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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 方啟泰(Chi-Tai Fang) | |
dc.contributor.author | Mathuros Tipayamongkholgul | en |
dc.contributor.author | 田蜜 | zh_TW |
dc.date.accessioned | 2021-06-15T04:17:30Z | - |
dc.date.available | 2010-12-16 | |
dc.date.copyright | 2010-03-12 | |
dc.date.issued | 2009 | |
dc.date.submitted | 2009-12-21 | |
dc.identifier.citation | 1. Gubler DJ (2006) Dengue/dengue haemorrhagic fever: history and current status. Novartis Found Symp 277:3-16: discussion -22, 71-3, 251-3
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/45388 | - |
dc.description.abstract | Dengue fever (DF) and Dengue hemorrhagic fever (DHF) have become a major concern in Thailand since 1950s. Despite intensive efforts in vector control, dengue epidemics continued to occur throughout this country in multi-annual cycles. Weather is considered to be the important factor in these cycles, but the extent to which the El Niño-Southern Oscillation (ENSO) is a driving force of dengue epidemics remains unclear. As well as, an increasing trend of incidence of dengue cases in Thailand have conjectured the effect of urbanization. The impacts of urban contexts on local dengue vulnerability have not been well investigated.
This dissertation examined the temporality between El Niño and the occurrence of dengue epidemics, constructed Poisson autoregressive models for incidences of dengue cases, and applied geospatial model to estimate the effect of socio-geographic factors and transmission intensity of dengue on the incidence of all reported dengue cases. Dengue surveillance data, climate data (global and local climate), and socio-geographic factors were analyzed. The strength of El Niño consistently positively correlated with the occurrence of dengue epidemics throughout time lag from 1 to 11 months in two selected regions of Thailand. Up to 22% (northern inland mountainous region) and 15% (southern tropical coastal region) of variations in the monthly incidence of dengue cases were attributable to global ENSO cycles. Province-level predictive models were fitted using 1996–2004 data and validated with out-of-fit data from 2005. Multivariate ENSO index remained an independent predictor in 10 of the 13 studied provinces. Analysis showed a significant neighborhood effect (ρ= 0.405, P<0.001), which implies that villages with geographical proximity shared a similar level of vulnerability to dengue. The two independent social factors associated with a higher incidence of dengue were a shorter distance to the nearest urban area (β = –0.133, P<0.05) and a smaller average family size (β = –0.102, P<0.05). Spatial error regression and Geographically Weight Regression (GWR) consistently presented positive effect of transmission intensity and spatial dependence upon the proximity villages. Spatial heterogeneity was clearly presented by GWR; the distinct patterns of transmission intensity across regions were relocated yearly. It also clearly expressed the higher intense transmission in north region than south region. In conclusion, El Niño was one of the important driving forces of dengue epidemics across geographically diverse regions in Thailand and indicated that the trend of increased dengue occurrence in rural Thailand arose in areas under stronger urban influence rather than in remote rural areas. Population mobility may increase transmission intensity urban area and neighboring area. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T04:17:30Z (GMT). No. of bitstreams: 1 ntu-98-D94842009-1.pdf: 1401518 bytes, checksum: 3dbc6b45c75a7712ecfdfb6489bb5876 (MD5) Previous issue date: 2009 | en |
dc.description.tableofcontents | Acknowledgements iii
Abstracts viii List of Tables x List of Figures xii Chapter 1: Background and Literature Review 1.1 Transmission model of dengue 1 1.2 Surveillance system of dengue and dengue situation in Thailand 1.2.1 Geography of Thailand 2 1.2.2 Surveillance system for dengue fever and 3 dengue hemorrhagic fever in Thailand 1.2.3 Case definition of dengue for surveillance system 3 1.2.4 Dengue situation in Thailand 4 1.3 Factors influencing dengue epidemic 1.3.1 Climate factors 5 1.3.2 Socio-geographical factors 7 1.3.3 Human behaviors 8 1.4. Rationale of the study and specific aims 9 Chapter 2: Effects of the El Niño-Southern Oscillation on Dengue Epidemics in Thailand, 1996–2005 2.1 Introduction 12 2.2 Materials and Methods 2.2.1 Study areas 14 2.2.2 Surveillance for dengue cases 15 2.2.3 Definition of dengue epidemic 15 2.2.4 El Nino and meteorological data 16 2.2.5 Statistical methods 17 2.2.6 Regression models 18 2.3 Results 2.3.1 Descriptive epidemiology 19 2.3.2 Influences of ENSO on local climate 19 2.3.3 Temporal correlation between ENSO indicators and 20 the occurrence of dengue epidemics 2.3.4 Temporal correlation between ENSO indicators and 20 the incidence of dengue cases 2.3.5 Effects of ENSO cycle on monthly incidence of dengue cases 21 2.3.6 Validation of models using out-of-fit data in each province 22 2.4 Discussion 22 Chapter 3: Socio-Geographic Factors in Vulnerability to Dengue in Thai Villages: A Spatial Regression Analysis 3.1 Introduction 28 3.2 Materials and Methods 3.2.1 Study area 30 3.2.2 Surveillance for dengue cases 31 3.2.3 Spatial data 32 3.2.4 Ordinary linear model 32 3.2.5 Spatial neighbors 33 3.2.6 Spatial autocorrelation 34 3.2.7 Spatially lagged model 34 3.3 Results 3.3.1 Descriptive epidemiology of dengue in village level 35 3.3.2 The association between socio-geographical factors 36 and dengue occurrences 3.3.3 The association between socio-geographical factors and 36 dengue occurrences and neighborhood effect 3.4 Discussion 37 Chapter 4: Spatio-temporal transition of dengue transmission in Thai villages 4.1 Introduction 41 4.2 Materials and Methods 4.2.1 Study area 42 4.2.2 Data and statistical analyses 43 4.2.3 Village specific incidence rate of DF and DHF 44 4.2.4 Determination of high incidence of DF/DHF 45 4.2.5 Ordinary least square regression 45 4.2.6 Spatial autocorrelation of residual of fitted model 46 4.2.7 Spatial regression models 47 4.2.8 Geographically weighted regression (GWR) 48 4.4 Results 4.3.1 General epidemiology of dengue in villages of 49 Prachuap Khiri-Khan 4.3.2 Spatial distribution of DF and DHF epidemics 49 4.3.3 Association between transmission intensity and incidence 50 of DF and DHF cases 4.5 Discussion 51 Chapter 5: Conclusion and Future Perspectives 56 References 57 | |
dc.language.iso | en | |
dc.title | 泰國選定地區1996-2008登革疫情的數學模式分析 | zh_TW |
dc.title | Modeling Dengue Epidemics in Selected Areas of Thailand, 1996–2008 | en |
dc.type | Thesis | |
dc.date.schoolyear | 98-1 | |
dc.description.degree | 博士 | |
dc.contributor.coadvisor | 金傳春(Chwan-Chuen King) | |
dc.contributor.oralexamcommittee | 蕭朱杏(Chuh-sing Kate Hsiao),黃景祥(Jing-Shian Hwang),柳中明(Chung-Ming Liu),溫在弘(Zhai-Hung Wen) | |
dc.subject.keyword | 泰國,登革疫情,模式分析, | zh_TW |
dc.subject.keyword | Thailand,Dengue,Epidemics,Modeling, | en |
dc.relation.page | 98 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2009-12-21 | |
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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