應用地理探勘技術預測登革熱傳播區域

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姓名

黃世翔(Shih-Hsiang Huang) 查詢紙本館藏

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企業管理學系

論文名稱

應用地理探勘技術預測登革熱傳播區域
(Applying Spatial Data Mining to Predict Dengue Transmission Area)

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摘要(中)

登革熱是爆發在熱帶和亞熱帶等區域的傳染性疾病，在這些區域不論是城市和郊區都有極高的染病機率，過去各爆發疫情的國家對登革熱防疫皆投入巨大的成本。本研究使用典型的空間自我相關分析驗證登革熱疫情位置具備空間相關性，並發展了空間群集分析和空間時序擴散分析兩個地理探勘技術，空間群集分析結合K-Means演算法和空間自我相關分析的結果利用可掌握的考量因素中找出哪些地理位置疫情特性相同，並以此依據進行擴散預測；空間時序擴散分析使用疫情病例資料產生空間時序資料，接著利用關聯方法的序列樣式分析找出疫情在空間的擴散路徑，利用此結果進行擴散預測。利用空間群集分析和空間時序擴散分析兩個方法在臺灣高雄地區登革熱疫情擴散區域預測的地理位置命中率為59.29%和68.40%，地理位置覆蓋率為91.46%和38.75%，皆高於一般的防疫進行方式；若作業成本和錯誤成本一樣的情況下，基於空間群集分析和空間時序擴散分析的防疫策略在成本上分別比一般防疫策略節省27.21%和47.28%，本研究提供了具備地理位置相關的疾病一個績效良好的疫情擴散預測模型，將此應用在實際防疫策略亦具備較好的成本控制效果。

摘要(英)

Dengue fever is a contagious disease that breaks out in tropical and subtropical regions. In these regions, both cities and suburbs have a high probability of infection. In the past, countries with outbreaks have invested huge costs in dengue epidemic prevention. This study uses classical spatial autocorrelation analysis to verify that the location of the dengue fever epidemic is spatially correlated, and develops two spatial data mining techniques, spatial clustering analysis and spatial time sequence diffusion analysis. Spatial clustering analysis combines the results of K-Means algorithm and spatial autocorrelation analysis to find out which geographic locations have the same epidemic characteristics from available consideration factors and use this basis to predict the spread region. Spatial time sequence diffusion analysis uses epidemic case data to generate space-time sequence data, then use the sequence pattern analysis to find the spread path of the epidemic in space and use this result to predict the spread region. Using the two methods of spatial clustering analysis and spatial time sequence diffusion analysis, the hit rates of the dengue fever epidemic area in Kaohsiung, Taiwan are 59.29% and 68.40%, and the coverage rates are 91.46% and 38.75%, which are higher than general epidemic prevention. If the operating cost and the error cost are the same, the epidemic prevention strategy based on spatial cluster analysis and spatial time sequence diffusion analysis saves 27.21% and 47.28% in cost, respectively, compared with general epidemic prevention strategies. This research provides a well-performing epidemic spread prediction model with geographically related diseases. This application research also has a better cost control effect in actual epidemic prevention strategies.

關鍵字(中)

★ 登革熱
★ 空間自我相關分析
★ 空間群集分析
★ 空間時序擴散分析

關鍵字(英)

★ Dengue
★ Spatial Autocorrelation Analysis
★ Spatial Clustering Analysis
★ Spatial Time Sequence Diffusion Analysis

