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Activity Number: 691
Type: Topic Contributed
Date/Time: Thursday, August 4, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics and the Environment
Abstract #320412 View Presentation
Title: Harnessing the Power of Twitter with Offline Contact Networks for Probabilistic Flu Forecasting
Author(s): Kusha Nezafati* and Leticia Ramirez-Ramirez and Yulia R. Gel
Companies: The University of Texas at Dallas and Mexico Autonomous Institute of Technology and The University of Texas at Dallas
Keywords: Biosurveillance ; Social Media ; Twitter ; Disease Dynamics ; Real-Time Forecasting ; Random Networks

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 disease prediction and control. In contrast, non-traditional data sources, such as online social media, create a new momentum for real-time epidemiological forecasting and have potential to revolutionize modern biosurveillance capabilities by predicting an event before its typical manifestation and before patient-healthcare interaction. In this talk we investigate utility of Twitter to serve as a proxy for yet unobserved or yet not publicly unavailable data on flu occurrence and propose a predictive platform for individual-specific disease dynamics by accounting for heterogeneous everyday social interactions, space-time, and socio-demographic information.

Authors who are presenting talks have a * after their name.

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