Bayesian geostatistical modelling for mapping schistosomiasis transmission

Vounatsou, P., Raso, G., Tanner, M., N'Goran, E. K. and Utzinger, J. (2009) Bayesian geostatistical modelling for mapping schistosomiasis transmission. Parasitology, 136 13: 1695-1705. doi:10.1017/S003118200900599X


Author Vounatsou, P.
Raso, G.
Tanner, M.
N'Goran, E. K.
Utzinger, J.
Title Bayesian geostatistical modelling for mapping schistosomiasis transmission
Journal name Parasitology   Check publisher's open access policy
ISSN 0031-1820
1469-8161
Publication date 2009-11-01
Sub-type Article (original research)
DOI 10.1017/S003118200900599X
Open Access Status Not Open Access
Volume 136
Issue 13
Start page 1695
End page 1705
Total pages 11
Editor R. S. Phillips
R. B. Gasser
L. H. Chappell
Place of publication Cambridge, United Kingdom
Publisher Cambridge University Press
Language eng
Abstract Schistosomiasis in China has been substantially reduced due to an effective control programme employing various measures including bovine and human chemotherapy, and the removal of bovines from endemic areas. To fulfil elimination targets, it will be necessary to identify other possible reservoir hosts for Schistosoma japonicum and include them in future control efforts. This study determined the infection prevalence of S. japonicum in rodents (0-9·21%), dogs (0-18·37%) and goats (6·9-46·4%) from the Dongting Lake area of Hunan province, using a combination of traditional coproparasitological techniques (miracidial hatching technique and Kato-Katz thick smear technique) and molecular methods [quantitative real-time PCR (qPCR) and droplet digital PCR (ddPCR)]. We found a much higher prevalence in goats than previously recorded in this setting. Cattle and water buffalo were also examined using the same procedures and all were found to be infected, emphasising the occurrence of active transmission. qPCR and ddPCR were much more sensitive than the coproparasitological procedures with both KK and MHT considerably underestimating the true prevalence in all animals surveyed. The high level of S. japonicum prevalence in goats indicates that they are likely important reservoirs in schistosomiasis transmission, necessitating their inclusion as targets of control, if the goal of elimination is to be achieved in China.
Formatted abstract
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.
Keyword Schistosomiasis
Schistosoma mansoni
Bayesian geostatistics
Non-stationarity
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ
Additional Notes Special Issue 13 (Control of schistosomiasis in sub-Saharan Africa)

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Public Health Publications
 
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Created: Sun, 20 Dec 2009, 10:00:54 EST