論文目次

中文摘要 i
Abstract ii
目錄 iii
圖目錄 vi
表目錄 vii
一、緒論 1
1-1 研究背景 1
1-2 研究動機 2
1-3 研究缺口 3
二、文獻探討 5
三、研究方法 9
3-1 研究方法流程架構 9
3-2 研究問題描述 10
3-3 資料格式及轉換運算定義 10
3-4 空間自我相關分析 12
3-4-1 全域型空間自我相關分析 13
3-4-2 區域型空間自我相關分析 15
3-4-3 空間自我相關分析的演算過程 16
3-5 空間群集分析 18
3-5-1 K-Means演算法 18
3-5-2 結合空間自我相關分析結果之空間群集分析的衡量指標 19
3-5-3 空間群集分析的演算過程 20
3-6 空間時序擴散分析 21
3-6-1 空間時序擴散分析概念 21
3-6-2 空間時序擴散分析階段說明 22
3-6-3 參數說明 23
3-6-4 空間時序擴散分析演算法 23
3-7 防疫策略擬定 25
3-7-1 一般防疫策略 25
3-7-2 基於空間群集分析的防疫策略 26
3-7-3 基於空間時序擴散分析的防疫策略 26
3-8 演算法績效衡量 27
3-9 成本模型估算 27
四、個案分析 29
4-1 個案資料蒐集與說明 29
4-1-1 登革熱疫情資料 29
4-1-2 地理位置資料 29
4-1-3 關鍵因素資料 30
4-2 個案資料探索 30
4-2-1 登革熱疫情歷年分佈 30
4-2-2 登革熱疫情逐月趨勢 31
4-2-3 登革熱疫情地區分佈 32
4-2-4 高雄地區病例分佈 33
4-2-5 境外移入病例分析 33
4-2-6 高雄地區病例數的區域變化 35
4-3 空間自我相關分析 35
4-3-1 高雄地區2014年的空間自我相關分析 35
4-3-2 高雄地區2015年的空間自我相關分析 37
4-4 空間群集分析 39
4-4-1 空間群集變數的選擇 39
4-4-2 群集結果空間自我相關指標分析 40
4-4-3 基於空間群集分析的防疫策略 41
4-5 空間時序擴散分析 42
4-5-1 空間時序擴散分析的參數設定 42
4-5-2 空間時序擴散分析的結果分析 42
4-5-3 基於空間時序擴散分析的防疫策略 43
4-6 防疫策略比較 43
4-7 成本估算 44
五、結論 46
5-1 理論意涵 46
5-2 實務意涵 47
5-3 研究貢獻 47
5-4 研究限制 48
5-5 研究發展 48
參考文獻 50

