Collins Bekoe1, Tatdow Pansombut2, Pakwan Riyapan2, Sampurna Kakchapati2, Aniruth Phon-On2. 1. Department of Mathematics and Computer Science, Faculty of Science and Technology, Prince of Songkla University, Pattani campus, Pattani, Thailand. 5720320706@email.psu.ac.th. 2. Department of Mathematics and Computer Science, Faculty of Science and Technology, Prince of Songkla University, Pattani campus, Pattani, Thailand.
Abstract
BACKGROUND: Dengue fever is one of the infectious diseases that is still a public health problem in Thailand. This study considers in detail, the geographic consequence, seasonal and pattern of dengue fever transmission among the 76 provinces of Thailand from 2003 to 2015. STUDY DESIGN: A cross-sectional study. METHODS: The data for the study was from the Department of Disease Control under the Bureau of Epidemiology, Thailand. The quarterly effects and location on the transmission of dengue was modeled using an alternative additive log-linear model. RESULTS: The model fitted well as illustrated by the residual plots and the Again, the model showed that dengue fever is high in the second quarter of every year from May to August. There was an evidence of an increase in the trend of dengue annually from 2003 to 2015. CONCLUSIONS: There was a difference in the distribution of dengue fever within and between provinces. The areas of high risks were the central and southern regions of Thailand. The log-linear model provided a simple medium of modeling dengue fever transmission. The results are very important in the geographic distribution of dengue fever patterns.
BACKGROUND:Dengue fever is one of the infectious diseases that is still a public health problem in Thailand. This study considers in detail, the geographic consequence, seasonal and pattern of dengue fever transmission among the 76 provinces of Thailand from 2003 to 2015. STUDY DESIGN: A cross-sectional study. METHODS: The data for the study was from the Department of Disease Control under the Bureau of Epidemiology, Thailand. The quarterly effects and location on the transmission of dengue was modeled using an alternative additive log-linear model. RESULTS: The model fitted well as illustrated by the residual plots and the Again, the model showed that dengue fever is high in the second quarter of every year from May to August. There was an evidence of an increase in the trend of dengue annually from 2003 to 2015. CONCLUSIONS: There was a difference in the distribution of dengue fever within and between provinces. The areas of high risks were the central and southern regions of Thailand. The log-linear model provided a simple medium of modeling dengue fever transmission. The results are very important in the geographic distribution of dengue fever patterns.
Entities:
Keywords:
Dengue; Infectious Disease; Linear Models; Thailand