肾综合征出血热时空分布及环境危险因素研究
|
摘要
背景:肾综合征出血热(hemorrhagic fever with renal syndrome, HFRS)在我国又称流行性出血热(epidemic hemorrhagic fever,EHF),是由汉坦病毒(Hantavirus,HV)引起的以高热、出血和肾功能损害为特征,严重危害人类健康的自然疫源性疾病。该病在我国分布广、发病多、疫区类型复杂,是危害人民健康和生命安全的重点传染病之一。我国是世界上HFRS发病最多的国家,累计病人数占世界报道病例数的90%以上,到目前为止,我国内陆31个省、自治区、直辖市均有HFRS的病例报告。虽然2000年以后我国HFRS总体发病人数下降较明显,但我国HFRS疫区的范围仍在不断扩大,高发区范围发生变迁,部分地区流行强度上升明显,如我国华北、东北的部分地区HFRS已成为当地危害最严重的传染病之一。另外,由于HFRS的季节性分布往往反映当地汉坦病毒的血清型特征,其带毒的动物宿主、应采取的防控措施均有所不同,我国自上世纪80年代开始HFRS的季节性分布变化明显,但在不同地区其具体的变化趋势及可能的环境影响因素尚不明确。因此,有必要选择全国范围、HFRS新发区、传统高发区在不同的空间尺度上研究不同类型疫区的时空动态及疫源地变迁规律,以及对不同类型疫区的环境影响因素开展探索性研究,对于理解我国HFRS的流行病学分布特征、为疫区类型变化的原因提供线索、划定重点防治区域、促进HFRS的预防与控制等具有重要意义。
目的:在不同的时空尺度上弄清我国HFRS的时空分布特征、疫区类型及变化、疫区扩散特征及其相关的环境影响因素,并以此为基础建立HFRS防治决策应用系统,以利于较全面地掌握我国HFRS的时空动态及传播规律,为采取有针对性的防控措施提供科学依据。
方法:收集整理全国1994～2008年、北京市1997～2006年、山东省1968～2005年HFRS疫情监测数据,分别在不同的时空尺度上与数字地图建立空间关联,建立HFRS流行的地理信息数据库。综合应用时空扫描聚类分析、空间扩散趋势面分析、主成分Poisson回归模型等方法对不同类型疫区HFRS的时空分布及环境影响因素进行研究,环境因素选择与自然环境及社会经济环境的变化直接或间接相关的土地利用数据开展研究。所采用的软件包括ArcGIS9.2、ENVI4.2、SaTScan8.0、Geoda 0.9i、Origin7.5、STATA10.0等软件。
结果:①我国2000年以后HFRS总体的流行强度呈下降趋势,但疫区范围存在较明显的扩展,出现较多新发疫区,仍有大约1/3的疫区流行强度上升;我国2000年后HFRS流行强度上升的地区主要分布于我国原有疫区的周边(东北、北部、西部及南部区域),而发病率下降区域相对较为集中,主要分布于我国中东部地区;2000年后扩展的主要疫区发生于原有疫区的西部、北部及南部周边区域,减少的疫区范围主要分布于我国西南部地区,北方减少的疫区范围较少。②建立的全国季节性分布预测地图显示我国HFRS季节性分布特征为:从东北向西南秋冬型疫区→双峰型疫区→春季型疫区→双峰型疫区→秋冬型疫区→双峰型疫区→春季型疫区交错分布的特点,其过渡类型的边界除北方、西北较明显外,其他地方基本不明显。③土地利用与我国春季型疫区、秋冬型及双峰型疫区HFRS的发生与流行密切相关,部分因素对HFRS传播流行的作用不同。如建筑用地对与秋冬型疫区为保护性因素,对于春季型疫区则为危险因素,对于双峰型疫区的影响介于二者之间,提示土地利用变化可能会导致HFRS疫区类型的转变(如城市化建设使建筑用地增加,导致家鼠型HFRS传播的风险增加,从而为解释近年来我国HFRS疫区类型普遍由秋冬型向双峰混合型转变的原因提供线索)。④土地利用类型对HFRS传播流行的影响存在多重共线性,对HFRS传播流行的影响主要来自不同土地利用类型组合所反映的生境。⑤应用空间趋势面模型建立HFRS疫区空间动态扩散速度向量地图显示山东省HFRS疫区由最初山东省南部的临沂、日照和潍坊地区向北、向东北、向西南扩散的过程。⑥系统分析了山东省HFRS流行强度变化、不同类型疫区的分布及动态变化特征、疫区类型转变特征,发现与山东省HFRS流行强度、疫区类型转变相关的三个流行阶段,其中第二阶段(1983～1985年)为山东省疫区类型转变的过渡阶段。⑦建立肾综合征出血热防治决策应用系统。
结论:本研究在收集整理全国、北京市及山东省历年HFRS疫情监测数据,建立HFRS流行的地理信息系统数据库的基础上,综合运用时空扫描聚类分析、空间扩散趋势分析、主成分Poisson回归模型等方法,对不同类型疫区的时间空间分布及环境影响因素进行研究,明确了HFRS的时空热点地区,阐述了HFRS疫区时空动态变迁、季节性分布及变化、流行强度变化等特征;明确了土地利用对不同类型疫区HFRS发生与流行的影响。与此同时,本研究还建立了HFRS防治决策应用系统。以上研究结果不仅为经济、旅游开发等可能导致的HFRS流行风险评估提供了数据基础,而且为探讨我国HFRS疫区类型的转变原因提供了线索,为指导不同类型疫区HFRS的防控策略提供了依据。
Background: Hemorrhagic fever with renal syndrome (HFRS), a rodent-borne disease caused by Hantaviruses (HV), is characterized by fever, acute renal dysfunction and hemorrhage manifestations. It is one of the key infectious diseases harming people's health and safety with wide distribution, high incidence and complicated type of endemic areas in mainland China. At present, HFRS is endemic in all 31 provinces, autonomous regions, and metropolitan areas in mainland Chian and accounts for 90% of the HFRS cases reported globally. Although the overall incidence of HFRS in China decreased obviously since 2000, the scope of HFRS endemic area is still expanding, and the areas of hotspot with high incidence are changing with significantly increasing incidence in some areas, such as some local places in northeast China and north China, being ranked as one of the most serious infectious diseases. Noticeably, seasonal pattern of HFRS cases is linked with the serotype of HVs, animal hosts. Also, the measures of prevention and control measures to be taken are different. Since 1980s, changes of seasonal pattern of HFRS cases had occurred popularly in mainland China, but the detail on transformation dynamics and its environmental factors in different regions is not clear. Therefore, it is necessary to study the spatial temporal dynamics and the rule of endemic area’s transformation at different spatial scales and type of endemic area by choosing the national level, a newly-established endemic region and a traditional high-incidence area, as well as to study the association between HFRS incidence for different types of endemic areas and the environmental factors. These will be helpful for understanding the spatiotemporal pattern of HFRS cases, providing clue to changes of endemic areas’types, identifying the targets of measures, and promoting prevention and control of HFRS.
