TY - JOUR
AU - Gosoniu, L.
AU - Vounatsou, P.
AU - Sogoba, N.
AU - Smith, T.
PY - 2006/11/01
Y2 - 2021/10/17
TI - Bayesian modelling of geostatistical malaria risk data
JF - Geospatial Health
JA - Geospat Health
VL - 1
IS - 1
SE - Original Articles
DO - 10.4081/gh.2006.287
UR - https://geospatialhealth.net/index.php/gh/article/view/287
SP - 127-139
AB - 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.
ER -