• 研究论文 •

### 基于地理信息系统的2009-2013年甲型H1N1流感的时空分析

1. 中山大学地理科学与规划学院,广东省城市化与地理环境空间模拟重点实验室,广州 510275
• 收稿日期:2016-05-13 修回日期:2016-08-24 出版日期:2016-11-25 发布日期:2016-11-24
• 作者简介:

作者简介：李美芳（1990- ）,女,江西抚州人,博士研究生,研究方向为空间流行病学及多智能体模型。E-mail:limfjx@foxmail.com

• 基金资助:
国家自然科学基金重点项目（41531176）

### Spatio-temporal analysis of influenza A (H1N1) in China during 2009-2013 based on GIS

Meifang LI(), Jinpei OU, Xia LI()

1. School of Geography and Planning, Sun Yat-sen University, Guangdong Key Laboratory for Urbanization and Geo-simulation, Guangzhou 510275, China
• Received:2016-05-13 Revised:2016-08-24 Online:2016-11-25 Published:2016-11-24

Abstract:

Influenza A (H1N1), an acute infectious disease, broken out throughout the world in 2009. It has not only become a hazard to human health but also resulted in great global economic loss. At present, many scholars have carried out studies on various disciplines related to influenza A (H1N1). However, few studies have investigated the spatio-temporal characteristics of influenza A (H1N1) based on Geographic Information System. In this study, we collected the monthly and yearly data of influenza A (H1N1) incidence in 31 provinces of China from 2009 to 2013 from the National Scientific Data Sharing Platform for Population and Health to explore the spatio-temporal distribution of influenza A (H1N1) in China. The spatial distribution of yearly incidence of influenza A (H1N1) in the five-year period was analyzed using global Moran's I, local Getis-Ord Gi*, and the Moran scatterplot. Among them, global Moran's I was used to analyze the average spatial correlation and significance level of the whole study area. Local Getis-Ord Gi* and the Moran scatterplot were used to analyze the instability and heterogeneity of local regions. In addition, we used the degree of concentration, time series model, and circular distribution analysis to examine seasonal variations of influenza A (H1N1) epidemics. The results showed that influenza A (H1N1) incidence in 2009 was significantly higher than that in other years. Moreover, there were prominent correlations between both spatial and temporal dimensions of influenza A (H1N1) incidence. As for global spatial autocorrelation, the global Moran's I results suggested that significantly positive spatial autocorrelations were associated with the yearly incidence of influenza A (H1N1) in 2010, 2012, and 2013, as well as the monthly incidence of influenza A (H1N1) from October to April. Regarding local spatial autocorrelation, the Moran scatterplots showed that an increasing number of adjacent provinces had high incidence from 2009 to 2013. Besides, the outcomes of local Getis-Ord Gi* indicated that the hot spots of the epidemics transferred from northwest China, including Xinjiang, Qinghai and Gansu, to southeast China. In terms of the seasonal characteristics of influenza A (H1N1) epidemics, the degree of concentration analysis showed that there were strong temporal aggregations of these epidemics every year. Additionally, using the time series model and circular distribution analysis, we identified that the peaks of incidence were clustered from October to April. Therefore, the findings of our study not only improved the understanding of the spatial variation and temporal dynamics of influenza A (H1N1) in China, but also provided an effective method for infectious disease surveillance.