陕西省HFRS疫情时空分异特征及影响要素研究

doi:10.12082/dqxxkx.2020.190420

地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (5): 1142-1152.doi: 10.12082/dqxxkx.2020.190420

• “空间综合人文学与社会科学”专辑 • 上一篇下一篇

陕西省HFRS疫情时空分异特征及影响要素研究

朱伶俐^1,², 任红艳^1,^*(), 丁凤², 鲁亮³, 吴思佳^1,², 崔成^1,⁴

1.中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京 100101
2.福建师范大学地理科学学院, 福州 350007
3.中国疾病预防控制中心传染病预防控制所媒介生物控制室传染病预防控制国家重点实验室,北京 102206
4.中国科学院大学资源与环境学院,北京 100190

收稿日期:2019-08-03 修回日期:2020-03-03 出版日期:2020-05-25 发布日期:2020-07-25
通讯作者: 任红艳 E-mail:renhy@igsnrr.ac.cn
作者简介:朱伶俐（1995— ）,女,江苏泰州人,硕士生,主要从事遥感与GIS应用研究。E-mail:linglizhu321@sina.com
基金资助:
国家自然科学基金项目(41571158);资源与环境信息系统国家重点实验室自由探索类项目(O8R8B6A0YA);国家重点研发计划(2016YFC1302602)

Spatiotemporal Variations and Influencing Factors of Hemorrhagic Fever with Renal Syndrome in Shaanxi Province

ZHU Lingli^1,², REN Hongyan^1,^*(), DING Feng², LU Liang³, WU Sijia^1,², CUI Cheng^1,⁴

1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2. College of Geographical Sciences, Fujian Normal University, Fuzhou 350007, China
3. State Key Laboratory for Infectious Disease Prevention and Control, Department of Vector Biology and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
4. College of Resources and Environment, University of Academy of Sciences, Beijing 100190 , China

Received:2019-08-03 Revised:2020-03-03 Online:2020-05-25 Published:2020-07-25
Contact: REN Hongyan E-mail:renhy@igsnrr.ac.cn
Supported by:
National Natural Science Foundation of China(41571158);State Key Laboratory of Resources and Environment Information Systems, Independent Innovation Project(O8R8B6A0YA);National Key Research and Development Program(2016YFC1302602)

摘要/Abstract

摘要：

肾综合征出血热（HFRS）主要是由鼠类携带传播汉坦病毒而引起的一类自然疫源性传染病,严重危害着人类健康。陕西省是我国HFRS疫情最严重的省份之一,发病率居全国前列,研究其疫情时空分异和影响要素对指导当地疫情防控具有重要意义。本研究基于2005—2017年县区尺度HFRS发病率数据,采用空间自相关、热点分析等方法分析陕西省疫情时空分异特征,并利用地理探测器探究影响疫情的主要自然环境和社会经济要素。结果表明：2005—2017年陕西省HFRS发病率明显高于全国水平,同时呈现明显的时间波动和空间聚集,平原面积占比、建设用地面积占比、人口密度等因素可以解释约20%的HFRS疫情空间分异;关中平原聚集了陕西省90%以上的高发病县区,其疫情亦呈现明显的空间分异性,主要受降水量、 NDVI、土地利用类型等因素的影响。由此可知,高发病县区聚集、且自然环境和社会经济条件明显不同的关中平原是陕西省HFRS疫情流行的关键地区。因此,建议陕西省HFRS疫情防控应当重点关注降水量、植被状况以及土地利用类型,特别是在土地城镇化水平较高、人口密度较大的关中平原进行有效的防控干预。

关键词: HFRS疫情, 时空分异, 影响要素, 地理探测器, 陕西省, 关中平原, 因子探测器, 交互探测器

Abstract:

Hemorrhagic Fever with Renal Syndrome (HFRS) is a rodent-borne endemic disease caused by Hantavirus, which poses an increasingly serious threat to public health, especially in China. In this country, Shaanxi Province is one of the top regions with the highest HFRS incidence in the past years. It is of great importance to explore the potential influences on the spatiotemporal variations of HFRS epidemics across this province, which would provide useful clues for local authorities making targeted interventions on this disease.The county-level HFRS incidence rates during 2005-2017, as well as some potential natural and socioeconomic variables, were collected and analyzed by using spatial auto-correlation and hot-spot analysis tools as well as a Geodetector tool to explore the spatiotemporal relationships between the incidence rates and the potential variables. The HFRS epidemics in Shaanxi Province were obviously higher than the national level and presented clear temporal fluctuation and spatial clustering at the county scale. More than 90% of the counties with relatively high HFRS incidence rates concentrated in the Guanzhong Plain where obvious spatial heterogeneity was also observed. Some variables including the percentage of plain area and construction land, and population density separately accounted for about 20% of spatial variations of the county-level epidemic across the whole province. By comparison, the spatial pattern of this epidemic in the Guanzhong Plain with no obvious socioeconomic differences was mainly affected by precipitation, normalized difference vegetation index, and land-use types. Thus, the Guanzhong Plain with both spatially clustering higher incidence rates and obviously differentiated natural and socioeconomic conditions was the crucial region of the HFRS prevalence across Shaanxi Province. We suggest that precipitation, vegetation conditions, and land-use types should be heavily considered by local authorities for making effective interventions on this disease across Shaanxi Province, especially in the Guanzhong Plain with relatively high land urbanization and population density.

