Presented by:
Yulia Gel University of Texas at Dallas, University of Waterloo
Date:
Tuesday 23rd August 2016 - 11:30 to 12:30
Venue:
INI Seminar Room 1
Abstract:
The prompt detection and
forecasting of infectious diseases with rapid transmission and high virulence
are critical in the effective defense against these diseases. Despite many
promising approaches in modern surveillance methodology, the lack of
observations for near real-time forecasting is still the key challenge
obstructing operational prediction and control of disease dynamics. For
instance, even CDC data for well monitored areas in USA are two weeks behind,
as it takes time to confirm influenza like illness (ILI) as flu, while two
weeks is a substantial time in terms of flu transmission. These
limitations have ignited the recent interest in searching for alternative near
real-time data sources on the current epidemic state and, in particular, in the
wealth of health-related information offered by modern social media. For example,
Google Flu Trends used flu-related searches to predict a future epidemiological
state at a local level, and more recently, Twitter and Wikipedia have also
proven to be a very valuable resource for a wide spectrum of public health
applications. In this talk we will review capabilities and limitations of such
social media data as early warning indicators of influenza dynamics in
conjunction with traditional time series epidemiological models and with more
recent random network approaches accounting for heterogeneous social
interaction patterns.