Outbreaks of infectious diseases must be detected early for effective control measures to be introduced. When dealing with large amounts of data, automated procedures can usefully supplement traditional surveillance methods, provided that the wide variety of patterns and frequencies of infections are taken into account. This paper describes a robust system developed to process weekly reports of infections received at the Communicable Disease Surveillance Centre. A simple regression algorithm is used to calculate suitable thresholds. Organisms exceeding their threshold are then flagged for further investigation.
Originally called the Journal of the Statistical Society of London when founded in 1838 and renamed the Journal of the Royal Statistical Society when the Society was granted its Royal Charter in 1887, what is now known as Series A of the journal (Statistics in Society) is essentially a journal of general statistical interest. It publishes papers whose appeal lies in their subject-matter rather than their technical statistical content. Medical, social, educational, legal, demographic and governmental issues are of particular concern. The journal encourages serious statistical thinking on issues of importance to society, especially on substantive matters aimed at taking debate outside the confines of the statistical profession. JSTOR provides a digital archive of the print version of Journal of the Royal Statistical Society, Series A: Statistics in Society. The electronic version of Journal of the Royal Statistical Society, Series A: Statistics in Society is available at http://www.interscience.wiley.com. Authorized users may be able to access the full text articles at this site.
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Journal of the Royal Statistical Society. Series A (Statistics in Society)
© 1996 Royal Statistical Society
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