Injury Prevention Research Institute, Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing, China; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China.
Injury Prevention Research Institute, Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing, China.
Ann Epidemiol. 2015 Feb;25(2):101-6. doi: 10.1016/j.annepidem.2014.10.015. Epub 2014 Oct 31.
Road traffic injuries have become a major public health problem in China. This study aimed to develop statistical models for predicting road traffic deaths and to analyze seasonality of deaths in China.
A seasonal autoregressive integrated moving average (SARIMA) model was used to fit the data from 2000 to 2011. Akaike Information Criterion, Bayesian Information Criterion, and mean absolute percentage error were used to evaluate the constructed models. Autocorrelation function and partial autocorrelation function of residuals and Ljung-Box test were used to compare the goodness-of-fit between the different models. The SARIMA model was used to forecast monthly road traffic deaths in 2012.
The seasonal pattern of road traffic mortality data was statistically significant in China. SARIMA (1, 1, 1) (0, 1, 1)12 model was the best fitting model among various candidate models; the Akaike Information Criterion, Bayesian Information Criterion, and mean absolute percentage error were -483.679, -475.053, and 4.937, respectively. Goodness-of-fit testing showed nonautocorrelations in the residuals of the model (Ljung-Box test, Q = 4.86, P = .993). The fitted deaths using the SARIMA (1, 1, 1) (0, 1, 1)12 model for years 2000 to 2011 closely followed the observed number of road traffic deaths for the same years. The predicted and observed deaths were also very close for 2012.
This study suggests that accurate forecasting of road traffic death incidence is possible using SARIMA model. The SARIMA model applied to historical road traffic deaths data could provide important evidence of burden of road traffic injuries in China.
道路交通伤害已成为中国的一个主要公共卫生问题。本研究旨在建立预测道路交通事故死亡人数的统计模型，并分析中国死亡的季节性。
采用季节性自回归综合移动平均（SARIMA）模型拟合 2000 年至 2011 年的数据。采用赤池信息量准则（Akaike Information Criterion）、贝叶斯信息量准则（Bayesian Information Criterion）和平均绝对百分比误差（mean absolute percentage error）评估构建的模型。残差的自相关函数和偏自相关函数以及Ljung-Box 检验用于比较不同模型的拟合优度。SARIMA 模型用于预测 2012 年每月的道路交通事故死亡人数。
中国道路交通事故死亡率数据的季节性模式具有统计学意义。SARIMA（1,1,1）（0,1,1）12 模型是各种候选模型中最佳拟合模型；赤池信息量准则、贝叶斯信息量准则和平均绝对百分比误差分别为-483.679、-475.053 和 4.937。拟合优度检验表明模型残差不存在自相关（Ljung-Box 检验，Q=4.86，P=.993）。SARIMA（1,1,1）（0,1,1）12 模型拟合 2000 年至 2011 年的死亡人数与同期观察到的道路交通事故死亡人数密切相关。2012 年的预测死亡人数与实际死亡人数也非常接近。
本研究表明，SARIMA 模型可用于准确预测道路交通事故死亡人数。SARIMA 模型应用于历史道路交通事故死亡数据，可以为中国道路交通事故伤害负担提供重要证据。
