Statistical Models for Biosurveillance of Multiple Organisms

Doyo G. Enki, Angela Noufaily, C. P. Farrington, Paul H. Garthwaite, Nick Andrews, André Charlett, Chris Lane


Outbreak detection systems for use with very large multiple surveillance databases must be suited both to the data available and to the requirements of full automation. We analysed twenty years‰Û ª data from a large laboratory surveillance database used for outbreak detection in England and Wales. Our aim is to inform the development of more effective outbreak detection algorithms. We describe the diversity of seasonal patterns, trends, artefacts and extra-Poisson variability that an effective multiple laboratory-based outbreak detection system must cope with. We provide empirical information to guide the selection of simple statistical models for automated surveillance of multiple organisms.

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Online Journal of Public Health Informatics * ISSN 1947-2579 *