2011-2018年我国登革热疫情时间序列分析及空间自相关分析

  目的  了解2011-2018年我国登革热疫情时空分布特征，对2019年我国登革热的发病情况进行预测。  方法  基于中国疾病预防控制信息系统中2011-2018年我国登革热的病例数据，借助R 3.6.0软件，使用自回归积分滑动平均模型（autoregressive integrated moving average model，ARIMA）对登革热的发病趋势进行描述和预测。基于国家人口与健康科学数据共享服务平台提供的2011-2016年全国以及各省市登革热发病率、发病人数数据，采用GeoDa 1.12软件进行全局、局部空间自相关分析，确定登革热时空热点区域。  结果  预测2019年全年登革热发病数为14 302人。2012年（Moran's I=-0.088，P=0.037）、2013年（Moran's I=-0.121，P=0.040）和2014（Moran's I=-0.076，P=0.045）年全国登革热发病呈现全局空间负相关关系，2016年（Moran's I=0.078，P=0.048）登革热发病呈现全局空间正相关关系。局部自相关分析结果显示，登革热发病高聚集区域主要在我国东南沿海地区。  结论  2019年我国登革热的流行无明显波动趋势，且疫情呈空间聚集性分布。

Time series analysis and spatial autocorrelation analysis of dengue data in China from 2011 to 2018

  Objective  To understand the spatial and temporal distribution characteristics of dengue fever in China from 2011 to 2018, and predict the incidence of dengue fever in China in 2019.  Methods  Based on the case data of dengue fever in China from 2011 to 2018 in the Chinese Disease Prevention and Control Information System, the trend of dengue fever was described and predicted by using the autoregressive integrated moving average model (ARIMA) with R 3.6.0 software. Based on the data of the incidence of dengue fever in the country, provinces and cities from 2011 to 2016 provided by the national scientific data sharing platform for population and health, global and local spatial autocorrelation analysis was performed using GeoDa 1.12 software to determine the dengue fever hotspots.  Results  The incidence of dengue fever was 14 302 in 2019, showing no disease outbreaks. The incidence of dengue fever in 2012(Moran's I=-0.088, P=0.037), 2013(Moran's I=-0.121, P=0.040) and 2014(Moran's I=-0.076, P=0.045) showed a global spatial negatively correlaton. In 2016(Moran's I=0.078, P=0.048), the incidence of dengue fever was positively correlated with global space. The results of local autocorrelation analysis showed that the high incidence of dengue fever was mainly in the southeast coastal areas of China.  Conclusions  In 2019, the epidemic of dengue fever in China showed no obvious fluctuation trend, and the epidemic situation showed spatial clustering distribution.