Objective: To study the temporal and spatial pattern of HFRS cases, endemic areas’type and its changes, the characteristics of HFRS endemic areas’outspread, as well as their environmental determinants at different temporal and spatial scales in mainland Chian and to farther establish the decision-making system for prevention and control of HFRS, in order to facilitate a more comprehensive understanding of spatiotemporal dynamics and their determinants of HFRS cases and provide a scientific basis for the targeted measures of prevention and control of HFRS in mainland China.
Methods: The surveillance data of HFRS epidemic from 1994 to 2008 in the whole country, 1997 to 2006 in Beijing, and 1968 to 2005 in Shandong Province were collected and processed. Based on the data, a GIS database of HFRS epidemic was established by linking these data to digital maps at different spatiotemporal scales. Spatiotemporal dynamics and their environmental determinants of HFRS cases for different type of endemic areas were studied by comprehensive application of cluster analysis of time-space scanning, spatial trend surface analysis, Poisson regression model with principal component, and so on. Here we chose and used land use data as environmental factors for these studies due to its direct or indirect relationship with changes of natural environment and socio-economic environment. Softwares such as ArcGIS9.2, ENVI4.2, SaTScan8.0, Geoda 0.9i, Origin7.5, and STATA10.0 were used in this study.
Results:
1. Since 2000, the overall incidence of HFRS decreases in mainland China, but the scope of HFRS endemic areas enlarge obviously with lots of newly-established endemic areas, and the incidence still increases in about 1/3 of all endemic areas. Since 2000, the HFRS endemic areas with increased incidence are distributed mainly surrounding the original endemic areas and located in the northeast, north, west and southern regions of the original endemic areas, while the endemic areas with declining incidence are relatively concentrated on the eastern and central region of China. In addition, the newly-established endemic areas of HFRS are mainly located in the western, northern and southern regions of original endemic areas, while the reduced endemic areas are mainly distributed in southwestern China and there are less endemic areas reduced in north China.
2. The predictive map of HFRS seasonal distribution shows the seasonal pattern of HFRS cases in mainland China as follows: from the northeast to southwest, the different type of endemic areas arrange according to this rank, the endemic areas of autumn/winter-type→endemic areas of bimodal type→endemic areas of spring-type→endemic areas of bimodal type→endemic areas of autumn/winter-type→endemic areas of bimodal type→endemic areas of spring-type. The borders between two types of endemic areas are vague in the most endemic areas except for the north, north-west China.
3. We find that the incidence in HFRS endemic areas of autumn/winter-type, spring-type, and bimodal type are all closely related to land use. It is not always the same on the influence to HFRS epidemic in different endemic areas resulted from some variables from land use. For instance, built-up land has the influence of protection to the endemic areas of autumn/winter-type, while it can add the epidemic risk for endemic areas of spring-type. Compared to endemic areas of endemic areas of autumn/winter-type, and spring-type, it has the in-between influence for the endemic areas of bimodal type. This result indicate that the change of land use may lead to the the change of type of HFRS endemic area. For instance, urbanization increases the area of built-up land, which results in the added risk of HFRS epidemic of spring-type and decease the risk of HFRS epidemic of autumn/winter-type, subsequently. The result can provide clue to the cause on the changing of endemic areas’type occurred popularly in the 1980s.
4. land-use types’influence on HFRS epidemic has this pattern of multi-collinearity and it usually come from the habitat reflected by the combination of different types of land use.
5. The velocity vector map to visualize spread of HFRS over time indicate that the rapid propagation of HFRS endemic areas from its epicenter in Rizhao, Linyi, Weifang regions in southern Shandong province and its spread north, north-east and south-west into new counties and foci. by using space trend surface model.
6. We demonstrated the spatial and temporal dynamic of scope of endemic areas and their changing patterns, seasonal pattern of HFRS cases and their changing patterns, as well as the spatiotemporal dynamics of HFRS cases and their changing patterns in Shandong Province. Three periods associated with HFRS incidence, seasonal pattern of HFRS cases and their changing patterns were identified and indicated that the second period (the period from 1983 to 1985) was the phase of the change of type of HFRS endemic area in Shandong Province.
