Bayesian modelling of geostatistical malaria risk data

  • L. Gosoniu Swiss Tropical Institute, Basel, Switzerland.
  • P. Vounatsou | penelope.vounatsou@unibas.ch Swiss Tropical Institute, Basel, Switzerland.
  • N. Sogoba Malaria Research and Training Center, Universite du Mali, Bamako, Mali.
  • T. Smith Swiss Tropical Institute, Basel, Switzerland.

Abstract

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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Published
2006-11-01
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Section
Original Articles
Keywords:
remote sensing, epidemiology, disease control, arthropod-borne viruses.
Statistics
  • Abstract views: 2004

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How to Cite
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