A multi-data source surveillance system to detect a bioterrorism attack during the G8 Summit in Scotland

Area of Science:

• Public Health
• Epidemiology
• Health Surveillance

Background:

• Large-scale events like the G8 Summit pose unique public health challenges due to potential disease outbreaks.
• Effective surveillance systems are crucial for early detection and response to public health threats.

Purpose of the Study:

• To deploy and evaluate a syndromic surveillance system for early detection of natural or intentional disease outbreaks during the 2005 G8 Summit.
• To integrate diverse clinical and non-clinical data streams for enhanced outbreak detection capabilities.

Main Methods:

• Development and implementation of a syndromic surveillance system within 18 weeks.
• Integration of clinical data (Accident & Emergency syndromes, General Practice codes) and non-clinical data (nurse helpline calls, lab orders, staff absenteeism).
• Utilized cumulative sum and log-linear regression models for signal detection, alongside a real-time fax-based track for unusual presentations.

Main Results:

• The system triggered 95 signals, with 13 investigated and four faxed for real-time assessment.
• Successfully identified a small cluster of gastroenteritis among the police force.
• Complemented traditional surveillance methods, leading to interventions that prevented further cases.

Conclusions:

• The deployed syndromic surveillance system proved effective in complementing traditional methods for early disease outbreak detection.
• The integrated data approach and detection algorithms facilitated timely public health interventions during a major international event.