Surveillance Systems for Outbreak Detection of Infectious Diseases

 

Date: Monday 14 May 2012

Time: 14:00- 17:35

Venue: Royal Statistical Society, 12 Errol Street,

            London EC1Y 8LX

Registration:

 

http://www.rss.org.uk/site/cms/contentEventViewEvent.asp?chapter=9&e=1286

 

Speakers:

                                                  

Paddy Farrington (The Open University, Milton Keynes)

Nick Andrews (Health Protection Agency, London)     

Kim Kavanagh (University of Strathclyde, Scotland)

Yann Le Strat (Institut de Veille Sanitaire, Paris)

Simon Spencer (University of Warwick)

 

 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 Scotland during the Influenza A H1N1v pandemic and beyond.

 

In Scotland, data from the 24 hour telephone helpline NHS24, has been routinely monitored since 2006 using an exception reporting system.  This talk describes the system and demonstrates usage during the Influenza A pandemic in 2009 and usage in subsequent influenza seasons.  The caveats of the system, in particular the influence of the media and “worried well” on the outputs are discussed as are possible developments and extensions to the current implementation.

 

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 France and compared to other methods.

 

4:45-5:35 Simon Spencer

A Bayesian outbreak detection model for campylobacteriosis in New Zealand.

 

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