登革熱之時間延遲統計模型之應用

淡江大學機構典藏 > 商管學院 > 統計學系暨研究所 > 學位論文 > Item 987654321/122830

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題名:	登革熱之時間延遲統計模型之應用 Prediction of Dengue Fever via Vector Indices and Meteorological Factors: a Time-lag Statistical Model for Mobile Device
作者:	曾耀霆 Tseng, Yao-Ting
關鍵詞:	case-crossover study;conditional logistic regression;propensity score;household density;vector surveillance;meteorology
日期:	2017-01
上傳時間:	2022-10-12 10:13:49 (UTC+8)
摘要:	Background: Dengue fever (DF) is the most rapidly spreading mosquito-borne viral disease in tropical and subtropical countries. In Taiwan, dengue incidence for the past ten years has been clustered in the southern part and is especially prevalent in Kaohsiung. Using the spatial and temporal patterns of dengue transmission in Kaohsiung from 2005 to 2012, this study investigates association and interaction of immature/adult mosquito indices, household density, and meteorological factors with DF. Method: We used the databases obtained from the Department of Health, Kaohsiung City Government and Environmental Protection Administration, and then conducted (1) a case-crossover study design and conditional logistic regression to explores the main/interaction effects of mosquito indices and weather on DF risk, and (2) logistic regression to construct prediction model for DF using the variables suggested by (1). We compared the odds ratios (OR) obtained from the above model with that from a propensity-adjusted approach. Result: Immature mosquito indices had strong interaction with DF in medium and high household density areas (e.g., adjusted ORs of Breteau Index were 1.04, 95% CI = [1.02, 1.06] and 1.06, 95% CI = [1.04, 1.08], respectively), and no association with DF in low household density area. Aedes aegypti index (AI) was significant to all low/medium/high household densities (adjusted ORs of AI were 1.29, 95% CI=[1.23,1.6], 1.49, 95% CI=[1.37,1.61], and 1.3, 95% CI=[1.21,1.39], respectively). Meanwhile, combination with 2-week lag rainfall, 2-month lag rainfall, 2-week lag temperature and relative humidity, resulted better prediction of DF incidence. In the propensity approach, AI, BI, CI, and HI positively influenced DF, with ORs of 1.03, 1.31, 1.04, and 1.03 respectively (all p-value < 0.0001). Conclusion: Meteorological conditions affect DF occurrence in a nonlinear way, and a single time-point rainfall variable is insufficient to fit it. Our study suggested that 2-week lag rainfall, temperature, humidity, and 2-month lag rainfall were related to higher probability of DF incidence. Immature mosquito indices are useful predictors for DF occurrence in medium and high household density areas, but not in the low household density area. Finally, we create Dengue fever risk alerting APP to prediction the incidence of DF next two week.
顯示於類別:	[統計學系暨研究所] 學位論文

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