Statistical Models for Biosurveillance of Multiple Organisms
PDF
HTML

How to Cite

Enki, D. G., Noufaily, A., Farrington, C. P., Garthwaite, P. H., Andrews, N., Charlett, A., & Lane, C. (2013). Statistical Models for Biosurveillance of Multiple Organisms. Online Journal of Public Health Informatics, 5(1). https://doi.org/10.5210/ojphi.v5i1.4396

Abstract

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.
https://doi.org/10.5210/ojphi.v5i1.4396
PDF
HTML
Authors own copyright of their articles appearing in the Online Journal of Public Health Informatics. Readers may copy articles without permission of the copyright owner(s), as long as the author and OJPHI are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. Share-alike: when posting copies or adaptations of the work, release the work under the same license as the original. For any other use of articles, please contact the copyright owner. The journal/publisher is not responsible for subsequent uses of the work, including uses infringing the above license. It is the author's responsibility to bring an infringement action if so desired by the author.