Bayesian modelling of geostatistical malaria risk data

• Main Authors: L. Gosoniu, P. Vounatsou, N. Sogoba, T. Smith
• Format: Article
• Language: English
• Published: PAGEPress Publications 2006-11-01
• Series: Geospatial Health
• Subjects: remote sensing, epidemiology, disease control, arthropod-borne viruses.
• Online Access: http://www.geospatialhealth.net/index.php/gh/article/view/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.