請用此 Handle URI 來引用此文件：
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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林先和(Hsien-Ho Lin) | |
| dc.contributor.author | Chu-Chang Ku | en |
| dc.contributor.author | 辜鉅璋 | zh_TW |
| dc.date.accessioned | 2021-05-15T17:50:39Z | - |
| dc.date.available | 2019-10-20 | |
| dc.date.available | 2021-05-15T17:50:39Z | - |
| dc.date.copyright | 2014-10-20 | |
| dc.date.issued | 2014 | |
| dc.date.submitted | 2014-08-19 | |
| dc.identifier.citation | Armbruster, B. and Brandeau, M. L. (2007a). Contact tracing to control infectious disease:when enough is enough. Health care management science, 10(4):341–355.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/4972 | - |
| dc.description.abstract | 傳染病模擬模型在流行病學中被用來探索一些在現實中難以探究的問題。
其中，個體化模擬模型(Agent-based model)利用在電腦中的虛擬個體模擬由複雜行為組成的系統。 近年來，由於電腦運算技術的進步，個體化模擬模型有許多的應用產生，然而對於如何擬合與校正個體化模擬模型的研究甚少。 本研究利用連續時間貝氏網路(Continuous-time Bayesian Networks)發展了一組具有統計界面的傳染病個體化模擬模型，並進一步以過去的擬和架構為基礎，發展出一套擬合程序。 我們成功將遺傳演算法中的數值點突變(Numerical mutation)及參數分組策略(Blocking strategy)應用於序列蒙地卡羅法(Sequential Monte Carlo)中，使擬合程序可以處理大量參數且來源各異的資料。 最後，我們以肺結核的接觸者追蹤政策為例，使用易感受-感染者-復原者模型(Susceptible-Infectious-Recovery model)來演示我們為個體化模擬模型從模型建構、估計到預測所發展的實證架構。 | zh_TW |
| dc.description.abstract | The simulation models in epidemiology were developed to answer the questions which were not easy to solve by observational studies in the real world.
In particular, Agent-based models (ABMs) were usually employed to deal with the complex system of disease transmission by simulating computational agents in the virtual world. However, the fitting scheme of ABMs is less developed than the applications.. With the aim of investigating disease dynamics and creating an interface for statistical analysis, we proposed a class of ABMs with Continuous-time Bayesian network, a temporal multivariate probability model. While retaining the strength of existing procedure for simulation model fitting based on sequential Monte Carlo, we set up an improved framework for fitting ABMs. We further synthesized the numerical mutation in genetic algorithm and the parameters augmentation in blocking Gibbs sampling in order to overcome the challenges of multidimensional parameters and multi-sources data. Using an example of Susceptible-Infectious-Recovery model for contact tracing in tuberculosis control, we briefly presented the properties of our proposed model and demonstrated its potential applications in the future. By including model construction, fitting, and forecasting, we formalized an empirical scheme for individual based models in simulating disease dynamics. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-15T17:50:39Z (GMT). No. of bitstreams: 1 ntu-103-R02849006-1.pdf: 1014267 bytes, checksum: 73c92bc423cec4045bcf0dd305c97e7f (MD5) Previous issue date: 2014 | en |
| dc.description.tableofcontents | 致謝 i
中文摘要 ii Abstract iii Contents iv List of Figures vii List of Tables viii 1 Introduction 1 1.1 Modern challenge of infectious disease control . . . . . . . . . . . . . . 1 1.2 Why simulation model? . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2.1 Agent-based models (ABMs) . . . . . . . . . . . . . . . . . . . 2 1.3 Challenges in ABMs construction for epidemiologist . . . . . . . . . . . 3 1.4 Fitting scheme and research gap . . . . . . . . . . . . . . . . . . . . . . 4 1.5 Example: Tuberculosis control in Taiwan . . . . . . . . . . . . . . . . . 5 1.6 Objective and outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2 Model Construction 7 2.1 An Agent-Based Model with Bayesian networks for modelling infectious diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1.1 Conceptual model: Disease Triangle Model . . . . . . . . . . . . 7 2.2 Dynamic Bayesian Networks (DBNs) . . . . . . . . . . . . . . . . . . . 