地理信息系统及卫星遥感图像在广东省登革热监测中的应用
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摘要
登革热是由伊蚊引起的一种急性热性传染病，主要分布于热带和亚热带的国家和地区，是分布最广、发病人数最多的虫媒病之一，估计全球每年有1亿例感染者。不但对人群健康造成极大威胁，而且对部队战斗力有重要影响。同时，该病毒可大量培养，其冻干粉可保存数年，可通过气溶胶和蚊虫引起感染，因此可以作为生物战剂与恐怖袭击的工具。在我国主要分布在东南沿海的广东、福建、海南和台湾四省，呈现明显的地域性，现已被列为我国新增的重要的传染病。同时，全球化趋势、自然生态系统的破坏使得该传染病的预防和控制工作越发复杂。由于登革热媒介伊蚊的种群受多种环境因素的影响，传统的监测和控制较为困难，因而新的监测手段的探索及应用成为传染病工作者所急需。为摸清影响登革热伊蚊动态变化规律，我们在常规的研究基础上，运用地理信息系统空间分析功能、卫星遥感图像提取环境因素信息功能、主成分分析压缩与综合指标功能，对影响登革热发病和媒介伊种群动态变化的因素进行了探讨，为建立登革热媒介伊蚊分布的模型、预测与控制提供理论依据。
一
我们选择高发区的广东省（潮州市）作为研究现场，分四个
部分进行了初步探讨。
第一部分：本研究首先从流行病学分析的角度，研究了登革
热伊蚊动态变化与气候因素的相关性，结果发现：与媒介伊蚊密
度有关的气象参数是：降雨量、日照时间、降雨天数、平均气温、
最低平均气温、相对湿度；经逐步回归分析得到回归方程：
八，＝24．800＋0．826 XI十0．020 X。－0．418 X。其中局代表最低平均
气温、XZ代表降雨量、X3代表相对湿度。登革热发病的L吗。
回归方程：P（l）＝川l＋e－（－’·”’＋‘·”’“）l，BI为伊蚊密度指数。
第二部分：为进一步探讨广东省登革热伊蚊的空间分布特征，
以潮州市为例，建立了潮州市的伊蚊媒介的地理信息系统，并对
其空间分布特征进行分析。结果显示：潮州市各监测点的伊蚊分
布不是随机的，而是存在一定的空间聚集性。各监测点距离水系
的不同，其伊蚊密度也不一样，且存在，距离水系越近，伊蚊密
度越高，高密度的监测点集中于水系附近。统计分析表明，1000 m
以内的监测点的伊蚊密度明显高于 1000 m范围以外的伊蚊密度
（尸25．354，P＜0刀1）。空间数据探索分析得出，伊蚊在空间分布
上具有空间自相关性。其空间分布图显示：每年的伊蚊疫情虽有
变化，但基本上仍保持高、中、低三类分布区域，伊蚊高密度区
主要集中于韩江三角的潮州市区湘桥区、潮安县的官塘镇和铁铺
镇等沿江地区；伊蚊密度低的区域为潮州地区的北部。枕…ng分
析最后产生四个交叉核验指标：反映预期值的偏性的MPE、估计
方差的RMSE、反映预期值与实测数据的一致性的ASE、预期误
差变异程度的RMSSE，本研究中分布图的四项指标均较为理想。
5
摘要 第四军医大学硕士学位论文
第三部分：我们利用ERDASS．5对卫星遥感图像所包含环境
因素信息提取的功能，探讨了登革热媒介伊蚊密度与标准化植被
指数之间的关系，结果得出：从NOAA－AVHRR卫星图片提取全
省1995年流行季节和非流行季节的NDVI得到，广东省全境全年
都有植被覆盖，流行季节和非流行季节的NDVI平均值分别为
142．95和 126．19。从提取的流行区域和非流行区域来看，流行区
域的平均NDVI为140．98，非流行区域的平均NDVI为124．02。
方差分析表明流行季节与非流行季节之间总体均数不等或不全
等，即流行季节的NDVI要高于非流行季节的NDVI（F＝96．943，P
＜0．of），对应的流行区域的 NDVI也高于非流行季节的 NDVI
（F＝56．348，P＜0．of），相关分析表明，与伊蚊密度相关的植被指
数有 7、8、9、10、12月的最大 NDVI，3、7、11、12的平均 NDVI
和 11月的最小NDVI（P＜0刀1），且存在：
fBI二一14586y0691b，Thlx－Sp十0325if＆zx．A呕＋0126llka，，－－Jul 十0．054lljin－NOv
0功．01，R’－0．80人其中广表示各市县的伊蚊密度，局佃．S6p、
XMax、。、XM。n、I、XMn－NOv分别表W 9月最大NDVI、8月最大NDVI、
7月平均 NDVI和 11月最小 NDVI。
在指标替换中，我们运用了Co七opng，分析表明：媒介分布
与发病地区呈现高度一致性，即媒介密度高的地区发病也高，且
与NDVI有较强的一致性。用NDVI对登革热发病、BI同时替代
进行三者协同时也取得了一致性的效果，即NDVI高的地域其发
病和媒介密度也关联性增高。
第四部分：运用主成分分析，对广东省各监测点的3类共门
个与登革热发病相关的指标分析得出初始特征值显示：第一、第
二、第三、第四主成分的特征值较大，分别为10．334，2．322，1石92，
6
摘要 第四军医大学硕士学位论文
Dengue fever is acute, hot property infectious disease, it is vector disease of distribution most widely, patient most, distributing primary in tropical and subtropical countries or regions, estimated at one hundred million cases to be infected annual in the world. Once being epidemiolocal, armed force will loss fight capability, Simultaneity, this virus can be large-scale cultured, its lyophilized powder can be preserved few years, it can be infected with aerosol and vector, so, it can be used in biological warfares and terror attacks. It mostly distributes in Guangdong, Fujian, Hainan and Taiwan at southeast along the coast. Meanwhile, because of global tendency and destroy of nature ecosystem, its prevention and control became more complicated. As vector aedes is affected by many environmental factors, its supervision and control is relatively difficulty, and new prevention means became quickly need. In order to find out the regularity of aedes vector dynamic change, we make use of spatial
