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- W3209397253 abstract "Dengue fever is a global life-threatening vector-borne disease which is mainly distributed by the vector Aedes Aegypti mosquito. It is known that the development and survivorship of this vector depends on surrounding climate. The dengue outbreak in Kelantan, Malaysia is alarming. The aim of the study was to compare the fixed effects Negative Binomial GLM and Bayesian Poisson GLMM in prediction of dengue incidence in Kelantan. The data involved daily number of reported dengue cases (1st January 2013–31st December 2017) in ten districts of Kelantan which was collected from Ministry of Health Malaysia. The climate variables, average daily temperature, relative humidity and rainfall (climatic data) were obtained from NASA’s Global Climate Change website, while the population data were from Department of Statistics Malaysia. Statistical modeling results revealed that the fixed effects Negative Binomial GLM failed to fit the daily dengue incidence when serious epidemic occurred. The spatio-temporal Bayesian Poisson GLMM model improved the prediction of dengue incidence. Relative humidity at lag 7 days and 21 days and average temperature at lag 21 days were found to be significant contributing factors of dengue incidence in Kelantan. The findings of the study are significant to respective local authorities in providing vital information for early dengue warning systems in a particular area. This is important for authorities to monitor and reduce dengue incidence in endemic areas and to safeguard the community from dengue outbreak." @default.
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- W3209397253 date "2021-01-01" @default.
- W3209397253 modified "2023-09-25" @default.
- W3209397253 title "Quantifying the Impact of Climatic Factors on Dengue Incidence Using Generalized Linear Mixed Model with Spatio-Temporal Bayesian Poisson Random Effects Approach" @default.
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- W3209397253 doi "https://doi.org/10.1007/978-981-16-7334-4_24" @default.
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