進階搜尋


   電子論文尚未授權公開,紙本請查館藏目錄
(※如查詢不到或館藏狀況顯示「閉架不公開」,表示該本論文不在書庫,無法取用。)
系統識別號 U0026-1807201814013200
論文名稱(中文) 探討家戶旅次與登革熱疫情擴散之關聯性研究-以臺南市為例
論文名稱(英文) The Relationship between Dengue Fever Diffusion and Population Movement – A Case Study of Tainan City
校院名稱 成功大學
系所名稱(中) 都市計劃學系
系所名稱(英) Department of Urban Planning
學年度 106
學期 2
出版年 107
研究生(中文) 何奕萱
研究生(英文) I-Hsuan Ho
學號 P26054070
學位類別 碩士
語文別 中文
論文頁數 74頁
口試委員 口試委員-謝宏昌
口試委員-陳彥仲
口試委員-黃泰霖
指導教授-李子璋
中文關鍵字 家戶旅次  登革熱  傳染病擴散  貝式統計  馬可夫鏈蒙地卡羅法 
英文關鍵字 Household travel survey  dengue fever  epidemic diffusion  Bayesian analysis  Markov chain Monte Carlo (MCMC) method 
學科別分類
中文摘要 近年來隨著運輸科技的進步,人口移動範圍大如跨國旅行,小至都會內部的移動皆變得快速且便捷。然而在縮短人類旅運時間的同時,許多藉由人類傳播的疾病擴散範圍也隨之大幅提升。其中登革熱為臺灣嚴重之地方疾病,曾發生數次流行疫情,近年來尤以2015年的疫情最為嚴峻。
登革熱的傳染途徑主要為人與病媒蚊交互感染所形成的傳播循環,因此其於空間中傳播的媒介可分為人以及病媒蚊兩種,其中病媒蚊的移動有其空間限制,人於空間的移動則範圍廣泛,且與家戶旅次分布的特性有高度相關,也因此對登革熱的擴散有極大的影響。此外由於登革熱疫情的形成為一複雜的交互作用,交通網絡因子、氣象因子、社會經濟因子、土地使用因子等皆為登革熱的重要影響因子。
本研究首先根據文獻探討與相關分析找出與病例數顯著相關的因子,並基於二項分布的假設之下分別建立登革熱位移式傳播模型與擴張式傳播模型,可說明人口移動對於登革熱疫情的影響能力,並以氣象因子、社會經濟因子與土地使用因子描述登革熱在一地區的群聚效果。本研究的模型利用貝式統計進行校估,最終模型顯示出人口移動、每週平均氣溫、每週累積雨量與交通使用面積比例與登革熱的傳播有關,並且本研究的模型能夠良好的描述現實數據,配適度高,可應用於後續疫情的預測,協助將登革熱對臺灣的威脅降低。
英文摘要 Traveling has been becoming more convenient with accessibility, affordability, and efficiency enhanced by the rapid development of transportation systems, aggravating the spread of contagious diseases. As an example, dengue fever, of which the prevention strategy has been a main challenge for the Taiwan government, has again drawn attentions after Tainan City suffered the most severe epidemic for decades in 2015.
Dengue fever is transmitted to human through the bite of mosquitoes infected with dengue virus. However, compared to the limited dispersal distance of mosquitoes, human hosts have greater mobility that is highly correlated with the travel characteristics, rendering travel distance as a key factor affecting the diffusion of dengue fever. In addition, dengue fever has a complex transmission mechanism involving transport networks, weather, socioeconomic status, demographic, and land-use patterns, which should all be taken into consideration in modeling the diffusion of this disease.
Prior to the epidemic model development based on binomial distribution, factors related to dengue fever diffusion in the literature were reviewed and highly correlated factors were analyzed using the data from the 2015 outbreak. This model includes two types of diffusion patterns, i.e. relocation diffusion and expansion diffusion, and was calibrated using Bayesian statistics the Markov chain Monte Carlo (MCMC) method. The results show that the spread of dengue fever is highly correlated with the population movement, average temperature, precipitation and traffic land-use ratio, as the model is well fitted to the data.
