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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 溫在弘 | zh_TW |
| dc.contributor.advisor | Tzai-Hung Wen | en |
| dc.contributor.author | 王成驊 | zh_TW |
| dc.contributor.author | Cheng-Hua Wang | en |
| dc.date.accessioned | 2024-02-22T16:17:16Z | - |
| dc.date.available | 2024-02-23 | - |
| dc.date.copyright | 2024-02-22 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-01-31 | - |
| dc.identifier.citation | Alfonseca, M., & Ortega, A. (2001). Determination of fractal dimensions from equivalent L systems. IBM Journal of Research ad Development, 45(9), 797-805.
Bang, J.-W., & Gillies, D. (2002). Using Bayesian networks with hidden nodes to recognise neural cell morphology. In M. Ishizuka, & A. Sattar (Ed.), PRICAI 2002: Trends in Artificial Intelligence. LNAI 2417, pp. 385-394. Tokyo, Japan: Springer-Verlag. Bellingeri, M., Bevacqua, D., Turchetto, M., Scotognella, F., Alfieri, R., Nguyen, N.-K.-K., . . . Cassi, D. (2022). Network structure indexes to forecast epidemic spreading in real-world complex networks. Frontiers in Physics, 1-11. Bernard, J., & McQuillan , I. (2023). Stochastic L-system inference from multiple string sequence inputs. Soft Computing, 27, 6783–6798. Bernard, J., & McQuillan, I. (2018). Inferring Stochastic L-Systems Using a Hybrid Greedy Algorithm. International Conference on Tools with Artificial Intelligence (pp. 1-8). Volos, Greece: IEEE. Bordeu, I., Amarteifio, S., Garcia-Millan, R., Walter, B., Wei, N., & Pruessner, G. (2019). Volume explored by a branching random walk on general graphs. Scientific Reports, 9(15590), 1-9. Brown, J. H., Gupta, V. K., Li, B.-L., Milne, B. T., Restrepo, C., & West, G. B. (2002). The fractal nature of nature: power laws, ecological complexity and biodiversity. Philosophical Transactions of the Royal Society B, 357, 619-626. Cheeseman, A. K., & Vrscay, E. R. (2022). Estimating the fractal dimensions of vascular networks and other branching structures: some words of caution. Mathematics, 10(839), 1-21. Chen, Y.-H., Fang, C.-T., & Huang, Y.-L. (2021). Effect of Non-lockdown Social Distancing and Testing-Contact Tracing During a COVID-19 Outbreak in Daegu, South Korea, February to April 2020: A Modeling Study. International Journal of Infectious Diseases, 110, 213-221. Da Costa, L. E., & Landry, J.-A. (2005). Generating grammatical plant models with genetic algorithms. Adaptive and Natural Computing Algorithms (pp. 230-234). Coimbra, Portugal: Springer. Fine, P. E., Mulholland, K., Scott, J., & Edmunds, W. (2023). Community Protection. In W. Orenstein, P. Offit, K. M. Edwards, & S. Plotkin, Plotkin's Vaccines (pp. 1603-1624). Elsevier. GISAID. (2023). GISAID. Retrieved from Wikipedia: https://en.wikipedia.org/wiki/GISAID Jacob, C. (2010). Branching processes: their role in epidemiology. International Journal of Environmental Research and Public Health, 7, 1186-1204. James, A., Plank, M. J., Hendy, S., Binny, R. N., Lustig, A., & Steyn, N. (2021). Model-free estimation of COVID-19 transmission dynamics from a complete outbreak. PLoS ONE, 16(3), 1-13. Keeling, M. J., & Rohani, P. (2008). Introduction to Simple Epidemic Models. In Modeling Infectious Diseases in Humans and Animals (pp. 16-34, 232-290). Princeton University Press. Kekkonen, H., Lassas, M., Saksman, E., & Siltanen, S. (2023). Random tree Besov priors - towards fractal imaging. Inverse Problems and Imaging, 17(2), 507-531. Lequime, S., Bastide, P., Dellicour, S., Lemey, P., & Baele, G. (2020). nosoi: A stochastic agent-based transmission chain simulation framework in R. Methods in Ecology and Evolution, 11, 1002-1007. Li, K., Ma, Y., & Príncipe, J. C. (2017). Automatic plant identification using stem automata. International Workshop on Machine Learning for Signal Processing (pp. 1-6). Tokyo, Japan: IEEE. Lok, K. (2017). Fractals, Lindenmayer-Systems and Dimensions. 