基于信息熵的中国自然疫源性疾病分布特征研究

doi:10.12082/dqxxkx.2019.190109

摘要/Abstract

摘要：

中国目前发现有43种自然疫源性疾病,其中鼠疫、人禽流感、疟疾、登革热等14种被列为国家法定传染病,对中国人民生命健康造成了很大的威胁。为了探讨中国自然疫源性疾病发病数量均衡/不均衡的区域分布规律,本文运用Shannon信息熵理论和空间自相关方法,基于2004-2015年14种自然疫源性疾病的发病数,对中国自然疫源性疾病进行了分析。研究结果表明：① 中国自然疫源性疾病发病数量均衡/不均衡的区域在空间上具有明显的西北-东南分异特征,并具有显著的空间自相关关系,高值聚集区和低值聚集区主要分布在以河北-云南连线上山脉分界线的两侧;② 自然因素是影响自然疫源性疾病发病均衡程度的主要因素,在温暖潮湿（即温度适宜、水分充足）的地区更容易发生多种疾病,疾病发病数量相近;在特定牲畜为主或特定蚊虫流行地区更容易发生单种疾病,疾病发病数量不均衡;③ 我国发病总数高的区域往往是因为单种疾病极其严重导致,而信息熵高的地区往往存在多种疾病发生,这两类地区在自然疫源性疾病防治上,需要根据地区特点采取不同措施。

关键词: 自然疫源性疾病, 信息熵, 空间分布, 影响因素, 自然因素, 空间自相关, 中国

Abstract:

To date, there have been 43 types of natural focus diseases reported in China, 14 of which are officially-recognized infectious diseases including plague, human-avian influenza, malaria, and dengue fever. Most natural focus diseases are characterized by strong pathogenicity, serious clinical behavior, high mortality rate, and high incidence rate. In 2008, the fever with thrombocytopenia syndrome emerged in China, and dengue fever broke out in Guangdong province in 2014. Natural focus diseases are great threats to Chinese, epsically in the context that there is currently no comprehensive method for acquiring the distribution characteristics of multiple diseases. The equilibrium degree in a region reflects the structure of the diseases in that region, and the distribution of the degree can help understand the distribution of multiple diseases. The paper used the quantity information of 14 natural focus diseases in China from 2004 to 2015, and applied Shannon information entropy theory to explore the spatial distribution pattern of the equilibrium degree of multiple natural focus diseases. Spatial autocorrelation analysis was adopted to detect the high incidence areas and low incidence areas. Finally, based on Pearson correlation coefficient analysis, the correlations among elevation, temperature, precipitation, NDVI, population, density of population, GDP, and information entropy were quantified. Results show that: (1) Anhui Province and Inner Mongolia Autonomous Region had the highest number of natural focus diseases. The information entropy of natural focus diseases in mainland China showed obvious northwest-southeast differentiation characteristics. The high-value aggregation areas and low-value aggregation areas were mainly distributed on the two sides of the boundary line of the mountains from Hebei Province to Yunnan Province. (2) Compared with social factors, natural factors were the main factors affecting the equilibrium degree of natural focus diseases. It was more prone to a variety of diseases in warm and humid areas with appropriate temperatures and adequate moisture. Single disease was more likely to occur in specific livestock or specific mosquitoes areas. (3) Areas with a high total number of cases usually resulted from a large number of cases of one disease, and these areas were less equilibrated, while areas with high information entropy usually resulted from many concentrated outbreaks of diseases. Our findings help understand the distribution characteristics of natural focus diseases in China, and demonstrate the potential of applying information entropy to analyze the prevention and control measures of natural focus diseases.

Key words: Natural focus disease, information entropy, spatial distribution, impact factors, natural factor, spatial autocorrelation analysis, China

丁晓彤, 余卓渊, 宋海慧, 谢云鹏, 吕可晶. 基于信息熵的中国自然疫源性疾病分布特征研究[J]. 地球信息科学学报, 2019, 21(12): 1877-1887.DOI:10.12082/dqxxkx.2019.190109

DING Xiaotong, YU Zhuoyuan, SONG Haihui, XIE Yunpeng, LV Kejing. Research on the Distribution of Natural Focus Diseases based on Information Entropy[J]. Journal of Geo-information Science, 2019, 21(12): 1877-1887.DOI:10.12082/dqxxkx.2019.190109

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地区	第一名	第二名	第三名	第四名	第五名
华北地区	布鲁氏菌病	出血热	斑疹伤寒	狂犬病	乙脑
东北地区	布鲁氏菌病	出血热	斑疹伤寒	疟疾	恙虫病
华东地区	疟疾	出血热	血吸虫病	恙虫病	布鲁氏菌病
华中地区	血吸虫病	疟疾	布鲁氏菌病	出血热	乙脑
华南地区	登革热	疟疾	狂犬病	出血热	斑疹伤寒
西南地区	疟疾	乙脑	斑疹伤寒	包虫病	狂犬病

R	高程	气温	降水	NDVI	人口数量	人口密度	GDP
气温	-0.306^**
降水	-0.300^**	0.860^**
NDVI	-0.442^**	0.260^**	0.354^**
人口数量	0.527^**	-0.261^**	-0.278^**	-0.403^**
人口密度	-0.167^**	-0.238^**	-0.181^**	-0.204^**	-0.084^**
GDP	0.259^**	-0.005	-0.041^*	-0.160^**	0.612^**	-0.037
信息熵	-0.391^**	0.463^**	0.424^**	0.298^**	-0.295^**	0.100^**	0.181^**