7. We established the GIS decision-making system of HFRS.
Conclusion: Based on the establishment of HFRS GIS database by collecting the data of HFRS epidemic in the country, Beijing and Shandong Province in this study, the spatial temporal distribution and environmental risk factors of HFRS for endemic areas of different types were studied by using synthetically space-time scanning cluster analysis, spatial trend surface model, Poisson regression combined with principal component analysis methods. This study detected the space-time hot spots of HFRS epidemics, and demonstrated the spatial and temporal dynamic of scope of endemic areas and their changing patterns, seasonal patterns of HFRS cases and their changing patterns, as well as the spatiotemporal dynamics of HFRS cases and their changing patterns. This study also identified the association between HFRS incidence of three-type endemic areas and land use. In addition, the GIS decision-making system of HFRS was established in the study. The results above not only provides the basis of data for the the risk assessment of HFRS epidemic resulted from economic and tour development, also can provide the clue for researching the cause on the changing of endemic areas’type and provide scientic basis for targeting the suitable measures of prevention and control according to different type of endemic areas in mainland China.
目的:在不同的时空尺度上弄清我国HFRS的时空分布特征、疫区类型及变化、疫区扩散特征及其相关的环境影响因素,并以此为基础建立HFRS防治决策应用系统,以利于较全面地掌握我国HFRS的时空动态及传播规律,为采取有针对性的防控措施提供科学依据。
方法:收集整理全国1994～2008年、北京市1997～2006年、山东省1968～2005年HFRS疫情监测数据,分别在不同的时空尺度上与数字地图建立空间关联,建立HFRS流行的地理信息数据库。综合应用时空扫描聚类分析、空间扩散趋势面分析、主成分Poisson回归模型等方法对不同类型疫区HFRS的时空分布及环境影响因素进行研究,环境因素选择与自然环境及社会经济环境的变化直接或间接相关的土地利用数据开展研究。所采用的软件包括ArcGIS9.2、ENVI4.2、SaTScan8.0、Geoda 0.9i、Origin7.5、STATA10.0等软件。
结果:①我国2000年以后HFRS总体的流行强度呈下降趋势,但疫区范围存在较明显的扩展,出现较多新发疫区,仍有大约1/3的疫区流行强度上升;我国2000年后HFRS流行强度上升的地区主要分布于我国原有疫区的周边(东北、北部、西部及南部区域),而发病率下降区域相对较为集中,主要分布于我国中东部地区;2000年后扩展的主要疫区发生于原有疫区的西部、北部及南部周边区域,减少的疫区范围主要分布于我国西南部地区,北方减少的疫区范围较少。②建立的全国季节性分布预测地图显示我国HFRS季节性分布特征为:从东北向西南秋冬型疫区→双峰型疫区→春季型疫区→双峰型疫区→秋冬型疫区→双峰型疫区→春季型疫区交错分布的特点,其过渡类型的边界除北方、西北较明显外,其他地方基本不明显。③土地利用与我国春季型疫区、秋冬型及双峰型疫区HFRS的发生与流行密切相关,部分因素对HFRS传播流行的作用不同。如建筑用地对与秋冬型疫区为保护性因素,对于春季型疫区则为危险因素,对于双峰型疫区的影响介于二者之间,提示土地利用变化可能会导致HFRS疫区类型的转变(如城市化建设使建筑用地增加,导致家鼠型HFRS传播的风险增加,从而为解释近年来我国HFRS疫区类型普遍由秋冬型向双峰混合型转变的原因提供线索)。④土地利用类型对HFRS传播流行的影响存在多重共线性,对HFRS传播流行的影响主要来自不同土地利用类型组合所反映的生境。⑤应用空间趋势面模型建立HFRS疫区空间动态扩散速度向量地图显示山东省HFRS疫区由最初山东省南部的临沂、日照和潍坊地区向北、向东北、向西南扩散的过程。⑥系统分析了山东省HFRS流行强度变化、不同类型疫区的分布及动态变化特征、疫区类型转变特征,发现与山东省HFRS流行强度、疫区类型转变相关的三个流行阶段,其中第二阶段(1983～1985年)为山东省疫区类型转变的过渡阶段。⑦建立肾综合征出血热防治决策应用系统。
结论:本研究在收集整理全国、北京市及山东省历年HFRS疫情监测数据,建立HFRS流行的地理信息系统数据库的基础上,综合运用时空扫描聚类分析、空间扩散趋势分析、主成分Poisson回归模型等方法,对不同类型疫区的时间空间分布及环境影响因素进行研究,明确了HFRS的时空热点地区,阐述了HFRS疫区时空动态变迁、季节性分布及变化、流行强度变化等特征;明确了土地利用对不同类型疫区HFRS发生与流行的影响。与此同时,本研究还建立了HFRS防治决策应用系统。以上研究结果不仅为经济、旅游开发等可能导致的HFRS流行风险评估提供了数据基础,而且为探讨我国HFRS疫区类型的转变原因提供了线索,为指导不同类型疫区HFRS的防控策略提供了依据。
Background: Hemorrhagic fever with renal syndrome (HFRS), a rodent-borne disease caused by Hantaviruses (HV), is characterized by fever, acute renal dysfunction and hemorrhage manifestations. It is one of the key infectious diseases harming people's health and safety with wide distribution, high incidence and complicated type of endemic areas in mainland China. At present, HFRS is endemic in all 31 provinces, autonomous regions, and metropolitan areas in mainland Chian and accounts for 90% of the HFRS cases reported globally. Although the overall incidence of HFRS in China decreased obviously since 2000, the scope of HFRS endemic area is still expanding, and the areas of hotspot with high incidence are changing with significantly increasing incidence in some areas, such as some local places in northeast China and north China, being ranked as one of the most serious infectious diseases. Noticeably, seasonal pattern of HFRS cases is linked with the serotype of HVs, animal hosts. Also, the measures of prevention and control measures to be taken are different. Since 1980s, changes of seasonal pattern of HFRS cases had occurred popularly in mainland China, but the detail on transformation dynamics and its environmental factors in different regions is not clear. Therefore, it is necessary to study the spatial temporal dynamics and the rule of endemic area’s transformation at different spatial scales and type of endemic area by choosing the national level, a newly-established endemic region and a traditional high-incidence area, as well as to study the association between HFRS incidence for different types of endemic areas and the environmental factors. These will be helpful for understanding the spatiotemporal pattern of HFRS cases, providing clue to changes of endemic areas’types, identifying the targets of measures, and promoting prevention and control of HFRS.
