Wang Yongbin, Xu Chunjie, Ren Jingchao, Wu Weidong, Zhao Xiangmei, Chao Ling, Liang Wenjuan, Yao Sanqiao
Department of Epidemiology and Health Statistics, School of Public Health, Xinxiang Medical University, Xinxiang, Henan, People's Republic of China.
Department of Occupational and Environmental Health, School of Public Health, Capital Medical University, Beijing, People's Republic of China.
Infect Drug Resist. 2020 Mar 5;13:733-747. doi: 10.2147/IDR.S238225. eCollection 2020.
Tuberculosis (TB) is a major public health problem in China, and contriving a long-term forecast is a useful aid for better launching prevention initiatives. Regrettably, such a forecasting method with robust and accurate performance is still lacking. Here, we aim to investigate its potential of the error-trend-seasonal (ETS) framework through a series of comparative experiments to analyze and forecast its secular epidemic seasonality and trends of TB incidence in China.
We collected the TB incidence data from January 1997 to August 2019, and then partitioning the data into eight different training and testing subsamples. Thereafter, we constructed the ETS and seasonal autoregressive integrated moving average (SARIMA) models based on the training subsamples, and multiple performance indices including the mean absolute deviation, mean absolute percentage error, root-mean-squared error, and mean error rate were adopted to assess their simulation and projection effects.
In the light of the above performance measures, the ETS models provided a pronounced improvement for the long-term seasonality and trend forecasting in TB incidence rate over the SARIMA models, be it in various training or testing subsets apart from the 48-step ahead forecasting. The descriptive results to the data revealed that TB incidence showed notable seasonal characteristics with predominant peaks of spring and early summer and began to be plunging at on average 3.722% per year since 2008. However, this rate reduced to 2.613% per year since 2015 and furthermore such a trend would be predicted to continue in years ahead.
The ETS framework has the ability to conduct long-term forecasting for TB incidence, which may be beneficial for the long-term planning of the TB prevention and control. Additionally, considering the predicted dropping rate of TB morbidity, more particular strategies should be formulated to dramatically accelerate progress towards the goals of the End TB Strategy.
结核病是中国的一个主要公共卫生问题，制定长期预测有助于更好地开展预防工作。遗憾的是，仍缺乏一种性能强大且准确的预测方法。在此，我们旨在通过一系列对比实验研究误差趋势季节性（ETS）框架分析和预测中国结核病发病率长期流行季节性和趋势的潜力。
我们收集了1997年1月至2019年8月的结核病发病率数据，然后将数据划分为八个不同的训练和测试子样本。此后，我们基于训练子样本构建了ETS和季节性自回归积分滑动平均（SARIMA）模型，并采用包括平均绝对偏差、平均绝对百分比误差、均方根误差和平均错误率在内的多个性能指标来评估它们的模拟和预测效果。
根据上述性能指标，ETS模型在结核病发病率的长期季节性和趋势预测方面比SARIMA模型有显著改进，无论是在除提前48步预测之外的各种训练或测试子集中。对数据的描述性结果显示，结核病发病率呈现出明显的季节性特征，主要高峰出现在春季和初夏，自2008年以来平均每年以3.722%的速度下降。然而，自2015年以来这一速度降至每年2.613%，并且预计这种趋势在未来几年将持续。
ETS框架有能力对结核病发病率进行长期预测，这可能有利于结核病防控的长期规划。此外，考虑到预测的结核病发病率下降速度，应制定更具体的策略以大幅加快实现终止结核病战略目标的进展。