8 2.2.1 Dynamic model for infectious diseases . . . . . . . . . . . . . . 9 2.3 An illustrative example: SIR model . . . . . . . . . . . . . . . . . . . . 10 2.3.1 Collect information . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.3.2 Identify nodes and form the agent . . . . . . . . . . . . . . . . . 12 2.3.3 Set the interaction between nodes . . . . . . . . . . . . . . . . . 12 2.3.4 Set the initial states . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.3.5 Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.3.6 Sub-model inference: basic reproductive numbers (R 0 ) . . . . . . 13 3 Fitting Scheme 22 3.1 General fitting scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.1.1 Bayesian approach: Sequential Monte Carlo . . . . . . . . . . . . 22 3.1.2 Frequentist approach: Genetic algorithm . . . . . . . . . . . . . 23 3.2 Fitting scheme for single dataset . . . . . . . . . . . . . . . . . . . . . . 25 3.2.1 Numerical mutation . . . . . . . . . . . . . . . . . . . . . . . . 26 3.2.2 Fitting scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.3 Fitting for multiple datasets . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.3.1 Blocking strategy . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.3.2 Reducing problem of spurious correlation . . . . . . . . . . . . . 28 3.3.3 Identifying the order of processes . . . . . . . . . . . . . . . . . 28 3.3.4 Fitting scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 4 Tuberculosis Dynamic Model 33 4.1 Tuberculosis control model . . . . . . . . . . . . . . . . . . . . . . . . . 33 4.1.1 Environments agents . . . . . . . . . . . . . . . . . . . . . . . . 33 4.1.2 Human agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 4.1.3 Tuberculosis agents . . . . . . . . . . . . . . . . . . . . . . . . . 34 4.1.4 Health care seeking . . . . . . . . . . . . . . . . . . . . . . . . . 34 4.1.5 Intervention model . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.2 Model fitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.3 Forecasting: Policy analysis . . . . . . . . . . . . . . . . . . . . . . . . 36 5 Results for TB Model 40 5.1 Population-level inference . . . . . . . . . . . . . . . . . . . . . . . . . 40 5.2 Individual-level inference . . . . . . . . . . . . . . . . . . . . . . . . . . 41 6 Discussion 46 6.1 Connection of the proposed agent-based model with epidemiological studies 46 6.2 Advantage of using continuous-time sampling . . . . . . . . . . . . . . . 47 6.3 Computation time saving . . . . . . . . . . . . . . . . . . . . . . . . . . 49 6.4 Numerical mutation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 6.5 Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 6.6 Tuberculosis control policy . . . . . . . . . . . . . . . . . . . . . . . . . 51 6.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 | |
| dc.language.iso | en | |
| dc.title | 使用動態貝氏網路建立傳染病個體化模擬模型：以肺結核介入政策為例 | zh_TW |
| dc.title | Using Dynamic Bayesian Networks for Agent-Based Modelling: Application in Tuberculosis Control | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 102-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 蕭朱杏(Chuhsing Kate Hsiao),方啟泰(Chi-Tai Fang) | |
| dc.subject.keyword | 個體化模擬模型,傳染病數理模型,動態貝氏網路,數值突變,肺結核,接觸者追蹤, | zh_TW |
| dc.subject.keyword | Agent-based model,Mathematical model for Infectious Disease,Continuous-time Bayesian Networks,Numerical mutation,Blocking Gibbs sampling,Tuberculosis,Contact tracing, | en |
| dc.relation.page | 59 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2014-08-19 | |
| dc.contributor.author-college | 公共衛生學院 | zh_TW |
| dc.contributor.author-dept | 流行病學與預防醫學研究所 | zh_TW |
| 顯示於系所單位： | 流行病學與預防醫學研究所 | |
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