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Spatio-temporal modelling and mapping of malaria in Angola.

dc.contributor.advisorMwambi, Henry Godwell.
dc.contributor.authorLima, Artemisa Aminata Soares.
dc.descriptionMaster of Science in Statistics. University of KwaZulu-Natal, Pietermaritzburg 2017.en_US
dc.description.abstractAbout half of the world's population is at risk of contracting malaria. The growing number of malaria cases and deaths due to this disease in Africa has become a major challenge to public health care sector. Malaria is reported to be the primary cause of mortality in Angola, hence major focus needs to be put on intervention and prevention methods that reduce the disease risk and mortality to a level which is no longer a public risk problem. The common risk factors for malaria can be linked to environmental, socio-economic, demographic and climatic factors. The mortality rate due to malaria in Angola is analyzed using spatial disease mapping models. Such models are widely used to study the disease incidence, spatial distribution of diseases, prediction of the disease outcome and also to inform intervention strategies in various regions across the world. The methodology used is based on the Bayesian hierarchical modelling (BHM) framework. Four models namely the Poisson-gamma model, Poisson log-normal model, conditional autoregressive (CAR) model as well as the convolution model were used to study the relative risk of malaria mortality at provincial level in Angola using the National malaria control programme data from the period of 2003 to 2010. The Deviance Information Criteria selection was applied to compare and select the best fitted model. A total of 109; 320 deaths due to malaria were observed during the period of 2003-2010 in Angola. The lowest crude death rate was estimated as 124.14 per 100,000 in Lunda Sul province and the highest was 1583.63 per 100,000 in Luanda province. The results revealed that when comparing the four fitted models, the convolution model when we fitted to the data with both spatial structured and unstructured random effects performed better than the other three models. The structured and unstructured random effects were used to capture variation of risk specific to a province and across provinces respectively. The risk maps revealed variation of risk among provinces with very high relative risks in the South-East parts of Angola. A full Bayesian approach was also applied to perform a spatial and spatio-temporal modeling of malaria prevalence in Angola among children under 5 years using the 2006-2007 and 2011 Angola malaria indicator survey (AMIS) data. The Bayesian logistic model was applied in the spatio-temporal analysis to investigate the relationship between malaria prevalence and some reported socio-economic and demographic factors for data collected over the years 2006-2007 and 2011. The space-time effect of the association between malaria and these factors has practical implications for informing strategies for malaria control. Other than temporal variation, the risks factors were also found to vary spatially. The study found that there was a significant difference in the effects of socioeconomic and demographic variations on malaria between these two time periods. Wealth has a negative relationship with malaria prevalence while age was found to have a positive linear relationship with malaria prevalence which is indicative that this covariate play an important role in contracting malaria. Children living in urban areas and those who had bed nets were less likely to contract malaria as compared to those who lived in rural areas and those who did not have bed nets. The temporal analysis show that the prevalence for malaria was lower in 2011 as compared to the 2006-2007 period.en_US
dc.subject.otherRelative risk.en_US
dc.subject.otherHierachical spatial models.en_US
dc.subject.otherMalaria spatial models.en_US
dc.subject.otherPublic health.en_US
dc.subject.otherSpatio-temporal models.en_US
dc.titleSpatio-temporal modelling and mapping of malaria in Angola.en_US


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