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01.12.2012 | Research | Ausgabe 1/2012 Open Access

Malaria Journal 1/2012

Mapping malaria risk among children in Côte d’Ivoire using Bayesian geo-statistical models

Malaria Journal > Ausgabe 1/2012
Giovanna Raso, Nadine Schur, Jürg Utzinger, Benjamin G Koudou, Emile S Tchicaya, Fabian Rohner, Eliézer K N’Goran, Kigbafori D Silué, Barbara Matthys, Serge Assi, Marcel Tanner, Penelope Vounatsou
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​1475-2875-11-160) contains supplementary material, which is available to authorized users.
Giovanna Raso, Nadine Schur contributed equally to this work.

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

GR, NS, BGK and EST performed the systematic searches and geo-referenced malaria survey data. GR, JU, BGK, FR, EKN, KDS, SA and BM contributed malaria survey data. GR and NS carried out the spatial analyses and interpretation of the data and drafted the manuscript. JU, BGK, EST, KDS, FR, EKN, SA, BM, MT and PV assisted with the interpretation of the data and the revision of the manuscript. All authors read and approved the final manuscript.



In Côte d’Ivoire, an estimated 767,000 disability-adjusted life years are due to malaria, placing the country at position number 14 with regard to the global burden of malaria. Risk maps are important to guide control interventions, and hence, the aim of this study was to predict the geographical distribution of malaria infection risk in children aged <16 years in Côte d’Ivoire at high spatial resolution.


Using different data sources, a systematic review was carried out to compile and geo-reference survey data on Plasmodium spp. infection prevalence in Côte d’Ivoire, focusing on children aged <16 years. The period from 1988 to 2007 was covered. A suite of Bayesian geo-statistical logistic regression models was fitted to analyse malaria risk. Non-spatial models with and without exchangeable random effect parameters were compared to stationary and non-stationary spatial models. Non-stationarity was modelled assuming that the underlying spatial process is a mixture of separate stationary processes in each ecological zone. The best fitting model based on the deviance information criterion was used to predict Plasmodium spp. infection risk for entire Côte d’Ivoire, including uncertainty.


Overall, 235 data points at 170 unique survey locations with malaria prevalence data for individuals aged <16 years were extracted. Most data points (n = 182, 77.4%) were collected between 2000 and 2007. A Bayesian non-stationary regression model showed the best fit with annualized rainfall and maximum land surface temperature identified as significant environmental covariates. This model was used to predict malaria infection risk at non-sampled locations. High-risk areas were mainly found in the north-central and western area, while relatively low-risk areas were located in the north at the country border, in the north-east, in the south-east around Abidjan, and in the central-west between two high prevalence areas.


The malaria risk map at high spatial resolution gives an important overview of the geographical distribution of the disease in Côte d’Ivoire. It is a useful tool for the national malaria control programme and can be utilized for spatial targeting of control interventions and rational resource allocation.
Authors’ original file for figure 1
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