Abstract Details
Activity Number:
|
580
|
Type:
|
Invited
|
Date/Time:
|
Thursday, August 7, 2014 : 8:30 AM to 10:20 AM
|
Sponsor:
|
Section on Statistics in Epidemiology
|
Abstract #310546
|
|
Title:
|
Tracking Epidemics with Google Flu Trends Data and a State-Space SEIR Model
|
Author(s):
|
Vanja Dukic*+ and Hedibert Lopes and Nicholas Polson
|
Companies:
|
University of Colorado at Boulder and University of Chicago and Booth School of Business
|
Keywords:
|
SEIR ;
state space models ;
Google Flu Trends ;
surveillance ;
particle learning ;
epidemics
|
Abstract:
|
In this talk we use Google Flu Trends data together with a sequential surveillance model based on the state-space methodology, to track the evolution of an epidemic process over time. We embed a classical mathematical epidemiology model (a susceptible-exposed-infected-recovered (SEIR) model) within the state-space framework, thereby allowing the classic SEIR dynamics to allow changes through time. The implementation of this model is based on a particle filtering algorithm, which learns about the epidemic process sequentially through time, and provides updated estimates of epidemic parameters and states with each new surveillance data point. We show how this approach, in combination with sequential Bayes factors, can serve as an on-line diagnostic tool for influenza pandemic. We take a close look at the Google Flu Trends data describing the spread of flu in the US during 2003-2009.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2014 program
|
2014 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Professional Development program, please contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Copyright © American Statistical Association.