IntroductionDengue fever, a mosquito-borne viral infection, is a growing threat to human health in tropical and subtropical areas worldwide. There is a demand from public officials for maps that capture the current distribution of dengue and maps that analyze risk factors to predict the future burden of disease.MethodsTo identify relevant articles, we searched Google Scholar, PubMed, BioMed Central, and WHOLIS (World Health Organization Library Database) for published articles with a specific set of dengue criteria between January 2002 and July 2013.ResultsAfter evaluating the currently available dengue models, we identified four key barriers to the creation of high-quality dengue maps: (1) data limitations related to the expense of diagnosing and reporting dengue cases in places where health information systems are underdeveloped; (2) issues related to the use of socioeconomic proxies in places with limited dengue incidence data; (3) mosquito ranges which may be changing as a result of climate changes; and (4) the challenges of mapping dengue events at a variety of scales.ConclusionAn ideal dengue map will present endemic and epidemic dengue information from both rural and urban areas. Overcoming the current barriers requires expanded collaboration and data sharing by geographers, epidemiologists, and entomologists. Enhanced mapping techniques would allow for improved visualizations of dengue rates and risks.
Outbreaks, epidemics and endemic conditions make dengue a disease that has emerged as a major threat in tropical and sub-tropical countries over the past 30 years. Dengue fever creates a growing burden for public health systems and has the potential to affect over 40% of the world population. The problem being investigated is to identify the highest and lowest areas of dengue risk. This paper presents "Similarity Search", a geospatial analysis aimed at identifying these locations within Kenya. Similarity Search develops a risk map by combining environmental susceptibility analysis and geographical information systems, and then compares areas with dengue prevalence to all other locations. Kenya has had outbreaks of dengue during the past 3 years, and we identified areas with the highest susceptibility to dengue infection using bioclimatic variables, elevation and mosquito habitat as input to the model. Comparison of the modelled risk map with the reported dengue epidemic cases obtained from the open source reporting ProMED and Government news reports from 1982-2013 confirmed the high-risk locations that were used as the Similarity Search presence cells. Developing the risk model based upon the bioclimatic variables, elevation and mosquito habitat increased the efficiency and effectiveness of the dengue fever risk mapping process.
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