Objective: To study the temporal and spatial pattern of HFRS cases, endemic areas’type and its changes, the characteristics of HFRS endemic areas’outspread, as well as their environmental determinants at different temporal and spatial scales in mainland Chian and to farther establish the decision-making system for prevention and control of HFRS, in order to facilitate a more comprehensive understanding of spatiotemporal dynamics and their determinants of HFRS cases and provide a scientific basis for the targeted measures of prevention and control of HFRS in mainland China.
Methods: The surveillance data of HFRS epidemic from 1994 to 2008 in the whole country, 1997 to 2006 in Beijing, and 1968 to 2005 in Shandong Province were collected and processed. Based on the data, a GIS database of HFRS epidemic was established by linking these data to digital maps at different spatiotemporal scales. Spatiotemporal dynamics and their environmental determinants of HFRS cases for different type of endemic areas were studied by comprehensive application of cluster analysis of time-space scanning, spatial trend surface analysis, Poisson regression model with principal component, and so on. Here we chose and used land use data as environmental factors for these studies due to its direct or indirect relationship with changes of natural environment and socio-economic environment. Softwares such as ArcGIS9.2, ENVI4.2, SaTScan8.0, Geoda 0.9i, Origin7.5, and STATA10.0 were used in this study.
Results:
1. Since 2000, the overall incidence of HFRS decreases in mainland China, but the scope of HFRS endemic areas enlarge obviously with lots of newly-established endemic areas, and the incidence still increases in about 1/3 of all endemic areas. Since 2000, the HFRS endemic areas with increased incidence are distributed mainly surrounding the original endemic areas and located in the northeast, north, west and southern regions of the original endemic areas, while the endemic areas with declining incidence are relatively concentrated on the eastern and central region of China. In addition, the newly-established endemic areas of HFRS are mainly located in the western, northern and southern regions of original endemic areas, while the reduced endemic areas are mainly distributed in southwestern China and there are less endemic areas reduced in north China.
2. The predictive map of HFRS seasonal distribution shows the seasonal pattern of HFRS cases in mainland China as follows: from the northeast to southwest, the different type of endemic areas arrange according to this rank, the endemic areas of autumn/winter-type→endemic areas of bimodal type→endemic areas of spring-type→endemic areas of bimodal type→endemic areas of autumn/winter-type→endemic areas of bimodal type→endemic areas of spring-type. The borders between two types of endemic areas are vague in the most endemic areas except for the north, north-west China.
3. We find that the incidence in HFRS endemic areas of autumn/winter-type, spring-type, and bimodal type are all closely related to land use. It is not always the same on the influence to HFRS epidemic in different endemic areas resulted from some variables from land use. For instance, built-up land has the influence of protection to the endemic areas of autumn/winter-type, while it can add the epidemic risk for endemic areas of spring-type. Compared to endemic areas of endemic areas of autumn/winter-type, and spring-type, it has the in-between influence for the endemic areas of bimodal type. This result indicate that the change of land use may lead to the the change of type of HFRS endemic area. For instance, urbanization increases the area of built-up land, which results in the added risk of HFRS epidemic of spring-type and decease the risk of HFRS epidemic of autumn/winter-type, subsequently. The result can provide clue to the cause on the changing of endemic areas’type occurred popularly in the 1980s.
4. land-use types’influence on HFRS epidemic has this pattern of multi-collinearity and it usually come from the habitat reflected by the combination of different types of land use.
5. The velocity vector map to visualize spread of HFRS over time indicate that the rapid propagation of HFRS endemic areas from its epicenter in Rizhao, Linyi, Weifang regions in southern Shandong province and its spread north, north-east and south-west into new counties and foci. by using space trend surface model.
6. We demonstrated the spatial and temporal dynamic of scope of endemic areas and their changing patterns, seasonal pattern of HFRS cases and their changing patterns, as well as the spatiotemporal dynamics of HFRS cases and their changing patterns in Shandong Province. Three periods associated with HFRS incidence, seasonal pattern of HFRS cases and their changing patterns were identified and indicated that the second period (the period from 1983 to 1985) was the phase of the change of type of HFRS endemic area in Shandong Province.
