基于地理信息系统的2009-2013年甲型H1N1流感的时空分析

doi:10.11821/dlyj201611011

摘要/Abstract

摘要：

甲型H1N1流感作为一种人群普遍易感的急性传染病,自2009年爆发以来,不仅对人类健康造成了危害,也给全球经济造成了巨大损失。为研究中国大陆地区2009年来甲型H1N1流感疫情的时空分布特征,以中国大陆地区31个省级行政区2009-2013年甲型H1N1流感发病率的时空数据为研究对象,采用空间自相关分析技术和季节性分析方法进行了较为全面的空间和时间分析。结果表明：甲型H1N1流感的发病率在空间上和时间上都具有较强的聚集性。空间上,甲流发病高值热点有从新疆、青海和甘肃等西北部地区逐渐南移至中国东南部沿海省份的趋势,且高发聚集省份有增多的趋势;时间上,每年10月至次年4月是中国甲流的高发时段,且在此期间空间上也存在较强的聚集性。

关键词: 甲型H1N1流感, 地理信息系统, 空间自相关, 季节性分析, 中国

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 G_i^*, 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 G_i^* 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 G_i^* 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.

Key words: influenza A (H1N1), geographic information system, spatial autocorrelation, seasonal analysis, China

李美芳, 欧金沛, 黎夏. 基于地理信息系统的2009-2013年甲型H1N1流感的时空分析[J]. 地理研究, 2016, 35(11): 2139-2152.

Meifang LI, Jinpei OU, Xia LI. Spatio-temporal analysis of influenza A (H1N1) in China during 2009-2013 based on GIS[J]. GEOGRAPHICAL RESEARCH, 2016, 35(11): 2139-2152.

图/表 10

图1

图2

表1

图3

图4

图5

表2

图6

表3

2009-2012年中国甲型H1N1流感发病率圆形分布计算结果"

省份	$sin α ?$	$cos α ?$	Z值	离差s	省份	$sin α ?$	$cos α ?$	Z值	离差s
全国	-0.761	0.013	7.610	40.055	河南	-0.757	0.013	3.102	38.879
北京	-0.934	0.007	41.881	39.594	湖北	-0.713	0.014	6.958	39.451
天津	-0.655	0.015	6.329	39.346	湖南	-0.727	0.021	15.660	28.383
河北	-0.680	0.019	4.193	33.413	广东	-0.775	0.007	5.100	54.100
山西	-0.680	0.023	6.039	28.557	广西	-0.637	0.017	18.276	36.384
内蒙古	-0.915	0.012	7.584	29.122	海南	-0.687	0.009	5.844	51.469
辽宁	-0.898	0.011	3.716	33.687	重庆	-0.921	0.010	8.699	32.745
吉林	-0.646	0.006	1.478	66.089	四川	-0.895	0.009	5.287	37.772
黑龙江	-0.969	0.005	3.398	38.967	贵州	-0.754	0.017	6.479	33.336
上海	-0.474	0.008	7.439	61.842	云南	-0.547	0.016	5.369	41.462
江苏	-0.735	0.011	5.276	44.155	西藏	-0.917	0.017	150.052	21.660
浙江	-0.678	0.016	18.058	37.996	陕西	-0.738	0.017	12.956	33.455
安徽	-0.814	0.010	3.546	42.300	甘肃	-0.803	0.015	13.011	33.152
福建	-0.492	0.016	9.598	42.476	青海	-0.977	0.007	32.187	25.612
江西	-0.687	0.016	4.259	36.516	宁夏	-0.699	0.013	17.099	41.964
山东	-0.651	0.017	3.303	36.294	新疆	-0.768	0.014	17.588	37.024

表3

图7

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时间(年-月)	Moran's I	Z值	空间格局	时间(年-月)	Moran's I	Z值	空间格局
2009	0.0486	1.2590		2011-01	0.1060	1.4862	聚集
2010	0.2573	2.7977	聚集	2011-02	0.1165	1.3919	聚集
2011	0.0410	0.7272		2011-03	0.0416	0.8656
2012	0.3649	3.9295	聚集	2011-04	-0.0430	-0.1021
2013	0.2876	2.9143	聚集	2011-05	0.0647	1.1631
2009-06	0.0903	1.2563		2011-06	-0.0166	0.4107
2009-07	0.0165	0.7451		2011-07	-0.0891	-0.9818
2009-08	0.0549	0.9795		2011-08	-0.0854	-0.8642
2009-09	0.0862	1.3290		2011-09	-0.0451	-0.2507
2009-10	0.0946	2.0096		2011-10	0.2481	2.7386	聚集
2009-11	-0.0055	0.4198		2011-11	0.0371	1.1820
2009-12	0.1317	1.5266	聚集	2011-12	-0.1318	-1.1439	扩散
2010-01	0.2860	3.0621	聚集	2012-01	-0.0886	-0.7990
2010-02	0.2274	2.4535	聚集	2012-02	-0.0259	0.2210
2010-03	0.1938	2.0997	聚集	2012-03	-0.0057	0.8874
2010-04	0.0682	1.0802		2012-04	0.3176	3.7126	聚集
2010-05	-0.1345	-1.0457	扩散	2012-05	0.1911	3.2458	聚集
2010-06	0.0612	1.0089		2012-06	0.0605	1.0356
2010-07	-0.0186	0.2815		2012-07	0.0168	0.5230
2010-08	0.0143	0.5808		2012-08	-0.0123	0.7385
2010-09	0.0758	1.3964		2012-09	-0.0275	0.2061
2010-10	0.0258	1.0889		2012-10	0.1315	2.1797	聚集
2010-11	0.1329	1.8950	聚集	2012-11	0.2033	2.8036	聚集
2010-12	-0.0118	0.2325		2012-12	0.1813	2.1514	聚集