云南疟疾疫情与蚊媒评价体系及地理信息系统研究
发布时间：2019-02-16 04:14
【摘要】：目的①建立疟疾疫情与蚊媒评价预测多因素复合模型;②构建云南疟疾疫情与蚊媒评价地理信息系统;③研究遥感植被指标NDVI 在疟疾疫情与蚊媒评价中的应用;④发展云南省疟疾蚊媒疫情危险性地图。方法①以云南省14 个疟疾流行县33 乡为研究现场,收集1984 2000 年疟疾疫情、蚊媒、防蚊防疟、气象、地理环境、人口与遥感生态学资料。疟疾疫情资料为疟疾发病率与死亡率; 27 个以微小按蚊为主要传播媒介的乡蚊媒资料为微小按蚊与中华按蚊人工每小时密度、滇东北6 个以嗜人按蚊为主要传播媒介的乡蚊媒资料为中华按蚊与嗜人按蚊叮人率;防蚊防疟措施资料为室内滞留喷洒与蚊帐使用比例;气象资料为月平均温度、月平均最高温度、月平均最低温度、月平均降雨量、月平均日照量;地理环境资料为经度、纬度、海拔与水田面积占耕地面积比例;人口资料为人口密度、农业人口密度、农业人口比例。从亚洲医学微型数据库空间决策系统中截取云南省1:1,000,000 电子地图。借助Arcview 与Erdas 软件提取33 个乡的经度、纬度与高程信息。由http://eosdata.gsfc.nasa.gov 下载NOAA/AVHRR NDVI 遥感生态学资料,其分辨率为8km×8km。NDVI=(Ch2-Ch1)/ (Ch2+Ch1)。②采用主成分分析与因子分析法研究气象、环境、遥感生态指标与蚊媒密度的关系,筛选蚊媒密度评价的主要因素;③采用层次分析法构建微小按蚊密度评价层次结构模型;④选择27 个以微小按蚊为主要传播媒介的乡中的15 个乡1984 1993 年相关数据作为建模的基础数据。以合计蚊媒密度为主导因子,采用灰色关联度分析方法研究18 项气象、环境、遥感NDVI 指标与合计蚊媒密度的灰色关联关系,依据灰色阈值筛选合计蚊媒密度的评价指标。以灰色关联序为评价指标赋权,采用加权方法合成变量E,研究E 与合计蚊媒密度Y 的定量关系,从而建立合计蚊媒密度与微小按蚊密度拟合评价模型。⑤以合计蚊媒密度为第一主导因子、以微小按蚊密度为第二主导因子,分别求评价指标与合计蚊媒密度和微小按蚊密度的灰色关联度,依据平均灰色关联度排列灰色关联序。为平均关联度最小的评价指标给定权重10~n(n =0),并以此基本单位为公差依次呈等差级数向关联度增大的方向递增评价指标的权重。以平均关联度最大的评价指标权数为基数,以2 为公比呈等比级数递增主导因子的权重,从而构建蚊媒密度综合评价模型。⑥求各评
[Abstract]:Objective 1 to establish a multivariate model of malaria epidemic situation and mosquito vector evaluation, 2 to construct Yunnan malaria epidemic situation and mosquito vector evaluation geographic information system, 3 to study the application of remote sensing vegetation index (NDVI) in malaria epidemic situation and mosquito vector evaluation. 4 Development of malaria mosquito vector risk map in Yunnan Province. Methods 1 data of malaria epidemic, mosquito vector, malaria control, meteorology, geographical environment, population and remote sensing ecology were collected from 33 villages in 14 endemic counties of Yunnan Province in 1984 ~ 2000. The data of malaria epidemic situation are malaria morbidity and mortality; The artificial hourly density of Anopheles minimus and Anopheles sinensis was found in 27 rural mosquito vectors with Anopheles minimus as the main transmission vector, and Anopheles sinensis and Anopheles anthropophagus in 6 rural mosquito vectors in northeast Yunnan. The data of anti-mosquito and malaria control measures are indoor residual spraying and the proportion of mosquito nets used, the meteorological data are monthly mean temperature, monthly average maximum temperature, monthly mean minimum temperature, monthly average rainfall, monthly average sunshine amount. The geographic and environmental data are longitude, latitude, elevation and paddy field area, and the population data are population density and agricultural population ratio. The 1: 1000000 electronic map of Yunnan Province was intercepted from the Asian medical microdatabase spatial decision system. The longitude, latitude and elevation of 33 villages were extracted by Arcview and Erdas software. The NOAA/AVHRR NDVI remote sensing ecological data were downloaded by http://eosdata.gsfc.nasa.gov with a resolution of 8km 脳 8km.NDVI = (Ch2-Ch1) / (Ch2 Ch1). 2 the principal component analysis and factor analysis were used to study the weather and environment. The relationship between remote sensing ecological index and mosquito vector density, and screening the main factors of mosquito vector density evaluation; (3) Analytic hierarchy process (AHP) was used to construct the hierarchical structure model of Anopheles minimus density evaluation, and (4) 15 townships of 27 villages in which Anopheles minimus were the main vector of transmission were selected as the basic data of the model during 1984 / 1993. The grey correlation between total mosquito vector density and total mosquito vector density was studied by using grey correlation analysis method. The grey correlation between total mosquito vector density and 18 meteorological, environmental, remote sensing NDVI indexes was studied, and the evaluation indexes of total mosquito vector density were screened according to grey threshold. The quantitative relationship between E and total mosquito vector density (Y) was studied by using the weighted method to synthesize the variable E with the grey relational order as the evaluation index. A fitting evaluation model of total mosquito vector density and Anopheles minimus density was established. 5 the total mosquito vector density was the first leading factor, and the Anopheles minimus density was the second leading factor. The grey correlation degree between the evaluation index and the total mosquito vector density and the Anopheles minimus density was obtained, and the grey relational order was arranged according to the average grey correlation degree. A weight of 10 n (n = 0) is given for the evaluation index with the smallest average correlation degree, and the weight of the evaluation index is incremented in turn in the direction of increasing the correlation degree as the basic unit of tolerance is equal difference series. A comprehensive evaluation model of mosquito vector density was constructed based on the weight of the evaluation index with the largest average correlation degree and the weight of the leading factor increasing by equal ratio series with 2 as the common ratio. 6.
