Bayesian Spatio - Temporal Modelling of Malaria Surveillance in Uganda

Malaria is one of the oldest infectious diseases that has had global health significance on humans for several centuries. In recent times, its burden has been concentrated in the Sub-Sahara Africa (SSA) region where almost 90% of the global malaria morbidity and mortality burden is shouldered. In these countries, transmission is high mainly due to suitable weather conditions, yet control and prevention activities are hampered by weak national health systems and low socioeconomic development. This situation leads to a significant loss of lives in endemic countries particularly in the vulnerable groups of children less than 5 years and pregnant women, as well as pain, suffering, and economic losses due to lost workdays. This further undermines socioeconomic development and perpetuates the vicious cycle of poverty in the affected countries. Uganda ranks number four among the 15 high-burdened countries, with the disease being the leading cause of hospitalization and death.

Autorentext

Julius Ssempiira is a Biostatistician trained in Bayesian and frequentist approaches and their applications in epidemiology and public health. He holds bachelor s and masters degrees in statistics from Makerere University (Uganda), and a PhD in Biostatistics and epidemiology from University of Basel (Switzerland).