistent monotonic upward trend, with annual cancer deaths estimated to reach approximately&#xa0;9,652&#xa0;by the year 2030.</p> Conclusion <p>&#xa0;The findings indicate a significant and ongoing epidemiological transition in Somalia. These forecasts provide a robust statistical baseline to assist the Somali Ministry of Health in strategic planning for oncology infrastructure, diagnostic capacity expansion, and the formal establishment of a National Cancer Registry to transition from reconstructed estimates to actual patient records.</p>

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Comparative analysis of single and hybrid time series models for forecasting of deaths from cancer in Somalia

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月30日

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  • Suhaib Mohamed Kahie Seiman,
  • Mustafe Mohamoud Abdi,
  • Saralees Nadarajah,
  • Abdisalam Hassan Muse

摘要

Background

 Cancer mortality is rising rapidly across the African continent, yet Somalia lacks localized national forecasting evidence due to decades of instability and the absence of a formal registry. This study aims to develop and compare high-precision time-series forecasting models to project cancer mortality trends in Somalia through 2030, providing a critical statistical baseline for proactive healthcare resource allocation.

Methods

 Annual cancer mortality data (1980–2021) were retrieved from the Global Burden of Disease (GBD) database. A rigorous comparative analysis of 12 representative forecasting configurations was conducted, comprising six single models (including ARIMA, ETS, TBATS, and the Theta method) and six optimized hybrid ensembles integrated with Artificial Intelligence (AI) proxies like Extreme Learning Machines (ELM) and Neural Network Autoregression (NNAR). To stabilize non-stationary trends and mitigate overfitting in a limited sample context (n = 42), second-order differencing (d = 2) and a 10-year rolling-origin cross-validation (tsCV) were implemented. Model performance was benchmarked using RMSE, MAPE, Theil’s U, Willmott’s Index of Agreement (WI), and Skill Scores.

Results

 All evaluated configurations achieved Theil’s U statistics below 1.0, indicating superior performance relative to the naive benchmark. The parsimonious ARIMA (2,2,1) model emerged as the most accurate configuration for long-term forecasting, achieving the lowest MAPE (1.127%) and RMSE (100.16) in the independent holdout set. The statistical superiority of ARIMA (2,2,1) was confirmed via the Diebold–Mariano test (p < 0.01) against the naive benchmark, outperforming complex hybrid ensembles which showed higher error accumulation over the 8-year horizon. Projections indicate a consistent monotonic upward trend, with annual cancer deaths estimated to reach approximately 9,652 by the year 2030.

Conclusion

 The findings indicate a significant and ongoing epidemiological transition in Somalia. These forecasts provide a robust statistical baseline to assist the Somali Ministry of Health in strategic planning for oncology infrastructure, diagnostic capacity expansion, and the formal establishment of a National Cancer Registry to transition from reconstructed estimates to actual patient records.