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Spatial prediction of Plasmodium falciparum prevalence in Somalia

Abdisalan M Noor12*, Archie CA Clements3, Peter W Gething4, Grainne Moloney5, Mohammed Borle5, Tanya Shewchuk6, Simon I Hay17 and Robert W Snow12

Author Affiliations

1 Malaria Public Health & Epidemiology Group, Centre for Geographic Medicine Research-Coast, Kenya Medical Research Institute/Wellcome Trust Research Programme, P.O. Box 43640, 00100 GPO, Nairobi, Kenya

2 Centre for Tropical Medicine, University of Oxford, John Radcliffe Hospital, Headington, Oxford, OX3 9DU, UK

3 School of Population Health, University of Queensland, Brisbane, Queensland, 4006, Australia

4 Centre for Geographic Health Research, School of Geography, University of Southampton, Southampton, SO17 1BJ, UK

5 United Nations Food and Agricultural Organization, Food Security Analysis Unit-Somalia, 3rd Floor, Kalson Towers, Parklands, P.O. Box 1230, Village Market, Nairobi, Kenya

6 United Nations Children's Fund, Somalia Support Centre, P.O. Box 44145, 00100, Nairobi, Kenya

7 Spatial Ecology and Epidemiology Group, Tinbergen building, Department of Zoology, University of Oxford, South Parks Road, Oxford, UK

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Malaria Journal 2008, 7:159  doi:10.1186/1475-2875-7-159

Published: 21 August 2008



Maps of malaria distribution are vital for optimal allocation of resources for anti-malarial activities. There is a lack of reliable contemporary malaria maps in endemic countries in sub-Saharan Africa. This problem is particularly acute in low malaria transmission countries such as those located in the horn of Africa.


Data from a national malaria cluster sample survey in 2005 and routine cluster surveys in 2007 were assembled for Somalia. Rapid diagnostic tests were used to examine the presence of Plasmodium falciparum parasites in finger-prick blood samples obtained from individuals across all age-groups. Bayesian geostatistical models, with environmental and survey covariates, were used to predict continuous maps of malaria prevalence across Somalia and to define the uncertainty associated with the predictions.


For analyses the country was divided into north and south. In the north, the month of survey, distance to water, precipitation and temperature had no significant association with P. falciparum prevalence when spatial correlation was taken into account. In contrast, all the covariates, except distance to water, were significantly associated with parasite prevalence in the south. The inclusion of covariates improved model fit for the south but not for the north. Model precision was highest in the south. The majority of the country had a predicted prevalence of < 5%; areas with ≥ 5% prevalence were predominantly in the south.


The maps showed that malaria transmission in Somalia varied from hypo- to meso-endemic. However, even after including the selected covariates in the model, there still remained a considerable amount of unexplained spatial variation in parasite prevalence, indicating effects of other factors not captured in the study. Nonetheless the maps presented here provide the best contemporary information on malaria prevalence in Somalia.