Bayesian geostatistical modeling of Malaria Indicator Survey data in Angola

Gosoniu, L. and Veta, A. M. and Vounatsou, P.. (2010) Bayesian geostatistical modeling of Malaria Indicator Survey data in Angola. PLoS ONE, Vol. 5, H. 3 , e9322.

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Official URL: http://edoc.unibas.ch/dok/A5842828

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The 2006-2007 Angola Malaria Indicator Survey (AMIS) is the first nationally representative household survey in the country assessing coverage of the key malaria control interventions and measuring malaria-related burden among children under 5 years of age. In this paper, the Angolan MIS data were analyzed to produce the first smooth map of parasitaemia prevalence based on contemporary nationwide empirical data in the country. Bayesian geostatistical models were fitted to assess the effect of interventions after adjusting for environmental, climatic and socio-economic factors. Non-linear relationships between parasitaemia risk and environmental predictors were modeled by categorizing the covariates and by employing two non-parametric approaches, the B-splines and the P-splines. The results of the model validation showed that the categorical model was able to better capture the relationship between parasitaemia prevalence and the environmental factors. Model fit and prediction were handled within a Bayesian framework using Markov chain Monte Carlo (MCMC) simulations. Combining estimates of parasitaemia prevalence with the number of children under we obtained estimates of the number of infected children in the country. The population-adjusted prevalence ranges from in Namibe province to in Malanje province. The odds of parasitaemia in children living in a household with at least ITNs per person was by 41% lower (CI: 14%, 60%) than in those with fewer ITNs. The estimates of the number of parasitaemic children produced in this paper are important for planning and implementing malaria control interventions and for monitoring the impact of prevention and control activities
Faculties and Departments:09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH) > Department of Epidemiology and Public Health (EPH) > Infectious Disease Modelling > Epidemiology and Transmission Dynamics (Smith)
UniBasel Contributors:Vounatsou, Penelope
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:Public Library of Science
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:31 Aug 2018 06:40
Deposited On:08 Jun 2012 06:46

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