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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 | 簡微 | zh_TW |
| dc.contributor.author | Wei Chien | en |
| dc.date.accessioned | 2024-08-19T17:00:38Z | - |
| dc.date.available | 2024-08-20 | - |
| dc.date.copyright | 2024-08-19 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-07-31 | - |
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I., & Hartskeerl, R. A. (2017). Environmental risk of leptospirosis infections in the Netherlands: Spatial modelling of environmental risk factors of leptospirosis in the Netherlands. PloS one, 12(10), e0186987. Rushworth, A., Lee, D., & Sarran, C. (2017). An adaptive spatiotemporal smoothing model for estimating trends and step changes in disease risk. Journal of the Royal Statistical Society Series C: Applied Statistics, 66(1), 141-157. Sahu, S. K., & Böhning, D. (2022). Bayesian spatio-temporal joint disease mapping of Covid-19 cases and deaths in local authorities of England. Spatial Statistics, 49, 100519. Schærström, A. (2009). Disease diffusion. International Encyclopedia of Human Geography, 222. Stoddard, S. T., Morrison, A. C., Vazquez-Prokopec, G. M., Paz Soldan, V., Kochel, T. J., Kitron, U., . . . Scott, T. W. (2009). The role of human movement in the transmission of vector-borne pathogens. PLoS neglected tropical diseases, 3(7), e481. Tang, W., Liao, H., Marley, G., Wang, Z., Cheng, W., Wu, D., & Yu, R. (2020). The changing patterns of coronavirus disease 2019 (COVID-19) in China: A tempogeographic analysis of the severe acute respiratory syndrome coronavirus 2 epidemic. Clinical infectious diseases, 71(15), 818-824. Tipayamongkholgul, M., & Lisakulruk, S. (2011). Socio-geographical factors in vulnerability to dengue in Thai villages: a spatial regression analysis. Geospatial health, 5(2), 191-198. Vazquez-Prokopec, G. M., Stoddard, S. T., Paz-Soldan, V., Morrison, A. C., Elder, J. P., Kochel, T. J., . . . Kitron, U. (2009). Usefulness of commercially available GPS data-loggers for tracking human movement and exposure to dengue virus. International Journal of Health Geographics, 8, 1-11. Verdonschot, P. F., & Besse-Lototskaya, A. A. (2014). Flight distance of mosquitoes (Culicidae): a metadata analysis to support the management of barrier zones around rewetted and newly constructed wetlands. Limnologica, 45, 69-79. 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Annals of the Association of American Geographers, 102(5), 1026-1037. 徐品翰（2023）。建立共識自組織對映於預測登革熱擴散的時空範圍。〔碩士論文，國立臺灣大學〕，臺灣博碩士論文知識加值系統。https://hdl.handle.net/11296/r6a36h 國史館臺灣文獻館採集組（2002）。臺灣地名辭書(卷七)臺南縣。國家圖書館臺灣記憶系統。 衛生福利部疾病管制署（2024）。登革熱／屈公病防治工作指引。衛生福利部疾病管制署。 藍奕青（2010）。帝國之守──日治時期臺灣的郡制與地方統治。〔碩士論文，國立臺灣師範大學〕，臺灣博碩士論文知識加值系統。https://hdl.handle.net/11296/zjbm9d | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/94823 | - |
| dc.description.abstract | 疾病傳播涉及複雜的空間擴散模式，傳統空間分析研究經常使用鄰近矩陣或是人流資料代表疾病擴散的空間結構，但這些事先給定的結構可能無法捕捉到真正的傳播關係。本研究採用條件自適應自迴歸模型( Conditional Autoregressive Adaptive Model )估計傳染病傳播的空間擴散模式，並使用2015年和2023年台南市登革熱疫情的案例進行分析。研究結果說明，模擬資料的分析結果確定了模型對傳播關係的準確估計能力，而套用於登革熱實際資料的模型分析結果顯示：模型估計出的地區關係以及其所反映的擴散關係與過往文獻描述的擴散趨勢吻合，且另外找到了過去文獻未曾觀察到的潛在遠距離擴散關係。此外，將估計出的空間結構轉換為網絡並進行分群後，可以發現兩年度的分群結果相似，且地區之間的互動關係可能與傳統分區有關。這說明聚落之間的互動關係具有穩定性和持續性，有助於在疫情初期快速找到高風險區域並實施相對應的措施。這項研究的成果能為疾病傳播的研究提供新的想法，透過模型估計空間權重矩陣，可以同時捕捉擴張型擴散和遷移型擴散的結構特徵，打破過去研究必須事先給定空間結構的限制，並增強我們對疾病空間擴散特性的理解。若能確實掌握將地區之間的擴散以及互動的空間結構，對於疾病預防和控制策略的將具有一定的參考價值。 | zh_TW |
