The overarching aim of the project was to provide accurate, up-to-date population estimates for Cameroon disaggregated by age and sex groups to support evidence-based vaccination campaigns in the country. These estimates, which are required at both health area and locality levels, will help the Programme Elargi de Vaccination (PEV) to improve measles and rubella vaccination campaigns in 2025.

The specific objectives were threefold:

1. to develop comprehensive, up-to-date population estimates at health area and locality levels;
2. to stratify these population estimates by specific age and sex groups relevant for vaccination, with a focus on 0-8 months, 9-11 months, 12-23 months, 24-59 months, and 5-9 years;
3. to provide these estimates at high spatial resolution as well as to provide estimates of the age-sex structured population at high resolution raster files (approximately 100m X 100m resolution) to support more effective vaccination campaign planning at very small area levels.

This project marks an important step in closing the data gaps that have limited effective vaccination planning in Cameroon. By combining official population data, micro-census information, geospatial variables, and advanced statistical methods, the team produced detailed and reliable population estimates by age and sex. These estimates are designed to support the operational needs of the Programme Elargi de Vaccination (PEV) as it prepares for the 2025 measles and rubella campaigns. The methods used in the project included hierarchical Bayesian models and the INLA-SPDE approach. This helped to create smooth estimates across space, measure uncertainty, and bring together data from different sources. These methods are especially useful in countries like Cameroon, where recent census data are not available. Even with strong methods, the project faced some challenges. Some input data were inconsistent, and there were cases where several Localités shared the same location on the map. A number of health areas also had missing or incorrect shapes. In addition, the data used came from different years (2020 to 2024), which could affect the precision of the estimates. However, checks on model performance showed good results, and the estimates are reliable for guiding vaccination planning.

Acknowledgements

We gratefully acknowledge Bluesquare for facilitating the funding and providing the input dataset, as well as for their valuable periodic feedback and constructive discussions. We also thank the entire WorldPop team for their continuous support.