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Predicting Local Dengue Transmission in Guangzhou, China, through the Influence of Imported Cases, Mosquito Density and Climate Variability

机译:通过输入病例,蚊子密度和气候变化的影响,预测中国广州的局部登革热传播

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摘要

Introduction: Each year there are approximately 390 million dengue infections worldwide. Weather variables have a significant impact on the transmission of Dengue Fever (DF), a mosquito borne viral disease. DF in mainland China is characterized as an imported disease. Hence it is necessary to explore the roles of imported cases, mosquito density and climate variability in dengue transmission in China. The study was to identify the relationship between dengue occurrence and possible risk factors and to develop a predicting model for dengue’s control and prevention purpose.Methodology and Principal Findings: Three traditional suburbs and one district with an international airport in Guangzhou city were selected as the study areas. Autocorrelation and cross-correlation analysis were used to perform univariate analysis to identify possible risk factors, with relevant lagged effects, associated with local dengue cases. Principal component analysis (PCA) was applied to extract principal components and PCA score was used to represent the original variables to reduce multi-collinearity. Combining the univariate analysis and prior knowledge, time-series Poisson regression analysis was conducted to quantify the relationship between weather variables, Breteau Index, imported DF cases and the local dengue transmission in Guangzhou, China. The goodness-of-fit of the constructed model was determined by pseudo-R2, Akaike information criterion (AIC) and residual test. There were a total of 707 notified local DF cases from March 2006 to December 2012, with a seasonal distribution from August to November. There were a total of 65 notified imported DF cases from 20 countries, with forty-six cases (70.8%) imported from Southeast Asia. The model showed that local DF cases were positively associated with mosquito density, imported cases, temperature, precipitation, vapour pressure and minimum relative humidity, whilst being negatively associated with air pressure, with different time lags.Conclusions: Imported DF cases and mosquito density play a critical role in local DF transmission, together with weather variables. The establishment of an early warning system, using existing surveillance datasets will help to control and prevent dengue in Guangzhou, China.
机译:简介:每年全世界大约有3.9亿登革热感染。天气因素对蚊子传播的病毒性疾病登革热(DF)的传播具有重大影响。 DF在中国大陆的特征是外来疾病。因此,有必要探讨传入病例,蚊子密度和气候变异性在中国登革热传播中的作用。本研究旨在确定登革热发生与可能的危险因素之间的关系,并为登革热的控制和预防目的建立预测模型。方法和主要发现:选择广州市的三个传统郊区和一个设有国际机场的地区地区。使用自相关和互相关分析来进行单变量分析,以识别与局部登革热病例相关的可能的危险因素,并具有相关的滞后效应。应用主成分分析(PCA)提取主成分,并使用PCA评分代表原始变量以减少多重共线性。结合单变量分析和先验知识,进行了时间序列Poisson回归分析,以量化天气变量,Breteau指数,进口DF病例与中国广州本地登革热传播之间的关系。通过伪R2,Akaike信息准则(AIC)和残差检验确定所构建模型的拟合优度。从2006年3月到2012年12月,总共有707例当地DF通报病例,从8月到11月是季节性分布。共有来自20个国家的65例通报的进口DF病例,其中46例(70.8%)从东南亚进口。该模型显示,当地DF病例与蚊虫密度,进口病例,温度,降水,蒸气压和最低相对湿度呈正相关,而与气压呈负相关,具有不同的时滞。在当地DF传输以及天气变量中起着至关重要的作用。利用现有的监视数据集建立预警系统将有助于控制和预防中国广州的登革热。

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