| dc.description.abstract | Disease transmission involves complex spatial diffusion patterns. Conventional spatial analysis often relies on spatial adjacency matrices or human mobility matrices, which may fail to capture true transmission relationships. This study employs a conditional autoregressive adaptive model to estimate the 2015 and 2023 dengue fever outbreak in Tainan, Taiwan, aiming to provide insights into spatial structure of disease diffusion. Through the model's estimation, we can simultaneously capture the distinctive structural characteristics of various diffusions, breaking free from the constraints of having to predefine spatial structures as required in previous studies. Simulated data confirms the model can accurately estimate transmission relationships. Analysis of actual data indicates the dengue outbreak exhibited both expansion diffusion to nearby districts and potential relocation diffusion to more distant regions. Furthermore, converting the estimated spatial structures into networks and performing clustering also helps to understand the interactions between regions. The results reveal that the clustering outcomes are similar for both years, and the interactions between regions may be related to traditional administrative divisions.
The study's results offer new insights into the spatial diffusion characteristics of diseases by estimating spatial weight matrices through the model. This approach overcomes the limitation of having to predefine spatial structures and enhances our understanding of disease transmission dynamics. Understanding the estimated spatial structure and interactions between regions can enhance our comprehension of the spatial diffusion characteristics of diseases and provide critical references for formulating effective epidemic prevention and control strategies. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-08-19T17:00:37Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-08-19T17:00:38Z (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 第一章 研究動機與目的 1 第一節 研究動機 1 第二節 研究目的 3 第二章 文獻回顧 5 第一節 疾病的空間自相關 5 第二節 疾病的空間分析與矩陣設定 7 第三節 利用變數時序關連性反映擴散特徵 8 第三章 研究方法與資料 10 第一節 研究流程 10 第二節 空間權重矩陣 11 第三節 條件自相關迴歸自適應模型 13 第四節 模擬資料產生與模型評估 18 第五節 研究範圍與研究資料 23 第四章 研究結果 27 第一節 模擬資料分析 27 第二節 實際登革熱資料分析 33 第五章 討論 42 第一節 登革熱的空間擴散結構 42 第二節 登革熱傳播網絡與區域社會經濟群聚 43 第三節 研究限制 47 第六章 結論 48 參考文獻 50 附錄 54 附錄一、模擬資料模型的估計值 54 附錄二、2015年實際資料模型自相關函數圖 59 附錄三、2023年實際資料模型自相關函數圖 61 附錄四、2015年模型實際值與估計值 63 附錄五、2023年模型實際值與估計值 65 | - |
| dc.language.iso | zh_TW | - |
| dc.title | 估計局部空間自相關呈現傳染病擴散特徵的空間結構：比較2015與2023年台南登革熱疫情擴散 | zh_TW |
| dc.title | Estimating Localized Spatial Autocorrelation for Characterizing Spatial Structures of Infectious Disease Transmission: Comparing Diffusion Patterns of Dengue Epidemics of Tainan City in 2015 and 2023 | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 余清祥;陳怡如 | zh_TW |
| dc.contributor.oralexamcommittee | Ching-Syang Yue;Vivian Yi-Ju Chen | en |
| dc.subject.keyword | 空間分析,空間權重矩陣,條件自相關迴歸模型,時空自適應模型,登革熱,疾病傳播,空間擴散, | zh_TW |
| dc.subject.keyword | spatial analysis,spatial weighted matrix,conditional autoregressive models,CAR adaptive model,dengue fever,disease transmission,spatial diffusion, | en |
| dc.relation.page | 66 | - |
| dc.identifier.doi | 10.6342/NTU202401989 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2024-08-02 | - |
| dc.contributor.author-college | 理學院 | - |
| dc.contributor.author-dept | 地理環境資源學系 | - |
| 顯示於系所單位： | 地理環境資源學系 | |
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