Forecasting Ethiopia's Neonatal Mortality Rate Toward the Sustainable Development Goal Target 3.2 Using Statistical and Deep Learning Models: A Time-Series Forecasting Study.
- 1Strategic Affairs Executive Office Ministry of Health Addis Ababa Ethiopia.
- 2College of Computational and Natural Science Addis Ababa University Addis Ababa Ethiopia.
Abstract
Background and aims
A high neonatal mortality rate (NMR) reflects underlying health inequalities, the quality of maternal and newborn care, and overall healthcare system performance. Despite substantial progress, Ethiopia remains off track to achieve Sustainable Development Goal (SDG) 3.2, which aims to reduce NMR to at least 12/1000 by 2030. This study compared statistical and deep learning models to forecast Ethiopia's NMR through 2030, assess the progress towards the SDG target, and provide evidence to inform policy.Methods
Annual NMR data were obtained from the WHO Health Inequality Data Repository. Autoregressive integrated moving average (ARIMA), long short-term memory (LSTM), multi-step LSTM, and a hybrid were developed using Python. For the deep learning models, the data were normalised using MinMax scaling and transformed into sequential time-series windows. Stationarity was assessed using rolling statistics and the Augmented Dickey-Fuller test, while ARIMA parameters were selected using autocorrelation and partial autocorrelation and diagnostic criteria. Model performance was evaluated on the test dataset using mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), symmetric MAPE (sMAPE), and mean absolute scaled error (MASE). The best-performing model was used to forecast NMR from 2024 to 2030.Results
Ethiopia's NMR decreased from 66.39 to 27.39 deaths per 1000 live births between 1977 and 2023. ARIMA offers the best performance, with MAE of 0.10, RMSE of 0.12, MAPE of 0.32, sMAPE of 0.32, and MASE of 0.11. The forecast indicates a gradual decline in Ethiopia's NMR, with the ARIMA model forecasting an NMR of 22.72 per 1000 live births (95% PI: 17.60-27.84) by 2030.Conclusion
Ethiopia's decline in NMR is insufficient to meet SDG target 3.2 by 2030. The ARIMA model's strong performance confirms traditional methods suit short-term forecasts. To further reduce NMR and advance the SDGs, accelerated implementation of evidence-based interventions, strengthened health systems, and ongoing monitoring are crucial.
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BibTeX
@article{kebede2026forecasting,
title = {{Forecasting Ethiopia's Neonatal Mortality Rate Toward the Sustainable Development Goal Target 3.2 Using Statistical and Deep Learning Models: A Time-Series Forecasting Study.}},
author = {Fentahun Bikale Kebede and Selam Kifle Dergba and Amanuel Worku},
journal = {Health science reports},
year = {2026},
month = oct,
doi = {10.1002/hsr2.73402},
url = {https://europepmc.org/article/MED/42910164},
note = {CC BY},
}
RIS
TY - JOUR
TI - Forecasting Ethiopia's Neonatal Mortality Rate Toward the Sustainable Development Goal Target 3.2 Using Statistical and Deep Learning Models: A Time-Series Forecasting Study.
AU - Fentahun Bikale Kebede
AU - Selam Kifle Dergba
AU - Amanuel Worku
PY - 2026
DA - 2026/10/21
JO - Health science reports
DO - 10.1002/hsr2.73402
UR - https://europepmc.org/article/MED/42910164
AB - Background and aims A high neonatal mortality rate (NMR) reflects underlying health inequalities, the quality of maternal and newborn care, and overall healthcare system performance. Despite substantial progress, Ethiopia remains off track to achieve Sustainable Development Goal (SDG) 3.2, which aims to reduce NMR to at least 12/1000 by 2030. This study compared statistical and deep learning models to forecast Ethiopia's NMR through 2030, assess the progress towards the SDG target, and provide evidence to inform policy.Methods Annual NMR data were obtained from the WHO Health Inequality Data Repository. Autoregressive integrated moving average (ARIMA), long short-term memory (LSTM), multi-step LSTM, and a hybrid were developed using Python. For the deep learning models, the data were normalised using MinMax scaling and transformed into sequential time-series windows. Stationarity was assessed using rolling statistics and the Augmented Dickey-Fuller test, while ARIMA parameters were selected using autocorrelation and partial autocorrelation and diagnostic criteria. Model performance was evaluated on the test dataset using mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), symmetric MAPE (sMAPE), and mean absolute scaled error (MASE). The best-performing model was used to forecast NMR from 2024 to 2030.Results Ethiopia's NMR decreased from 66.39 to 27.39 deaths per 1000 live births between 1977 and 2023. ARIMA offers the best performance, with MAE of 0.10, RMSE of 0.12, MAPE of 0.32, sMAPE of 0.32, and MASE of 0.11. The forecast indicates a gradual decline in Ethiopia's NMR, with the ARIMA model forecasting an NMR of 22.72 per 1000 live births (95% PI: 17.60-27.84) by 2030.Conclusion Ethiopia's decline in NMR is insufficient to meet SDG target 3.2 by 2030. The ARIMA model's strong performance confirms traditional methods suit short-term forecasts. To further reduce NMR and advance the SDGs, accelerated implementation of evidence-based interventions, strengthened health systems, and ongoing monitoring are crucial.
SN - 2398-8835
ER -