本文選題：瘧疾 + 時空分析��； 參考：《華中科技大學》2015年博士論文

【摘要】：目的：分析湖北省本地瘧疾發(fā)病的時空分布特征,識別瘧疾流行的高危風險區(qū)域和高危時段。評價氣象因素在湖北省本地瘧疾流行中的作用,建立瘧疾流行的氣象預測模型。探討ARIMA模型預測湖北省本地瘧疾發(fā)病率的可行性,預測瘧疾發(fā)病趨勢。本研究可以為湖北省本地瘧疾疫情的監(jiān)測和預測提供決策支持,最終為指導瘧疾的防控,合理的分配衛(wèi)生資源提供科學的參考依據和理論指導。 方法：(1)采用Cochran-Armitage趨勢檢驗分析2004～2011年湖北省本地瘧疾疫情發(fā)展變化的長期趨勢。繪制2004～2011年湖北省各縣(市、區(qū))本地瘧疾年度發(fā)病率分布圖。(2)采用ArcGIS10.1軟件的全局Moran'sI空間自相關分析整個研究區(qū)域瘧疾發(fā)病是否存在空間自相關。采用ArcGIS10.1局部Moran's/空間自相關分析方法和單純空間掃描統計方法兩種局部空間聚集性研究方法來確定2004～2011年湖北省本地瘧疾流行的高風險區(qū)域。利用單純時間掃描,時空掃描研究瘧疾發(fā)病的時間和時空分布特點,確定發(fā)病的高風險時段和高風險區(qū)域。(3)運用Spearman等級相關分析不同區(qū)域尺度瘧疾發(fā)病率與氣象因素相關性。采用多元回歸逐步回歸分析法篩選影響瘧疾發(fā)病率氣象因素,利用氣象因素對瘧疾發(fā)病變化進行回歸擬合。(4)本研究將棗陽市瘧疾流行程度按月病人數占全年病人總數的構成比,分為低發(fā)月、中發(fā)月、高發(fā)月。應用逐步判別分析方法利用氣象因素對棗陽市未來瘧疾流行程度進行判別。(5)采用2004～2009年湖北省本地瘧疾發(fā)病率構建ARIMA模型,2010年1-12月的數據用于檢驗模型,并評價模型的擬合及預測效果。 結果：(1)湖北省2004～2011年不同年份瘧疾發(fā)病率整體上呈顯著下降的趨勢。全局空間自相關分析結果提示湖北省的本地瘧疾發(fā)病具有一定的空間聚集性。2004～2011年,局部Moran's I空間自相關發(fā)現11個瘧疾發(fā)病高�？h,高危縣瘧疾發(fā)病率的中位數從2004年58.1/10萬降至2011年0.79/10萬；單純空間掃描分析結果發(fā)現2004～2011年湖北省有11個聚集地區(qū),其中一級聚集區(qū)8個,二級聚集區(qū)3個；時空聚集性分析結果發(fā)現一級聚集區(qū)域,其中一級聚集區(qū)域13個縣,聚集時段從2004年4月-2007年11月。(2)湖北省與棗陽市兩種區(qū)域尺度上2004～2009年的瘧疾發(fā)病率周期性變化與年中氣候的周期變動明顯相關,與氣溫相關指標和降雨量的相關性較為顯著,相關系數多在0.7左右。湖北省每個月瘧疾發(fā)病率的變動78.1%可歸因于當月和前一個月平均氣溫(MeanT-01)和之前2個月的平均最低氣溫(MinT-2);棗陽市每個月瘧疾發(fā)病率的變動67.8%可歸因于當月和前一個月平均最高氣溫(MaxT-01)。(3)本研究利用氣象因素建立判別函數對棗陽市瘧疾流行程度進行判別,首先對32個氣象因子進行逐步判別,最終引入判別方程的氣象因子有MinT0、MaxT0和D-012。判別函數的準確率為73.61%,具有一定的判別效果。(4)利用湖北省2004～2009年的每月本地瘧疾發(fā)病率建立模型,結果顯示ARIMA(1,1,1)(1,1,0)12模型擬合效果相對最優(yōu),預測發(fā)病變動趨勢與實際發(fā)病趨勢完全一致,實際值均在預測值的95%可信區(qū)間范圍,表明模型預測值與實際情況基本一致,擬合效果好。 結論：(1)2004～2011年,研究發(fā)現湖北省本地瘧疾發(fā)病率呈顯著下降的趨勢。湖北省的瘧疾發(fā)病具有空間聚集性,高危風險地區(qū)仍然存在,高危風險地區(qū)主要位于嗜人按蚊和中華按蚊復媒瘧區(qū)。(2)利用氣象因素擬合全省和瘧疾高發(fā)縣的瘧疾發(fā)病率取得較好的效果,構建的判別函數能準確地判斷棗陽市瘧疾高、中、低發(fā)月份的出現時間。(3)構建的ARIMA模型對湖北省瘧疾發(fā)病情況的擬合結果滿意,預測效果良好,可用于預測湖北省未來瘧疾的變動趨勢。
[Abstract]:Objective: to analyze the temporal and spatial distribution characteristics of malaria in Hubei Province, identify the high-risk area and high risk period of malaria, evaluate the role of meteorological factors in the local malaria epidemic in Hubei Province, establish the meteorological prediction model of malaria epidemic, and discuss the feasibility of the ARIMA model to predict the local malaria incidence in Hubei Province, and predict the malaria incidence. This study can provide decision-making support for the monitoring and prediction of local malaria epidemic in Hubei province. Finally, it provides scientific reference and theoretical guidance for guiding malaria control and rational distribution of health resources.
