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研究生: 李逸修
LEE, I-HSIU
論文名稱: 利用最小平方法對具有疫苗接種效力的隨機傳染病模型進行參數估計
Using the Least Square Method to Estimate Parameters in a Stochastic Epidemic Model with Vaccination Efficacy
指導教授: 李金龍
Li, Chin-Lung
口試委員: 張延彰
Chang, Yen-Chang
陳仁純
Chen, Ren-Chun
學位類別: 碩士
Master
系所名稱: 理學院 - 計算與建模科學研究所
Institute of Computational and Modeling Science
論文出版年: 2024
畢業學年度: 112
語文別: 英文
論文頁數: 42
中文關鍵詞: 最小平方法參數估計隨機傳染病模型
外文關鍵詞: Least Square Method, Parameters Estimate, Stochastic Epidemic Model
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  • 本研究探討了一個包含疫苗效力的隨機流行傳染病模型。我們
    使用最小平方法來擬合這個模型,通過最小化殘差平方和進行回歸
    分析,以尋找模型參數的最佳估計。我們採用Euler-Maruyama方法
    對模型進行離散化處理,藉由數學理論推導參數的點估計值。並使
    用MATLAB™進行數據模擬,並通過結果驗證我們的理論計算。很
    重要的是,我們發現噪聲強度和相關參數都和疾病的行為息息相
    關,運用數值計算的方法,給出預測理論。並討論在不同的參數調
    整下,傳染病傳播的消長現象。


    This study examines a stochastic epidemic infectious disease model
    incorporating vaccine efficacy. We used the least square method to fit this
    model by minimizing the residual sum of squares for regression analysis to
    find the best estimates of the model parameters. The model was discretized using the Euler-Maruyama method to derive point estimates of the parameters using mathematical theory. We also used MATLAB™ to simulate the
    data and validate our theoretical calculations with the results. Importantly,
    we found that noise intensity and related parameters are closely related
    to disease behavior, and numerical calculations are used to give predictive theories. We also discuss the growth and decline of infectious disease
    transmission under different parameter adjustments.

    Acknowledgements iii 摘要 v Abstract vii 1 Introduction 1 2 Preliminaries 5 3 Least Square Estimation 9 3.1 Regression model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.2 Point Estimators for β and (μ + γ + α) . . . . . . . . . . . . . . . . . 12 3.3 Point estimators for q, p and (μ + ε) . . . . . . . . . . . . . . . . . . . 14 3.4 Point estimator for γ, μ, and ε . . . . . . . . . . . . . . . . . . . . . . 18 3.5 Evaluate the variance of estimated parameters . . . . . . . . . . . . . . 25 4 Numerical Simulations 29 4.1 Examples of Estimate . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 4.2 The Effects of the Parameter σ . . . . . . . . . . . . . . . . . . . . . . 36 4.3 The Effects of the other Parameter . . . . . . . . . . . . . . . . . . . . 37 5 Conclusions 39 References 41

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