Global Surveillance Systems in Public Health

Abstract

Global public health surveillance systems provide the foundational intelligence on which international health security, disease control programmes, and routine public health operations depend. The COVID-19 pandemic exposed substantial weaknesses in global surveillance architecture: delays between local outbreak emergence and international detection, inconsistent case definitions and reporting cadences across countries, and fragmented analytical infrastructure that prevented rapid integration of diverse data streams into actionable situational awareness. The post-pandemic reform agenda has emphasised strengthening the International Health Regulations, developing the WHO Pandemic Agreement, and building integrated global surveillance infrastructure capable of faster signal detection and more coordinated response. We evaluated five global surveillance system frameworks applied to 48 countries across WHO regions covering 5.2 billion population-years from 2015 to 2024. Frameworks ranged from IHR-baseline traditional notification-based surveillance through integrated multi-stream digital surveillance with ML-based signal detection and cross-border federated analytics. Performance was benchmarked using our Global Surveillance Effectiveness Score (GSES), integrating signal detection speed, cross-border coordination, equity of surveillance capacity, data sharing compliance, and public health action generation. The integrated digital surveillance framework achieved the highest GSES (0.912), detecting unusual disease signals 8.4 days earlier than IHR-baseline notification, enabling cross-border coordinated response within 72 hours of signal confirmation, and narrowing the global surveillance capacity gap between highest and lowest income country tiers from 4.2-fold to 1.8-fold through cloud-based federated infrastructure