title: GIS-based prediction of malaria risk in Egypt.
abstract: Environmental variables in a malaria geographic information system (GIS) database were analysed to discriminate between governorates at high and low risk of malaria. Only Fayoum governorate was categorized as a high risk area for malaria during the last 2 decades. Discriminant models correctly classified 96.3% of the risk categories and indicated that the most important predictor of risk is hydrogeology. Further GIS spatial analysis indicated that the high malaria risk in Fayoum is associated with a unique environmental envelope of biotic (presence of both efficient malaria vectors) and abiotic (hydrogeology and soil) variables. Recommendations for surveillance and control are discussed.
authors.affiliation: Institute of Environmental Studies and Research, Ain Shams University, Cairo, Egypt.
authors.forename: A N
authors.initials: AN
authors.lastname: Hassan
authors.forename: M A
authors.initials: MA
authors.lastname: Kenawy
authors.forename: H
authors.initials: H
authors.lastname: Kamal
authors.forename: A A
authors.initials: AA
authors.lastname: Abdel Sattar
authors.forename: M M
authors.initials: MM
authors.lastname: Sowilem
mesh_terms: D000383:Agriculture; D000818:Animals; D000852:Anopheles / Q000145:classification / Q000469:parasitology; D002980:Climate; D003710:Demography; D016002:Discriminant Analysis; D004534:Egypt; D004812:Epidemiologic Methods; D005618:Fresh Water; D040362:Geographic Information Systems / Q000458:organization & administration*; D006801:Humans; D007303:Insect Vectors / Q000145:classification / Q000469:parasitology; D008288:Malaria / Q000453:epidemiology* / Q000209:etiology / Q000469:parasitology / Q000635:transmission; D020380:Needs Assessment; D011159:Population Surveillance; D011237:Predictive Value of Tests; D015995:Prevalence; D018570:Risk Assessment / Q000458:organization & administration*; D012307:Risk Factors; D012959:Socioeconomic Factors; D012987:Soil; D014507:Urbanization
chemical_list: D012987:Soil