參考文獻

[1] J. M. de Araujo Lobo, “Koutango: under reported arboviral disease in West Africa,” 2012.
[2] WHO, Dengue and severe dengue, 2017.
[3] J. D. Stanaway, D. S. Shepard, E. A. Undurraga, Y. A. Halasa, L. E. Coffeng, O. J. Brady, S. I. Hay, N. Bedi, I. M. Bensenor, and C. A. Castañeda-Orjuela, “The global burden of dengue: an analysis from the Global Burden of Disease Study 2013,” The Lancet infectious diseases, vol. 16, no. 6, pp. 712-723, 2016.
[4] O. Dyer, “Dengue: Philippines declares national epidemic as cases surge across South East Asia,” BMJ: British Medical Journal (Online), vol. 366, 2019.
[5] S. Bhatt, P. W. Gething, O. J. Brady, J. P. Messina, A. W. Farlow, C. L. Moyes, J. M. Drake, J. S. Brownstein, A. G. Hoen, and O. Sankoh, “The global distribution and burden of dengue,” Nature, vol. 496, no. 7446, pp. 504-507, 2013.
[6] G. C. Perng, T.-C. Ho, H.-I. Shih, C.-H. Lee, P.-W. Huang, C.-H. Chung, N.-Y. Ko, W.-C. Ko, and Y.-W. Chien, “Seroprevalence of Zika and dengue virus antibodies among migrant workers, Taiwan, 2017,” Emerging infectious diseases, vol. 25, no. 4, pp. 814, 2019.
[7] C.-Y. Sher, H. T. Wong, and Y.-C. Lin, “The Impact of Dengue on Economic Growth: The Case of Southern Taiwan,” International Journal of Environmental Research and Public Health, vol. 17, no. 3, pp. 750, 2020.
[8] D.-L. Luh, C.-C. Liu, Y.-R. Luo, and S.-C. Chen, “Economic cost and burden of dengue during epidemics and non-epidemic years in Taiwan,” Journal of infection and public health, vol. 11, no. 2, pp. 215-223, 2018.
[9] J. Hassard, K. R. Teoh, and T. Cox, “Estimating the economic burden posed by work-related violence to society: a systematic review of cost-of-illness studies,” Safety science, vol. 116, pp. 208-221, 2019.
[10] T. Shiri, K. Khan, K. Keaney, G. Mukherjee, N. D. McCarthy, and S. Petrou, “Pneumococcal disease: a systematic review of health utilities, resource use, costs, and economic evaluations of interventions,” Value in Health, 2019.
[11] S. Tricarico, H. C. McNeil, D. W. Cleary, M. G. Head, V. Lim, I. K. S. Yap, C. C. Wie, C. S. Tan, M. N. Norazmi, and I. Aziah, “Pneumococcal conjugate vaccine implementation in middle-income countries,” Pneumonia, vol. 9, no. 1, pp. 6, 2017.
[12] A. M. Ayob, "Dengue Spread Model using Climate Variables."
[13] R. Rappuoli, S. Black, and D. E. Bloom, “Vaccines and global health: In search of a sustainable model for vaccine development and delivery,” Science Translational Medicine, vol. 11, no. 497, pp. eaaw2888, 2019.
[14] A. Honda, N. Krucien, M. Ryan, I. S. N. Diouf, M. Salla, M. Nagai, and N. Fujita, “For more than money: willingness of health professionals to stay in remote Senegal,” Human resources for health, vol. 17, no. 1, pp. 28, 2019.
[15] U. L. Lestari, C. Anwar, and R. Ristiawati, "Spatial Analysis Case DHF (Dengue Hemorrhagic Fever) in The District Pekalongan Year 2015-2017." pp. 140-148.
[16] E. P. Astuti, P. W. Dhewantara, H. Prasetyowati, M. Ipa, C. Herawati, and K. Hendrayana, “Paediatric dengue infection in Cirebon, Indonesia: a temporal and spatial analysis of notified dengue incidence to inform surveillance,” Parasites & vectors, vol. 12, no. 1, pp. 186, 2019.
[17] M. Pujianto, M. Raharjo, and N. Nurjazuli, “Spatial Pattern Analysis on Dengue Hemorrhagic Fever (DHF) Disease in Tanjung Emas Port Area using Moran Index,” International Journal of English Literature and Social Sciences (IJELS), vol. 5, no. 2, 2020.
[18] S. Mahmood, A. Irshad, J. M. Nasir, F. Sharif, and S. H. Farooqi, “Spatiotemporal analysis of dengue outbreaks in Samanabad town, Lahore metropolitan area, using geospatial techniques,” Environmental monitoring and assessment, vol. 191, no. 2, pp. 55, 2019.
[19] L. Tanner, M. Schreiber, J. G. Low, A. Ong, T. Tolfvenstam, Y. L. Lai, L. C. Ng, Y. S. Leo, L. T. Puong, and S. G. Vasudevan, “Decision tree algorithms predict the diagnosis and outcome of dengue fever in the early phase of illness,” PLoS neglected tropical diseases, vol. 2, no. 3, 2008.
