Mapping malaria risk in West Africa using a Bayesian nonparametric non-stationary model

• Published in: Computational statistics & data analysis Vol. 53; no. 9; pp. 3358 - 3371
• Main Authors: Gosoniu, L., Vounatsou, P., Sogoba, N., Maire, N., Smith, T.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.07.2009; Elsevier
• Series: Computational Statistics & Data Analysis
• Subjects: Applications; Biology, psychology, social sciences; Exact sciences and technology; General topics; Mathematics; Multivariate analysis; Numerical analysis; Numerical analysis. Scientific computation; Numerical methods in probability and statistics; Probability and statistics; Sciences and techniques of general use; Statistics; Africa; Statistical distribution; Spatial correlation; Prediction theory; Non parametric estimation; Multivariate analysis; Covariate; Stochastic method; Stochastic process; Spline approximation; Numerical approximation; Markov chain; Database; Stationary process; Approximation theory; Bayes estimation; Human; Mixed distribution; Monte Carlo method; Data analysis; Smoothing methods; Prediction; Statistical association; Statistical estimation; Statistical method; Spatial process; Numerical analysis; Statistical computation; Simulation; Correlation analysis; Smoothing; Environment; Filtering theory
• ISSN: 0167-9473; 1872-7352
• DOI: 10.1016/j.csda.2009.02.022

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.