Tracking Infectious Diseases Outbreaks with Networks-Based Models

• Activity Number: 580
• Type: Invited
• Date/Time: Thursday, August 7, 2014 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Lilia Leticia Ramirez Ramirez*+ and Yulia R. Gel and Mark Dredze and Octavio Gutierrez-Garcia
• Companies: Instituto Tecnológico Autónomo de México and University of Waterloo and Johns Hopkins University and Instituto Tecnológico Autónomo de México
• Keywords: Epidemics ; Networks ; Simulation ; Outbreak ; Control Measures ; Health

Abstract

The prompt detection and forecasting of infectious diseases with rapid transmission and high virulence are critical for the effective defense against these diseases, and the lack of observations for near real-time forecasting remains the key challenge in modern epidemiology. This has ignited the recent interest in searching for alternative data sources on the current epidemic state and, in particular, in the wealth of information offered by modern social media. E.g., Google Flu Trends uses flu-related searches to assess an epidemiological state at a local level, and more recently, Twitter has proven to be a valuable resource for a wide spectrum of public health applications. However, nearly all work on social media to date focuses on producing national disease trends, i.e. searches are aggregated over space, do not account for heterogeneity of individuals, different sociodemographic patterns. In this talk, we discuss a new approach bridging the framework of dynamic compartmental models with random networks of social contacts and based on real-time health-related Twitter inputs as initialization for operational individual-specific forecasting of flu.