Bayesian spatial risk prediction of Schistosoma mansoni infection in Western Cote d'Ivoire using a remotely-sensed digital elevation model

Beck-Wörner, Christian, Raso, Giovanna, Vounatsou, Penelope, N'Goran, Eliézer K., Rigo, Gergely, Parlow, Eberhard and Utzinger, Jürg (2007) Bayesian spatial risk prediction of Schistosoma mansoni infection in Western Cote d'Ivoire using a remotely-sensed digital elevation model. The American Journal of Tropical Medicine and Hygiene, 76 5: 956-963.


Author Beck-Wörner, Christian
Raso, Giovanna
Vounatsou, Penelope
N'Goran, Eliézer K.
Rigo, Gergely
Parlow, Eberhard
Utzinger, Jürg
Title Bayesian spatial risk prediction of Schistosoma mansoni infection in Western Cote d'Ivoire using a remotely-sensed digital elevation model
Formatted title
Bayesian spatial risk prediction of Schistosoma mansoni infection in Western Côte d'Ivoire using a remotely-sensed digital elevation model
Journal name The American Journal of Tropical Medicine and Hygiene   Check publisher's open access policy
ISSN 0002-9637
Publication date 2007-05-01
Year available 2007
Sub-type Article (original research)
Volume 76
Issue 5
Start page 956
End page 963
Total pages 8
Editor James W. Kazura
Place of publication Northbrook, IL, U.S.A.
Publisher American Society of Tropical Medicine and Hygiene
Language eng
Subject 1117 Public Health and Health Services
Formatted abstract
An important epidemiologic feature of schistosomiasis is the focal distribution of the disease. Thus, the identification of high-risk communities is an essential first step for targeting interventions in an efficient and cost-effective manner. We used a remotely-sensed digital elevation model (DEM), derived hydrologic features (i.e., stream order, and catchment area), and fitted Bayesian geostatistical models to assess associations between environmental factors and infection with Schistosoma mansoni among more than 4,000 school children from the region of Man in western Côte d’Ivoire. At the unit of the school, we found significant correlations between the infection prevalence of S. mansoni and stream order of the nearest river, water catchment area, and altitude. In conclusion, the use of a freely available 90 m high-resolution DEM, geographic information system applications, and Bayesian spatial modeling facilitates risk prediction for S. mansoni, and is a powerful approach for risk profiling of other neglected tropical diseases that are pervasive in the developing world.
Copyright © 2007 by The American Society of Tropical Medicine and Hygiene
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Unknown

Document type: Journal Article
Sub-type: Article (original research)
Collections: Excellence in Research Australia (ERA) - Collection
School of Public Health Publications
 
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Created: Fri, 20 Mar 2009, 22:52:03 EST by Mary-Anne Marrington on behalf of School of Public Health