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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.

Original publication

DOI

10.1016/j.sste.2013.09.001

Type

Journal article

Journal

Spat Spatiotemporal Epidemiol

Publication Date

12/2013

Volume

7

Pages

25 - 36

Keywords

ACD, CAR, CPO, Conditional-autoregressive, DIC, ESRI, EVI, Environmental System Research Institute, GF, GIS, GMRF, GPS, GRUMP, Gaussian field, Gaussian markov random field, Global Rural and Urban Mapping Project, HMIS, Health Management Information System, INLA, Integrated Nested Laplace Approximation, JAXA, Japan Aerospace Exploration Agency, MAUP, MCMC, MODIS, MODerate-resolution Imaging Spectro-radiometer, Malaria, Markov Chain Monte Carlo, Ministry of Health and Social Services, MoHSS, Modifiable Areal Unit Problem, NASA, NVBDCP, Namibia, National Aeronautics and Space Administration, National Vector-Borne and Disease Control Programme, PCD, PHS, RDT, Rapid Diagnostic Test, SPA, Service Provision Assessments, Spatio-temporal, TRMM, TSI, Tropical Rainfall Measuring Mission, WHO, World Health Organisation, ZIP, Zero-Inflated Poisson, active case detection, conditional auto-regressive, conditional predictive ordinate, deviance information criterion, enhanced vegetation index, geographic information system, global positioning system, passive case detection, public health sector, temperature suitability index, Bayes Theorem, Humans, Malaria, Models, Statistical, Namibia, Spatio-Temporal Analysis