Bayesian modelling of geostatistical malaria risk data

Submitted: 23 December 2014
Accepted: 23 December 2014
Published: 1 November 2006
Abstract Views: 4242
PDF: 1357
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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.

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Gosoniu, L., Vounatsou, P., Sogoba, N., & Smith, T. (2006). Bayesian modelling of geostatistical malaria risk data. Geospatial Health, 1(1), 127–139. https://doi.org/10.4081/gh.2006.287

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