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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 陳秀熙 | |
dc.contributor.author | Chen-Yang Hsu | en |
dc.contributor.author | 許辰陽 | zh_TW |
dc.date.accessioned | 2021-06-15T04:27:07Z | - |
dc.date.available | 2009-09-16 | |
dc.date.copyright | 2009-09-16 | |
dc.date.issued | 2009 | |
dc.date.submitted | 2009-08-20 | |
dc.identifier.citation | VI References
1. Becker, N.G., Analysis of Infectious Disease Data. 1989, London, New York: Chapman and Hall. 2. Peter F. Wright, K.M.N., Infectious Disease, J.G.B. Sherwood L. Gorbach, Neil R. Blacklow Editor. 2004, Lippincott Williams & Wilkins: Philadelphia. p. 1987 - 1989. 3. Bridges, C.B., M.J. Kuehnert, and C.B. Hall, Transmission of influenza: implications for control in health care settings. Clinical Infectious Diseases, 2003. 37(8): p. 1094-101. 4. Myers, S.W., Clinical Observations in Recent Epidemic of Influenza. Boston Medical and Surgical Journal, 1919. 180: p. 98-101. 5. Carrat, F., et al., Time lines of infection and disease in human influenza: a review of volunteer challenge studies. American Journal of Epidemiology, 2008. 167(7): p. 775-85. 6. Fiore, A.E., et al., Prevention and control of influenza: recommendations of the Advisory Committee on Immunization Practices (ACIP), 2008. Morbidity & Mortality Weekly Report, 2008. Recommendations & Reports. 57(RR-7): p. 1-60. 7. Stephenson, I. and M. Zambon, The epidemiology of influenza. Occupational Medicine, 2002. 52(5): p. 241-7. 8. Cunha, B.A., Influenza: historical aspects of epidemics and pandemics. Infectious Disease Clinics of North America, 2004. 18(1): p. 141-55. 9. Glezen, W.P., Clinical practice. Prevention and treatment of seasonal influenza. New England Journal of Medicine, 2008. 359(24): p. 2579-85. 10. Call, S.A., et al., Does this patient have influenza?[see comment]. JAMA, 2005. 293(8): p. 987-97. 11. Monto, A.S., et al., Clinical signs and symptoms predicting influenza infection.[see comment]. Archives of Internal Medicine, 2000. 160(21): p. 3243-7. 12. Leekha, S., et al., Duration of influenza A virus shedding in hospitalized patients and implications for infection control. Infection Control & Hospital Epidemiology, 2007. 28(9): p. 1071-6. 13. Jefferson, T., et al., Antivirals for influenza in healthy adults: systematic review.[see comment][erratum appears in Lancet. 2006 Jun 24;367(9528):2060]. Lancet, 2006. 367(9507): p. 303-13. 14. Sato, M., et al., Viral shedding in children with influenza virus infections treated with neuraminidase inhibitors. Pediatric Infectious Disease Journal, 2005. 24(10): p. 931-2. 15. Brankston, G., et al., Transmission of influenza A in human beings.[see comment]. The Lancet Infectious Diseases, 2007. 7(4): p. 257-65. 16. Henle W, H.G., Strokes J JR, Maris E.P., Experimental exposure of human subjects to viruses of influenza. The Journal of Immunology, 1946. 52(2): p. 145-65. 17. Hall, C.B., et al., Viral shedding patterns of children with influenza B infection. Journal of Infectious Diseases, 1979. 140(4): p. 610-3. 18. James C. Thomas, D.J.W., ed. Epidemiologic Methods for the Study of Infectious Disease. 1st ed. 2001, Oxford University Press: United States. 19. Ferguson, N.M., et al., Strategies for mitigating an influenza pandemic. Nature, 2006. 442(7101): p. 448-52. 20. Davey, V.J., et al., Effective, robust design of community mitigation for pandemic influenza: a systematic examination of proposed US guidance. PLoS ONE [Electronic Resource], 2008. 3(7): p. e2606. 21. Milne, G.J., et al., A small community model for the transmission of infectious diseases: comparison of school closure as an intervention in individual-based models of an influenza pandemic. PLoS ONE [Electronic Resource], 2008. 3(12): p. e4005. 22. Monto, A.S., Interrupting the transmission of respiratory tract infections: theory and practice. Clinical Infectious Diseases, 1999. 28(2): p. 200-4. 23. Greenwood, M., On the Statistical Measure of Infectious Disease. Journal of Hygiene, Cambridge, 1931. 31: p. 336-351. 