【学位授予单位】：中国疾病预防控制中心
【学位级别】：博士
【学位授予年份】：2005
【分类号】：R181.8
本文编号：2424034
[Abstract]:Objective 1 to establish a multivariate model of malaria epidemic situation and mosquito vector evaluation, 2 to construct Yunnan malaria epidemic situation and mosquito vector evaluation geographic information system, 3 to study the application of remote sensing vegetation index (NDVI) in malaria epidemic situation and mosquito vector evaluation. 4 Development of malaria mosquito vector risk map in Yunnan Province. Methods 1 data of malaria epidemic, mosquito vector, malaria control, meteorology, geographical environment, population and remote sensing ecology were collected from 33 villages in 14 endemic counties of Yunnan Province in 1984 ~ 2000. The data of malaria epidemic situation are malaria morbidity and mortality; The artificial hourly density of Anopheles minimus and Anopheles sinensis was found in 27 rural mosquito vectors with Anopheles minimus as the main transmission vector, and Anopheles sinensis and Anopheles anthropophagus in 6 rural mosquito vectors in northeast Yunnan. The data of anti-mosquito and malaria control measures are indoor residual spraying and the proportion of mosquito nets used, the meteorological data are monthly mean temperature, monthly average maximum temperature, monthly mean minimum temperature, monthly average rainfall, monthly average sunshine amount. The geographic and environmental data are longitude, latitude, elevation and paddy field area, and the population data are population density and agricultural population ratio. The 1: 1000000 electronic map of Yunnan Province was intercepted from the Asian medical microdatabase spatial decision system. The longitude, latitude and elevation of 33 villages were extracted by Arcview and Erdas software. The NOAA/AVHRR NDVI remote sensing ecological data were downloaded by http://eosdata.gsfc.nasa.gov with a resolution of 8km 脳 8km.NDVI = (Ch2-Ch1) / (Ch2 Ch1). 2 the principal component analysis and factor analysis were used to study the weather and environment. The relationship between remote sensing ecological index and mosquito vector density, and screening the main factors of mosquito vector density evaluation; (3) Analytic hierarchy process (AHP) was used to construct the hierarchical structure model of Anopheles minimus density evaluation, and (4) 15 townships of 27 villages in which Anopheles minimus were the main vector of transmission were selected as the basic data of the model during 1984 / 1993. The grey correlation between total mosquito vector density and total mosquito vector density was studied by using grey correlation analysis method. The grey correlation between total mosquito vector density and 18 meteorological, environmental, remote sensing NDVI indexes was studied, and the evaluation indexes of total mosquito vector density were screened according to grey threshold. The quantitative relationship between E and total mosquito vector density (Y) was studied by using the weighted method to synthesize the variable E with the grey relational order as the evaluation index. A fitting evaluation model of total mosquito vector density and Anopheles minimus density was established. 5 the total mosquito vector density was the first leading factor, and the Anopheles minimus density was the second leading factor. The grey correlation degree between the evaluation index and the total mosquito vector density and the Anopheles minimus density was obtained, and the grey relational order was arranged according to the average grey correlation degree. A weight of 10 n (n = 0) is given for the evaluation index with the smallest average correlation degree, and the weight of the evaluation index is incremented in turn in the direction of increasing the correlation degree as the basic unit of tolerance is equal difference series. A comprehensive evaluation model of mosquito vector density was constructed based on the weight of the evaluation index with the largest average correlation degree and the weight of the leading factor increasing by equal ratio series with 2 as the common ratio. 6.
【学位授予单位】：中国疾病预防控制中心
【学位级别】：博士
【学位授予年份】：2005
【分类号】：R181.8
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