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Forecasting under-five stunting in Ethiopia using classical and machine learning time series models.
Rewina Tilahun Gessese1, Jenberu Mekurianew Kelkay2, Fetlework Gubena Arage3
1International Institute for Primary Health Care -Ethiopia, Addis Ababa, Ethiopia.
Plos One
|April 22, 2026
Summary
Ethiopia’s under-five stunting prevalence is projected to remain high through 2030, exceeding national and Sustainable Development Goal targets. Current nutrition interventions require evaluation and adjustment to improve child health outcomes.
Area of Science:
- Public Health
- Epidemiology
- Child Nutrition
Background:
- Under-five stunting in Ethiopia exceeds 30%, a significant public health concern with long-term child development impacts.
- Accurate forecasting is crucial for effective policy guidance and targeted interventions in child malnutrition.
Purpose of the Study:
- To forecast the prevalence of under-five stunting in Ethiopia from 2025 to 2030.
- To utilize historical data and advanced time series modeling for accurate predictions.
Main Methods:
- Collected annual under-five stunting data (2000-2024) from WHO Global Health Observatory.
- Developed and evaluated multiple time series models: ARIMA, ETS, MLP, and LSTM.
- Selected the best-performing ETS model based on MAE, MAPE, and R² for forecasting.
Main Results:
- The Exponential Smoothing (ETS) model showed superior predictive accuracy (MAE=1.09, MAPE=2.71%, R²=0.903).
- Forecasts predict a gradual decline in stunting from 33.95% in 2025 to 31.95% in 2030.
- Projected 2029 prevalence (32.35%) and 2030 forecast significantly exceed national and SDG targets.
Conclusions:
- Ethiopia's under-five stunting rates are projected to remain above national and SDG targets by 2030.
- Current nutrition strategies appear insufficient, necessitating program evaluation and policy adjustments.
- Evidence-based policy revisions are recommended to address persistent child malnutrition challenges.
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