Details

Autor(en) / Beteiligte

• Gosoniu, L
• Vounatsou, P
• Sogoba, N
• Smith, T

Titel

Bayesian modelling of geostatistical malaria risk data

Ist Teil von

• Geospatial health, 2006-11, Vol.1 (1), p.127-139

Ort / Verlag

Italy: PAGEPress Publications

Erscheinungsjahr

2006

Link zum Volltext

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• 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.