Bayesian modelling of geostatistical malaria risk data

Geospat Health. 2006 Nov;1(1):127-39. doi: 10.4081/gh.2006.287.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bayes Theorem*
  • Geographic Information Systems / statistics & numerical data*
  • Malaria / epidemiology*
  • Malaria / mortality
  • Malaria / prevention & control
  • Malaria / transmission
  • Mali / epidemiology
  • Markov Chains
  • Models, Statistical
  • Risk Assessment / statistics & numerical data*