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• W2145363485 abstract "Bayesian geostatistical models applied to malaria risk data quantify the environment-disease relations, identify significant environmental predictors of malaria transmission and provide model-based predictions of malaria risk together with their precision. These models are often based on the stationarity assumption which implies that spatial correlation is a function of distance between locations and independent of location. We relax this assumption and analyse malaria survey data in Mali using a Bayesian non-stationary model. Model fit and predictions are based on Markov chain Monte Carlo simulation methods. Model validation compares the predictive ability of the non-stationary model with the stationary analogue. Results indicate that the stationarity assumption is important because it influences the significance of environmental factors and the corresponding malaria risk maps." @default.
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• W2145363485 date "2006-11-01" @default.
• W2145363485 modified "2023-10-03" @default.
• W2145363485 title "Bayesian modelling of geostatistical malaria risk data" @default.
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• W2145363485 doi "https://doi.org/10.4081/gh.2006.287" @default.
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