Surveillance
Systems for Outbreak Detection of Infectious Diseases
Date: Monday 14 May 2012
Time: 14:00- 17:35
Venue: Royal Statistical Society,
Registration:
http://www.rss.org.uk/site/cms/contentEventViewEvent.asp?chapter=9&e=1286
Speakers:
Paddy Farrington (The
Open University,
Nick Andrews (Health
Protection Agency,
Kim Kavanagh (
Yann Le Strat (Institut
de Veille Sanitaire, Paris)
Simon Spencer (
2:00-2:50 Nick Andrews & Paddy Farrington
Automated outbreak
detection for multiple infections by Poisson regression.
This talk will describe the
outbreak detection system which has been in routine use for some two decades at
the Health Protection Agency. We will illustrate some of its advantages and
limitations, and describe current plans for improvements to it.
2:50-3:40
Kim Kavanagh
Syndromic surveillance
of influenza-like illness in
In
3:55-4:45
Yann Le Strat
An extreme value theory
approach for the early detection of time clusters with application to the
surveillance of Salmonella.
A new method based on an
extreme value theory approach is proposed to generate a warning system for the
early detection of time clusters applied to public health surveillance data.
This method is applied to Salmonella surveillance in
4:45-5:35
Simon Spencer
A Bayesian outbreak
detection model for campylobacteriosis in
Identifying potential
outbreaks of campylobacteriosis from a background of sporadic cases is made
more difficult by the large spatial and temporal variation in incidence. By assuming
that outbreaks are characterised by brief, spatially-localised periods of
increased risk, it becomes possible to use a Bayesian hierarchical model to
calculate an outbreak probability for each potential disease cluster.
Meeting organised by General
Applications Section
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