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SUMMARY:The Role of Modern Social Media Data in Surveillance and Predictio
 n of Infectious Diseases: from Time Series to Networks - Yulia Gel (Univer
 sity of Texas at Dallas\; University of Waterloo)
DTSTART:20160823T103000Z
DTEND:20160823T113000Z
UID:TALK67007@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:The prompt detection and forecasting of infectious diseases wi
 th rapid transmission and high virulence are critical in the effective def
 ense against these diseases. Despite many promising approaches in modern s
 urveillance methodology\, the lack of observations for near real-time fore
 casting is still the key challenge obstructing operational prediction and 
 control of disease dynamics. For instance\, even CDC data for well monitor
 ed areas in USA are two weeks behind\, as it takes time to confirm influen
 za like illness (ILI) as flu\, while two weeks is a substantial time in te
 rms of flu transmission.&nbsp\; These limitations have ignited the recent 
 interest in searching for alternative near real-time data sources on the c
 urrent epidemic state and\, in particular\, in the wealth of health-relate
 d information offered by modern social media. For example\, Google Flu Tre
 nds used flu-related searches to predict a future epidemiological state at
  a local level\, and more recently\, Twitter and Wikipedia have also prove
 n to be a very valuable resource for a wide spectrum of public health appl
 ications. In this talk we will review capabilities and limitations of such
  social media data as early warning indicators of influenza dynamics in co
 njunction with traditional time series epidemiological models and with mor
 e recent random network approaches accounting for heterogeneous social int
 eraction patterns.
LOCATION:Seminar Room 1\, Newton Institute
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