title: Mosquito habitat and dengue risk potential in Kenya: alternative methods to traditional risk mapping techniques.
abstract: 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.
authors.affiliation: Department of Geography and GeoInformation Science, George Mason University, Fairfax, USA.
authors.forename: David F
authors.initials: DF
authors.lastname: Attaway
authors.affiliation: Department of Global and Community Health, George Mason University, Fairfax, USA.
authors.forename: Kathryn H
authors.initials: KH
authors.lastname: Jacobsen
authors.affiliation: Department of Geography and GeoInformation Science, George Mason University, Fairfax, USA.
authors.forename: Allan
authors.initials: A
authors.lastname: Falconer
authors.affiliation: Department of Geography and GeoInformation Science, George Mason University, Fairfax, USA.
authors.forename: Germana
authors.initials: G
authors.lastname: Manca
authors.affiliation: ESRI, Redlands, USA.
authors.forename: Lauren
authors.initials: L
authors.lastname: Rosenshein Bennett
authors.affiliation: Department of Geography and GeoInformation Science, George Mason University, Fairfax, USA.
authors.forename: Nigel M
authors.initials: NM
authors.lastname: Waters
mesh_terms: D000330:Aedes / Q000502:physiology / Q000821:virology; D000818:Animals; D002980:Climate; D003715:Dengue / Q000453:epidemiology / Q000209:etiology*; D004196:Disease Outbreaks / Q000706:statistics & numerical data; D017753:Ecosystem; D040362:Geographic Information Systems; D062305:Geographic Mapping; D006801:Humans; D007303:Insect Vectors / Q000502:physiology / Q000821:virology; D007630:Kenya / Q000453:epidemiology; D008962:Models, Theoretical; D011156:Population Density; D012307:Risk Factors; D016036:Seroepidemiologic Studies