Modelled gridded population estimates for Kwilu Province in the Democratic Republic of Congo version 4.3

Nnanatu, Chris, Yankey, Ortis, Chaudhuri, Somnath, Chamberlain, Heather, Lazar, Attila and Tatem, Andrew (2025) Modelled gridded population estimates for Kwilu Province in the Democratic Republic of Congo version 4.3. University of Southampton doi:10.5258/SOTON/WP00836 [Dataset]

Abstract

This data release provides gridded population estimates (spatial resolution of 3 arc-seconds, approximately 100-metre grid cells) for Kwilu 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 2022 as well as settlement extents and geospatial covariates, to model and estimate population numbers at grid cell level using a Bayesian hierarchical statistical modelling framework. The approach facilitated accounting for the multiple levels of variability within the data while simultaneously quantifying for uncertainties in parameter estimates. These model-based population estimates can be considered as most accurately representing the year 2022, which is the period following the PDRS survey data collection for Kwilu. Although the methods were robust enough to explicitly account for key random biases and adjust for potential systematic biases within the observed datasets, it is important to note that some systematic biases arising from other sources may remain. These data were produced by the WorldPop Research Group at the University of Southampton. The work was part of the GRID3 – Phase 2 Scaling project, with funding from the Gates Foundation (INV-044979). Project partners included GRID3 Inc, the Center for Integrated Earth System Information (CIESIN) within the Columbia Climate School at Columbia University, and WorldPop at the University of Southampton. A robust Bayesian joint (hurdle) population modelling approach was developed to estimate population density whilst at same time accounting for probability of settlement detection. The final statistical modelling was conceived, designed, and implemented by Chris Nnanatu. Data processing was done by Ortis Yankey, while project oversight was provided by Attila Lazar, and Andy Tatem. The PDRS data from the malaria insecticide treated net (ITN) distribution campaigns were collected, processed, anonymised, and shared by the PNLP and the implementing partners. The settlement extent data was prepared and shared by CIESIN (2024). The data has been clipped to GRID3-CIESIN health area extent (version 6.0) (CIESIN, 2025).

This record has no associated files available for download.

Contributors

Creator: Chris Nnanatu

Creator: Ortis Yankey

Creator: Somnath Chaudhuri

Creator: Heather Chamberlain

Creator: Attila Lazar

Creator: Andrew Tatem

Download statistics