analysis function of geographic information systems (GIS), extracting environmental agent information's function of satellite remote sensing and compressing index's function of principal component analysis's (PCA), explore affecting factor on dengue and aedes, and provide theory evidences to construction distribution model, prediction and prevention of aedes vector.
We select Guangdong (Chaozhou) with high incidence of dengue disease as our study site, and have accidence exploration with fourth sects.
Sect I : exploring relation between vector dynamic change and climate factors from epidemical analysis' view. The results indicate: Meteorology parameters correlating with aedes density are rainfall, sunlight, average air temperature, lowest average air temperature and relative humidity. Stepwise regression analysis leads to the regression equation,
Viz.: YBI = 24.800 + 0.826X1 + 0.020X 2- 0.418 X 3 (X1 represents lowest average air temperature, X2 rainfall and X3, relative humidity).
The logistic regression equation is
Sect II : For further exploring character of vector spatial distribution in Guangdong, we established GIS of vector in Chaozhou and analyzed spatial autocorrelation, the results show that aedes distribution of supervision spots in Chaozhou wasn't stochastic rather than existing stated spatial cluster. All supervision spots have different densities with different distance, and the more the
supervision spots are close to water, the more the supervision spots are occupied and higher in density. Statistic analysis show that the density of the spots within 1000 meters to waterside is significantly higher than the spots which exceed 1000 meters to waterside (F=25.354, P<0.01). Exploratory spatial data analysis (ESDA) demonstrates aedes's spatial distribution has spatial autocorrelation. Its spatial distribution maps show that annual aedes epidemic situation change but basically maintain high, middle and low incidence three types distribution area, high density primary distributed in Xianqiao district, Guangtang tower and Tiepu tower of Chao'an county, and the low density area is north of Chaozhou. Kriging finally bring four cross-validation indices, they are MPE (mean prediction error) which reflecting predictive bias, RMSE (root-mean- square error) which estimating variance, ASE (average standard error) which reflecting consistency between predicate value and true value, and RMSSE (root-mean- square standard error) which being predictive error variation degree. Four indices of this study were ideal.