7. We established the GIS decision-making system of HFRS.
Conclusion: Based on the establishment of HFRS GIS database by collecting the data of HFRS epidemic in the country, Beijing and Shandong Province in this study, the spatial temporal distribution and environmental risk factors of HFRS for endemic areas of different types were studied by using synthetically space-time scanning cluster analysis, spatial trend surface model, Poisson regression combined with principal component analysis methods. This study detected the space-time hot spots of HFRS epidemics, and demonstrated the spatial and temporal dynamic of scope of endemic areas and their changing patterns, seasonal patterns of HFRS cases and their changing patterns, as well as the spatiotemporal dynamics of HFRS cases and their changing patterns. This study also identified the association between HFRS incidence of three-type endemic areas and land use. In addition, the GIS decision-making system of HFRS was established in the study. The results above not only provides the basis of data for the the risk assessment of HFRS epidemic resulted from economic and tour development, also can provide the clue for researching the cause on the changing of endemic areas’type and provide scientic basis for targeting the suitable measures of prevention and control according to different type of endemic areas in mainland China.
引文
1.沈建中,吴光华.近年肾综合症出血热流行病学研究进展.医学动物防制,2002;18(1):4-7
2.陈化新,罗成旺.中国肾综合征出血热监测.中华流行病学杂志,2002;23(1):63-66
3.连志浩主编.流行病学.第三版.北京:人民卫生出版社,1994.5:1
4. Carlos CS. Use of geographic information system in epidemiology (GIS-Epi). Bull PAHO,1996;17(1):1-6
5. Briggs DJ, Elliott P. The use of geographic information system in studies on environment and health. Wld Hlth Statist Quart,1995; 48:85-94
6. Clarke KC, Mclafferty SL, Tempalski BL. On epidemicology and geographic information system: a review and discussion of future directions. Emerging Infect Dis, 1996; 2(2):85-92
7. Kamel Boulos M.N, Roudsari A.V., and Carson E.R. Health geomatics: An enabling suite of technologies in health and healthcare. J Bio Inf, 2001; 34(9):195-219
8. Krieger N. Place, space, and health: GIS and epidemiology. Epidemiology, 2003 ;14(4):384-385
9.陈述彭,卢学军,周成虎.地理信息系统导论.北京:科学出版社,1999.5
10.梅安新,彭望,秦其明等.遥感导论.北京:高等教育出版社,2001.7
1沈建中,吴光华.近年肾综合征出血热流行病学研究现状、问题与展望.医学动物防制,2002;18(9):504-8
2卫生部疾病控制司.流行性出血热防治手册.北京:人民卫生出版社,1998
3 Zhang WY, Fang LQ, Jiang JF, Hui FM, Glass GE, Yan L, Xu Yf, Zhao WJ, Yang H, Liu W, Cao WC. Predicting the risk of Hantavirus infection in Beijing, People’s Republic of China. Am J Trop Med Hyg, 2009; 80(4):678-83
4陈化新,丘福禧,赵秀芹等.中国肾综合征出血热不同年代不同地区流行季节分布特点.中华实验和临床病毒学杂志,1994;8(3):197-202
5 Kulldorff M: A spatial scan statistic. Communications in Statistics:Theory and Methods 1997, 26:1481-1496
6 Kulldorff M, Nagarwalla N: Spatial disease clusters: detection and inference. Stat Med 1995, 14:799-810
7 Kulldorff M, Feuer EJ, Miller BA, Freedman LS: Breast cancer clusters in the northeast United States: a geographic analysis. Am J Epidemiol 1997, 146:161-70
1. Zhang XC, Hu JY, Zhou SL, Guan ZZ. Study on epidemic state in Beijing. Chin J Vector Bio & Control. 2001; 12: 218-21
2. Dwass M. Modified randomization tests for nonparametric hypotheses. Ann Math Statist. 1957;28:181-7
3. Kulldorff M, Athas WF, Feurer EJ, Miller BA, Key CR. Evaluating cluster alarms: a space-time scan statistic and brain cancer in Los Alamos, New Mexico. Am J Public Health. 1998;88:1377-80
4. Chaput EK, Meek JI, Heimer R. Spatial analysis of human granulocytic ehrlichiosis near Lyme, Connecticut. Emerg Infect Dis. 2002;8:943-8
1. Waller LA, Gotway CA. Applied spatial statistics for public health data. New York: John Wiley & Sons; 2004
2. Moore DA. Spatial diffusion of raccoon rabies in Pennsylvania, USA. Prev Vet Med. 1999;40:19–32
3. Berke O. Estimation and prediction in the spatial linear model. Water Air Soil Pollution. 1999;110:215–37
1. Tanser FC, Le Sueur D. The application of geographical information systems to important public health problems in Africa. Int J Health Geogr, 2002, 9: 4
