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Bayesian geostatistical modelling for mapping schistosomiasis transmission

Published online by Cambridge University Press:  02 June 2009

P. VOUNATSOU ,
G. RASO ,
M. TANNER ,
E. K. N'GORAN  and
J. UTZINGER
P. VOUNATSOU*
Affiliation:
Department of Public Health and Epidemiology, Swiss Tropical Institute, P.O. Box, CH-4002 Basel, Switzerland
G. RASO
Affiliation:
Division of Epidemiology and Social Medicine, School of Population Health, The University of Queensland, Public Health Building, Herston Road, Brisbane, Queensland 4006, Australia Molecular Parasitology Laboratory, Queensland Institute of Medical Research, 300 Herston Road, Brisbane, Queensland 4006, Australia
M. TANNER
Affiliation:
Department of Public Health and Epidemiology, Swiss Tropical Institute, P.O. Box, CH-4002 Basel, Switzerland
E. K. N'GORAN
Affiliation:
UFR Biosciences, Université de Cocody-Abidjan, 22 BP 582, Abidjan 22, Côte d'Ivoire Centre Suisse de Recherches Scientifiques, 01 BP 1303, Abidjan 01, Côte d'Ivoire
J. UTZINGER
Affiliation:
Department of Public Health and Epidemiology, Swiss Tropical Institute, P.O. Box, CH-4002 Basel, Switzerland
*
*Corresponding author: Penelope Vounatsou, Department of Public Health and Epidemiology, Swiss Tropical Institute, P.O. Box, CH-4002 Basel, Switzerland. Tel: +41 61 284-8109. Fax: +41 61 284-8105. E-mail: [email protected]
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Summary

Progress has been made in mapping and predicting the risk of schistosomiasis using Bayesian geostatistical inference. Applications primarily focused on risk profiling of prevalence rather than infection intensity, although the latter is particularly important for morbidity control. In this review, the underlying assumptions used in a study mapping Schistosoma mansoni infection intensity in East Africa are examined. We argue that the assumption of stationarity needs to be relaxed, and that the negative binomial assumption might result in misleading inference because of a high number of excess zeros (individuals without an infection). We developed a Bayesian geostatistical zero-inflated (ZI) regression model that assumes a non-stationary spatial process. Our model is validated with a high-quality georeferenced database from western Côte d'Ivoire, consisting of demographic, environmental, parasitological and socio-economic data. Nearly 40% of the 3818 participating schoolchildren were infected with S. mansoni, and the mean egg count among infected children was 162 eggs per gram of stool (EPG), ranging between 24 and 6768 EPG. Compared to a negative binomial and ZI Poisson and negative binomial models, the Bayesian non-stationary ZI negative binomial model showed a better fit to the data. We conclude that geostatistical ZI models produce more accurate maps of helminth infection intensity than the spatial negative binomial ones.

Keywords

SchistosomiasisSchistosoma mansoniBayesian geostatisticsnon-stationarityoverdispersionzero-inflated modelinfection intensityCôte d'Ivoire
Type
SECTION 1 ADVOCACY AND DEFINING AREAS IN NEED OF CONTROL
Information
Parasitology , Volume 136 , Special Issue 13: Control of schistosomiasis in sub-Saharan Africa , November 2009 , pp. 1695 - 1705
Copyright
Copyright © Cambridge University Press 2009

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