[20] V. J. Lee, D. Lye, Y. Sun, and Y. Leo, “Decision tree algorithm in deciding hospitalization for adult patients with dengue haemorrhagic fever in Singapore,” Tropical Medicine & International Health, vol. 14, no. 9, pp. 1154-1159, 2009.
[21] Y. L. Hii, H. Zhu, N. Ng, L. C. Ng, and J. Rocklöv, “Forecast of dengue incidence using temperature and rainfall,” PLoS neglected tropical diseases, vol. 6, no. 11, 2012.
[22] S. Wongkoon, M. Jaroensutasinee, and K. Jaroensutasinee, “Weather factors influencing the occurrence of dengue fever in Nakhon Si Thammarat, Thailand,” Trop Biomed, vol. 30, no. 4, pp. 631-41, 2013.
[23] T.-C. Chan, T.-H. Hu, and J.-S. Hwang, “Daily forecast of dengue fever incidents for urban villages in a city,” International journal of health geographics, vol. 14, no. 1, pp. 9, 2015.
[24] L. E. Hugo, J. A. Jeffery, B. J. Trewin, L. F. Wockner, N. T. Yen, N. H. Le, L. T. Nghia, E. Hine, P. A. Ryan, and B. H. Kay, “Adult survivorship of the dengue mosquito Aedes aegypti varies seasonally in central Vietnam,” PLoS neglected tropical diseases, vol. 8, no. 2, 2014.
[25] P. Siriyasatien, A. Phumee, P. Ongruk, K. Jampachaisri, and K. Kesorn, “Analysis of significant factors for dengue fever incidence prediction,” BMC bioinformatics, vol. 17, no. 1, pp. 166, 2016.
[26] S. Sang, W. Yin, P. Bi, H. Zhang, C. Wang, X. Liu, B. Chen, W. Yang, and Q. Liu, “Predicting local dengue transmission in Guangzhou, China, through the influence of imported cases, mosquito density and climate variability,” PloS one, vol. 9, no. 7, 2014.
[27] F. Ibrahim, T. Faisal, M. M. Salim, and M. N. Taib, “Non-invasive diagnosis of risk in dengue patients using bioelectrical impedance analysis and artificial neural network,” Medical & biological engineering & computing, vol. 48, no. 11, pp. 1141-1148, 2010.
[28] F. Ibrahim, M. N. Taib, W. A. B. W. Abas, C. C. Guan, and S. Sulaiman, “A novel dengue fever (DF) and dengue haemorrhagic fever (DHF) analysis using artificial neural network (ANN),” Computer methods and programs in biomedicine, vol. 79, no. 3, pp. 273-281, 2005.
[29] A. Ghaderi, B. M. Sanandaji, and F. Ghaderi, “Deep forecast: deep learning-based spatio-temporal forecasting,” arXiv preprint arXiv:1707.08110, 2017.
[30] M. Hossain, B. Rekabdar, S. J. Louis, and S. Dascalu, "Forecasting the weather of Nevada: A deep learning approach." pp. 1-6.
[31] P. Siriyasatien, S. Chadsuthi, K. Jampachaisri, and K. Kesorn, “Dengue epidemics prediction: A survey of the state-of-the-art based on data science processes,” IEEE Access, vol. 6, pp. 53757-53795, 2018.
[32] A. Ashiquzzaman, A. K. Tushar, M. R. Islam, D. Shon, K. Im, J.-H. Park, D.-S. Lim, and J. Kim, "Reduction of overfitting in diabetes prediction using deep learning neural network," IT Convergence and Security 2017, pp. 35-43: Springer, 2018.
[33] A. Fuentes, S. Yoon, S. C. Kim, and D. S. Park, “A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition,” Sensors, vol. 17, no. 9, pp. 2022, 2017.
[34] K. Kesorn, P. Ongruk, J. Chompoosri, A. Phumee, U. Thavara, A. Tawatsin, and P. Siriyasatien, “Morbidity rate prediction of dengue hemorrhagic fever (DHF) using the support vector machine and the Aedes aegypti infection rate in similar climates and geographical areas,” PloS one, vol. 10, no. 5, 2015.
[35] K. D. Sharma, R. S. Mahabir, K. M. Curtin, J. M. Sutherland, J. B. Agard, and D. D. Chadee, “Exploratory space-time analysis of dengue incidence in Trinidad: a retrospective study using travel hubs as dispersal points, 1998–2004,” Parasites & vectors, vol. 7, no. 1, pp. 341, 2014.
[36] C. Chauhan, S. K. Behura, B. Debruyn, D. D. Lovin, B. W. Harker, C. Gomez-Machorro, A. Mori, J. Romero-Severson, and D. W. Severson, “Comparative expression profiles of midgut genes in dengue virus refractory and susceptible Aedes aegypti across critical period for virus infection,” PLoS One, vol. 7, no. 10, 2012.
