World Immunisation Week 24-30 April

In conversations about immunisation and vaccines in Africa, data is often expected to provide clear, unified answers, coverage rates, gaps, and progress. Yet behind these numbers lies a system that is deliberately complex: a multi-system but coordinated approach to data. While it may appear fragmented at first glance, this model is in fact one of the key reasons immunisation programmes across the continent can function, adapt, and improve in highly varied contexts.

Unlike a single, centralised system, the multi-system approach recognises that African countries operate within diverse realities. Health systems differ in infrastructure, digital capacity, workforce availability, and geography. In some areas, health workers rely on paper-based reporting and in others, digital platforms enable real-time data capture. Attempting to impose a single system across these contexts would risk excluding those least equipped to comply. Instead, the current model allows countries to use systems that match their realities while aligning with shared standards set by organisations such as the World Health Organization and UNICEF.

This flexibility is particularly important for data quality and reliability.  Immunisation data is drawn from multiple sources: routine administrative reports from health facilities, population-based surveys, supply chain data, and disease surveillance systems. No single source is perfect. Administrative data may overestimate coverage, while surveys, though more accurate, are periodic and limited in scope. By combining and comparing these streams, stakeholders are able to triangulate insights, identify discrepancies, and arrive at a more accurate picture of vaccine coverage and gaps. This process, often supported by joint estimation mechanisms, ensures that decisions are not based on a single dataset that may be flawed.

The multi-system approach also builds resilience into immunisation programmes. Across parts of Africa, health systems must contend with disruptions, ranging from conflict and displacement to climate-related events and public health emergencies. In such environments, relying on a single data system would create vulnerability. If one system fails or is delayed, others can continue to provide visibility. This redundancy allows programmes to maintain continuity, ensuring that vaccine delivery and monitoring do not halt when challenges arise.

Equally important is the balance between national ownership and global coordination. Governments remain the primary producers and users of immunisation data, drawing on their Health Management Information Systems to guide day-to-day decisions on where to allocate vaccines, which communities to prioritise, and how to respond to emerging gaps. At the same time, multilateral organisations help standardise indicators, validate data, and produce harmonised estimates that enable global comparison and funding decisions. This dual structure ensures that data remains both locally relevant and internationally credible.

Crucially, this approach reflects how decisions are actually made in practice. Health officials and practitioners do not rely on a single figure; they interpret data through context, considering geography, access, population movement, and system capacity. A coverage rate is not just a number but a signal that must be understood alongside realities on the ground. The multi-system model supports this layered interpretation, making data more usable for real-world decision-making.

To this end, the effectiveness of immunisation efforts in Africa depends not only on vaccines themselves, but on the systems that guide their delivery. The coordinated multi-system approach works because it embraces complexity rather than attempting to eliminate it. By combining flexibility with alignment, and diversity with shared standards, it produces something more valuable than uniformity: reliable, context-aware intelligence that enables better decisions, stronger programmes, and more equitable vaccine access across the continent.