Bayesian modelling of geostatistical malaria risk data.
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.
Gosoniu
L
L
Swiss Tropical Institute, Basel, Switzerland.
Vounatsou
P
P
Sogoba
N
N
Smith
T
T
D001499:Bayes Theorem; D040362:Geographic Information Systems / Q000706:statistics & numerical data*; D008288:Malaria / Q000453:epidemiology* / Q000401:mortality / Q000517:prevention & control / Q000635:transmission; D008302:Mali / Q000453:epidemiology; D008390:Markov Chains; D015233:Models, Statistical; D018570:Risk Assessment / Q000706:statistics & numerical data*