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系統識別號 U0026-1908202012351100
論文名稱(中文) 航空運輸與跨國傳染病抵達時間-以新冠肺炎為例
論文名稱(英文) Airline Transportation and Arrival Time of International Disease Spread – A Case Study of Covid-19
校院名稱 成功大學
系所名稱(中) 測量及空間資訊學系
系所名稱(英) Department of Geomatics
學年度 108
學期 2
出版年 109
研究生(中文) 邱垂盛
研究生(英文) Chui-Sheng Chiu
學號 P66074117
學位類別 碩士
語文別 英文
論文頁數 72頁
口試委員 口試委員-溫在宏
口試委員-許聿廷
指導教授-郭佩棻
中文關鍵字 傳染病傳播  全球機場網路  生存模型  新冠肺炎  有效距離 
英文關鍵字 Disease spreading  World airport network  Covid-19  effective distance  survival model 
學科別分類
中文摘要 在全球化發展的趨勢下,人們在國際間移動變得更加快速便利。但這也連帶加速傳染病擴散並使傳播範圍從區域上升至全球性的危害。在不同交通運具中,跨國空運是最快速的交通方式,全球機場路網也超越地理距離限制將各國連為一體,同時加快疾病傳播速度。航空運輸無疑是現有傳染病全球傳播中一重要的影響因子。本研究主要目的在於結合效現有之有效距離(effective distance)與傳染路徑選擇方式後,預測新冠肺炎(Covid-19)各國首例病例抵達時間。
本研究使用2017年IATA(International Air Transport Association)航班資料庫,以各機場間的航班比估算各機場間的有效距離。在疾病傳達路徑選擇方式中,過去文獻多以最短路徑(即有效距離加總最小的路徑)假設疾病於機場網路間傳播,而忽略了其他傳播路徑的可能性。僅少數研究提出隨機行走的概念,納入所有疾病可能的路徑。
其次,上述文獻多以數值模擬結果(如GLEAMviz)來佐證其提出方法之預測能力。雖然數值模擬模型可以建立動態的疾病傳播狀態,但仍與現實情況有落差。綜上,本研究選用網路新聞、WHO和ProMED-mail等線上資源,蒐集真實疾病抵達資料,並比較最短路徑有效距離與隨機行走有效距離模型預測新冠肺炎抵達時間之準確性。最後,代入有效距離於傳統存活模型與空間存活模型以有效距離估算疾病抵達各國的機率函數。
研究結果顯示:(1)從中國武漢機場到各國機場的有效距離與新冠肺炎各國的抵達時間有顯著正相關;(2)考慮兩種疾病傳遞路徑之有效距離與抵達時間的相關性後,發現隨機行走(random walk)有效距離並沒有優於最短路徑有效距離,與現有文獻結果不同;(3)空間存活模型結合有效距離可有效估算各國疾病抵達的風險。本研究貢獻為未來各國可依實際航空運量,估算有效距離與在不同時間點下,疾病首例抵達的時間與風險值,協助相關單位制定更精確的應對措施與公衛方案。
英文摘要 In the era of globalization, convenient transportation increases international trade and travel. Among all travel modes, the air transport network plays an important role in worldwide transportation. However, the level of disease transmission has also changed from regional to global. By using the 2017 IATA (International Air Transport Association) flight database, this study aims to define the relationship between the effective distance of world airport network (WAN) and Covid-19 arrival time in different countries.
Although numerous studies tried to define the relationship between air traffic and disease spreading, their model still have several limitations. First, most of them chose the shortest path (the path with the smallest total of effective distance) as the propagation path of diseases, and ignored the possibility of other transmission paths, such as random walk. Second, most of them used numerical simulation results (such as GLEAMviz) to support the predictive power of their proposed methods instead of using real disease arrival time. The simulation result can’t reflect the real world condition. In order to solve the above problems, this study collected real disease arrival data by using WHO, ProMED, and online news. We also built the Covid-19 arrival time prediction model by using shortest path effective distance and the random walk effective distance respectively, and then compared their performance. Last, we applied effective distance into the traditional survival model and spatial survival model to estimate the Covid-19 importation probability function of different countries.
The results show that the effective distance has higher correlation with arrival date of Covid-19 than the geographical distance. The R-square of OLS model of effective distance is 0.37, which is higher than geographic distance model (0.15). Second, the correlation between the random walk effective distance of historical flight data and the arrival time is not higher than the shortest path effective distance, which is inconsistent with the previous studies. Third, the spatial survival model can better estimate the risk of disease importation in various countries by using the effective distance than other models. The results can help the authority to design international epidemic prevention and control strategies.
論文目次 ABSTRACT i
ACKNOWLEDGEMENT iv
CONTENTS vi
LIST OF TABLES viii
LIST OF FIGURES ix
CHAPTER 1 1
INTRODUCTION 1
1.1 Background 1
1.2 Study Goals 3
CHAPTER 2 5
LITERATURE REVIEW 5
2.1 Literature of Covid-19 5
2.2 Spread of epidemics by different travel modes 6
2.3 Epidemiological geographic prediction system and platform 11
2.4 Summary 13
CHAPTER 3 15
METHODOLOGY and STUDY DATA 15
3.1 Study Data 15
3.1.1 Disease data 15
3.1.2 Airline data 19
3.2 Methodology 21
3.2.1 Disease spreading simulation 22
3.2.2 Effective distance 26
3.2.3 Survival model 31
3.3 Workflow 35
CHAPTER 4 37
RESULTS 37
4.1 Correlation of Effective distance and disease arrival time 37
4.1.1 the effective distance through random walk or shortest path (all data) 39
4.1.2 Order estimation with effective distance 46
4.1.3 Summary of relationship of effective distance and arrival time 47
4.2 Disease Spreading Hazard Estimation 47
4.2.1 Traditional Cox model result and effective distance model 49
4.2.2 Spatial survival model result 54
CHAPTER 5 58
DISCUSSION and CONCLUSIONS 58
5.1 Discussion and conclusions 58
5.2 Study limitations and Future works 60
REFERENCES 62
Appendix 65
Comprehensive effective distance 65
IATA data format 65

參考文獻 Adiga, A., Venkatramanan, S., Schlitt, J., Peddireddy, A., Dickerman, A., Bura, A., ... & Machi, D. (2020). Evaluating the impact of international airline suspensions on the early global spread of COVID-19. medRxiv.