Methods: (1) Cochran-Armitage trend test was used to analyze the long-term trend of the development and changes of local malaria epidemic in Hubei province for 2004~2011 years. The annual distribution map of annual incidence of malaria in each county (city, district) of Hubei province was drawn. (2) the global Moran'sI spatial autocorrelation analysis was used to analyze the incidence of malaria in the whole study area by the ArcGIS10.1 software. The spatial autocorrelation is not existed. Two local spatial aggregation methods are used to determine the high risk area of the local malaria epidemic in Hubei province in 2004~2011 years by using the ArcGIS10.1 local Moran's/ spatial autocorrelation analysis method and the simple spatial scanning statistics method. The high risk period and high risk area of the disease were determined. (3) the correlation between the incidence of malaria and the meteorological factors at different regional scales was analyzed by Spearman level correlation analysis. The meteorological factors affecting the incidence of malaria were screened by multiple regression stepwise regression analysis, and the change of malaria incidence was fitted with the weather factors. (4) the study was carried out. The incidence of malaria prevalence in Jujube was divided into the proportion of the number of patients in the whole year of the total number of patients in the whole year, which were divided into low hair month, mid month and high month. Using the stepwise discriminant analysis method, the epidemic degree of malaria in the future of Jujube was judged by meteorological factors. (5) the ARIMA model was constructed by the local malaria incidence of 2004~2009 years in Hubei Province, 1-12 in 2010. The monthly data are used to test the model and evaluate the fitting and prediction effect of the model.
Results: (1) the incidence of malaria in different years in Hubei province was significantly decreased in 2004~2011 years. The results of global spatial autocorrelation analysis suggested that the local malaria incidence in Hubei province had a certain spatial aggregation.2004 to 2011, and the local Moran's I spatial autocorrelation found 11 high risk counties of malaria, and the incidence of malaria in high-risk counties. The median from 58.1/10 million in 2004 to 0.79/10 million in 2011; the results of simple spatial scanning analysis found that there were 11 aggregated areas in Hubei province in 2004~2011 years, of which 8 were gathered in the first class and 3 in the two level, and the results of spatial and temporal aggregation analysis found the first order aggregation area, including 13 counties in the first class gathering area, and the aggregation period from April 2004 -2 In November, 007 years. (2) the periodic changes of the incidence of malaria in 2004~2009 years in Hubei and Jujube were significantly related to the cycle changes of climate during the year. The correlation with the temperature related indexes and rainfall was more significant, and the correlation coefficient was about 0.7. The change of malaria incidence rate of 78.1% in Hubei province was attributable to the month of the month. The average temperature (MeanT-01) and the average minimum temperature of the previous 2 months (MinT-2) were used in the previous month, and the change of malaria incidence in Jujube was 67.8% due to the average maximum temperature of the month and the previous month (MaxT-01). (3) a discriminant function of meteorological factors was used to distinguish the malaria prevalence in Jujube, and the first to 32 gases. The meteorological factors of the image factor were gradually discriminate, and the meteorological factors that finally introduced the discriminant equation were MinT0, the accuracy rate of the MaxT0 and D-012. discriminant functions was 73.61%. (4) the model was established by using the monthly local malaria incidence rate of 2004~2009 years in Hubei province. The results showed that the fitting effect of ARIMA (1,1,1) (1,1,0) 12 model was relatively optimal and predicted. The trend of the incidence of the disease is exactly the same as the actual incidence trend. The actual values are in the range of 95% confidence interval of the predicted value, which shows that the model prediction value is basically the same as the actual situation, and the fitting effect is good.
Conclusions: (1) 2004~2011 years, the incidence of local malaria in Hubei province was found to be significantly decreased. The incidence of malaria in Hubei province was spatially aggregated, high-risk areas still existed, and high-risk areas were mainly located in Anopheles Anopheles and Anopheles sinensis. (2) the use of meteorological factors to fit malaria and malaria in high incidence counties of malaria. The disease incidence rate has achieved good results. The constructed discriminant function can accurately determine the occurrence time of the high, middle and low onset months of malaria in Jujube. (3) the constructed ARIMA model is satisfied with the fitting results of malaria incidence in Hubei Province, and the prediction effect is good, which can be used to predict the trend of the change of the future malaria in Hubei province.
【學位授予單位】：華中科技大學
【學位級別】：博士
【學位授予年份】：2015
【分類號】：R531.3

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