Development of Bayesian geostatistical models with applications in malaria epidemiology

Plasmodium falciparum malaria is a leading infectious disease and a major cause of morbidity and mortality in large areas of the developing world, especially Africa. Accurate estimates of the burden of the disease are useful for planning and implementing malaria control interventions and for monitoring the impact of prevention and control activities. Information on the population at risk of malaria can be compared to existing levels of service provision to identify underserved populations and to target interventions to high priority areas. The current available statistics for malaria burden are not reliable because of the poor malaria case reporting systems in most African countries and the lack of national representative malaria surveys. Accurate maps of malaria distribution together with human population totals are valuable tools for generating valid estimates of population at risk. Empirical mapping of the geographical patterns of malaria transmission in Africa requires field survey data on prevalence of infection. The Mapping Malaria Risk in Africa (MARA) is the most comprehensive database on malariological survey data across all sub-Sahara Africa. Transmission of malaria is environmentally driven because it depends on the distribution and abundance of mosquitoes, which are sensitive to environmental and climatic conditions. Estimating the environment-disease relation, the burden of malaria can be predicted at places where data on transmission are not available. Malaria data collected at fixed locations over a continuous study area (geostatistical data) are correlated in space because common exposures of the disease influence malaria transmission similarly in neighboring areas. Geostatistical models take into account spatial correlation by introducing location-specific random effects. Geographical dependence is considered as a function of the distance between locations. These models are highly parametrized. State-of-the-art Bayesian computation implemented via Markov chain Monte Carlo (MCMC) simulation ...