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Mapping malaria risk in West Africa using a Bayesian nonparametric non-stationary model

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Title: Mapping malaria risk in West Africa using a Bayesian nonparametric non-stationary model
Authors: Gosoniu, L.1 laura.gosoniu@unibas.ch, Vounatsou, P.1, Sogoba, N.2, Maire, N.1, Smith, T.1
Source: Computational Statistics & Data Analysis. Jul2009, Vol. 53 Issue 9, p3358-3371. 14p.
Subject Terms: Risk of malaria, Disease mapping, Mathematical models in medicine, Bayesian analysis, Nonparametric statistics, Stationary processes
Geographic Terms: West Africa
Abstract: Abstract: Malaria transmission is highly influenced by environmental and climatic conditions but their effects are often not linear. The climate-malaria relation is unlikely to be the same over large areas covered by different agro-ecological zones. Similarly, spatial correlation in malaria transmission arisen mainly due to spatially structured covariates (environmental and human made factors), could vary across the agro-ecological zones, introducing non-stationarity. Malaria prevalence data from West Africa extracted from the “Mapping Malaria Risk in Africa” database were analyzed to produce regional parasitaemia risk maps. A non-stationary geostatistical model was developed assuming that the underlying spatial process is a mixture of separate stationary processes within each zone. Non-linearity in the environmental effects was modeled by separate P-splines in each agro-ecological zone. The model allows smoothing at the borders between the zones. The P-splines approach has better predictive ability than categorizing the covariates as an alternative of modeling non-linearity. Model fit and prediction was handled within a Bayesian framework, using Markov chain Monte Carlo (MCMC) simulations. [Copyright &y& Elsevier]
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Database: GreenFILE
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Abstract:Abstract: Malaria transmission is highly influenced by environmental and climatic conditions but their effects are often not linear. The climate-malaria relation is unlikely to be the same over large areas covered by different agro-ecological zones. Similarly, spatial correlation in malaria transmission arisen mainly due to spatially structured covariates (environmental and human made factors), could vary across the agro-ecological zones, introducing non-stationarity. Malaria prevalence data from West Africa extracted from the “Mapping Malaria Risk in Africa” database were analyzed to produce regional parasitaemia risk maps. A non-stationary geostatistical model was developed assuming that the underlying spatial process is a mixture of separate stationary processes within each zone. Non-linearity in the environmental effects was modeled by separate P-splines in each agro-ecological zone. The model allows smoothing at the borders between the zones. The P-splines approach has better predictive ability than categorizing the covariates as an alternative of modeling non-linearity. Model fit and prediction was handled within a Bayesian framework, using Markov chain Monte Carlo (MCMC) simulations. [Copyright &y& Elsevier]
ISSN:01679473
DOI:10.1016/j.csda.2009.02.022