Statistical outbreak detection methods for large multiple surveillance systems

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Statistical outbreak detection involves computer-based monitoring of the numbers of cases of disease (for example, the numbers of individuals with a particular infection) over time. If that number rises above a certain threshold, an alert is declared and an investigation takes place to identify the cause of the increase. In the UK, such automated systems have been in use since the early 1990s to track many thousands of different infections on a weekly basis.The present project is to improve and evaluate the methods used for statistical outbreak detection applied to systems involving large numbers of infections. Because such systems must track large numbers of very different infections with very different patterns and frequencies, the statistical method used to calculate the threshold for each infection needs to be versatile, robust and fast. We will seek to improve the current system in several ways. We will try new methods for calculating thresholds which rely on fewer assumptions; we will improve the handling of anomalies in the data; we will try to increase the chance of detecting outbreaks that start with a gradual increase rather than a great surge; and we will seek ways to take account of the delay between a person becoming ill and finding out the cause of the illness. We will also study how to evaluate the system and how to avoid too many false alerts. Finally, we will undertake a large evaluation study to compare different methods for detecting outbreaks.We hope that the research will benefit the public by improving the detection of infectious disease outbreaks in the UK, by detecting them earlier and more reliably. If successful, the project will help public health officials make better decisions, and will reduce the burden of infectious diseases in the UK.