Identifying and visualizing spatial patterns and hot spots of clinically-confirmed dengue fever cases and female 'Aedes aegypti' mosquitoes in Jeddah, Saudi Arabia

Abstract

Understanding the distribution of dengue fever in time and space is the foundation for its control and management programmes. Different technologies, especially the Geographic Information System (GIS) and its tools and methods, have been used to illustrate and visualize the prevalence of some mosquito-borne diseases and abundance of their vectors. The aim of this study was to illustrate the spatial distribution and spatial pattern of this disease and female 'Aedes aegypti' mosquitoes in the epidemic-prone area of Jeddah, and also to show the hot spot districts with the highest risk levels. The study was conducted in Jeddah county, Saudi Arabia. The clinically-confirmed cases registries of dengue fever have been continuously and systematically collected since 2006 by the Dengue Fever Operation Room of Jeddah Health Affairs. The computerized databases of these two government departments have recorded weekly notifications of dengue fever cases and its vector (female 'Aedes' mosquito). The female 'Aedes' mosquito counts and identification were provided by the laboratory of mosquito, which belongs to the Jeddah Municipality. Two GIS techniques were used to achieve the aims of this study. The multi-distance spatial cluster (Ripley's K-function) was used to estimate the spatial pattern and distribution while the Getis Ord Gi* statistic was used to model and visualize the hot spots and the risk models. The results showed that the spatial patterns and distribution of dengue fever cases from 2006 to 2009 were clustered at multiple distances with statistically significant clustering. They also showed that most 'Aedes' mosquitoes were clustered while some of them were dispersed at larger distances, especially in 2007, 2008, 2009 and 2010. Also, areas with various risk levels of dengue fever and its vector were identified in different geographical locations (districts) for different epidemic years using the Getis-Ord Gi*. Identifying dengue fever and its vector cluster and hot spots can be greatly enhanced through the use of a variety of analytical techniques that are available in the Geographic Information System. Getis-Ord Gi* and multi-distance spatial cluster (Ripley's K-function) can be implemented as routine procedures along with dengue fever control and prevention programmes.