2.杨崇俊,刘冬林,杨洪志等.高访问量的“SARS网络地理信息系统”解决方案.遥感学报,2003,7:256-9
3.韩光红,张习坦,方立群等.我国重要自然疫源地地理信息系统的建立及应用研究.军事医学科学院院刊, 2004, 28:123-5
1.陈化新,罗成旺.中国肾综合征出血热监测.中华流行病学杂志,2002;23(1):63-6
2.方立群,曹务春,陈化新等.应用地理信息系统分析中国肾综合征出血热的空间分布.中华流行病学杂志,2003;24(4):265-8
3. Lin HL, Liu QY, Guo JQ, Zhang JB, Wang JF, Chen HX, 2007. Analysis of the geographic distribution of HFRS in Liaoning province between 2000 and 2005. BMC Public Health 7: 207
4. Bi P, Tong SL, Donald K, Ni JF, 2002. Climatic, reservoir and occupational variables and the transmission of hemorrhagic fever with renal syndrome in China. Int J Epidemiol 31: 189–93
5. Chen HX, Luo CW, Chen F, Wang XH, Yang JH, Ma LJ, Hu JS, Sun HY, Yao ZH, Qiu JC, 1999. Study and surveillance of HFRS in China. Chin Public Health 15: 616–23
6.韩光红,张习坦,方立群等.我国重要自然疫源地地理信息系统的建立及应用研究.传染病信息, 2001, 14:127-9
7. Yan L, Fang LQ, Huang HG, Zhang LQ, Feng D, Zhao WJ, Zhang WY, Li XW, Cao WC, 2007. Landscape elements and Hantaan virus-related hemorrhagic fever with renal syndrome, People’s Republic of China. Emerg Infect Dis 3: 1301–6
8. Zuo SQ, Zhang PH, Jiang JF, Zhan L, Wu XM, Zhao WJ, Wang RM, Tang F, Dun Z, Cao WC, 2008. Seoul virus in patients and rodents from Beijing, China. Am J Trop Med Hyg 78: 833–7
9. Jiang JF, Zuo SQ, Zhang WY, Wu XM, Tang F, de Vlas SJ, Zhao WJ, Zhang PH, Dun Z, Wang RM, Cao WC, 2008. Prevalence and genetic diversities of hantaviruses in rodents in Beijing, China. Am J Trop Med Hyg 78: 98–105
1.陈述彭,鲁学军,周成虎.地理信息系统导论.北京:科学出版社,1999
2.邬伦,刘瑜,张晶等.地理信息系统原理、方法和应用.北京:科学出版社,2001
3.李立明,叶冬青.流行病学.第5版.北京:人民卫生出版社,2003
4.韩光红,张习坦.地理信息系统及其在流行病学研究中的应用.中华流行病学杂志,2001,22(1):65-67
5.宫鹏,徐冰,梁松.用遥感和地理信息系统研究传染病时空分布.中国科学C辑生命科学,2006,36(2):184-192
6.方立群,曹务春.3S技术在自然疫源性疾病中的应用及发展前景.中国人兽共患病杂志,2004,20(9): 807-810
7. Nelson KE, Williams CM, Graham NMH. Infectious disease epidemiology. Sudbury: Jones and Bartlett, 2004
8. Meselson M, Guillemin J, Hugh-Jones M, et al. The Sverdlovsk anthrax outbreak of 1979. Science, 1994, 266:1202-1208
9. Conrad E.R. Pennsylvania combats west Nile virus with ArcPad, internet. http://www.esri.com/library/brochures/pdfs/health-west-nile-virus.pdf, December 14, 2005
10. Spear RC, Hubbard A, Liang S, et al. Disease transmission models for public health decision making: toward an approach for designing intervention strategies for schistosomiasis japonica. Environ Health Perspect, 2002, 110:907-915
11. Poulstrup A, Hansen HL. Use of GIS and exposure modeling as tools in a study of cancer incidence in a population exposed to airborne dioxin. Environ Health Perspect, 2004, 112:1032-1036
12. Nuckols JR, Ward MH, Jarup L. Using geographic information systems for exposure assessment in environmental epidemiology studies. Environ Health Perspect, 2004, 112:1007-1015
13. Tran A, Deparis X, Dussart P, et al. Dengue Spatial and Temporal Patterns, French Guiana, 2001. Emerg Inf Dis, 2004, 10: 615-621
14. Graham AJ, Atkinson PM, Danson FM. Spatial analysis for epidemiology. Acta Tropica, 2004, 91:219-225
15. Gosselin P, Lebel G, Rivest S, et al. The integrated system for public health monitoring of west Nile virus (ISPHM-WNV): a real-time GIS for surveillance and decision-making. Int J Health Geogr, 2005, 4:20-32
16. Grover-Kopec E, Kawano M, Klaver RW, et al. An online operational rainfall-monitoring resource for epidemic malaria early warning systems in Africa. Malaria Journal, 2005, 4:6-10
17.梅安新,彭望琭,秦其明等.遥感导论.北京:高等教育出版社,2001
18. Beck LR, Lobitz BM, Wood BL. Remote sensing and human health: new sensors and new opportunities. Emerg Inf Dis, 2000, 6: 217-226
19.王劲峰,孙英君,韩卫国等.空间分析引论.地理信息世界,2004,2(5):6-10
20. Anselin L, Syabri I, Kho Y. GeoDa: An introduction to spatial data analysis. http://www.sal.uiuc.edu/stuff/stuff-sum/stuff/stuff-sum/pdf/geoda.pdf, May 5, 2005
21. Waller LA, Gotway CA. Applied spatiall statistics for public health data. New Jersey: John Wiley & sons,Inc, 2004
22.武继磊,王劲峰,郑晓瑛等.空间数据分析技术在公共卫生领域的应用.地理科学进展,2003,22(3):220-228
23.李立明,詹思延,吴系科等.流行病学进展.第10卷.北京:北京医科大学出版社,2002
24.赵仲堂.流行病学研究方法与应用.第2版.北京:科学出版社,2005
