Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/98194
標題: 台灣地區2015年至2017年高致病性禽流感疫情之空間分析及影響疫情之環境因素
Identify spatial clusters of the poultry farms and the associated environmental factors during the highly pathogenic avian Influenza outbreak in Taiwan, 2015 to 2017
作者: 何于蓁
Yu-Chen He
關鍵字: 空間分析;空間自相關;熱區;禽流感;環境因子;逐步多變量分析;spatial analysis;spatial autocorrelation;hot spot;avian Influenza;environmental factors;stepwise multivariate logistic regression analysis
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摘要: 
自2014年以來,由中國發源的H5高致病性禽流感演化分枝clade 2.3.4.4,藉由候鳥以飛快的速度擴散至世界各地。至今,此高致病性禽流感病毒演化分枝於禽類養殖場中分離出,造成大量禽類的死亡及撲殺,使得禽類養殖產業的經濟貿易嚴重受創,如:嚴禁家禽產品出口等防控措施。台灣地區2015年至2017年各年分別有1004間、37間及182間禽場檢測為高致病性禽流感陽性,2015年流行的亞型為H5N2、H5N3及H5N8,2016年流行的亞型為H5N2及H5N8,而2017年則為H5N2、H5N6及H5N8。由於高致病性禽流感除了對禽類具有高致病性外,也有突變成禽傳人或人傳人之病毒的可能性,具有公共衛生上的重大威脅。本研究利用台灣地區2015年至2017年爆發禽流感的案例禽場藉由Global Moran's I及Local Moran's I分析其空間分布上的聚集與熱區,並用逐步多變數迴歸分析找出形成熱區之環境因子。藉由點為的分佈,可以發現明顯集中於雲林縣西部及屏東縣北部。研究上利用Global Moran's I分析,分析結果指出台灣地區的禽流感疫情於半徑3公里內的範圍中有高度的空間自相關。隨後,藉由Local Moran's I方法分析疫情的熱區,其結果顯示台灣地區2015年至2017年禽流感的疫情熱區位於雲林縣西部及屏東縣北部。下一步,利用逐步多變數迴歸分析,分析熱區和非熱區區域內與17個環境因子變項的關聯性,根據分析結果得知台灣地區禽流感疫情與未登記的水禽場密度、單位面積下水陸禽飼養活動指數、高度的禽場密度及作植物耕作面積覆蓋率等4個環境因子有關,指出台灣地區禽流感的疫情與禽場過度集中、水陸禽的飼養活動範圍過近及留鳥的存在有關。最後,利用迴歸分析的結果繪製風險地圖,預測台灣地區禽流感疫情的高風險區域。由於高致病性禽流感除了對禽類具有高致病性外,也有突變成禽傳人或人傳人之病毒的可能性,具有公共衛生上的重大威脅。本研究藉由分析所找出的環境因子與繪製風險地圖,希望能協助政府提升台灣未來禽流感之監測及防疫策略。

Since 2014, new subtypes of high pathogenic avian influenza virus (HPAIV) descendant from H5 clade 2.3.4.4 viruses have emerged from China and spread to worldwide rapidly through the migrating bird flyway. To date, no human case had been reported, but the new clade of HPAIV has caused among poultry farms, even result in huge socio-economic impacts due to the losses of birds killed by the disease or by culling, and from the disruption of trade and market activities imposed by disease control measures such as movement restrictions and a temporary ban of poultry product exports. From 2015 to 2017 in Taiwan, there were 1004 poultry farms, 37 poultry farms and 182 poultry farms confirmed as HPAIV positive were reported. The subtypes caused the HPAIV epidemic in 2015 were H5N2, H5N3 and H5N8. In 2016, the subtypes of HPAIV were H5N2 and H5N8. And in 2017, the subtypes of HPAIV were H5N2, H5N6 and H5N8. HPAIV causes the serious symptom in poultry, and will be the great threaten in public health if it is possible that HPAIV transmit to human from poultry or human. The objectives of this study is to identify the spatial clusters, hot spot, with Global Moran's I and Local Moran's I analysis methods and find out the associated environmental factors resulting in the clustering with the stepwise multivariate logistic regression analysis. According to the distribution of the HPAIV case-points, most points were in the western of Yunlin County and in the north of Pingtung County. The result of Global Moran's I indicated that the distribution of the HPAIV cases in Taiwan had the spatial autocorrelation in the radius of 3km distance. Through the Local Moran's I, spatial analysis, the hot spots of the HPAIV outbreaks in 2015 to 2017 in Taiwan were in the western of Yunlin County and in the north of Pingtung County. Next, we examined 17 different variables between the hot spots and non-hot spots by the stepwise multivariate logistic regression analysis. Based on the result of the stepwise multivariate logistic regression analysis, the four risk factors strongly associated with the HPAIV hot spots throughout three consecutive years were the farm density of non-register waterfowl farms, the index of waterfowl-chicken mixed farming per unit area, highly degree of farm density and the coverage of the cropping farms. That meant the outbreaks of HPAIV in Taiwan were related to the concentration of poultry farms were too over, the active area of waterfowl and non-waterfowl were to closed and the exist of resident birds. Final we used the results of the stepwise multivariate logistic regression analysis, we developed the risk map and predict the high risk area of the HPAIV outbreak in Taiwan in the future. This study points out the environmental factors and developed the risk map, which will assist in future surveillance and disease control among the poultry farms for avian influenza epidemic in Taiwan.
URI: http://hdl.handle.net/11455/98194
Rights: 同意授權瀏覽/列印電子全文服務,2018-08-29起公開。
Appears in Collections:微生物暨公共衛生學研究所

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