Bayesian modelling of geostatistical malaria risk data
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 station...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
PAGEPress Publications
2006-11-01
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| Series: | Geospatial Health |
| Subjects: | |
| Online Access: | http://www.geospatialhealth.net/index.php/gh/article/view/287 |
| Summary: | 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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| ISSN: | 1827-1987 1970-7096 |