論文目次 第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的與重要性 2
第三節 研究流程 3
第二章 文獻回顧 4
第一節 登革熱概述 4
第二節 人口移動與傳染病傳播 12
第三章 研究設計與方法 17
第一節 研究範圍 17
第二節 研究資料 17
第三節 研究設計 25
第四章 研究結果 33
第一節 人口移動與登革熱疫情之關聯 33
第二節 敘述統計與相關分析 40
第三節 登革熱疫情模擬模型校估 48
第五章 模型驗證 57
第一節 模型的穩定性與一致性 57
第二節 模型驗證 62
第三節 模型可攜性(portability)測試 65
第六章 結論與建議 68
第一節 研究結論與建議 68
第二節 研究限制 69
第三節 研究貢獻 70
參考文獻 71
參考文獻 Aagaard‐Hansen, J., Nombela, N., & Alvar, J. (2010). Population movement: a key factor in the epidemiology of neglected tropical diseases. Tropical Medicine & International Health, 15(11), 1281-1288. doi: 10.1111/j.1365-3156.2010.02629.x
Anker, M., & Arima, Y. (2011). Male–female differences in the number of reported incident dengue fever cases in six Asian countries. Western Pacific Surveillance and Response Journal : WPSAR, 2(2), 17-23. doi: 10.5365/WPSAR.2011.2.1.002
Banu, S., Hu, W., Hurst, C., & Tong, S. (2011). Dengue transmission in the Asia-Pacific region: impact of climate change and socio-environmental factors. Trop Med Int Health, 16(5), 598-607. doi: 10.1111/j.1365-3156.2011.02734.x
Barmak, D. H., Dorso, C. O., Otero, M., & Solari, H. G. (2011). Dengue epidemics and human mobility. Phys Rev E Stat Nonlin Soft Matter Phys, 84. doi: 10.1103/PhysRevE.84.011901
Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). Ucinet 6 for Windows: Software for Social Network Analysis. Harvard: Analytic Technologies.
Brownstein, J. S., Wolfe, C. J., & Mandl, K. D. (2006). Empirical Evidence for the Effect of Airline Travel on Inter-Regional Influenza Spread in the United States. PLoS Medicine, 3(10), e401. doi: 10.1371/journal.pmed.0030401
Cliff, A. D. (1981). Spatial diffusion: an historical geography of epidemics in an island community (Vol. 14): CUP Archive.
Colón-González, F. J., Fezzi, C., Lake, I. R., & Hunter, P. R. (2013). The Effects of Weather and Climate Change on Dengue. PLOS Neglected Tropical Diseases, 7(11), e2503. doi: 10.1371/journal.pntd.0002503
Colizza, V., Barrat, A., Barthelemy, M., Valleron, A. J., & Vespignani, A. (2007). Modeling the worldwide spread of pandemic influenza: Baseline case and containment interventions. PLoS Med, 4. doi: 10.1371/journal.pmed.0040013
de Mattos Almeida, M. C., Caiaffa, W. T., Assunção, R. M., & Proietti, F. A. (2007). Spatial Vulnerability to Dengue in a Brazilian Urban Area During a 7-Year Surveillance. Journal of Urban Health, 84(3), 334-345. doi: 10.1007/s11524-006-9154-2
Delmelle, E., Hagenlocher, M., Kienberger, S., & Casas, I. (2016). A spatial model of socioeconomic and environmental determinants of dengue fever in Cali, Colombia. Acta Trop, 164, 169-176. doi: 10.1016/j.actatropica.2016.08.028
Epstein, J. M., Goedecke, D. M., Yu, F., Morris, R. J., Wagener, D. K., & Bobashev, G. V. (2007). Controlling Pandemic Flu: The Value of International Air Travel Restrictions. PloS one, 2(5), e401. doi: 10.1371/journal.pone.0000401
ESRI. (2011). ArcGIS Desktop: Release 10. Redlands: CA: Environmental Systems Research Institute.
Gardner, L., & Sarkar, S. (2013). A Global Airport-Based Risk Model for the Spread of Dengue Infection via the Air Transport Network. PloS one, 8(8), e72129. doi: 10.1371/journal.pone.0072129
Gould, P. R. (1969). Spatial Diffusion, Resource Paper No. 4.
