|
Activity Number:
|
319
- Innovative Approaches to the Study of an Epidemic
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Wednesday, August 5, 2020 : 10:00 AM to 2:00 PM
|
|
Sponsor:
|
Section on Statistics in Epidemiology
|
|
Abstract #312648
|
|
|
Title:
|
Early Detection of Flu Season by Applying a Modified Bayesian Online Change Point Detection to Real-Time Flu Data Obtained from the AutoRegression with General Online Data Model
|
|
Author(s):
|
Jialiang Liu* and Sumihiro Suzuki
|
|
Companies:
|
University of North Texas Health Science Center and University of North Texas Health Science Center
|
|
Keywords:
|
Bayesian;
online change point detection;
big data;
flu surveillance
|
|
Abstract:
|
To improve flu surveillance, this study tested the feasibility of early detection of flu season onset by applying the modified Bayesian online change point detection (BOCPD) algorithm to real-time flu activity data obtained from the AutoRegression with General Online (ARGO) data model. The ARGO model uses Google flu-related search query data and historical CDC flu activity data to estimate flu activity in a real-time fashion. We used change point detection methods on the ARGO data to predict the dates of flu season onset and compared them to those reported by the CDC from 2007 to 2015. In applying the BOCPD algorithm to the ARGO data, we developed systematic ways to satisfy the necessary assumptions of the BOCPD algorithm making it more robust and practical for flu surveillance, and we proposed a method to determine informative change points that may signal the onset of flu seasons. Our strategy exhibits a high accuracy of prediction with the proportion of correct prediction being 86%. Additionally, our strategy on average detected flu season onset three weeks prior to the official flu season onset.
|
Authors who are presenting talks have a * after their name.
