Automated biosurveillance data from England and Wales, 1991-2011

Doyo G. Enki, Angela Noufaily, Paul H. Garthwaite, Nicholas Andrews, Andre Charlett, Christopher Lane, C. Paddy Farrington

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)

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. To inform the development of more effective outbreak detection algorithms, we analyzed 20 years of data (1991 - 2011) from a large laboratory surveillance database used for outbreak detection in England and Wales. The data relate to 3,303 distinct types of infectious pathogens, with a frequency range spanning 6 orders of magnitude. Several hundred organism types were reported each week. We describe the diversity of seasonal patterns, trends, artifacts, and extra-Poisson variability to which an effective multiple laboratory-based outbreak detection system must adjust. We provide empirical information to guide the selection of simple statistical models for automated surveillance of multiple organisms, in the light of the key requirements of such outbreak detection systems, namely, robustness, flexibility, and sensitivity.

Original languageEnglish
Pages (from-to)35-42
Number of pages8
JournalEmerging Infectious Diseases
Volume19
Issue number1
DOIs
Publication statusPublished - Jan 2013

Fingerprint

Dive into the research topics of 'Automated biosurveillance data from England and Wales, 1991-2011'. Together they form a unique fingerprint.

Cite this