2011-2018年我国登革热疫情时间序列分析及空间自相关分析

杨慧欣; 赵晨皓; 雒静静; 胡芳芳; 张思文; 王太君; 甄清

doi:10.16462/j.cnki.zhjbkz.2019.10.018

2011-2018年我国登革热疫情时间序列分析及空间自相关分析

doi: 10.16462/j.cnki.zhjbkz.2019.10.018

130021 长春, 吉林大学公共卫生学院

详细信息

通讯作者:
甄清, E-mail: zq415@sina.com

中图分类号: R181
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出版历程
- 收稿日期: 2019-05-30
- 修回日期: 2019-08-05
- 刊出日期: 2019-10-10

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

Jilin University School of Public Health, Changchun 130021 China

More Information

Corresponding author: ZHEN Qing, E-mail: zq415@sina.com

摘要

摘要: 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。
- 登革热 /
- 时间序列 /
- 全局自相关分析 /
- 局部自相关分析 /
- 发病预测
Abstract: 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.
- Dengue fever /
- Time series /
- Global spatial autocorrelation analysis /
- Spatial clustering analysis /

HTML全文

图 1 2011-2018年全国登革热发病率时间序列

Figure 1. Time series of national dengue fever incidence rates from 2011 to 2018

下载: 全尺寸图片幻灯片

图 2 2011-2016年全国登革热患者发病年龄分布变化

Figure 2. Age distribution change of dengue patients in China from 2011 to 2016

下载: 全尺寸图片幻灯片

图 3 2011-2016年我国登革热的发病月份分布

Figure 3. Distribution of the incidence of dengue fever in China from 2011 to 2016

下载: 全尺寸图片幻灯片

图 4 差分与季节性差分后的自相关和偏自相关图

Figure 4. Autocorrelation and partial autocorrelation plots after difference and season difference

下载: 全尺寸图片幻灯片

图 5 2011-2016年中国大陆登革热全局型空间自相关分析和局部自相关分析示意图

Figure 5. Schematic diagram of global spatial autocorrelation analysis and local autocorrelation analysis of dengue fever in China from 2011 to 2016

下载: 全尺寸图片幻灯片

表 1 模型选择表

Table 1. Model selection table

模型参数	AIC	RMSE	Ljung-Box P值
(010)(010)	151.250 7	0.552 8	< 0.001
(110)(010)	143.086 3	0.519 6	0.055
(011)(010)	142.591 5	0.518 0	0.018
(010)(110)	139.425 3	0.501 8	0.007
(010)(011)	126.968 5	0.441 8	0.035
(111)(010)	140.638 7	0.503 4	0.001
(010)(111)	128.722 3	0.422 1	0.018
(110)(110)	136.696 4	0.489 5	0.110
(011)(011)	125.819 9	0.439 1	0.069
(111)(110)	133.012 6	0.470 6	0.038
(111)(011)	122.575 1	0.423 9	0.085
(110)(111)	127.150 9	0.407 3	0.078
(011)(111)	126.539 9	0.406 1	0.070
(111)(111)	123.633 1	0.391 7	0.051

下载: 导出CSV

表 2 2019年全国登革热发病预测表

Table 2. National dengue fever morbidity forecast table in 2019

预测月份	预测值	95% CI预测值范围
2019.1	50.404 40	14.46~207.82
2019.2	48.918 14	10.71~289.03
2019.3	40.470 22	7.92~279.17
2019.4	55.526 18	9.51~463.63
2019.5	94.503 85	13.93~984.42
2019.6	137.548 70	18.02~1 714.02
2019.7	480.783 14	47.48~9 410.66
2019.8	1 689.394 40	122.16~56 570.20
2019.9	5 003.354 18	267.67~294 224.05
2019.10	5 093.178 19	258.43~332 296.33
2019.11	1 437.776 99	94.09~57 382.31
2019.12	176.286 24	17.39~3 454.57

下载: 导出CSV

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