25. Kulldorff M and Information Management Services, Inc: SaTScanTM v6.0: Software for the spatial and space-time scan statistics. http://www.satscan.org/, December 10, 2005
26. Krivoruchko K, Gotway CA. Creating exposure maps using kriging. http://www.esri.com/software/arcgis/arcgisxtensions/geostatistical/research_papers.html, September 1, 2006
27. Tanser FC, Sueur DL. The application of geographical information systems to important public health problems in Africa. Int J Health Geogr, 2002, 1: 4-12
28.方立群,曹春香,陈国胜等.地理信息系统应用于我国高致病性禽流感的空间分布及环境因素分析.中华流行病学杂志,2005,26(11):839-846
29. Fang L, Zhong S, Cao w, et al. Studies on applications of remote sensing and geographic information system in surveillance and prediction of highly pathogenic avian influenza. Asia-Pacific Space Outlook, 2006,10:17-23
2.陈化新,罗成旺.中国肾综合征出血热监测.中华流行病学杂志,2002;23(1):63-66
3.连志浩主编.流行病学.第三版.北京:人民卫生出版社,1994.5:1
4. Carlos CS. Use of geographic information system in epidemiology (GIS-Epi). Bull PAHO,1996;17(1):1-6
5. Briggs DJ, Elliott P. The use of geographic information system in studies on environment and health. Wld Hlth Statist Quart,1995; 48:85-94
6. Clarke KC, Mclafferty SL, Tempalski BL. On epidemicology and geographic information system: a review and discussion of future directions. Emerging Infect Dis, 1996; 2(2):85-92
7. Kamel Boulos M.N, Roudsari A.V., and Carson E.R. Health geomatics: An enabling suite of technologies in health and healthcare. J Bio Inf, 2001; 34(9):195-219
8. Krieger N. Place, space, and health: GIS and epidemiology. Epidemiology, 2003 ;14(4):384-385
9.陈述彭,卢学军,周成虎.地理信息系统导论.北京:科学出版社,1999.5
10.梅安新,彭望,秦其明等.遥感导论.北京:高等教育出版社,2001.7
1沈建中,吴光华.近年肾综合征出血热流行病学研究现状、问题与展望.医学动物防制,2002;18(9):504-8
2卫生部疾病控制司.流行性出血热防治手册.北京:人民卫生出版社,1998
3 Zhang WY, Fang LQ, Jiang JF, Hui FM, Glass GE, Yan L, Xu Yf, Zhao WJ, Yang H, Liu W, Cao WC. Predicting the risk of Hantavirus infection in Beijing, People’s Republic of China. Am J Trop Med Hyg, 2009; 80(4):678-83
4陈化新,丘福禧,赵秀芹等.中国肾综合征出血热不同年代不同地区流行季节分布特点.中华实验和临床病毒学杂志,1994;8(3):197-202
5 Kulldorff M: A spatial scan statistic. Communications in Statistics:Theory and Methods 1997, 26:1481-1496
6 Kulldorff M, Nagarwalla N: Spatial disease clusters: detection and inference. Stat Med 1995, 14:799-810
7 Kulldorff M, Feuer EJ, Miller BA, Freedman LS: Breast cancer clusters in the northeast United States: a geographic analysis. Am J Epidemiol 1997, 146:161-70
1. Zhang XC, Hu JY, Zhou SL, Guan ZZ. Study on epidemic state in Beijing. Chin J Vector Bio & Control. 2001; 12: 218-21
2. Dwass M. Modified randomization tests for nonparametric hypotheses. Ann Math Statist. 1957;28:181-7
3. Kulldorff M, Athas WF, Feurer EJ, Miller BA, Key CR. Evaluating cluster alarms: a space-time scan statistic and brain cancer in Los Alamos, New Mexico. Am J Public Health. 1998;88:1377-80
4. Chaput EK, Meek JI, Heimer R. Spatial analysis of human granulocytic ehrlichiosis near Lyme, Connecticut. Emerg Infect Dis. 2002;8:943-8
1. Waller LA, Gotway CA. Applied spatial statistics for public health data. New York: John Wiley & Sons; 2004
2. Moore DA. Spatial diffusion of raccoon rabies in Pennsylvania, USA. Prev Vet Med. 1999;40:19–32
3. Berke O. Estimation and prediction in the spatial linear model. Water Air Soil Pollution. 1999;110:215–37
1. Tanser FC, Le Sueur D. The application of geographical information systems to important public health problems in Africa. Int J Health Geogr, 2002, 9: 4
2.杨崇俊,刘冬林,杨洪志等.高访问量的“SARS网络地理信息系统”解决方案.遥感学报,2003,7:256-9
3.韩光红,张习坦,方立群等.我国重要自然疫源地地理信息系统的建立及应用研究.军事医学科学院院刊, 2004, 28:123-5
1.陈化新,罗成旺.中国肾综合征出血热监测.中华流行病学杂志,2002;23(1):63-6
2.方立群,曹务春,陈化新等.应用地理信息系统分析中国肾综合征出血热的空间分布.中华流行病学杂志,2003;24(4):265-8
3. Lin HL, Liu QY, Guo JQ, Zhang JB, Wang JF, Chen HX, 2007. Analysis of the geographic distribution of HFRS in Liaoning province between 2000 and 2005. BMC Public Health 7: 207
4. Bi P, Tong SL, Donald K, Ni JF, 2002. Climatic, reservoir and occupational variables and the transmission of hemorrhagic fever with renal syndrome in China. Int J Epidemiol 31: 189–93
5. Chen HX, Luo CW, Chen F, Wang XH, Yang JH, Ma LJ, Hu JS, Sun HY, Yao ZH, Qiu JC, 1999. Study and surveillance of HFRS in China. Chin Public Health 15: 616–23
