Analysis of the geographical patterns of malaria transmission in KwaZulu-Natal, South Africa using Bayesian spatio-temporal modelling.

M. Sc. University of KwaZulu-Natal, Durban 2013.

Malaria is one of the most important public health issues that is still affecting millions of people around the world, especially in Africa. Africa accounted for 80% of the 216 million cases worldwide and 91% of deaths. It poses serious economic burdens on communities and countries at large. However, through temporal and spatial mapping of the disease populations at risk can be identified timeously and resources distributed accordingly. Since malaria is a climatic disease geostatistical approaches can be utilised in modelling its spatial distribution. Bayesian geostatistical methods enable the mathematical descriptions of the environment-disease association. Significant environmental predictors of malaria transmission can be identified which can also allow for the development of a malaria epidemic prediction model. This model can serve as a surveillance system for early detection and containment of the disease. Therefore, it is crucial to understand the complex dynamics of malaria transmission so malaria control programmes can be more effective and efficient in managing this public health issue. In South Africa, malaria is transmitted in 3 provinces: KwaZulu-Natal, Mpumalanga and Limpopo. Although malaria is highly seasonal in these areas and KwaZulu-Natal has experienced tremendous achievements in decreasing morbidity and mortality due to malaria, it still remains in an unstable condition that needs constant control and surveillance. The aim of this study was to investigate which environmental/climatic variables are drivers of malaria incidence in KwaZulu-Natal and subsequently develop methods to produce risk maps using Bayesian spatio-temporal modelling. It emerged from the research that the main environmental/climatic drivers of malaria incidence in KwaZulu-Natal were the day temperature of the previous month, altitude and forest land cover type. This was due to the different ways these three factors affect the three-way interaction of the vector, the parasite and the human host. The predicted risk maps showed that incidence rates ranged from 0.2 to 5 per 1000 inhabitants in the study area. This prediction was based on only the climatic factors, however, non-climatic factors also affect malaria transmission through vector control strategies like Indoor Residual Spraying among others.