Noufaily Angela

There has been much research on statistical methods of prospective outbreak detection that are aimed at identifying unusual clusters of one syndrome or disease, and some work on multivariate surveillance methods. In England and Wales, automated laboratory surveillance of infectious diseases has been undertaken since the early 1990’s. The statistical methodology of this automated system is described in. However, there has been little research on outbreak detection methods that are suited to large, multiple surveillance systems involving thousands of different organisms.

Objective

To look at the diversity of the patterns displayed by a range of organisms, and to seek a simple family of models that adequately describes all organisms, rather than a well-fitting model for any particular organism.

There has been much interest in the use of statistical surveillance systems over the last decade, prompted by concerns over bio-terrorism, the emergence of new pathogens such as SARS and swine flu, and the persistent public health problems of infectious disease outbreaks. In the United Kingdom (UK), statistical surveillance methods have been in routine use at the Health Protection Agency (HPA) since the early 1990s and at Health Protection Scotland (HPS) since the early 2000s (1,2). These are based on a simple yet robust quasi-Poisson regression method (1). We revisit the algorithm with a view to improving its performance.

Objective

To improve the performance of the England and Wales large scale multiple statistical surveillance system for infectious disease outbreaks with a view to reducing the number of false reports, while retaining good power to detect genuine outbreaks.