South Africa is at the southern fringe of sub-Saharan African countries which persist in
experiencing malaria transmission. The purpose of the study is to analyse the geographical
and temporal patterns of malaria transmission from 2000 to 2011 using Bayesian geostatistical
modelling in Limpopo Province, South Africa. Hereafter, develop malaria case
data-driven spatio-temporal models to assess malaria transmission in Limpopo Province.
Malaria case data was acquired from the South African Medical Research Council (MRC).
Population data was acquired from AfriPopo; and Normalised Difference Vegetation Index
(NDVI), Land Surface Temperature (LST) and Land Cover data were acquired from
MODerate-resolution Imaging Spectro-radiometer (MODIS). Rainfall, Altitude and distance
to water bodies’ data were acquired from African Data Dissemination Service (ADDS),
United States Geological Survey (USGS) and Environmental Systems Research Institute
(ESRI), respectively. Bayesian spatio-temporal incidence models were formulated for Gibbs
variable selection and models were fitted using the best set of environmental factors. Modelbased
predictions were obtained over a regular grid of 1 x 1km. spatial resolution covering
the entire province and expressed as rates of per 1 000 inhabitants for the year 2010. To
assess the performance of the predicted malaria incidence risk maps, the predictions and field
observations were compared.
The best set of environmental factors selected by variable selection was Altitude and the night
temperature of two months before the case was reported. The environmental factors were then
used for model fitting and all of the covariates were important on malaria risk. Predictions
were done using all the environmental factors. The predictions showed that Vhembe and
Mopani district municipalities have high malaria transmission as compared to other district
municipalities in Limpopo Province. Assessment of predictive performance showed scatter
plots with the coefficient of determination ( R² ). The values representing the statistical
correlation represented by the coefficient of determination ( R² ) were 0.9798 (January),
0.8736 (February), 0.8152 (March), 0.8861 (April), 0.9949 (May), 0.3838 (June), 0.7794
(July), 0.9235 (September), 0.8966 (October), 0.9834 (November) and 0.8958 (December).
August had two values reported and predicted which resulted in R² of 1. The numbers of the
The produced malaria incidence maps can possibly be considered as one of the baselines for future malaria control programmes. The results highlighted the risk factors of malaria in Limpopo Province which are the most important characteristics of malaria transmission. / M.Sc. University of KwaZulu-Natal, Durban, 2013.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:ukzn/oai:http://researchspace.ukzn.ac.za:10413/11316 |
| Date | 17 October 2014 |
| Creators | Mgabisa, Aphelele Ronnie. |
| Contributors | Gebreslasie, Michael T. |
| Source Sets | South African National ETD Portal |
| Language | en_ZA |
| Detected Language | English |
| Type | Thesis |
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