【摘要】：目的①建立瘧疾疫情與蚊媒評(píng)價(jià)預(yù)測(cè)多因素復(fù)合模型;②構(gòu)建云南瘧疾疫情與蚊媒評(píng)價(jià)地理信息系統(tǒng);③研究遙感植被指標(biāo)NDVI 在瘧疾疫情與蚊媒評(píng)價(jià)中的應(yīng)用;④發(fā)展云南省瘧疾蚊媒疫情危險(xiǎn)性地圖。方法①以云南省14 個(gè)瘧疾流行縣33 鄉(xiāng)為研究現(xiàn)場(chǎng),收集1984 2000 年瘧疾疫情、蚊媒、防蚊防瘧、氣象、地理環(huán)境、人口與遙感生態(tài)學(xué)資料。瘧疾疫情資料為瘧疾發(fā)病率與死亡率; 27 個(gè)以微小按蚊為主要傳播媒介的鄉(xiāng)蚊媒資料為微小按蚊與中華按蚊人工每小時(shí)密度、滇東北6 個(gè)以嗜人按蚊為主要傳播媒介的鄉(xiāng)蚊媒資料為中華按蚊與嗜人按蚊叮人率;防蚊防瘧措施資料為室內(nèi)滯留噴灑與蚊帳使用比例;氣象資料為月平均溫度、月平均最高溫度、月平均最低溫度、月平均降雨量、月平均日照量;地理環(huán)境資料為經(jīng)度、緯度、海拔與水田面積占耕地面積比例;人口資料為人口密度、農(nóng)業(yè)人口密度、農(nóng)業(yè)人口比例。從亞洲醫(yī)學(xué)微型數(shù)據(jù)庫空間決策系統(tǒng)中截取云南省1:1,000,000 電子地圖。借助Arcview 與Erdas 軟件提取33 個(gè)鄉(xiāng)的經(jīng)度、緯度與高程信息。由http://eosdata.gsfc.nasa.gov 下載NOAA/AVHRR NDVI 遙感生態(tài)學(xué)資料,其分辨率為8km×8km。NDVI=(Ch2-Ch1)/ (Ch2+Ch1)。②采用主成分分析與因子分析法研究氣象、環(huán)境、遙感生態(tài)指標(biāo)與蚊媒密度的關(guān)系,篩選蚊媒密度評(píng)價(jià)的主要因素;③采用層次分析法構(gòu)建微小按蚊密度評(píng)價(jià)層次結(jié)構(gòu)模型;④選擇27 個(gè)以微小按蚊為主要傳播媒介的鄉(xiāng)中的15 個(gè)鄉(xiāng)1984 1993 年相關(guān)數(shù)據(jù)作為建模的基礎(chǔ)數(shù)據(jù)。以合計(jì)蚊媒密度為主導(dǎo)因子,采用灰色關(guān)聯(lián)度分析方法研究18 項(xiàng)氣象、環(huán)境、遙感NDVI 指標(biāo)與合計(jì)蚊媒密度的灰色關(guān)聯(lián)關(guān)系,依據(jù)灰色閾值篩選合計(jì)蚊媒密度的評(píng)價(jià)指標(biāo)。以灰色關(guān)聯(lián)序?yàn)樵u(píng)價(jià)指標(biāo)賦權(quán),采用加權(quán)方法合成變量E,研究E 與合計(jì)蚊媒密度Y 的定量關(guān)系,從而建立合計(jì)蚊媒密度與微小按蚊密度擬合評(píng)價(jià)模型。⑤以合計(jì)蚊媒密度為第一主導(dǎo)因子、以微小按蚊密度為第二主導(dǎo)因子,分別求評(píng)價(jià)指標(biāo)與合計(jì)蚊媒密度和微小按蚊密度的灰色關(guān)聯(lián)度,依據(jù)平均灰色關(guān)聯(lián)度排列灰色關(guān)聯(lián)序。為平均關(guān)聯(lián)度最小的評(píng)價(jià)指標(biāo)給定權(quán)重10~n(n =0),并以此基本單位為公差依次呈等差級(jí)數(shù)向關(guān)聯(lián)度增大的方向遞增評(píng)價(jià)指標(biāo)的權(quán)重。以平均關(guān)聯(lián)度最大的評(píng)價(jià)指標(biāo)權(quán)數(shù)為基數(shù),以2 為公比呈等比級(jí)數(shù)遞增主導(dǎo)因子的權(quán)重,從而構(gòu)建蚊媒密度綜合評(píng)價(jià)模型。⑥求各評(píng)
[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.
【學(xué)位授予單位】：中國(guó)疾病預(yù)防控制中心
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2005
【分類號(hào)】：R181.8

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