Reframing pandemic preparedness: an intelligence-led solidarity model for multi-layered emergencies

Area of Science:

• Global Health Security
• Epidemiology
• Public Health Policy

Background:

• Current global health preparedness systems are fragmented and disease-specific, failing to address multi-layered emergencies.
• Overlapping risks from disease outbreaks, climate shocks, and conflict overwhelm fragile health systems.
• Existing approaches suffer from siloed financing, poor multi-sectoral intelligence integration, and limited institutional autonomy.

Purpose of the Study:

• To examine structural gaps in current global health preparedness.
• To highlight the potential of integrated epidemic intelligence and regional coordination for anticipatory preparedness.
• To propose an intelligence-led solidarity model for resilient health systems.

Main Methods:

• Structured policy analysis of publicly available literature.
• Examination of institutional experiences, particularly from the Africa Centres for Disease Control and Prevention (Africa CDC).
• Analysis of how combining surveillance data across health, environmental, and mobility domains can improve interventions.

Main Results:

• Integrated epidemic intelligence and regional coordination can foster more anticipatory and context-responsive preparedness.
• The Africa CDC's model demonstrates combining diverse data streams for earlier, targeted interventions.
• Siloed financing, limited multi-sectoral intelligence, and constrained autonomy are key structural gaps.

Conclusions:

• An intelligence-led solidarity model, integrating equitable resources, regional intelligence, and predictable financing, strengthens preparedness.
• Preparedness should be viewed as a shared global public good, aligning data, resources, and governance.
• This framework enables coordinated, adaptive, and forward-looking responses to complex, concurrent crises, enhancing global health security.