Key words: Hemorrhagic Fever with Renal Syndrome (HFRS), spatio-temporal characteristics, influencing factors, Shaanxi Province, Geodetector, Guanzhong Plain, factor detector, interaction detector

朱伶俐, 任红艳, 丁凤, 鲁亮, 吴思佳, 崔成. 陕西省HFRS疫情时空分异特征及影响要素研究[J]. 地球信息科学学报, 2020, 22(5): 1142-1152.DOI:10.12082/dqxxkx.2020.190420

ZHU Lingli, REN Hongyan, DING Feng, LU Liang, WU Sijia, CUI Cheng. Spatiotemporal Variations and Influencing Factors of Hemorrhagic Fever with Renal Syndrome in Shaanxi Province[J]. Journal of Geo-information Science, 2020, 22(5): 1142-1152.DOI:10.12082/dqxxkx.2020.190420

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参考文献 37

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要素	数据	变量	时间	数据源
气象要素	温度	年均温度	2005—2017	中国气象数据网 data.cma.cn
气象要素	降水量	年均降水量	2005—2017	中国气象数据网 data.cma.cn
景观要素	NDVI	年度NDVI	2005—2017	中国科学院资源环境科学数据中心资源环境数据云平台 www.resdc.cn
	地形因子	DEM、坡度、坡向、地形起伏度	—
	地貌类型	平原、台地、丘陵、山地	—
	土壤质地	砂土、粉砂土、黏土	—
社会经济要素	土地利用类型	耕地、林地、水域、建设用地	2005、2010、2015
	GDP	公里格网GDP	2005、2010、2015
	人口密度	公里格网人口密度	2005、2010、2015

年份	交互作用1	q	交互作用2	q	交互作用3	q
2005	平原∩坡向	0.727	平原∩粉砂土	0.710	林地∩人口密度	0.652
2006	林地∩人口密度	0.624	水域∩人口密度	0.583	台地∩人口密度	0.578
2007	林地∩人口密度	0.610	平原∩林地	0.602	平原∩坡度	0.598
2008	平原∩降水量	0.603	平原∩地形起伏度	0.570	平原∩林地	0.567
2009	平原∩地形起伏度	0.693	平原∩林地	0.688	平原∩坡度	0.654
2010	平原∩地形起伏度	0.797	平原∩林地	0.790	平原∩坡度	0.763
2011	平原∩坡度	0.786	平原∩地形起伏度	0.756	平原∩林地	0.738
2012	平原∩NDVI	0.531	NDVI∩砂土	0.505	平原∩降水量	0.490
2013	台地∩黏土	0.564	平原∩温度	0.547	降水量∩丘陵	0.544
2014	平原∩降水量	0.662	平原∩坡度	0.659	平原∩温度	0.620
2015	平原∩坡度	0.671	NDVI∩建设用地	0.657	平原∩温度	0.653
2016	平原∩温度	0.730	平原∩坡度	0.712	林地∩建设用地	0.710
2017	耕地∩建设用地	0.613	平原∩坡度	0.603	地形起伏度∩建设用地	0.592

年份	交互作用1	q	交互作用2	q	交互作用3	q
2005	降水量∩建设用地	0.885	林地∩人口密度	0.826	林地∩GDP	0.818
2006	NDVI∩温度	0.906	温度∩建设用地	0.855	黏土∩建设用地	0.839
2007	降水量∩建设用地	0.837	降水量∩人口密度	0.820	降水量∩黏土	0.793
2008	降水量∩建设用地	0.910	温度∩降水量	0.862	温度∩水域	0.857
2009	降水量∩建设用地	0.874	降水量∩人口密度	0.869	降水量∩耕地	0.835
2010	林地∩人口密度	0.850	NDVI∩建设用地	0.829	降水量∩建设用地	0.823
2011	降水量∩耕地	0.877	水域∩粉砂土	0.804	降水量∩黏土	0.794
2012	降水量∩水域	0.911	降水量∩黏土	0.839	降水量∩耕地	0.810
2013	NDVI∩水域	0.754	降水量∩水域	0.700	降水量∩未利用地	0.694
2014	NDVI∩粉砂土	0.788	NDVI∩温度	0.767	降水量∩NDVI	0.764
2015	降水量∩黏土	0.872	降水量∩建设用地	0.841	温度∩降水量	0.822
2016	降水量∩砂土	0.841	降水量∩粉砂土	0.841	林地∩建设用地	0.828
2017	林地∩人口密度	0.808	降水量∩耕地	0.731	NDVI∩砂土	0.731

陕西省HFRS疫情时空分异特征及影响要素研究

Spatiotemporal Variations and Influencing Factors of Hemorrhagic Fever with Renal Syndrome in Shaanxi Province

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