Beyond aberration detection, coping with multiple exceedances in a national syndromic surveillance service


Public Health England uses data from four national syndromic surveillance systems to support public health programmes and identify unusual activity. Each system monitors a wide range of respiratory, gastrointestinal and other syndromes at a local, regional and national level. As a result, over 12,000 ‘signals’ (combining syndrome and geography) need to be assessed each day to identify aberrations. In this webinar I will describe how the ‘big data’ collected daily are translated into useful information for public health surveillance. Firstly, how statistical methods have been developed to automatically create ‘alarms’ when unusual activity is detected. Secondly, how the large number of automated alarms are ‘prioritised’ resulting in a manageable number of alarms to investigate using epidemiological methods. Finally, I will describe a risk assessment process developed to further investigate prioritised alarms and decide which require further public health action.


Roger Morbey, Statistical Project Lead, Real-time syndromic surveillance team, National Infection Service, Public Health England

Roger is a Chartered Statistician currently studying for a PhD by publication at Warwick University in “The practical application of statistical methods to improve the utility of syndromic surveillance in England.” His work involves developing and maintaining aberration detection methods for syndromic surveillance and the application of statistical methods to surveillance data.

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Event/Publication Date: 
October, 2016

March 15, 2017

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National Syndromic
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The National Syndromic Surveillance Program (NSSP) is a collaboration among states and public health jurisdictions that contribute data to the BioSense Platform, public health practitioners who use local syndromic surveillance systems, Center for Disease Control and Prevention programs, other federal agencies, partner organizations, hospitals, healthcare professionals, and academic institutions.

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