Sect III : We use function of ERDAS8.5 which distill environmental factors from remote sensing image, and explore the relation between vector density and normalized difference vegetation index (NDVI).The result lead that vegetation covered all over Guangdong all through year, the mean NDVI of epidemical season and non- epidemical season were 142.95, 126.19, respectively. And epidemical area and non-epidemical area were 140.98, 140.98,
respe
一
我们选择高发区的广东省（潮州市）作为研究现场，分四个
部分进行了初步探讨。
第一部分：本研究首先从流行病学分析的角度，研究了登革
热伊蚊动态变化与气候因素的相关性，结果发现：与媒介伊蚊密
度有关的气象参数是：降雨量、日照时间、降雨天数、平均气温、
最低平均气温、相对湿度；经逐步回归分析得到回归方程：
八，＝24．800＋0．826 XI十0．020 X。－0．418 X。其中局代表最低平均
气温、XZ代表降雨量、X3代表相对湿度。登革热发病的L吗。
回归方程：P（l）＝川l＋e－（－’·”’＋‘·”’“）l，BI为伊蚊密度指数。
第二部分：为进一步探讨广东省登革热伊蚊的空间分布特征，
以潮州市为例，建立了潮州市的伊蚊媒介的地理信息系统，并对
其空间分布特征进行分析。结果显示：潮州市各监测点的伊蚊分
布不是随机的，而是存在一定的空间聚集性。各监测点距离水系
的不同，其伊蚊密度也不一样，且存在，距离水系越近，伊蚊密
度越高，高密度的监测点集中于水系附近。统计分析表明，1000 m
以内的监测点的伊蚊密度明显高于 1000 m范围以外的伊蚊密度
（尸25．354，P＜0刀1）。空间数据探索分析得出，伊蚊在空间分布
上具有空间自相关性。其空间分布图显示：每年的伊蚊疫情虽有
变化，但基本上仍保持高、中、低三类分布区域，伊蚊高密度区
主要集中于韩江三角的潮州市区湘桥区、潮安县的官塘镇和铁铺
镇等沿江地区；伊蚊密度低的区域为潮州地区的北部。枕…ng分
析最后产生四个交叉核验指标：反映预期值的偏性的MPE、估计
方差的RMSE、反映预期值与实测数据的一致性的ASE、预期误
差变异程度的RMSSE，本研究中分布图的四项指标均较为理想。
5
摘要 第四军医大学硕士学位论文
第三部分：我们利用ERDASS．5对卫星遥感图像所包含环境
因素信息提取的功能，探讨了登革热媒介伊蚊密度与标准化植被
指数之间的关系，结果得出：从NOAA－AVHRR卫星图片提取全
省1995年流行季节和非流行季节的NDVI得到，广东省全境全年
都有植被覆盖，流行季节和非流行季节的NDVI平均值分别为
142．95和 126．19。从提取的流行区域和非流行区域来看，流行区
域的平均NDVI为140．98，非流行区域的平均NDVI为124．02。
方差分析表明流行季节与非流行季节之间总体均数不等或不全
等，即流行季节的NDVI要高于非流行季节的NDVI（F＝96．943，P
＜0．of），对应的流行区域的 NDVI也高于非流行季节的 NDVI
（F＝56．348，P＜0．of），相关分析表明，与伊蚊密度相关的植被指
数有 7、8、9、10、12月的最大 NDVI，3、7、11、12的平均 NDVI
和 11月的最小NDVI（P＜0刀1），且存在：
fBI二一14586y0691b，Thlx－Sp十0325if＆zx．A呕＋0126llka，，－－Jul 十0．054lljin－NOv
0功．01，R’－0．80人其中广表示各市县的伊蚊密度，局佃．S6p、
XMax、。、XM。n、I、XMn－NOv分别表W 9月最大NDVI、8月最大NDVI、
7月平均 NDVI和 11月最小 NDVI。
在指标替换中，我们运用了Co七opng，分析表明：媒介分布
与发病地区呈现高度一致性，即媒介密度高的地区发病也高，且
与NDVI有较强的一致性。用NDVI对登革热发病、BI同时替代
进行三者协同时也取得了一致性的效果，即NDVI高的地域其发
病和媒介密度也关联性增高。
第四部分：运用主成分分析，对广东省各监测点的3类共门
个与登革热发病相关的指标分析得出初始特征值显示：第一、第
二、第三、第四主成分的特征值较大，分别为10．334，2．322，1石92，
6
摘要 第四军医大学硕士学位论文
Dengue fever is acute, hot property infectious disease, it is vector disease of distribution most widely, patient most, distributing primary in tropical and subtropical countries or regions, estimated at one hundred million cases to be infected annual in the world. Once being epidemiolocal, armed force will loss fight capability, Simultaneity, this virus can be large-scale cultured, its lyophilized powder can be preserved few years, it can be infected with aerosol and vector, so, it can be used in biological warfares and terror attacks. It mostly distributes in Guangdong, Fujian, Hainan and Taiwan at southeast along the coast. Meanwhile, because of global tendency and destroy of nature ecosystem, its prevention and control became more complicated. As vector aedes is affected by many environmental factors, its supervision and control is relatively difficulty, and new prevention means became quickly need. In order to find out the regularity of aedes vector dynamic change, we make use of spatial
