Ding Lei, Ding Shu-Jun, Zhang Meng, Wang Xian-Jun, Li Zhong, Zhao Zhong-Tang
Institute of Epidemiology and Health Statistics, School of Public Health, Shandong University, Jinan 250012, China.
Zhonghua Liu Xing Bing Xue Za Zhi. 2012 Jul;33(7):698-701.
To explore the characteristics of temporal distribution and epidemic trend of autumn-winter type scrub typhus using the time series analysis.
Based on the data of scrub typhus collected from Shandong Diseases Reporting Information System from 2006 to 2011, both spectral analysis and moving average analysis were used to analyze the annual data of scrub typhus while scrub typhus incidence in 2012 - 2014 was forecasted. Seasonal decomposition analysis was applied to analyze the monthly data from January of 2006 to October of 2011, followed by Autoregressive Integrated Moving Average Model (ARIMA) which was constructed to forecast case number in November and December of 2011 and compared to the actual incidence.
The results of spectral analysis showed that the prevalence of autumn-winter type scrub typhus had a feature of '3-year-periodicity'. A long-term up-trend was confirmed by method of moving average analysis, with annually case numbers of 310, 337 and another number of 366 forecasted for 2012 to 2014, respectively, with the annual increase rate as 9% per-year. Data from analysis of monthly data of scrub typhus showed that through multiple seasonal decomposition analysis, the results indicated that the prevalence of this disease possessed a typical autumn-winter type. The seasonality indexes for scrub typhus in October and November were 8.454 and 2.230, respectively, while others were less than 1.000. The ARIMA(0,1,1) (0,1,0)(12) model of (1-B)(1-B(12))X(t) = (1-0.811B)u(t) that was used to forecast the prevalence of autumn-winter type scrub typhus and was constructed with the residual error of 16 lags as white noise. The Box-Jenkins test statistic for the model was 3.116, giving a P value of 0.999. The model fitted the data well. Good accordance was achieved between the observed values and the forecasted values of scrub typhus in November and December of 2011 which was produced by the ARIMA model, and all observed values were within the forecasted 95%CI.
The prevalence of autumn-winter type scrub typhus showed a 3-year-periodicity, with a long-term up-trend, and the case numbers of 2012 to 2014 were forecasted, rising on the end with an increasing rate of 9% per year, which occurred seasonally with October as the peak time in every year. The ARIMA(0,1,1) (0,1,0)(12) model seemed to be quite appropriate in predicting the autumn-winter type scrub typhus.
采用时间序列分析方法探讨秋冬型恙虫病的时间分布特征和流行趋势。
基于2006年至2011年山东省疾病报告信息系统收集的恙虫病数据，运用谱分析和移动平均分析方法对恙虫病的年度数据进行分析，并对2012 - 2014年的恙虫病发病率进行预测。应用季节分解分析方法对2006年1月至2011年10月的月度数据进行分析，随后构建自回归积分移动平均模型（ARIMA）预测2011年11月和12月的病例数，并与实际发病率进行比较。
谱分析结果显示，秋冬型恙虫病的流行具有“3年周期性”特征。移动平均分析方法证实存在长期上升趋势，预测2012年至2014年的年病例数分别为310、337及另一数值366，年增长率为9%。恙虫病月度数据分析结果表明，通过多次季节分解分析，结果显示该疾病的流行具有典型的秋冬型。恙虫病10月和11月的季节性指数分别为8.454和2.230，其他月份均小于1.000。用于预测秋冬型恙虫病流行情况的ARIMA(0,1,1) (0,1,0)(12)模型为(1 - B)(1 - B(12))X(t) = (1 - 0.811B)u(t)，构建该模型时以16阶滞后的残差作为白噪声。该模型的Box - Jenkins检验统计量为3.116，P值为0.999。该模型对数据拟合良好。ARIMA模型预测的2011年11月和12月恙虫病的观测值与预测值之间具有良好的一致性，所有观测值均在预测的95%置信区间内。
秋冬型恙虫病的流行呈3年周期性，具有长期上升趋势，预测了2012年至2014年的病例数，年末呈上升趋势，年增长率为9%，且每年以10月为发病高峰季节出现。ARIMA(0,1,1) (0,1,0)(12)模型在预测秋冬型恙虫病方面似乎较为合适。
