Health Map Senegal tackles spatial health inequalities by equipping authorities with high-resolution, disaggregated data for local policy formulation. By applying Random Forest machine learning, the project generates 100m-resolution population grids that provide a precise foundation for health planning. 

A key focus is identifying “zero-dose” children by combining these population estimates with Bayesian models of DTP vaccination coverage. Additionally, WorldPop will use geoprocessing to calculate travel times to facilities, exposing geographical barriers to care. 

The project integrates these findings into detailed district health profiles, overlaying population data with socioeconomic factors (such as education and occupation) to highlight priority intervention zones. To make this data actionable, an interactive Shiny App dashboard will enable the Senegal Ministry of Health and partners like UNICEF and WHO to monitor trends and target resources where they are needed most. 

Project Outputs 

• High-resolution population estimates (100m resolution) for total population and specific age/sex cohorts. 
• Vaccination coverage models and uncertainty estimates for DTP1, DTP2, and DTP3. 
• District-level estimates of zero-dose children, identifying under-vaccinated children. 
• Travel time isochrones and accessibility maps highlighting inequalities in reach to health infrastructure. 
• Demographic and health profiles for health districts to guide local policy and resource allocation. 
• Interactive Monitoring Dashboard (Shiny App) for real-time data exploration and comparative analysis.

Link image: Hann, Senegal, Peter A Harrison, 2015, CC BY-NC 2.0