6.韩光红,张习坦,方立群等.我国重要自然疫源地地理信息系统的建立及应用研究.传染病信息, 2001, 14:127-9
7. Yan L, Fang LQ, Huang HG, Zhang LQ, Feng D, Zhao WJ, Zhang WY, Li XW, Cao WC, 2007. Landscape elements and Hantaan virus-related hemorrhagic fever with renal syndrome, People’s Republic of China. Emerg Infect Dis 3: 1301–6
8. Zuo SQ, Zhang PH, Jiang JF, Zhan L, Wu XM, Zhao WJ, Wang RM, Tang F, Dun Z, Cao WC, 2008. Seoul virus in patients and rodents from Beijing, China. Am J Trop Med Hyg 78: 833–7
9. Jiang JF, Zuo SQ, Zhang WY, Wu XM, Tang F, de Vlas SJ, Zhao WJ, Zhang PH, Dun Z, Wang RM, Cao WC, 2008. Prevalence and genetic diversities of hantaviruses in rodents in Beijing, China. Am J Trop Med Hyg 78: 98–105
1.陈述彭,鲁学军,周成虎.地理信息系统导论.北京:科学出版社,1999
2.邬伦,刘瑜,张晶等.地理信息系统原理、方法和应用.北京:科学出版社,2001
3.李立明,叶冬青.流行病学.第5版.北京:人民卫生出版社,2003
4.韩光红,张习坦.地理信息系统及其在流行病学研究中的应用.中华流行病学杂志,2001,22(1):65-67
5.宫鹏,徐冰,梁松.用遥感和地理信息系统研究传染病时空分布.中国科学C辑生命科学,2006,36(2):184-192
6.方立群,曹务春.3S技术在自然疫源性疾病中的应用及发展前景.中国人兽共患病杂志,2004,20(9): 807-810
7. Nelson KE, Williams CM, Graham NMH. Infectious disease epidemiology. Sudbury: Jones and Bartlett, 2004
8. Meselson M, Guillemin J, Hugh-Jones M, et al. The Sverdlovsk anthrax outbreak of 1979. Science, 1994, 266:1202-1208
9. Conrad E.R. Pennsylvania combats west Nile virus with ArcPad, internet. http://www.esri.com/library/brochures/pdfs/health-west-nile-virus.pdf, December 14, 2005
10. Spear RC, Hubbard A, Liang S, et al. Disease transmission models for public health decision making: toward an approach for designing intervention strategies for schistosomiasis japonica. Environ Health Perspect, 2002, 110:907-915
11. Poulstrup A, Hansen HL. Use of GIS and exposure modeling as tools in a study of cancer incidence in a population exposed to airborne dioxin. Environ Health Perspect, 2004, 112:1032-1036
12. Nuckols JR, Ward MH, Jarup L. Using geographic information systems for exposure assessment in environmental epidemiology studies. Environ Health Perspect, 2004, 112:1007-1015
13. Tran A, Deparis X, Dussart P, et al. Dengue Spatial and Temporal Patterns, French Guiana, 2001. Emerg Inf Dis, 2004, 10: 615-621
14. Graham AJ, Atkinson PM, Danson FM. Spatial analysis for epidemiology. Acta Tropica, 2004, 91:219-225
15. Gosselin P, Lebel G, Rivest S, et al. The integrated system for public health monitoring of west Nile virus (ISPHM-WNV): a real-time GIS for surveillance and decision-making. Int J Health Geogr, 2005, 4:20-32
16. Grover-Kopec E, Kawano M, Klaver RW, et al. An online operational rainfall-monitoring resource for epidemic malaria early warning systems in Africa. Malaria Journal, 2005, 4:6-10
17.梅安新,彭望琭,秦其明等.遥感导论.北京:高等教育出版社,2001
18. Beck LR, Lobitz BM, Wood BL. Remote sensing and human health: new sensors and new opportunities. Emerg Inf Dis, 2000, 6: 217-226
19.王劲峰,孙英君,韩卫国等.空间分析引论.地理信息世界,2004,2(5):6-10
20. Anselin L, Syabri I, Kho Y. GeoDa: An introduction to spatial data analysis. http://www.sal.uiuc.edu/stuff/stuff-sum/stuff/stuff-sum/pdf/geoda.pdf, May 5, 2005
21. Waller LA, Gotway CA. Applied spatiall statistics for public health data. New Jersey: John Wiley & sons,Inc, 2004
22.武继磊,王劲峰,郑晓瑛等.空间数据分析技术在公共卫生领域的应用.地理科学进展,2003,22(3):220-228
23.李立明,詹思延,吴系科等.流行病学进展.第10卷.北京:北京医科大学出版社,2002
24.赵仲堂.流行病学研究方法与应用.第2版.北京:科学出版社,2005
25. Kulldorff M and Information Management Services, Inc: SaTScanTM v6.0: Software for the spatial and space-time scan statistics. http://www.satscan.org/, December 10, 2005
26. Krivoruchko K, Gotway CA. Creating exposure maps using kriging. http://www.esri.com/software/arcgis/arcgisxtensions/geostatistical/research_papers.html, September 1, 2006
27. Tanser FC, Sueur DL. The application of geographical information systems to important public health problems in Africa. Int J Health Geogr, 2002, 1: 4-12
28.方立群,曹春香,陈国胜等.地理信息系统应用于我国高致病性禽流感的空间分布及环境因素分析.中华流行病学杂志,2005,26(11):839-846
29. Fang L, Zhong S, Cao w, et al. Studies on applications of remote sensing and geographic information system in surveillance and prediction of highly pathogenic avian influenza. Asia-Pacific Space Outlook, 2006,10:17-23