Can syndromic surveillance data detect local outbreaks of communicable disease? A model using a historical cryptosporidiosis outbreak

Sarah O'Brien, Duncan L. Cooper, N. Q. Verlander, G. E. Smith, A. Charlett, E. Gerard, L. Willocks, S. O'Brien

    Research output: Contribution to journalArticlepeer-review

    Abstract

    A national UK surveillance system currently uses data from a health helpline (NHS Direct) in an attempt to provide early warning of a bio-terrorist attack, or an outbreak caused by a more common infection. To test this syndromic surveillance system we superimposed data from a historical outbreak of cryptosporidiosis onto a statistical model of NHS Direct call data. We modelled whether calls about diarrhoea (a proxy for cryptosporidiosis) exceeded a statistical threshold, thus alerting the surveillance team to the outbreak. On the date that the public health team were first notified of the outbreak our model predicted a 4% chance of detection when we assumed that one-twentieth of cryptosporidiosis cases telephoned the helpline. This rose to a 72% chance when we assumed nine-tenths of cases telephoned. The NHS Direct surveillance system is currently unlikely to detect an event similar to the cryptosporidiosis outbreak used here and may be most suited to detecting more widespread rises in syndromes in the community, as previously demonstrated. However, the expected rise in NHS Direct call rates, should improve early warning of outbreaks using call data. © 2005 Cambridge University Press.
    Original languageEnglish
    Pages (from-to)13-20
    Number of pages7
    JournalEpidemiology and infection
    Volume134
    Issue number1
    Publication statusPublished - Feb 2006

    Fingerprint

    Dive into the research topics of 'Can syndromic surveillance data detect local outbreaks of communicable disease? A model using a historical cryptosporidiosis outbreak'. Together they form a unique fingerprint.

    Cite this