Modelled gridded population estimates for Mongala Province in the Democratic Republic of Congo version 4.2
Modelled gridded population estimates for Mongala Province in the Democratic Republic of Congo version 4.2
This data release provides gridded population estimates (spatial resolution of 3 arc-seconds, approximately 100-metre grid cells) for Mongala province in the Democratic Republic of Congo (DRC), along with estimates of the number of people belonging to various age-sex groups. The project team used the Pre-Distribution Registration Survey (PDRS) data from the National Malaria Control Programme (PNLP) collected as part of anti-malarial campaigns in the DRC for 2023, settlement extent and geospatial covariates to model and estimate population numbers at grid cell level using a Bayesian statistical hierarchical modelling framework. The approach facilitated simultaneous accounting for the multiple levels of variability within the data. It also allowed the quantification of uncertainties in parameter estimates. These model-based population estimates can be considered as most accurately representing the year 2023. This time period corresponds to the PDRS survey date for Mongala. Although the methods were robust enough to explicitly account for key random biases within the datasets, it is noted that systematic biases, which may arise from sources other than random errors within the observed data collection process, are most likely to remain.
population, age-sex, data analysis, DRC
University of Southampton
KS, Krishnaveni
d4546605-d9e5-4255-8638-393609a3dc2a
Yankey, Ortis
9965d053-8afb-462f-b7fe-b270e21f2ec1
Nnanatu, Chris
24be7c1b-a677-4086-91b4-a9d9b1efa5a3
Abbott, Thomas
6dd117e8-cac5-4862-a3fd-ddbf1cbe94bb
Chamberlain, Heather
cb939de7-ac47-440e-aeb8-a2e36c110785
Lazar, Attila
d7f835e7-1e3d-4742-b366-af19cf5fc881
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e
KS, Krishnaveni
d4546605-d9e5-4255-8638-393609a3dc2a
Yankey, Ortis
9965d053-8afb-462f-b7fe-b270e21f2ec1
Nnanatu, Chris
24be7c1b-a677-4086-91b4-a9d9b1efa5a3
Abbott, Thomas
6dd117e8-cac5-4862-a3fd-ddbf1cbe94bb
Chamberlain, Heather
cb939de7-ac47-440e-aeb8-a2e36c110785
Lazar, Attila
d7f835e7-1e3d-4742-b366-af19cf5fc881
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e
KS, Krishnaveni, Yankey, Ortis, Nnanatu, Chris, Abbott, Thomas, Chamberlain, Heather, Lazar, Attila and Tatem, Andrew
(2025)
Modelled gridded population estimates for Mongala Province in the Democratic Republic of Congo version 4.2.
University of Southampton
doi:10.5258/SOTON/WP00785
[Dataset]
Abstract
This data release provides gridded population estimates (spatial resolution of 3 arc-seconds, approximately 100-metre grid cells) for Mongala province in the Democratic Republic of Congo (DRC), along with estimates of the number of people belonging to various age-sex groups. The project team used the Pre-Distribution Registration Survey (PDRS) data from the National Malaria Control Programme (PNLP) collected as part of anti-malarial campaigns in the DRC for 2023, settlement extent and geospatial covariates to model and estimate population numbers at grid cell level using a Bayesian statistical hierarchical modelling framework. The approach facilitated simultaneous accounting for the multiple levels of variability within the data. It also allowed the quantification of uncertainties in parameter estimates. These model-based population estimates can be considered as most accurately representing the year 2023. This time period corresponds to the PDRS survey date for Mongala. Although the methods were robust enough to explicitly account for key random biases within the datasets, it is noted that systematic biases, which may arise from sources other than random errors within the observed data collection process, are most likely to remain.
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Published date: 13 March 2025
Keywords:
population, age-sex, data analysis, DRC
Identifiers
Local EPrints ID: 500997
URI: http://eprints.soton.ac.uk/id/eprint/500997
PURE UUID: cb9aea8b-e2ef-423e-8af8-ebe2289c1535
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Date deposited: 20 May 2025 17:02
Last modified: 22 May 2025 02:12
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