This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 160
Type: Topic Contributed
Date/Time: Monday, August 2, 2010 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Defense and National Security
Abstract - #307959
Title: Using Bayesian Predictive Distributions to Identify Outbreaks of Influenza-Like Illness
Author(s): Owen Devine*+ and Howard Burkom
Companies: CDC and The Johns Hopkins University
Address: MS E-87, Atlanta, GA, 30333,
Keywords: Bayesian Methods ; Zero Inflated Poisson ; Influenza ; Over Dispersion ; Random Effects Models

ILINet is a surveillance network comprised of volunteer health care providers who supply CDC with weekly reports on the proportion of patient encounters meeting influenza-like illness (ILI) criteria. CDC uses this system both to monitor trends in ILI and to identify potential influenza outbreaks. Due to substantial variation across weeks in both the number and specialties of the participating providers, frequentist approaches for outbreak identification can be highly unstable. We use Bayesian methods to generate predictive distributions of the ILI percentage during non-influenza periods enabling estimation of the probability of observing a surveyed rate if the area/week is in a non-influenza state. The models account for heterogeneity in the specialties of reporting providers over time and reflect the substantial over dispersion and excess zero counts common in ILI surveillance data.

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