Evaluating Early Warning Surveillance Systems: Measuring Performance and Impact for Global Health Security and Pandemic Preparedness

Early warning surveillance systems are increasingly central to global efforts to detect, verify, and respond to emerging public health threats. Event-based surveillance (EBS), participatory surveillance, digital epidemiology platforms, and integrated intelligence systems have significantly expanded the ability of national, regional, and global networks to detect signals of potential outbreaks earlier than traditional surveillance approaches. Yet despite major investments in surveillance infrastructure — particularly following the COVID-19 pandemic — there remains limited evidence on how to systematically evaluate the performance, effectiveness, and real-world impact of these systems. While evaluation frameworks exist for indicator-based surveillance, fewer standardized methods apply to early detection systems that rely on complex networks, informal data streams, and rapidly evolving technologies. Without robust evaluation methods and evidence of impact, it remains difficult to demonstrate the value of surveillance investments or identify best practices. Global and regional initiatives — including WHO and Africa CDC early warning frameworks, GOARN, and emerging collaborative public health intelligence approaches — highlight cross-boundary challenges, yet questions remain about how to measure their contribution to outbreak detection, response timeliness, and broader goals of global health security and solidarity. This Research Topic will bring together multidisciplinary perspectives on how early warning surveillance systems are evaluated and how their benefits are quantified, drawing especially on success stories from around the world that can be applied and adapted to new contexts. The collection will highlight practical evaluation experiences from countries and international networks, methodological innovations for surveillance evaluation, and emerging approaches to measuring the value of early detection, providing a platform for advancing this science and documenting how early warning systems contribute to a more resilient and responsive global health ecosystem. By bridging operational experience with methodological advances, this Research Topic seeks to establish a stronger evidence base for how early warning systems contribute to timely detection, improved response, and reduced public health impact of emerging threats. We welcome contributions that address evaluation frameworks for EBS and other early warning systems; metrics for timeliness, sensitivity, specificity, and signal verification; methods for quantifying the real-world impact of early detection; and approaches to evaluation in resource-constrained and cross-border settings. The collection will support policymakers, surveillance practitioners, and researchers with practical tools and worked examples for assessing surveillance performance and demonstrating the value of early warning systems in strengthening global health security. Suitable themes for manuscripts include but are not limited to: 1. Frameworks and methods: evaluation approaches for event-based and signal-based surveillance; performance metrics (timeliness, sensitivity, specificity, signal verification); cost-effectiveness analyses; indicators of preparedness and system maturity 2. Country-level evaluations: national EBS implementation and performance assessment; integration with indicator-based surveillance; multi-sector evaluation across human, animal, and environmental health; operational and governance lessons 3. Regional and global surveillance networks: evaluation of regional early warning platforms and networked intelligence systems; performance assessment of global detection mechanisms including GOARN; cross-border surveillance collaboration 4. Digital and participatory surveillance: validation and benchmarking of AI-assisted signal detection; performance assessment of social media, open-source, and novel data stream tools; evaluation of participatory epidemiology platforms; algorithm auditing and reproducibility 5. Measuring the impact of early detection: case studies linking surveillance to outbreak mitigation; evidence of reduced response times and improved containment; counterfactual and natural-experiment approaches to impact attribution; implications for pandemic preparedness policy 6. Evaluation in resource-constrained and equity-focused contexts: adapted evaluation methods for low- and middle-income settings; community-based and participatory surveillance performance; equity-focused evaluation of global surveillance infrastructure; locally-led and sustainable approaches Topic editors: Stephanie Salyer, Carlos L. Correa-Martinez Source: https://www.frontiersin.org/research-topics/82803/evaluating-early-warning-surveillance-systems-measuring-performance-and-impact-for-global-health-security-and-pandemic-preparedness