Spatially Explicit Burden Estimates of Malaria in Tanzania: Bayesian Geostatistical Modeling of the Malaria Indicator Survey Data

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dc.contributor.authorGosoniu, Laura
dc.contributor.authorMsengwa, Amina S.
dc.contributor.authorLengeler, Christian
dc.contributor.authorVounatsou, Penelope
dc.date.accessioned2016-05-20T05:56:00Z
dc.date.available2016-05-20T05:56:00Z
dc.date.issued2012
dc.description.abstractA national HIV/AIDS and malaria parasitological survey was carried out in Tanzania in 2007–2008. In this study the parasitological data were analyzed: i) to identify climatic/environmental, socio-economic and interventions factors associated with child malaria risk and ii) to produce a contemporary, high spatial resolution parasitaemia risk map of the country. Bayesian geostatistical models were fitted to assess the association between parasitaemia risk and its determinants. Bayesian kriging was employed to predict malaria risk at unsampled locations across Tanzania and to obtain the uncertainty associated with the predictions. Markov chain Monte Carlo (MCMC) simulation methods were employed for model fit and prediction. Parasitaemia risk estimates were linked to population data and the number of infected children at province level was calculated. Model validation indicated a high predictive ability of the geostatistical model, with 60.00% of the test locations within the 95% credible interval. The results indicate that older children are significantly more likely to test positive for malaria compared with younger children and living in urban areas and better-off households reduces the risk of infection. However, none of the environmental and climatic proxies or the intervention measures were significantly associated with the risk of parasitaemia. Low levels of malaria prevalence were estimated for Zanzibar island. The population-adjusted prevalence ranges from in Kaskazini province (Zanzibar island) to in Mtwara region. The pattern of predicted malaria risk is similar with the previous maps based on historical data, although the estimates are lower. The predicted maps could be used by decision-makers to allocate resources and target interventions in the regions with highest burden of malaria in order to reduce the disease transmission in the country.en_US
dc.identifier.citationGosoniu, L., Msengwa, A., Lengeler, C. and Vounatsou, P., 2012. Spatially explicit burden estimates of malaria in Tanzania: Bayesian geostatistical modeling of the malaria indicator survey data. PLoS One, 7(5), p.e23966.en_US
dc.identifier.doi10.1371/journal.pone.0023966
dc.identifier.urihttp://hdl.handle.net/20.500.11810/2171
dc.language.isoenen_US
dc.titleSpatially Explicit Burden Estimates of Malaria in Tanzania: Bayesian Geostatistical Modeling of the Malaria Indicator Survey Dataen_US
dc.typeJournal Article, Peer Revieweden_US
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