Mapping tuberculosis prevalence in Africa using a Bayesian geospatial analysis.

ABSTRACT:

Background

Worldwide, tuberculosis (TB) remains the leading cause of death from infectious diseases. Africa is the second most-affected region, accounting for a quarter of the global TB burden, but there is limited evidence whether there is subnational variation of TB prevalence across the continent. Therefore, this study aimed to estimate sub-national and local TB prevalence across Africa.

Methods

We compiled geolocated data from 50 population-based surveys across 14 African countries. A total of 212 data points were identified and linked to covariates assembled from publicly available sources. Bayesian geostatistical modelling was used to predict TB prevalence across Africa, and results were aggregated to estimate number of TB cases at national and subnational levels.

R

SUBMITTER: Liyew AM 

PROVIDER: S-EPMC12117007 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature