Further Improvement for Public Health Planning: WorldPop Brings High-Resolution Population Estimates to DRC

A new study led by Senior Research Fellow, Dr Gianluca Boo has introduced a powerful new Bayesian model to estimate population distribution in Kasaï-Oriental province, Democratic Republic of the Congo (DRC), offering critical insights in a region where public health data has long been scarce.

At the heart of this advanced research is a method that combines satellite data, micro-census surveys, and statistical modelling to produce population estimates at fine spatial resolution –  approximately one hectare. The model jointly estimates building and population counts, using data from 213 micro-census clusters alongside 292 open-access geospatial datasets. These datasets include variables such as land use, night-time light intensity, proximity to major roads, and distance to healthcare facilities.

For 2024, the model estimates that around 4.1 million people live in Kasaï-Oriental, with a credible range of 3.4 to 4.8 million. This is the first time such granular, high-confidence data has been available for this province.

“This level of detail marks a significant improvement,” said Gianluca Boo, “It enables local health authorities and international partners to more effectively plan services like vaccine delivery, maternal health interventions, and emergency response.”

The study builds on years of prior work by WorldPop and was supported by the GRID3 Mapping for Health project, funded by Gavi, the Vaccine Alliance, and led by Professor Andy Tatem. It also leverages open tools like the wopr R package, which facilitates access to the WorldPop Open Population Repository, an important resource for researchers and policymakers worldwide.

By tackling the persistent challenge of data scarcity in low-resource settings, this model not only enhances the accuracy of population statistics in Kasaï-Oriental but also sets a precedent for similar efforts across other underserved regions in Africa and beyond.