analysis function of geographic information systems (GIS), extracting environmental agent information's function of satellite remote sensing and compressing index's function of principal component analysis's (PCA), explore affecting factor on dengue and aedes, and provide theory evidences to construction distribution model, prediction and prevention of aedes vector.
We select Guangdong (Chaozhou) with high incidence of dengue disease as our study site, and have accidence exploration with fourth sects.
Sect I : exploring relation between vector dynamic change and climate factors from epidemical analysis' view. The results indicate: Meteorology parameters correlating with aedes density are rainfall, sunlight, average air temperature, lowest average air temperature and relative humidity. Stepwise regression analysis leads to the regression equation,
Viz.: YBI = 24.800 + 0.826X1 + 0.020X 2- 0.418 X 3 (X1 represents lowest average air temperature, X2 rainfall and X3, relative humidity).
The logistic regression equation is
Sect II : For further exploring character of vector spatial distribution in Guangdong, we established GIS of vector in Chaozhou and analyzed spatial autocorrelation, the results show that aedes distribution of supervision spots in Chaozhou wasn't stochastic rather than existing stated spatial cluster. All supervision spots have different densities with different distance, and the more the
supervision spots are close to water, the more the supervision spots are occupied and higher in density. Statistic analysis show that the density of the spots within 1000 meters to waterside is significantly higher than the spots which exceed 1000 meters to waterside (F=25.354, P<0.01). Exploratory spatial data analysis (ESDA) demonstrates aedes's spatial distribution has spatial autocorrelation. Its spatial distribution maps show that annual aedes epidemic situation change but basically maintain high, middle and low incidence three types distribution area, high density primary distributed in Xianqiao district, Guangtang tower and Tiepu tower of Chao'an county, and the low density area is north of Chaozhou. Kriging finally bring four cross-validation indices, they are MPE (mean prediction error) which reflecting predictive bias, RMSE (root-mean- square error) which estimating variance, ASE (average standard error) which reflecting consistency between predicate value and true value, and RMSSE (root-mean- square standard error) which being predictive error variation degree. Four indices of this study were ideal.
Sect III : We use function of ERDAS8.5 which distill environmental factors from remote sensing image, and explore the relation between vector density and normalized difference vegetation index (NDVI).The result lead that vegetation covered all over Guangdong all through year, the mean NDVI of epidemical season and non- epidemical season were 142.95, 126.19, respectively. And epidemical area and non-epidemical area were 140.98, 140.98,
respe
引文
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high risk for malaria transmission[J]. Am J Trop Med Hyg., 1994; 51(3):271-280.
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