Baker, M. G., Thornley, C. N., Mills, C., Roberts, S., Perera, S., Peters, J., & Wilson, N. (2010). Transmission of pandemic A/H1N1 2009 influenza on passenger aircraft: retrospective cohort study. Bmj, 340, c2424.
Balcan, D., Gonçalves, B., Hu, H., Ramasco, J. J., Colizza, V., & Vespignani, A. (2010). Modeling the spatial spread of infectious diseases: The GLobal Epidemic and Mobility computational model. Journal of computational science, 1(3), 132-145.
Browne, A., St-Onge Ahmad, S., Beck, C. R., & Nguyen-Van-Tam, J. S. (2016). The roles of transportation and transportation hubs in the propagation of influenza and coronaviruses: a systematic review. Journal of travel medicine, 23(1), tav002.
Cai, J., Xu, B., Chan, K. K. Y., Zhang, X., Zhang, B., Chen, Z., & Xu, B. (2019). Roles of different transport modes in the spatial spread of the 2009 influenza A (H1N1) pandemic in mainland China. International Journal of Environmental Research and Public Health, 16(2), 222.
Crepey, P., Alvarez, F. P., & Barthélemy, M. (2006). Epidemic variability in complex networks. Physical Review E, 73(4), 046131.
Foxwell, A. R., Roberts, L., Lokuge, K., & Kelly, P. M. (2011). Transmission of influenza on international flights, May 2009. Emerging infectious diseases, 17(7), 1188.
Furuya H.(2007) Risk of transmission of airborne infection during train commute based on mathematical model. Environ Health Prev Med, 12:78–83.
Gautreau, A., Barrat, A., & Barthelemy, M. (2008). Global disease spread: statistics and estimation of arrival times. Journal of theoretical biology, 251(3), 509-522.
Gupta, J. K., Lin, C. H., & Chen, Q. (2012). Risk assessment of airborne infectious diseases in aircraft cabins. Indoor air, 22(5), 388-395.
Hertzberg, V. S., & Weiss, H. (2016). On the 2-row rule for infectious disease transmission on aircraft. Annals of global health, 82(5), 819-823.
Iannelli, F., Koher, A., Brockmann, D., Hövel, P., & Sokolov, I. M. (2017). Effective distances for epidemics spreading on complex networks. Physical Review E, 95(1), 012313.
Li, Q., Guan, X., Wu, P., Wang, X., Zhou, L., Tong, Y., ... & Xing, X. (2020). Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia. New England Journal of Medicine.
Longini Jr, I. M., Fine, P. E., & Thacker, S. B. (1986). Predicting the global spread of new infectious agents. American journal of epidemiology, 123(3), 383-391.
Marsden A. (2003) Influenza outbreak related to air travel. Med J; 179:172–73.
Moser MR, Bender TR, Margolis HS et al.(1979) . An outbreak of influenza aboard a comercial airliner. Am J Epidemiol; 10:1–6.
Nah, K., Mizumoto, K., Miyamatsu, Y., Yasuda, Y., Kinoshita, R., & Nishiura, H. (2016). Estimating risks of importation and local transmission of Zika virus infection. PeerJ, 4, e1904.
Neatherlin J, Cramer EH, Dubray C et al.(2013) Influenza A(H1N1)pdm09 during air travel. Travel Med Infect Dis; 11:110–18.
Pan, R. K., & Saramäki, J. (2011). Path lengths, correlations, and centrality in temporal networks. Physical Review E, 84(1), 016105.
Peng, X., Xu, X., Li, Y., Cheng, L., Zhou, X., & Ren, B. (2020). Transmission routes of 2019-nCoV and controls in dental practice. International Journal of Oral Science, 12(1), 1-6.
Quan S, Zhixing T, Meng J., (2013) Age structure and group behavior: a cluster of influences on influenza diffusion within public transport stations. J Pure Appl Microbiol; 7:695–701.
Tatem, A. J., Hay, S. I., & Rogers, D. J. (2006). Global traffic and disease vector dispersal. Proceedings of the National Academy of Sciences, 103(16), 6242-6247.
Troko J, Myles P, Gibson J et al. (2011). Is public transport a risk factor for acute respiratory infection? BMC Infect Dis; 1:16.
Tsai, Y. S., Huang, C. Y., Wen, T. H., Sun, C. T., & Yen, M. Y. (2011). Integrating epidemic dynamics with daily commuting networks: building a multilayer framework to assess influenza A (H1N1) intervention policies. Simulation, 87(5), 385-405.
Walters, J. A., Tan, D. J., White, C. J., & Wood‐Baker, R. (2018). Different durations of corticosteroid therapy for exacerbations of chronic obstructive pulmonary disease. Cochrane Database of Systematic Reviews, (3).
Wagner, B. G., Coburn, B. J., & Blower, S. (2009). Calculating the potential for within-flight transmission of influenza A (H1N1). BMC medicine, 7(1), 81.
Zhang L, Peng Z, Ou J et al.(2009) . Protection by face masks against influenza A(H1N1)pdm09 virus on trans-Pacific passenger aircraft, 2009. Emerg Infect Dis 2009; 19:1403–10.
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