Spatio-temporal diffusion pattern and hotspot detection of dengue in Chachoengsao province, Thailand

• المؤلفون الرئيسيون: Phaisarn Jeefoo, Nitin Kumar Tripathi, Marc Souris
• مؤلفون آخرون: Asian Institute of Technology Thailand
• التنسيق: مقال
• منشور في: 2018
• الموضوعات: Environmental Science; Medicine
• الوصول للمادة أونلاين: https://repository.li.mahidol.ac.th/handle/123456789/11934
• المؤسسة: Mahidol University

In recent years, dengue has become a major international public health concern. In Thailand it is also an important concern as several dengue outbreaks were reported in last decade. This paper presents a GIS approach to analyze the spatial and temporal dynamics of dengue epidemics. The major objective of this study was to examine spatial diffusion patterns and hotspot identification for reported dengue cases. Geospatial diffusion pattern of the 2007 dengue outbreak was investigated. Map of daily cases was generated for the 153 days of the outbreak. Epidemiological data from Chachoengsao province, Thailand (reported dengue cases for the years 1999-2007) was used for this study. To analyze the dynamic space-time pattern of dengue outbreaks, all cases were positioned in space at a village level. After a general statistical analysis (by gender and age group), data was subsequently analyzed for temporal patterns and correlation with climatic data (especially rainfall), spatial patterns and cluster analysis, and spatio-temporal patterns of hotspots during epidemics. The results revealed spatial diffusion patterns during the years 1999-2007 representing spatially clustered patterns with significant differences by village. Villages on the urban fringe reported higher incidences. The space and time of the cases showed outbreak movement and spread patterns that could be related to entomologicand epidemiologic factors. The hotspots showed the spatial trend of dengue diffusion. This study presents useful information related to the dengue outbreak patterns in space and time and may help public health departments to plan strategies to control the spread of disease. The methodology is general for space-time analysis and can be applied for other infectious diseases as well. © 2010 by the authors; licensee MDPI, Basel, Switzerland.