Full title: Leveraging HDSS demographic data & alternative health datasets to improve population estimation and malaria chemoprevention coverage. Upper East Region Ghana.

In Ghana’s Upper East Region, a new initiative is bridging data gaps to improve health planning and malaria interventions. While current systems provide detailed statistics for specific areas, researchers need to predict population sizes in neighbouring districts to ensure effective health coverage across the entire region. 

To tackle this, WorldPop in collaboration with the Navrongo Health and Demographic Surveillance System (NHDSS) Ghana are working to integrate  demographic surveillance data with geospatial layers (such as building footprints and nighttime lights), using machine learning and Bayesian frameworks. By incorporating routine health data like bednet distributions and vaccination records, the team will produce high-resolution (1×1 km) population maps. 

These maps act as the essential “denominator” to accurately measure RTS,S malaria vaccine coverage for children under five. This approach allows health officials to identify geographical gaps in delivery and prioritize health equity.  

To ensure long-term impact, the project will deliver an interactive Shiny App dashboard for real-time policy decisions and provides capacity-strengthening workshops for local researchers in spatial data analysis. This ensures that Ghana’s malaria surveillance remains data-driven and sustainable. 

The expected outputs and deliverables for the project include: 

• High-resolution population maps, consisting of gridded surfaces (1×1 km resolution) for total and under-five populations across the Upper East Region. 
• RTS,S coverage maps at high resolution to identify low-performing areas and geographical targeting gaps for the malaria vaccine. 
• An interactive Shiny App dashboard, providing a user-friendly platform for policymakers to visualize population estimates, intervention coverage, and model uncertainty. 
• Capacity-strengthening workshops designed for HDSS staff and local researchers, covering topics such as spatial data analysis, Bayesian statistics, and GIS. 
• Peer-reviewed publications, specifically a manuscript on the methodological framework for population prediction and a second manuscript on RTS,S coverage estimation and sensitivity analysis. 
• Policy briefs and conference presentations intended to support the dissemination of findings to a broader audience. 

Link image: UNICEF/UNI657287/Noorani