@article{Gosoniu_Vounatsou_Sogoba_Smith_2006, title={Bayesian modelling of geostatistical malaria risk data}, volume={1}, url={https://www.geospatialhealth.net/gh/article/view/287}, DOI={10.4081/gh.2006.287}, abstractNote={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.}, number={1}, journal={Geospatial Health}, author={Gosoniu, L. and Vounatsou, P. and Sogoba, N. and Smith, T.}, year={2006}, month={Nov.}, pages={127–139} }