Assessing the risk for dengue fever based on socioeconomic and environmental variables in a geographical information system environment.
An important option in preventing the spread of dengue fever (DF) is to control and monitor its vector (Aedes aegypti) as well as to locate and destroy suitable mosquito breeding environments. The aim of the present study was to use a combination of environmental and socioeconomic variables to model areas at risk of DF. These variables include clinically confirmed DF cases, mosquito counts, population density in inhabited areas, total populations per district, water access, neighbourhood quality and the spatio-temporal risk of DF based on the average, weekly frequency of DF incidence. Out of 111 districts investigated, 17 (15%), covering a total area of 121 km2, were identified as of high risk, 25 (22%), covering 133 km2, were identified as of medium risk, 18 (16%), covering 180 km2, were identified as of low risk and 51 (46%), covering 726 km2, were identified as of very low risk. The resultant model shows that most areas at risk of DF were concentrated in the central part of Jeddah county, Saudi Arabia. The methods used can be implemented as routine procedures for control and prevention. A concerted intervention in the medium- and high-risk level districts identified in this study could be highly effective in reducing transmission of DF in the area as a whole.
Ecosystem Management, School of Environmental and Rural Sciences, Faculty of Arts and Sciences, University of New England, Armidale, NSW 2351, Australia. hkhormi@une.edu.au
Hassan M
HM
Khormi
Lalit
L
Kumar
D000330:Aedes / Q000821:virology*; D000818:Animals; D003715:Dengue / Q000453:epidemiology* / Q000517:prevention & control; D004777:Environment; D040362:Geographic Information Systems; D014943:Global Health; D006801:Humans; D015994:Incidence; D007300:Insect Control / Q000379:methods; D008962:Models, Theoretical; D018570:Risk Assessment / Q000379:methods*; D012529:Saudi Arabia / Q000453:epidemiology; D012959:Socioeconomic Factors