Estimation of malaria incidence in northern Namibia in 2009 using Bayesian conditional-autoregressive spatial-temporal models.

Journal article

As malaria transmission declines, it becomes increasingly important to monitor changes in malaria incidence rather than prevalence. Here, a spatio-temporal model was used to identify constituencies with high malaria incidence to guide malaria control. Malaria cases were assembled across all age groups along with several environmental covariates. A Bayesian conditional-autoregressive model was used to model the spatial and temporal variation of incidence after adjusting for test positivity rates and health facility utilisation. Of the 144,744 malaria cases recorded in Namibia in 2009, 134,851 were suspected and 9893 were parasitologically confirmed. The mean annual incidence based on the Bayesian model predictions was 13 cases per 1000 population with the highest incidence predicted for constituencies bordering Angola and Zambia. The smoothed maps of incidence highlight trends in disease incidence. For Namibia, the 2009 maps provide a baseline for monitoring the targets of pre-elimination.

Authors: Alegana, V; Atkinson, P; Wright, J; Kamwi, R; Uusiku, P

• Journal: Spatial and spatio-temporal epidemiology
• Volume: 7
• Pages: 25-36
• Publication date: 2013-12-01
• DOI: 10.1016/j.sste.2013.09.001
• EISSN: 1877-5853
• ISSN: 1877-5845
• Language: English
• Keywords: Models, Statistical; Spatio-Temporal Analysis; Bayes Theorem; Malaria; Namibia; Humans