Ecosystem Management, School of Environmental and Rural Sciences, Faculty of Arts and Sciences, University of New England, Armidale, NSW 2351, Australia.
Sci Total Environ. 2011 Oct 15;409(22):4713-9. doi: 10.1016/j.scitotenv.2011.08.028. Epub 2011 Sep 8.
Dengue fever (DF) and its impacts are growing environmental, economic, and health concerns in Saudi Arabia. In this study, we have attempted to model areas with humans at risk of dengue fever prevalence, depending on the spatial relationship between dengue fever cases and different socioeconomic parameters. We have developed new methods to verify the quality of neighborhoods from high resolution satellite images based on several factors such as density of houses in each neighborhood in each district, width of streets, and roof area of houses. In the absence of detailed neighborhood quality information being available for each district, we felt this factor would best approximate the reality on the ground at local scales. Socioeconomic parameters, such as population numbers, population density, and neighborhood quality were analyzed using Geographically Weighted Regression (GWR) to create a prediction model identifying levels of risk of dengue and to describe the association between DF cases and the related socio-economic factors. Descriptive analysis was used to characterize dengue fever victims among Saudis and non-Saudis in various age groups. The results show that there was a strong positive association between dengue fever cases and socioeconomic factors (R²=0.80). The prevalence among Saudis was higher compared to non-Saudis in 2006 and 2007, while the prevalence among non-Saudis was higher in 2008, 2009 and 2010. For age groups, DF was more prevalent in adults between the ages of 16 and 60, accounting for approximately 74% of all reported cases in 2006, 67% in 2007, 81% in 2008, 87% in 2009, and 81% in 2010.
登革热（DF）及其影响在沙特阿拉伯日益成为环境、经济和健康方面令人关注的问题。在这项研究中，我们试图根据登革热病例与不同社会经济参数之间的空间关系，建立模型来预测存在登革热风险的人类活动区域。我们开发了新的方法，基于房屋密度、街道宽度和房屋屋顶面积等因素，从高分辨率卫星图像中验证社区的质量。由于每个区都没有详细的社区质量信息，我们认为这个因素最能反映当地社区的实际情况。人口数量、人口密度和社区质量等社会经济参数，使用地理加权回归（GWR）进行分析，以创建一个预测模型，识别登革热风险水平，并描述登革热病例与相关社会经济因素之间的关联。描述性分析用于描述不同年龄组的沙特人和非沙特登革热患者的特征。结果表明，登革热病例与社会经济因素之间存在很强的正相关关系（R²=0.80）。2006 年和 2007 年，沙特人的登革热患病率高于非沙特人，而 2008 年、2009 年和 2010 年，非沙特人的登革热患病率则更高。按年龄组划分，2006 年、2007 年、2008 年、2009 年和 2010 年，16 至 60 岁成年人中的登革热发病率分别约占所有报告病例的 74%、67%、81%、87%和 81%。
