Tackling public health data gaps through Bayesian high-resolution population estimation: a case study of Kasaï-Oriental, Democratic Republic of the Congo
Tackling public health data gaps through Bayesian high-resolution population estimation: a case study of Kasaï-Oriental, Democratic Republic of the Congo
Most low- and middle-income countries face significant public health challenges, exacerbated by the lack of reliable demographic data supporting effective planning and intervention. In such data-scarce settings, statistical models combining geolocated survey data with geospatial datasets enable the estimation of population counts at high spatial resolution in the absence of dependable demographic data sources. This study introduces a Bayesian model jointly estimating building and population counts, combining geolocated survey data and gridded geospatial datasets. The model provides population estimates for the Kasaï-Oriental province, Democratic Republic of the Congo (DRC), at a spatial resolution of approximately one hectare. Posterior estimates are aggregated across health zones and health areas to offer probabilistic insights into their respective populations. The model exhibits a -0.28 bias, 0.47 inaccuracy, and 0.95 imprecision using scaled residuals, with robust 95% credible intervals. The estimated population of Kasaï-Oriental for 2024 is approximately 4.1 million, with a credible range of 3.4 to 4.8 million. Aggregations by health zones and health areas reveal significant variations in population estimates and uncertainty levels, particularly between the provincial capital, Mbuji-Mayi and the rural hinterland. High-resolution Bayesian population estimates allow flexible aggregation across spatial units while providing probabilistic insights into model uncertainty. These estimates offer a unique resource for the public health community working in Kasaï-Oriental, for instance, in support of a better-informed allocation of vaccines to different operational boundaries based on the upper bound of the 95% credible intervals.
Boo, Gianluca
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Darin, Edith
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Chamberlain, Heather R.
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Hosner, Roland
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Akilimali, Pierre K.
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Kazadi, Henri Marie
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Nnanatu, Chibuzor C.
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Lázár, Attila N.
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Tatem, Andrew J.
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4 September 2025
Boo, Gianluca
d49f7aaa-6d95-4e36-b9be-e469911c4a3d
Darin, Edith
868fa688-2567-4dbd-aa12-3dcc91f2aa8d
Chamberlain, Heather R.
cb939de7-ac47-440e-aeb8-a2e36c110785
Hosner, Roland
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Akilimali, Pierre K.
597bbadd-75fa-459b-a0fa-f991b7b6cc3b
Kazadi, Henri Marie
4041c08d-d51d-4fd3-ab3d-1dd09274938f
Nnanatu, Chibuzor C.
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Lázár, Attila N.
d7f835e7-1e3d-4742-b366-af19cf5fc881
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Boo, Gianluca, Darin, Edith, Chamberlain, Heather R., Hosner, Roland, Akilimali, Pierre K., Kazadi, Henri Marie, Nnanatu, Chibuzor C., Lázár, Attila N. and Tatem, Andrew J.
(2025)
Tackling public health data gaps through Bayesian high-resolution population estimation: a case study of Kasaï-Oriental, Democratic Republic of the Congo.
PLOS Global Public Health, 5 (9), [e0005072].
(doi:10.1371/journal.pgph.0005072).
Abstract
Most low- and middle-income countries face significant public health challenges, exacerbated by the lack of reliable demographic data supporting effective planning and intervention. In such data-scarce settings, statistical models combining geolocated survey data with geospatial datasets enable the estimation of population counts at high spatial resolution in the absence of dependable demographic data sources. This study introduces a Bayesian model jointly estimating building and population counts, combining geolocated survey data and gridded geospatial datasets. The model provides population estimates for the Kasaï-Oriental province, Democratic Republic of the Congo (DRC), at a spatial resolution of approximately one hectare. Posterior estimates are aggregated across health zones and health areas to offer probabilistic insights into their respective populations. The model exhibits a -0.28 bias, 0.47 inaccuracy, and 0.95 imprecision using scaled residuals, with robust 95% credible intervals. The estimated population of Kasaï-Oriental for 2024 is approximately 4.1 million, with a credible range of 3.4 to 4.8 million. Aggregations by health zones and health areas reveal significant variations in population estimates and uncertainty levels, particularly between the provincial capital, Mbuji-Mayi and the rural hinterland. High-resolution Bayesian population estimates allow flexible aggregation across spatial units while providing probabilistic insights into model uncertainty. These estimates offer a unique resource for the public health community working in Kasaï-Oriental, for instance, in support of a better-informed allocation of vaccines to different operational boundaries based on the upper bound of the 95% credible intervals.
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journal.pgph.0005072
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Accepted/In Press date: 29 July 2025
Published date: 4 September 2025
Identifiers
Local EPrints ID: 505805
URI: http://eprints.soton.ac.uk/id/eprint/505805
ISSN: 2767-3375
PURE UUID: 62e3111a-c5ba-474a-86ee-0972c93bad86
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Date deposited: 20 Oct 2025 16:38
Last modified: 21 Oct 2025 02:05
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Author:
Gianluca Boo
Author:
Roland Hosner
Author:
Pierre K. Akilimali
Author:
Henri Marie Kazadi
Author:
Chibuzor C. Nnanatu
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