Peer-reviewed article
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.
Godfrey Musuka, Al-Umra Umar, Ibrahim Dadari, Enos Moyo, Oscar Mano et al. ·
Summary
Written by AI, not yet AICS's view · claude-haiku-4-5-20251001This narrative review examines how machine learning, geospatial mapping, and microplanning approaches are being used across Sub-Saharan Africa to identify and reach zero-dose and under-immunised children. The review finds that these technologies can improve the precision of identifying missed children and targeting outreach, but significant challenges remain in data quality, technical capacity, and system integration. The authors recommend standardising data collection, training the workforce, and embedding these tools into national health systems for sustainable impact.
Key points
- Machine learning models using demographic and mobility data have enhanced precision in identifying zero-dose children for targeted outreach
- Geospatial mapping enables real-time visualisation of immunization deserts and spatial distribution of missed communities to guide resource allocation
- Microplanning integrated with digital tools and community engagement improves follow-up and reduces missed vaccination opportunities
- Data quality, interoperability, and technical capacity gaps at sub-national level limit scalability in remote and fragile settings
- Fragmented investments and limited integration into national health information systems threaten sustainability of these approaches
Limitations
As a narrative review, this synthesis does not provide a systematic meta-analysis of effectiveness and may not capture all relevant studies uniformly. The review does not specify which Sub-Saharan African countries have successfully implemented these approaches or provide comparative effectiveness data across different contexts.
Abstract
Immunization inequities persist across Sub-Saharan Africa, with significant numbers of zero-dose and under-immunised children contributing to preventable morbidity and mortality. This narrative review critically examines the integration and effectiveness of machine learning, geospatial mapping, and microplanning strategies in identifying and reaching these vulnerable populations. The review's primary objective is to synthesise current evidence on how these innovative approaches are being applied within routine immunization systems to address persistent coverage gaps. A systematic search of peer-reviewed literature and grey sources was conducted, focusing on studies and programmatic reports from 2015 to 2025. The review analyses methodological trends, implementation experiences, and outcome data related to machine learning algorithms for risk profiling, geospatial technologies for mapping and targeting, and microplanning tools for local-level action. Data extraction and thematic synthesis were guided by the WHO framework for immunization equity. Findings demonstrate that machine learning models, utilizing demographic, health system, and mobility data, have enhanced the precision of zero-dose child identification, enabling more targeted outreach interventions. Geospatial mapping has further enabled real-time visualisation of immunization deserts and the spatial distribution of missed communities, supporting resource allocation and deployment of mobile teams. Microplanning, when integrated with digital tools and community engagement, has shown promise in translating high-level data into actionable local strategies, improving follow-up, and reducing missed vaccination opportunities. Despite these advancements, several challenges persist. Data quality and interoperability issues limit the scalability of machine learning and geospatial solutions, particularly in remote or fragile settings. Capacity gaps at the sub-national level, including technical skills and digital infrastructure, impede effective microplanning and data use. Furthermore, the sustainability of these approaches is threatened by fragmented investments and limited integration into national health information systems. Opportunities exist to strengthen the routine immunization system by standardising data collection, investing in workforce training, and fostering cross-sectoral collaboration. The review recommends prioritising the development of interoperable platforms, expanding context-specific pilot projects, and embedding evaluation mechanisms to track impact and equity outcomes. Policymakers are urged to leverage the demonstrated benefits of machine learning.