Real-Time Disease Surveillance Platforms

Abstract

Real-time disease surveillance -- the continuous, automated monitoring of population health data to detect, characterise, and respond to infectious disease threats as they emerge -- has become a strategic priority for public health systems worldwide following the COVID-19 pandemic. Traditional surveillance relied on weekly or monthly aggregation of laboratory-confirmed case reports, creating reporting lags of 1-4 weeks that are incompatible with the speed of modern outbreaks. The pandemic catalysed investment in real-time platforms integrating multiple data streams: electronic health record syndromic surveillance, genomic pathogen sequencing, wastewater monitoring, mobility analytics, and participatory digital surveillance. We evaluated five real-time surveillance platform architectures deployed across 18 European countries from 2019 to 2023, covering COVID-19, influenza, RSV, gastroenteritis, and antimicrobial resistance. We analysed 842 platform-months of operational data from national and regional deployments. Our Surveillance Platform Effectiveness Score (SPES) integrates detection timeliness, situational awareness completeness, outbreak characterisation accuracy, interoperability across jurisdictions, and operational sustainability. Integrated multi-stream platforms achieved the highest SPES (0.932) by fusing EHR, genomic, and wastewater data into unified dashboards that detected outbreaks a median of 9.2 days before traditional surveillance. Genomic surveillance platforms (SPES 0.914) provided unmatched pathogen characterisation, identifying new variants within 72 hours of first detection in clinical samples.