期刊
COMPUTATIONAL STATISTICS & DATA ANALYSIS
卷 53, 期 9, 页码 3358-3371出版社
ELSEVIER
DOI: 10.1016/j.csda.2009.02.022
关键词
-
资金
- Swiss National Foundation [3252B0-102136/1]
Malaria transmission is highly influenced by environmental and climatic conditions but their effects are often not linear. The climate-malaria relation is unlikely to be the same over large areas covered by different agro-ecological zones. Similarly, spatial correlation in malaria transmission arisen mainly due to spatially structured covariates (environmental and human made factors), could vary across the agro-ecological zones, introducing non-stationarity. Malaria prevalence data from West Africa extracted from the Mapping Malaria Risk in Africa database were analyzed to produce regional parasitaemia risk maps. A non-stationary geostatistical model was developed assuming that the underlying spatial process is a mixture of separate stationary processes within each zone. Non-linearity in the environmental effects was modeled by separate P-splines in each agro-ecological zone. The model allows smoothing at the borders between the zones. The P-splines approach has better predictive ability than categorizing the covariates as an alternative of modeling non-linearity. Model fit and prediction was handled within a Bayesian framework, using Markov chain Monte Carlo (MCMC) simulations. (C) 2009 Elsevier B.V. All rights reserved.
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