1-37. University of Groningen. Mandelbrot, B. B. (1983). The fractal geometry of nature. H. B. Fenn and Company. Menczer, F., Fortunato, S., & Davis, C. A. (2020). Network Models. In F. Menczer, S. Fortunato, & C. A. Davis, A First Course in Network Science (pp. 120-149). Cambridge University Press. Montgomery, D. C. (2013). Guidelines for Designing Experiments. In D. C. Montgomery, Design and Analysis of Experiments (8 ed., p. 17). John Wiley & Sons, Inc. Perera, D., Perks, B., Potemkin, M., Liu, A., Gordon, P. M., Gill, M., . . . van Marle, G. (2021). Reconstructing SARS-CoV-2 infection dynamics through the phylogenetic inference of unsampled sources of infection. PLoS ONE, 16(12), 1-16. Price, C. A., Ogle, K., White, E. P., & Weitz, J. S. (2009). Evaluating scaling models in biology using hierarchical Bayesian approaches. Ecology Letters, 12, 641-651. Renton, M., Costes, E., Godin, C., & Guédon, Y. (2006). Integrating markov chain models and L-systems to simulate the architectural development of apple trees. International Symposium on Modelling in Fruit Research and Orchard Management. 707, pp. 63-70. ISHS Acta Horticulturae: VII. Riley, S., Eames, K., Isham, V., Mollison, D., & Pieter, T. (2015). Five challenges for spatial epidemic models. Epidemics, 10, 68-71. Roanes-Lozano, E., & Solano-Macías, C. (2021). Using Fractals and Turtle Geometry to Visually Explain the Spread of a Virus to Kids: A STEM Multitarget Activity. Mathematics in Computer Science, 15, 689-699. Santos, E., & Coelho, R. C. (2009). Obtaining L-Systems Rules from Strings. Third Southern Conference on Computational Modeling (pp. 143-149). Rio Grande, Brazil: IEEE. Suhartono, S., Kurniawan, F., & Imran, B. (2018). Identification of virtual plants using bayesian networks based on parametric L-system. International Journal of Advances in Intelligent Informatics, 4(1), 40-52. Sun, H., Kryven, I., & Bianconi, G. (2022). Critical time-dependent branching process modelling epidemic spreading with containment measures. Journal of Physics A: Mathematical and Theoretical, 55(224006), 1-23. Ziff, A. L., & Ziff, R. M. (2020). Fractal kinetics of COVID-19 pandemic (with update 3/1/20). medRxiv, 1-15. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/91697 | - |
| dc.description.abstract | 在流行病早期階段的冪次律成長現象描述了經驗的規模關係，隱含傳播過程潛在著自相似機制。除此之外，相較於傳統的隔室模型，由傳播鏈與感染者組成的傳播樹能夠提供研究人員更多流行病的圖像化資訊。由於現有研究缺乏使用傳播樹揭示流行病中的自相似性，本研究目的為尋找在流行病早期的傳播樹自相似模式。本研究發展增階演算法 (OIA)推論條件式感染連結度集合來作為一個自相似模式。OIA已被施行於48項因子水準的模擬研究以尋找自相似模式、和一個真實流行病案例以重建流行病的傳播樹。結果發現末端節點的增加會打破早期流行病真實傳播樹中自相似特徵，並減低重建過程的品質。除此之外，OIA在尋找模式與重建過程中很穩健，其意涵為辨識出來的自相似模式能夠用於比較不同流行病在早期階段的傳播特性比如超級傳播者及其出現的可能性。故OIA作為一個貝氏樹演算法，成功地在模擬研究與流行病真實案例中推論出條件式感染連結度之重複模式以達成本研究目的。 | zh_TW |
| dc.description.abstract | A power-law phenomenon of growth in the early stage of epidemics describes an empirical scaling relationship, suggesting a self-similar mechanism underlying the spreading process. In addition, the transmission trees formulated by transmission chains and infectives can provide researchers with more graphical information on epidemics than conventional compartment models. Due to current studies lacking the use of transmission trees to unveil the self-similarity in epidemics, the purpose of this article is to identify self-similar patterns of transmission trees in the early stage of epidemics. This article developed an Order-increasing Algorithm (OIA) inferring the sets of conditional infectious connectivity as a self-similar pattern. The OIA was implemented in both simulation studies of 48 levels of factors to identify self-similar