A Spatial, Temporal, and Geographic Analysis of the Increasing Incidence of Dengue Fever in Kenya and Africa

While many studies have focused on the causes of dengue fever and the occurrence of specific outbreaks, little research has focused on the increasing spatial range, increasing incidence of infected individuals, and the use of algorithms/models to increase risk mapping in Kenya and Africa. GIS incorporates methods to evaluate results, identify geographic locations, and incorporate risk when dengue occurrence data are limited. This dissertation first outlines the limitations to dengue mapping then examines the strengths, weaknesses, opportunities, and threats (SWOT) to determine the limitations for mapping. Next, a similarity search approach was used to map dengue risk in Kenya. This method estimated the high and low risk areas for dengue by evaluating the similarity between environmental layers, population density, and elevation with respect to dengue and mosquito occurrence points. This analysis identified the most cost-effective locations to target dengue prevention activities such as vector control and public awareness campaigns. Finally, a PAT approach was used to map dengue susceptibility across the African continent using the Query Expression Editor and QFit tools. High susceptible risk areas were estimated by comparing peer reviewed literature, determining susceptibility for risk, and then improving the visualization through mapping. The Query Expression Editor evaluated dengue susceptibility risk based on environmental high risk parameters. The QFit tool provided a methodology to estimate high susceptibility risk between points of occurrence and a set of raster datasets. This dissertation contributes to improving risk analysis and susceptibility risk for disease through the localized visualization of risk and situational awareness between health officials, geographers, epidemiologists, and entomologists.