24. Frost, W.H., Some Conceptions of Epidemics in General. American Journal of Epidemiology, 1976. 103: p. 141-151. 25. 行政院主計處, 戶口及住宅普查:戶數及常住人口數 (1956-2000). 中華民國統計年鑑, 民國97年. 中華民國96年(97. 9) p. 25-26 26. 行政院主計處, 戶口及住宅普查:普通住戶有人居住單位、人數 (1966-2000). 中華民國統計年鑑, 民國97年. 中華民國96年(97. 9) p. 27-28 27. Spiegelhalter DJ, T.A., Best NG., Computation on Bayesian graphical models., in Bayesian Statistics, B.J. Bernardo JM, Dawid AP, Smith AFM, Editor. 1996., Oxford University Press. p. 407-25. 28. Centers for Disease, C. and Prevention, Influenza B virus outbreak on a cruise ship--Northern Europe, 2000. MMWR - Morbidity & Mortality Weekly Report, 2001. 50(8): p. 137-40. 29. Lyytikainen, O., et al., Influenza A outbreak among adolescents in a ski hostel. European Journal of Clinical Microbiology & Infectious Diseases, 1998. 17(2): p. 128-30. 30. Wright, P., Influenza in the family.[see comment][comment]. New England Journal of Medicine, 2000. 343(18): p. 1331-2. 31. Welliver, R., et al., Effectiveness of oseltamivir in preventing influenza in household contacts: a randomized controlled trial.[see comment]. JAMA, 2001. 285(6): p. 748-54. 32. Viboud, C., et al., Risk factors of influenza transmission in households.[see comment]. British Journal of General Practice, 2004. 54(506): p. 684-9. 33. Christina E. Mills, J.M.R., Marc Lipsitch, Transmissibility of 1918 pandemic influenza. Nature December, 2004. 432(7019): p. 904-906. 34. Longini, I.M., Jr. and J.S. Koopman, Household and community transmission parameters from final distributions of infections in households. Biometrics, 1982. 38(1): p. 115-26. 35. Grassly, N.C. and C. Fraser, Mathematical models of infectious disease transmission. Nature Reviews, 2008. Microbiology. 6(6): p. 477-87. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/45558 | - |
dc.description.abstract | 摘 要
背景 感染鏈模式在1989年由Becker 提出,主要用於分析家戶內的疾病傳播資料。雖然感染鏈模式在基於條件二項分佈以及感染世代的架構下適合用於分析疾病傳播之演進,但此一模式有其固有之限制。首先,識別度(identifiability)以及過度參數化(overparameterization)使的將無法觀測的潛在的感染鏈模式運用於分析感染症常見的實際觀察資料時有所困難。其次,連續世代之逃脫感染聯合機率乃基於世代間之轉移以及條件獨立假設,此一假設在感染鏈模式中並未詳細闡明。再者,由 Becker 所提出的二項模式無法進一步分析資料中不同階層的變異性對於逃脫機率之影響。 藉著運用感染鏈模式的觀念,本文基於隨機過程建構了聯合機率;並運用貝氏共軛對方法在模式中納入參數的不確定性。貝氏階層模式(Bayesian Hierarchical model) 則運用於分析資料中不同層級的變異性,使得模式運用更符合生物特性,也使不同假說之比較得以進行。 方法與材料 本文首先以隨機過程方法探討感染鏈模式之建構。感染鏈模式可以一階馬可夫鏈的方式建立;基於隨機過程之概念,可以推演出兩個簡化模式:Greenwood 以及 Reed-Frost 模式的轉移機率矩陣。貝氏共軛對可運用於加入對於參數不確定性的描述,並建立包含此一特性的轉移機率矩陣。 貝氏階層模式則運用於分析家戶資料中在世代階層以及家戶階層中的變異,並且加入個體間的變異。 此一家戶資料是收集台北縣以群體為基礎的家戶資料。病例定義乃基於臨床診斷。個體資料收集了性別、年齡、施打流感疫苗情形以及診斷為病例之日期。家戶資料則收集了家戶內成員以及家戶人數。 本文首先運用General Becker’s model 以及 Becker’s GLM model 分析資料。貝氏階層模式則用於進一步分析不同階層間的變異以及衡量個體因素差異對於罹病的影響。 結果 General Becker’s model 分別考慮世代效應與不考慮世代效應之模式下得到的逃脫感染機率估計值約為 0.90-0.98。 過度參數化以及辨識度的問題表現於信賴區間過寬以及估計值超出合理範圍。 Becker 提出的數種模式中,以家戶隨機效應模式採用Greenwood 假說可得到與資料最佳之配適。 如同General Becker’s model, Berker’s GLM model 也有過度參數化以及辨識度的問題。 貝氏階層模式分析顯示模式中必須包含參數在世代階層以及家戶階層的隨機效應。 結論 本文藉著提出貝氏共軛對方法以及貝氏階層模式,使感染鏈模式的應用得以更為廣泛,也使的感染鏈模式中的統計問題得以解決。這些基於隨機過程的新模式,使應用模式分析流行性感冒資料更精確也更符合感染症的傳播特性。 | zh_TW |
dc.description.abstract | Abstract
Introduction Proposed by Becker in 1989, chain model is used to analyze spread of disease within household. Although the chain model is suitable for modeling elaboration of disease in the frame of conditional binomial distribution and the setting of generation, there exist several inherent problems. First, identifiability and overparameterization hampered the application of the unobserved chain model to the observed size data, which is the often encountered type of data in infectious disease. Second, the joint probability in the successive generations of disease spreading is based on the assumption of conditional independence and the transition from generation to generation, which have not been addressed clearly. Third, incorporating the heterogeneity occurred from different levels of data structure is not possible in the standard approach of chain model proposed by Becker. By using the concept of chain model, we delineated the construction of joint probability by stochastic process and using Bayesian conjugated approach to