patterns and a real epidemic case to reconstruct transmission trees for epidemics. The finding highlighted that the increasing of leaf nodes would break the self-similar feature in real epidemic transmission trees in the early stage and reduce the quality of the reconstruction process. In addition, the OIA was robust in the processes of identification and reconstruction, implying that the identified self-similar patterns could be used to compare spreading characteristics, such as super-spreaders and their likelihood of occurrences, among different epidemics in the early stage. In conclusion, the OIA, as a Bayesian tree algorithm, successfully infers the repeated patterns of conditional infectious connectivity in both simulation studies and a real epidemic case to fulfill the purpose of this article. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-02-22T16:17:16Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-02-22T16:17:16Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
謝辭 ii 中文摘要 iii Abstract iv Content (目次) vi Content of Figures (圖次) ix Content of Tables (表次) xi 1 Introduction 1 1.1 Background 1 1.1.1 Power Law Phenomenon in COVID-19 1 1.1.2 Studying Transmission Trees as a Possible Approach 2 1.1.3 Self-similar Patterns in Transmission Trees 4 1.2 Literature Review 4 1.2.1 Branching Processes Relating to Self-similarity 5 1.2.2 Bayesian Networks Relating to Self-similarity 6 1.2.3 Inverse Problem of L-systems Relating to Self-similarity 7 1.3 Purpose of Research 9 2 Method 10 2.1 Basic Terms and Formula 10 2.1.1 Terms of Trees 10 2.1.2 Formula of Bayes’ Theorem 13 2.2 Main Algorithm 14 2.2.1 Bayesian Inference of Trees of Degree Type 14 2.2.2 Difference of Transmission Expectation 19 2.2.3 Minimum Sum of Distance 20 2.2.4 Order-increasing Algorithm 21 2.3 Experiments 23 2.3.1 Design of Simulation Studies 23 2.3.2 Application in Real Epidemic 31 3 Results 37 3.1 Experiment in Simulation Studies 37 3.1.1 Conceptual Framework of Experiment Outputs in Simulation Studies 37 3.1.2 Summary Table of Results 39 3.1.3 One Factor Analysis 43 3.1.4 Two Factor Analysis 44 3.2 Experiment in Real Epidemic 48 3.2.1 Overview of the Real Sample Tree 48 3.2.2 Conceptual Framework of Experiment Outputs in Real Epidemic 49 3.2.3 Summary Table of Results 51 3.2.4 Evaluation of Performance 51 4 Discussion 56 4.1 Significance 56 4.2 Findings 56 4.3 Limitations and Future Research 58 5 Reference 60 Appendix I: Pseudocode of OIA 64 | - |
| dc.language.iso | en | - |
| dc.subject | 冪次律 | zh_TW |
| dc.subject | 早期階段 | zh_TW |
| dc.subject | 傳播樹 | zh_TW |
| dc.subject | 自相似 | zh_TW |
| dc.subject | 貝氏推論 | zh_TW |
| dc.subject | 增階演算法 (OIA) | zh_TW |
| dc.subject | transmission tree | en |
| dc.subject | power-law | en |
| dc.subject | Order-increasing Algorithm (OIA) | en |
| dc.subject | Bayesian inference | en |
| dc.subject | self-similar | en |
| dc.subject | early stage | en |
| dc.title | 在早期流行病傳播過程中自相似性之貝氏樹演算法 | zh_TW |
| dc.title | A Bayesian Tree Algorithm of Self-similarity in Early Stage Epidemic Transmission Process | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 林楨家;蔡政安 | zh_TW |
| dc.contributor.oralexamcommittee | Jen-Jia Lin;Chen-An Tsai | en |
| dc.subject.keyword | 冪次律,早期階段,傳播樹,自相似,貝氏推論,增階演算法 (OIA), | zh_TW |
| dc.subject.keyword | power-law,early stage,transmission tree,self-similar,Bayesian inference,Order-increasing Algorithm (OIA), | en |
| dc.relation.page | 64 | - |
| dc.identifier.doi | 10.6342/NTU202400399 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2024-02-02 | - |
| dc.contributor.author-college | 共同教育中心 | - |
| dc.contributor.author-dept | 統計碩士學位學程 | - |
| 顯示於系所單位： | 統計碩士學位學程 | |
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