[37] H. L. Nguyen, T. H. Duong, C. P. Nguyen, D. C. Nguyen, T. P. Chiem, M. H. Nguyen, T. N. M. Nguyen, and H. V. Nguyen, “Specific K-mean clustering-based perceptron for dengue prediction,” International Journal of Intelligent Information and Database Systems, vol. 10, no. 3-4, pp. 269-288, 2017.
[38] N. Mathur, V. S. Asirvadam, S. C. Dass, and B. S. Gill, "Visualization of dengue incidences for vulnerability using K-means." pp. 569-573.
[39] P. Manivannan, and P. I. Devi, "Dengue fever prediction using K-means clustering algorithm." pp. 1-5.
[40] M. A. Johansson, N. G. Reich, A. Hota, J. S. Brownstein, and M. Santillana, “Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico,” Scientific reports, vol. 6, pp. 33707, 2016.
[41] M. Gharbi, P. Quenel, J. Gustave, S. Cassadou, G. La Ruche, L. Girdary, and L. Marrama, “Time series analysis of dengue incidence in Guadeloupe, French West Indies: forecasting models using climate variables as predictors,” BMC infectious diseases, vol. 11, no. 1, pp. 166, 2011.
[42] S. Bhatnagar, V. Lal, S. D. Gupta, and O. P. Gupta, “Forecasting incidence of dengue in Rajasthan, using time series analyses,” Indian journal of public health, vol. 56, no. 4, pp. 281, 2012.
[43] C. C. Ho, and C.-Y. Ting, "Time series analysis and forecasting of dengue using open data." pp. 51-63.
[44] A. Lal, T. Ikeda, N. French, M. G. Baker, and S. Hales, “Climate variability, weather and enteric disease incidence in New Zealand: time series analysis,” PLoS One, vol. 8, no. 12, 2013.
[45] H. Lin, L. Yang, Q. Liu, T. Wang, S. R. Hossain, S. C. Ho, and L. Tian, “Time series analysis of Japanese encephalitis and weather in Linyi City, China,” International journal of public health, vol. 57, no. 2, pp. 289-296, 2012.
[46] F. A. Siregar, T. Makmur, and S. Saprin, "Forecasting dengue hemorrhagic fever cases using ARIMA model: a case study in Asahan district." p. 012032.
[47] A. L. Buczak, B. Baugher, S. M. Babin, L. C. Ramac-Thomas, E. Guven, Y. Elbert, P. T. Koshute, J. M. S. Velasco, V. G. Roque Jr, and E. A. Tayag, “Prediction of high incidence of dengue in the Philippines,” PLoS neglected tropical diseases, vol. 8, no. 4, 2014.
[48] D. H. Barmak, C. O. Dorso, M. Otero, and H. G. Solari, “Dengue epidemics and human mobility,” Physical Review E, vol. 84, no. 1, pp. 011901, 2011.
[49] D. H. Barmak, C. O. Dorso, and M. Otero, “Modelling dengue epidemic spreading with human mobility,” Physica A: Statistical Mechanics and its Applications, vol. 447, pp. 129-140, 2016.
[50] L. C. de Castro Medeiros, C. A. R. Castilho, C. Braga, W. V. de Souza, L. Regis, and A. M. V. Monteiro, “Modeling the dynamic transmission of dengue fever: investigating disease persistence,” PLOS neglected tropical diseases, vol. 5, no. 1, 2011.
[51] P. Bajardi, C. Poletto, J. J. Ramasco, M. Tizzoni, V. Colizza, and A. Vespignani, “Human mobility networks, travel restrictions, and the global spread of 2009 H1N1 pandemic,” PloS one, vol. 6, no. 1, 2011.
[52] L. A. Rvachev, and I. M. Longini Jr, “A mathematical model for the global spread of influenza,” Mathematical biosciences, vol. 75, no. 1, pp. 3-22, 1985.
[53] M. C. Gonzalez, C. A. Hidalgo, and A.-L. Barabasi, “Understanding individual human mobility patterns,” nature, vol. 453, no. 7196, pp. 779-782, 2008.
[54] A. D. Cliff, J. Ord, P. Haggett, and G. Versey, Spatial diffusion: an historical geography of epidemics in an island community: CUP Archive, 1981.
[55] L. Anselin, “Local indicators of spatial association—LISA,” Geographical analysis, vol. 27, no. 2, pp. 93-115, 1995.
[56] J. MacQueen, "Some methods for classification and analysis of multivariate observations." pp. 281-297.
[57] M. J. Zaki, “SPADE: An efficient algorithm for mining frequent sequences,” Machine learning, vol. 42, no. 1-2, pp. 31-60, 2001.

指導教授

許秉瑜(Ping-Yu Hsu)

審核日期

2020-7-29

推文