incorporate the uncertainty of parameters. Bayesian Hierarchical model was used to tackle the problem of multilevel heterogeneity in the data to apply the chain model with more biological plausibility and potent for hypothesis testing. Material and Methods Revisiting the chain model in the viewpoint of stochastic process was performed. The chain model was reformed into first-order Markov process and the transition matrix was derived for two simplified models, Greenwood and Reed-Frost methods, based on the concept of stochastic process. Bayesian conjugated approach was applied to incorporate the uncertainty of the parameters. The corresponding transition matrix for two aforementioned models based on stochastic concept and Bayesian conjugated approach was constructed. Bayesian Hierarchical model was then applied to incorporate the heterogeneity of generation level and household level and accommodate individual characters to the chain model using household data. The data is a population-based household data collected in Taipei county using clinical diagnosis of influenza as the definition of case. Information on individual level such as gender, age, status of vaccination and the date of diagnosis was collected. Information on household level such as the specific household one belongs to, number of household members was obtained. General Becker’s model and Becker’s GLM model was first applied. The Bayesian Hierarchical model was then used to model the multilevel heterogeneity and evaluating the effect of individual factors. Results General Becker’s model with and without generation effect was fitted with the estimated escape probability around 0.90 – 0.98. Overparameterization and identifiability was reflected by wide confidence interval and the unreasonable value of estimates. Of models proposed by Becker, random household effect model with Greenwood assumption fits the data best. The effect of individual character was also observed by applying models to data treating those who were vaccinated as immune or still susceptible. As it is in general Becker’s mode, Becker’s GLM model was also subject to the problems of overparameterization and identifiability. Bayesian Hierarchical model was then applied, which revealed the necessity to allow parameters to vary in household and generation levels. Conclusion The current thesis expanded chain binomial model by proposing a novel Bayesian conjugated and hierarchical model under Greenwood assumption to solve several statistical problems that cannot be solved in the previous chain model. These new statistical models under the concept of stochastic process can provide more precise and biological plausibility for studying the outbreak of influenza. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T04:27:07Z (GMT). No. of bitstreams: 1 ntu-98-R96842028-1.pdf: 2886535 bytes, checksum: ce22b8329bc016c4781430ae7f77aae1 (MD5) Previous issue date: 2009 | en |
dc.description.tableofcontents | Contents
摘 要 1 Abstract 4 Introduction 4 Material and Methods 5 Results 6 Conclusion 6 I.0B Introduction 8 II. Literature Review 12 2.1 Pathogenesis of influenza infection 12 2.2 Clinical features and time lines of viral shedding 12 2.3 Transmission of influenza virus 14 2.4 Epidemic chain model 15 2.5 Chain binomial model and general Becker’s model 16 2.5.1 Derivation of complete likelihood in general Becker’s model with generation effect 18 2.5.2 Derivation of complete likelihood in general Becker’s model without generation effect 19 2.6 Two simplified chain models – Greenwood model and Reed-Frost Model 19 2.6.1 Greenwood model 20 2.6.2 Reed-Frost model 20 2.7 Generalized linear model approach 21 Figure 2.2.1 Time Lines of influenza infection. 25 Table 2.5.1 Probability of each generation based on chain binomial model in household size four with one introductory case. 26 Table 2.5.2 Chain probability of different household size and the corresponding outbreak size. 27 Table 2.5.1.1 Complete likelihood of general Becker’s model with generation effect. 