Bridging immunization gaps in Sub-Saharan Africa: a narrative review of microplanning, geospatial, and machine learning approaches to reach zero-dose children and under-immunised children

This peer-reviewed narrative review examines how machine learning, geospatial mapping, and microplanning approaches are being used to identify and reach zero-dose and under-immunised children in Sub-Saharan Africa. The review synthesises peer-reviewed and grey literature from 2015 to 2025, using thematic synthesis guided by the WHO framework for immunization equity, and focuses on methodological trends, implementation experiences, and outcome data related to risk profiling, spatial targeting, and local-level planning. 

Key insights

• Machine learning models using demographic, health system, and mobility data can help improve the precision of zero-dose child identification and support more targeted outreach.
• Geospatial mapping can help visualise immunization deserts and missed communities in real time, informing resource allocation and mobile team deployment.
• Microplanning, especially when combined with digital tools and community engagement, can translate high-level data into practical local strategies, improve follow-up, and reduce missed vaccination opportunities.
• Data quality, interoperability, and limited integration with national health information systems remain major barriers to scaling these approaches.
• Sub-national capacity gaps, including limited technical skills and digital infrastructure, constrain effective use of machine learning, geospatial tools, and data-driven microplanning.
• The review recommends standardised data collection, interoperable platforms, workforce training, cross-sectoral collaboration, context-specific pilots, and embedded evaluation mechanisms to monitor impact and equity outcomes.

Thumbnail image credit: Gavi