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    Please use this identifier to cite or link to this item: http://ir.lib.ksu.edu.tw/handle/987654321/29428


    Title: 運用自回歸分析預測登革熱發病率:以屏東縣為例
    其他題名: Prediction of dengue incidence using auto-regression models: case study of Ping-Tung County
    Authors: 周志韋
    Chou, Chih-Wei
    指導教授: 李志賢
    Lee, Chih-Sheng
    Keywords: 登革熱;時間序列分析;發病率;自回歸
    Dengue fever;time series analysis;incidence rate;auto-regression
    Date: 2018
    Issue Date: 2018-12-03 16:04:04 (UTC+8)
    Abstract: 登革熱感染案例的每月時間序列數據是收集屏東縣1998年至2015年的數據, 運用回歸方式使用天氣、社會經濟和氣候變化等參數預測登革熱發病率。自回歸(Auto-regression, AR)編入模型顯著提高屏東縣登革熱的預測相關性。AR 模型可以更有效地幫助預測登革熱並協助控制,預測能力和預測模型的穩定性將隨著較長時間週期所獲得得更多數據而有所增加。預測及掌握疾病的所有面向是一項艱鉅的任務,但較新的技術將有助於克服此困難,特別是本研究討論了屏東縣幾個顯著的爆發均有更好的預測性能 。
    Monthly time series data of dengue infection cases was collected from 1998 to 2015 in Ping-Tung County. Regression technique applied to predict dengue incidence rate by using weather, social-economic and climate change parameters. Auto-regression (AR) was then embedded into IR models, and to significantly improve the predictability of dengue for Ping-Tung County. The integration models of AR would be used to assist an efficient dengue control. The predictive power and robustness of predictive models would be improved with additional data over longer time periods. Capturing all aspects of the disease is a daunting task, but newer techniques may help overcome the difficulties. In particular, this study discussed several significant outbreaks in Ping-Tung County with better performance.
    Appears in Collections:[環境工程系所] 博碩士論文

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