title: Estimating malaria burden in Nigeria: a geostatistical modelling approach.
abstract: This study has produced a map of malaria prevalence in Nigeria based on available data from the Mapping Malaria Risk in Africa (MARA) database, including all malaria prevalence surveys in Nigeria that could be geolocated, as well as data collected during fieldwork in Nigeria between March and June 2007. Logistic regression was fitted to malaria prevalence to identify significant demographic (age) and environmental covariates in STATA. The following environmental covariates were included in the spatial model: the normalized difference vegetation index, the enhanced vegetation index, the leaf area index, the land surface temperature for day and night, land use/landcover (LULC), distance to water bodies, and rainfall. The spatial model created suggests that the two main environmental covariates correlating with malaria presence were land surface temperature for day and rainfall. It was also found that malaria prevalence increased with distance to water bodies up to 4 km. The malaria risk map estimated from the spatial model shows that malaria prevalence in Nigeria varies from 20% in certain areas to 70% in others. The highest prevalence rates were found in the Niger Delta states of Rivers and Bayelsa, the areas surrounding the confluence of the rivers Niger and Benue, and also isolated parts of the north-eastern and north-western parts of the country. Isolated patches of low malaria prevalence were found to be scattered around the country with northern Nigeria having more such areas than the rest of the country. Nigeria's belt of middle regions generally has malaria prevalence of 40% and above.
authors.affiliation: Swiss Tropical and Public Health Institute, Basel. dozieonyiri@yahoo.co.uk.
authors.forename: Nnadozie
authors.initials: N
authors.lastname: Onyiri
mesh_terms: D003710:Demography; D062305:Geographic Mapping; D006801:Humans; D008288:Malaria / Q000453:epidemiology*; D015233:Models, Statistical; D009549:Nigeria / Q000453:epidemiology; D015995:Prevalence; D018570:Risk Assessment; D013696:Temperature; D019371:Topography, Medical; D014887:Weather