Bayesian spatio-temporal modelling of malaria surveillance in Uganda

The launch of Roll Back Malaria (RBM) initiative in the early 2000s marked the first serious international efforts to control, prevent and treat malaria in endemic countries of sub-Sahara Africa unparalleled since the demise of the global malaria eradication campaign in the 1960s. These efforts have led to accelerated scale-up of highly proven malaria interventions, that is, insecticide-treated nets, indoor residual spraying, and case management with artemisinin-based combination therapies. This has been followed by a decline of malaria morbidity and mortality in high endemic countries including Uganda which is ranked in the top six high-burden countries.
Despite these achievements, malaria remains a major global public health problem and a leading cause of hospitalization and death in Uganda. The RBM support in Uganda has been extended to malaria surveillance specifically to strengthen the national Health Management Information System (HMIS), and periodical implementation of nationally representative household surveys such as Malaria Indicator Survey (MIS) and Demographic Health Surveys (DHS), and facility assessment surveys.
The availability of these large datasets notwithstanding, their utilization remains low and the information extracted is limited to national averages that neither take into account subnational heterogeneities and disparities nor evaluate the effects of interventions and other important confounders on malaria burden changes in space and time.
In this thesis, we developed Bayesian hierarchical geostatistical and spatio-temporal models for malaria surveillance data collected in Uganda during 2009-2017 to estimate malaria burden, assess interventions and health system-related effects on its changes, and forecast malaria cases to support early warning systems. These models fitted via Markov Chain Monte Carlo (MCMC) simulations offer the flexibility to incorporate correlation of malaria in space and time and can easily be extended to capture complex relationships.