Parasitology



Bayesian geostatistical prediction of the intensity of infection with Schistosoma mansoni in East Africa


A. C. A. CLEMENTS a1a2, R. MOYEED a3 and S. BROOKER a1c1
a1 Department of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK
a2 Schistosomiasis Control Initiative, Department of Infectious Disease Epidemiology, Imperial College London, London, UK
a3 School of Mathematics and Statistics, University of Plymouth, Plymouth, UK

Article author query
clements ac   [PubMed][Google Scholar] 
moyeed r   [PubMed][Google Scholar] 
brooker s   [PubMed][Google Scholar] 

Abstract

A Bayesian geostatistical model was developed to predict the intensity of infection with Schistosoma mansoni in East Africa. Epidemiological data from purpose-designed and standardized surveys were available for 31458 schoolchildren (90% aged between 6 and 16 years) from 459 locations across the region and used in combination with remote sensing environmental data to identify factors associated with spatial variation in infection patterns. The geostatistical model explicitly takes into account the highly aggregated distribution of parasite distributions by fitting a negative binomial distribution to the data and accounts for spatial correlation. Results identify the role of environmental risk factors in explaining geographical heterogeneity in infection intensity and show how these factors can be used to develop a predictive map. Such a map has important implications for schisosomiasis control programmes in the region.

(Received April 7 2006)
(Revised June 14 2006)
(Accepted June 16 2006)
(Published Online September 6 2006)


Key Words: Bayesian models; geostatistical prediction; negative binomial distribution; Schistosoma mansoni; schistosomiasis; East Africa.

Correspondence:
c1 Department of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK. Tel: +44 (0)20 7927 2614. Fax: +44 (0)20 7927 2918. E-mail: simon.brooker@lshtm.ac.uk


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