28 Table 2.5.2.1 Complete likelihood of general Becker’s model without generation effect. 29 III Material and Methods 30 3.1 Revisiting Greenwood model and Reed-Frost model with Markov chain concept 30 3.1.1 Greenwood model 30 3.1.2 Reed-Frost model 32 3.2 The incorporation of random effect 35 3.2.1 Greenwood model with random household effect 35 Bayesian posterior distribution 37 3.2.2 Random household effect under Reed-Frost assumption 38 Bayesian posterior distribution 39 3.3 Bayesian Hierarchical model 41 Figure 3.3.1 Acyclic graphic model for random intercept of household and generation under Greenwood assumption. 42 Figure 3.3.2 Acyclic graphic model for random intercept of generation under Greenwood assumption. 44 Figure 3.3.3 Acyclic graphic model for random intercept of household under Greenwood assumption. 45 Figure 3.3.4 Acyclic graphic model for random slope of generation under Reed-Frost assumption. 47 Figure 3.3.5 Acyclic graphic model for random slope of household under Reed-Frost assumption 48 Figure 3.3.6 Acyclic graphic model for random slope of 49 household and generation under Reed-Frost assumption. 49 3.3.1 Hypothesis testing for Greenwood and Reed-Frost model. 50 3.4 Data 50 3.5 Likelihood 53 Table3.4.1 Influenza outbreak size within household. 56 Table 3.4.2 Unobserved and observed data structure based on chain binomial model with one introductory case. 57 Table 3.5.1 Complete likelihood of observed data 58 IV Results 59 4.1 General Becker’s model 59 4.2 Becker’s GLM approach 60 4.3 Bayesian Hierarchical approach 62 Table 4.1.1 Estimated results of general Becker’s model with generation effect. 64 Table 4.1.2 Estimated result of general Becker’s model without generation effect. 65 Table 4.1.3 Estimated results of random household effect model applying Greenwood and Reed-Frost assumptions. 66 Table 4.1.4 Fitted frequencies for Becker’s models 67 Table 4.2.1 Becker’s GLM approach: Greenwood model 68 Table 4.2.2 Becker’s GLM approach: general Greenwood model 69 Table 4.2.3 Becker’s GLM approach: Reed-Frost model 70 Table 4.2.4 Becker’s GLM approach: general Reed-Frost model 71 Table 4.2.5 Becker’s GLM approach: univariate analysis of covariate effect based on Greenwood model 72 Table 4.2.6 Becker’s GLM approach: univariate analysis covariate effect based on Reed-Frost model 73 Table 4.2.7 Becker’s GLM approach: multivariate analysis of covariate effect based on Greenwood model 74 Table 4.2.8 Becker’s GLM approach: multivariate analysis of covariate effect based on Reed-Frost model 75 Table 4.2.9 Becker’s GLM approach: AIC values 76 Table 4.3.1 Estimated results of random intercept model with cluster effect on household 77 Table 4.3.2 Estimated results of random intercept model with cluster effect on generation 78 Table 4.3.3 Estimated results of random intercept model with j as covariate and cluster effect on household 79 Table 4.3.4 Estimated results of random intercept model with j as covariate and cluster effect on generation 80 Table 4.3.5 Estimated result of random slope model with cluster effect on household 81 Table 4.3.6 Estimated results of random slope model with cluster effect on generation 82 Table 4.3.7 Estimated results of random intercept and random slope model with cluster effect on generation 83 Table 4.3.8 AIC values of random effect models 84 V Discussion 85 VI References 91 | |
dc.language.iso | en | |
dc.title | 應用感染鍊二項模式及隨機概念分析流行性感冒家戶資料 - 運用貝式統計 | zh_TW |
dc.title | Application of Chain Binomial Model with Stochastic Concept to Influenza Household Data
- A Bayesian Approach | en |
dc.type | Thesis | |
dc.date.schoolyear | 97-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 戴政,張淑惠,黃崑明,嚴明芳 | |
dc.subject.keyword | 家戶資料,流行性感冒,貝氏統計,感染鏈模式, | zh_TW |
dc.subject.keyword | household data,influenza,Bayesian,chain binimoal model, | en |
dc.relation.page | 95 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2009-08-20 | |
dc.contributor.author-college | 公共衛生學院 | zh_TW |
dc.contributor.author-dept | 流行病學研究所 | zh_TW |
顯示於系所單位: | 流行病學與預防醫學研究所 |
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