Statistical methodological issues in mapping historical schistosomiasis survey data
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- 作者：Fr¨¦d¨¦rique Chammartin ; Eveline H¨¹rlimann ; Giovanna Raso ; Eli¨¦zer K. N¡¯Goran ; J¨¹rg Utzinger ; Penelope Vounatsou
- 关键词：Schistosomiasis ; Mapping ; Bayesian geostatistics ; Geostatistical variable selection ; Block of covariates ; Cô ; te d&rsquo ; Ivoire
- 刊名：Acta Tropica
- 出版年：2013
- 出版时间：November, 2013
- 年：2013
- 卷：128
- 期：2
- 页码：345-352
- 全文大小：1836 K
文摘
For schistosomiasis and other neglected tropical diseases for which resources for control are still limited, model-based maps are needed for prioritising spatial targeting of control interventions and surveillance of control programmes. Bayesian geostatistical modelling has been widely and effectively used to generate smooth empirical risk maps. In this paper, we review important issues related to the modelling of schistosomiasis risk, including Bayesian computation of large datasets, heterogeneity of historical survey data, stationary and isotropy assumptions and novel approaches for Bayesian geostatistical variable selection. We provide an example of advanced Bayesian geostatistical variable selection based on historical prevalence data of Schistosoma mansoni in C?te d¡¯Ivoire. We include a ¡°parameter expanded normal mixture of inverse-gamma¡± prior for the regression coefficients, which in turn allows selection of blocks of covariates, particularly categorical variables. The implemented Bayesian geostatistical variable selection provided a rigorous approach for the selection of predictors within a Bayesian geostatistical framework, identified the most important predictors of S. mansoni infection risk and led to a more parsimonious model compared to traditional selection approaches that ignore the spatial structure in the data. In conclusion, statistical advances in Bayesian geostatistical modelling offer unique opportunities to account for important inherent characteristics of the Schistosoma infection, and hence Bayesian geostatistical models can guide the spatial targeting of control interventions.