Time series analysis and spatial autocorrelation analysis of dengue data in China from 2011 to 2018
10.16462/j.cnki.zhjbkz.2019.10.018
- Author:
Hui-xin YANG
1
;
Chen-hao ZHAO
;
Jing-jing LUO
;
Fang-fang HU
;
Si-wen ZHANG
;
Tai-jun WANG
;
Qing ZHEN
Author Information
1. Jilin University School of Public Health, Changchun 130021 China
- Publication Type:Research Article
- Keywords:
Dengue fever;
Time series;
Global spatial autocorrelation analysis;
Spatial clustering analysis
- From:
Chinese Journal of Disease Control & Prevention
2019;23(10):1250-1254
- CountryChina
- Language:Chinese
-
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.
