Molla Md Maeen, Hossain Md Sifat, Ali Md Ayub, Islam Md Raqibul, Sultana Mst Papia, Roy Dulal Chandra
Department of Statistics, Pirojpur Science and Technology University, Pirojpur, Bangladesh.
Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh.
PLoS One. 2025 Mar 4;20(3):e0314466. doi: 10.1371/journal.pone.0314466. eCollection 2025.
Sustainable Development Goal 3 (SDG 3), focusing on ensuring healthy lives and well-being for all, holds global significance and is particularly vital for Bangladesh. Neonatal Mortality Rate (NMR), Under-5 Mortality Rate (U5MR), Maternal Mortality Ratio (MMR) and Death Rate Due to Road Traffic Injuries (RTI) are considered responsible indicators of SDG 3 progress in Bangladesh. The objective of the study is to forecast these indicators of Bangladesh up to 2030 and compare these forecasts with predetermined 2030 targets. The data is obtained from the World Bank's (WB) website.
For forecasting, time series models were employed, specifically Autoregressive Integrated Moving Average- ARIMA (0,2,1) with Akaike Information Criterion (AIC) 94.6 for NMR and ARIMA (2,1,2) with AIC 423.2 for U5MR, selected based on their lowest AIC values. Additionally, Machine Learning (ML) models, including Bidirectional Recurrent Neural Networks (BRNN) and Elastic Neural Networks (ENET), were employed for all the indicators.
ENET demonstrates superior performance compared to both BRNN and ARIMA in the context of NMR, achieving a Root Mean Absolute Error (RMAE) of 0.603446 and a Root Mean Square Error (RMSE) of 0.451162. Furthermore, when considering U5MR, MMR, and Death Rate Due to RTI, ENET consistently exhibits lower error metrics compared to the alternative models. Following the time series and ML analyses, a consistent trend emerges in the forecasted values for NMR and U5MR, which consistently fall below their respective 2030 targets. This promising finding suggests that Bangladesh is making significant progress toward meeting its 2030 targets for NMR and U5MR. However, in the cases of MMR and Death Rate Due to RTI, the forecasted values exceeded 2030 targets. This indicates that Bangladesh faces challenges in meeting the 2030 targets for MMR and Death Rate Due to RTI.
The analyses underscore the importance of SDG 3 in Bangladesh and its progress towards ensuring healthy lives and well-being for all. While there is optimism regarding NMR and U5MR, more focused efforts may be needed to address the challenges posed by MMR and Death Rate Due to RTI to align with the 2030 targets. This study contributes valuable insights into Bangladesh's journey toward sustainable development in the realm of health and well-being.
可持续发展目标3(SDG 3)聚焦于确保所有人享有健康生活和福祉,具有全球意义,对孟加拉国尤为重要。新生儿死亡率(NMR)、五岁以下儿童死亡率(U5MR)、孕产妇死亡率(MMR)以及道路交通伤害死亡率(RTI)被视为孟加拉国SDG 3进展的关键指标。本研究的目的是预测孟加拉国这些指标直至2030年,并将这些预测与预先设定的2030年目标进行比较。数据取自世界银行(WB)网站。
为进行预测,采用了时间序列模型,具体而言,针对NMR采用自回归积分移动平均模型ARIMA(0,2,1),赤池信息准则(AIC)为94.6;针对U5MR采用ARIMA(2,1,2),AIC为423.2,这些模型是根据其最低AIC值选定的。此外,还对所有指标采用了机器学习(ML)模型,包括双向递归神经网络(BRNN)和弹性神经网络(ENET)。
在NMR方面,与BRNN和ARIMA相比,ENET表现更优,平均绝对误差(RMAE)为0.603446,均方根误差(RMSE)为0.451162。此外,在考虑U5MR、MMR和RTI死亡率时,与其他模型相比,ENET的误差指标始终较低。经过时间序列和ML分析,NMR和U5MR的预测值呈现出一致趋势,始终低于各自的2030年目标。这一有前景的发现表明,孟加拉国在实现2030年NMR和U5MR目标方面取得了重大进展。然而,在MMR和RTI死亡率方面,预测值超过了2030年目标。这表明孟加拉国在实现2030年MMR和RTI死亡率目标方面面临挑战。
分析强调了SDG 3在孟加拉国的重要性以及该国在确保所有人享有健康生活和福祉方面取得的进展。虽然对NMR和U5MR有乐观预期,但可能需要更有针对性的努力来应对MMR和RTI死亡率带来的挑战,以符合2030年目标。本研究为孟加拉国在健康和福祉领域迈向可持续发展的进程提供了宝贵见解。