Spatial Modelling of Malaria Incidence and its Risk Factors in Namibia

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Namibia University of Science and Technology


Malaria distribution is known to be geographical and temporal heterogeneous, with cases fluctuating across space and time, and climatic conditions are largely connected with regard to malaria occurrence, both temporally and spatially. Millions of dollars have been spent on malaria control in Namibia to achieve the goal of reducing malaria incidence from 13 to less than 1 malaria case per 1000 population in 2016 and becoming malaria free by 2020. However, malaria still remains a major public health challenge in Namibia, primarily in the KavangoWest and East, Ohangwena, and Zambezi regions. The primary purpose of this research was to fit a spatial model to profile spatial variation in malaria incidence (MoHSS) and to investigate possible associations between disease risk and environmental factors in these areas. To explain disease trends, identify malaria risk factors, and locate malaria hotspots, the INLA package in R software was used to fit a range of models, including nonspatial, spatial, and spatio-temporal models. Malaria data for 2018 to 2020 were obtained from the Ministry of Health and Social Services, while monthly weather data for 2018 to 2020 were obtained from SASSCAL, and population estimates for each constituency were used to project the population for 2018 to 2020. Since more than 96% of the 2018-2019 reported malaria cases were from the Kavango East and West, Zambezi, and Ohangwena regions, and more than 80% in 2020, this study was restricted to those areas. A hierarchical Bayesian CAR model was fitted to these datasets to investigate climatic and other related factors that could explain the spatial/ temporal variation of malaria infection in Namibia. Average rainfall received on an annual basis and maximum temperature were found to have a significant spatial and temporal variation on malaria infection. Every mm increase in annual rainfall in a specific constituency in each year increases annual mean malaria cases by 0.6% in that constituency. Also, for every one ◦C increase in annual maximum temperature in a certain constituency, it will increase the annual mean cases of malaria by v 0.6%. The posterior means of the time main effect (year t) showed a visible slightly increasing global trend from 2018 to 2020. Constituencies in the Kavango outskirts East andWest regions revealed a high spatial risk and posterior relative risk (RR: 1.57 to 1.78). Both unstructured random effects (spatial and temporal) as well as temporal structured random effects revealed a significant variation of malaria. Future studies should consider examining all possible putative sources of malaria transmission including travel histories and networks, and treatment seeking behavior and should mostly focus on finding and mapping potential anopheles mosquito habitat that was missed in this study due to a lack of information in the datasets on anopheles mosquito breeding locations (e.g., irrigated agriculture). vi


A thesis submitted in partial fulfilment of the requirements for the degree of Master of Science in Applied Statistics in the Department of Mathematics and Statistics Faculty of Health and Applied Sciences Namibia University of Science and Technology.


Spatial Modelling, Malaria Incidence, Risk Factors, Malaria Incidence Namibia


Katale, R.N. (2022). Spatial Modelling of Malaria Incidence and its Risk Factors in Namibia. (Unpublished master's thesis). Namibia University of Science and Technology, Windhoek.