The world’s first comprehensive, automated outbreak detection system which will monitor over 3,000 infections and is ready to run during Olympics 2012 was developed by a researcher at The Open University.
Paddy Farrington, Professor of Statistics at the OU, began work on the outbreak detection system while he was at the Health Protection Agency (HPA) in the early 1990s. The system has proved its worth over the years and will be run by HPA during the period of the London 2012 Olympics.
The system has already contributed to the detection and control of numerous outbreaks of infections including:
- An outbreak of Salmonella enteritidis phage type 14b in 2009; an outbreak of Salmonella Java in 2010; an outbreak of Salmonella Poona in 2012
- The system has been implemented in Sweden, southern Germany, the Netherlands, Denmark; and been adapted to detect excess mortality in Belgium
- Enhanced surveillance using the system is also planned for the 2014 Commonwealth Games, Glasgow
The system is based on a set of algorithms known as Robust Poisson Regression (RPR). Outbreak detection starts with the detection of an unusual number of reported cases of a particular infection in a given time and space. Computer programs are used to compare the observed number of cases with expected values. When an increase is detected, the program raises an alert, which epidemiologists assess to determine if further investigation is warranted. If an outbreak is confirmed, further investigations follow and control measures are taken.
“Much interest in the use of statistical surveillance systems has been driven by concerns over bio-terrorism, the emergence of new pathogens like SARS and swine flu, and the persistent public health problems of infectious disease outbreaks,” said Professor Farrington, who is working on a new version of the system.
“Our system is the first to offer a comprehensive way to detect such outbreaks. A challenge in designing large multiple outbreak detection systems is to control the proportion of false alarms without impairing the detection of genuine outbreaks. Through improvements to the existing system, we managed to reduce the number of false alarms by 50 per cent without impairing performance.”
