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. 2025 May 27;5(1):194.
doi: 10.1038/s43856-025-00831-9.

Mapping tuberculosis prevalence in Africa using a Bayesian geospatial analysis

Alemneh Mekuriaw Liyew  1   2   3 , Eyob Alemayehu Gebreyohannes #  4   5   6 , Andre Python  7   8 , Archie C A Clements  9 , Beth Gilmour  10   4 , Peter W Gething  10   11 , Punam Amratia  11   12 , Kefyalew Addis Alene #  10   4
Affiliations

Mapping tuberculosis prevalence in Africa using a Bayesian geospatial analysis

Alemneh Mekuriaw Liyew et al. Commun Med (Lond). .

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.

Results: Here we estimate 1.28 million TB cases (95% uncertainty interval [UI] 0.14-4.87) across 14 countries, with marked spatial variations. The highest cases are estimated in Nigeria (460,247 95% UI 7954-1,783,106), and Mozambique (120,622 95%UI 20,027-321,177) while the lowest in Guinea-Bissau (1952 95%UI 154-7365) and Rwanda (2207 95% UI 1050-9225). National TB prevalence range from 0.25 to 7.32 per 1000 with significant variation at higher spatial resolution. Temperature (°C) (OR = 1.27; 95% CrI: 1.20-1.35), precipitation (mm) (OR = 1.34; 95% CrI: 1.26-1.40), and access to city (minute) (OR = 1.21; 95% CrI: 1.14-1.25) are positively associated with TB prevalence, while altitude (m) (OR = 0.83; 95% CrI: 0.78-0.87) is negatively associated.

Conclusions: We find substantial variations in TB prevalence at national, sub-national, and local levels in Africa. These considerable spatial variations suggest the need for geographically targeted interventions to control TB in Africa.

Plain language summary

Tuberculosis (TB) is a bacterial infection that mostly affects the lungs and can lead to death if untreated. It is common in Africa and the national incidence is well-documented. We examined subnational and local TB prevalence across the continent. Our findings show significant variations in TB prevalence, not only between countries but also within regions of the same country. The subnational and local variations in TB prevalence identified in this study suggest the importance of targeted interventions, especially in resource-limited settings. These findings could be used to direct resources more efficiently towards high-risk areas.

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Conflict of interest statement

Competing interests: All authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1. Predicted TB prevalence in Africa at the country level.
Figure 1 shows the median TB prevalence at the national level. The red colour represents areas with the highest prevalence, while dark blue indicates areas with the lowest prevalence. Areas with no data available are shown in grey.
Fig. 2
Fig. 2. Predicted TB prevalence in Africa at the sub-national level.
Figure 2 displays the median TB prevalence at the regional level. Red represents regions with the highest prevalence, while dark blue indicates regions with the lowest prevalence. Grey is used for areas where no data is available.
Fig. 3
Fig. 3. Predicted mean prevalence of TB in Africa at 5 ×5 km spatial resolution.
The highest mean estimates of TB prevalences at pixel level were represented by red colour while the lowest estimates were indicated by dark blue. Grey colour shows areas with no data is available.
Fig. 4
Fig. 4. The interquartile range of predicted prevalence of TB in Africa at 5 × 5 km spatial resolution.
The highest interquartile range estimates of TB prevalences at pixel level were represented by red colour while the lowest estimates were indicated by dark blue. Grey colour shows areas with no data is available.
See this image and copyright information in PMC

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