Gould, W. T. S. (2009). Population and development. from http://site.ebrary.com/id/10267085
Grais, R. F., Hugh Ellis, J., & Glass, G. E. (2003). Assessing the impact of airline travel on the geographic spread of pandemic influenza. European Journal of Epidemiology, 18(11), 1065-1072. doi: 10.1023/A:1026140019146
Gubler, D. J. (2011). Dengue, Urbanization and Globalization: The Unholy Trinity of the 21(st) Century. Tropical Medicine and Health, 39(4 Suppl), 3-11. doi: 10.2149/tmh.2011-S05
Harrington, L. C., Scott, T. W., Lerdthusnee, K., Coleman, R. C., Costero, A., Clark, G. G., . . . Edman, J. D. (2005). Dispersal of the dengue vectors aedes aegypti within and between rural communities. The American Journal of Tropical Medicine and Hygiene, 72(2), 209-220. doi: doi:https://doi.org/10.4269/ajtmh.2005.72.209
Johnson, R. A. (2009). Statistics: principles and methods: John Wiley & Sons.
Kan, C. C., Lee, P. F., Wen, T. H., Chao, D. Y., Wu, M. H., Lin, N. H., . . . Pai, L. (2008). Two clustering diffusion patterns identified from the 2001-2003 dengue epidemic, Kaohsiung, Taiwan. Am J Trop Med Hyg, 79(3), 344-352.
Lynch, C., & Roper, C. (2011). The Transit Phase of Migration: Circulation of Malaria and Its Multidrug-Resistant Forms in Africa. PLoS Medicine, 8(5), e1001040. doi: 10.1371/journal.pmed.1001040
Mary, E. W. (1995). Travel and the Emergence of Infectious Diseases. Emerging Infectious Disease journal, 1(2), 39. doi: 10.3201/eid0102.950201
Murray, N. E. A., Quam, M. B., & Wilder-Smith, A. (2013). Epidemiology of dengue: past, present and future prospects. Clinical Epidemiology, 5, 299-309. doi: 10.2147/CLEP.S34440
Naish, S., Dale, P., Mackenzie, J. S., McBride, J., Mengersen, K., & Tong, S. (2014). Climate change and dengue: a critical and systematic review of quantitative modelling approaches. BMC Infectious Diseases, 14(1), 167. doi: 10.1186/1471-2334-14-167
Nakhapakorn, K., & Tripathi, N. K. (2005). An information value based analysis of physical and climatic factors affecting dengue fever and dengue haemorrhagic fever incidence. International Journal of Health Geographics, 4(1), 13. doi: 10.1186/1476-072x-4-13
Perez, L., & Dragicevic, S. (2009). An agent-based approach for modeling dynamics of contagious disease spread. International Journal of Health Geographics, 8(1), 50. doi: 10.1186/1476-072x-8-50
Pim, M., & Lisbeth, H. (2000). Malaria on the Move: Human Population Movement and Malaria Transmission. Emerging Infectious Disease journal, 6(2), 103. doi: 10.3201/eid0602.000202
Sanna, M., & Hsieh, Y. H. (2017). Ascertaining the impact of public rapid transit system on spread of dengue in urban settings. Sci Total Environ, 598, 1151-1159. doi: 10.1016/j.scitotenv.2017.04.050
Spiegelhalter, D., Thomas, A., Best, N., & Lunn, D. (2003). WinBUGS user manual: version.
Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & Van Der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64(4), 583-639. doi: doi:10.1111/1467-9868.00353
Stoddard, S. T., Forshey, B. M., Morrison, A. C., Paz-Soldan, V. A., Vazquez-Prokopec, G. M., Astete, H., . . . Scott, T. W. (2013). House-to-house human movement drives dengue virus transmission. Proceedings of the National Academy of Sciences of the United States of America, 110(3), 994-999. doi: 10.1073/pnas.1213349110
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 Negl Trop Dis, 3(7), e481. doi: 10.1371/journal.pntd.0000481
Tatem, A. J. (2014). Mapping population and pathogen movements. International Health, 6(1), 5-11. doi: 10.1093/inthealth/ihu006
Tatem, A. J., Rogers, D. J., & Hay, S. I. (2006). Global Transport Networks and Infectious Disease Spread. Advances in parasitology, 62, 293-343. doi: 10.1016/S0065-308X(05)62009-X
Wen, T.-H., Lin, M.-H., & Fang, C.-T. (2012). Population Movement and Vector-Borne Disease Transmission: Differentiating Spatial–Temporal Diffusion Patterns of Commuting and Noncommuting Dengue Cases. Annals of the Association of American Geographers, 102(5), 1026-1037. doi: 10.1080/00045608.2012.671130
WHO. (2014). The dengue strategic plan for the Asia Pacific Region (2008–2015). Retreived on June, 11, 2014.
Wu, P. C., Guo, H. R., Lung, S. C., Lin, C. Y., & Su, H. J. (2007). Weather as an effective predictor for occurrence of dengue fever in Taiwan. Acta Trop, 103(1), 50-57. doi: 10.1016/j.actatropica.2007.05.014
Wu, P. C., Lay, J. G., Guo, H. R., Lin, C. Y., Lung, S. C., & Su, H. J. (2009). Higher temperature and urbanization affect the spatial patterns of dengue fever transmission in subtropical Taiwan. Sci Total Environ, 407(7), 2224-2233. doi: 10.1016/j.scitotenv.2008.11.034
內政部戶政司統計處(2015)。人口數及人口增加率。
內政部國土測繪中心(2015)。國土利用調查。
王淑鶯, 伍安怡, 朱雅婷, 何宗憲, 何慈娟, 余佳益, . . . 羅玉枝(2016)。登革熱的台灣經驗:從流行病學及臨床到基礎科學的新視野:科技部台灣重要新興感染症研究計畫成果報告。台北市:科技部。
季鈞管理顧問股份有限公司 (2013)。臺南市運輸系統整體規劃期中報告書。臺南市: 臺南市政府。
林政宏(2007)。台灣地區登革熱擴散之空間分析。臺灣大學地理環境資源學研究所。
凌瑞賢(2004)。 運輸規劃原理與實務。鼎漢國際工程顧問股份有限公司。
疾病管制局(2011)。埃及斑蚊及白線斑蚊消長因子及因應對策研究。
陳弘道(2008)。台灣南部地區登革熱擴散地圖及環境影響因子分析。國立高雄師範大學地理研究所。
陳璽文, 孫春在(2010)。網路拓樸的遞移性: 以流行性傳染病的潛在感染風險為例。國立交通大學資訊科學與工程研究所。
黃宜庭(2011)。人口旅運動態與登革熱傳播的時空分析。臺灣大學地理環境資源學研究所。
黃基森(1991)。臺灣地區斑蚊生態及其與登革熱流行之關係。中華昆蟲特刊, 第六號,頁 105-127。
黃基森(1993)。臺灣登革熱流行區斑蚊生態及綜合防治。國立臺灣大學植物病蟲害研究所。
葛應欽(1989)。登革熱流行病學-登革熱在台灣的流行。The Kaohsiung Journal of Medical Sciences, 5(1),頁 1-11。
臺南市政府衛生局(2015)。臺南市政府衛生局登革熱專區。 Retrieved 12月25日, 2015, from http://data.tainan.gov.tw/dataset/dengue-dist
衛生署疾病管制局(2013)。登革熱病媒蚊生態及習性介紹。
衛生福利部疾病管制署(2016a)。登革熱/屈公病/茲卡病毒感染症防治工作指引。臺北市:衛生福利部疾病管制署。
衛生福利部疾病管制署(2016b)。登革熱傳染病防治工作手冊。
蘇明道(2006)。建立時間-空間擴散動態模型評估登革熱流行的空間危險區域與防疫策略。行政院衛生署疾病管制局九十五年科技研究發展計畫(計畫編號:DOH95-DC-1026)。
論文全文使用權限
  • 同意授權校內瀏覽/列印電子全文服務,於2022-08-01起公開。
  • 同意授權校外瀏覽/列印電子全文服務,於2022-08-01起公開。


  • 如您有疑問,請聯絡圖書館
    聯絡電話:(06)2757575#65773
    聯絡E-mail:etds@email.ncku.edu.tw