Abstract

Sub-Saharan Africa faces a high disease burden in communicable diseases and an increasing burden in non-communicable diseases with a strong spatial and temporal structure. More recently, increased funding for research from donor initiatives has generated high-quality household data volume, but there is a high demand for biostatisticians to analyse these data locally and quickly resulting in a lack of capacity for advanced data analysis.

Globally, the fields of geographical epidemiology and public health surveillance have benefited from combined advances in hierarchical model building and in geographical information systems. Exploring and characterising a variety of spatial patterns of diseases at the disaggregated fine geographical resolution has become possible.

Donor funded initiatives exist to address the dearth in statistical capacity, but few initiatives have been led by African institutions. The Sub-Saharan African Consortium for Advanced Biostatistics (SSACAB) aims to improve biostatistical capacity in Africa according to the needs identified by African institutions, through collaborative masters and doctoral training in biostatistics.

In this special contributed session, we will bring together the work resulting in our effort to build capacity in recent developments in diseases mapping (both Bayesian and frequentist) and especially the classes of Bayesian hierarchical space time models that has been used to characterize the patterns of communicable and non-communicable disease burden in SSA through SSACAB and behind.