This paper is in the following e-collection/theme issue:

Use Cases in Human, Animal, and Zoonotic Communicable/Infectious Disease Surveillance (129) Theme Issue: Bayesian Methods In Medical Research (26) Validity, Reliability, and Bias (89) Influenza and Influenza-Like-Illness (ILI) Surveillance (203)

Published on in Vol 6, No 1 (2014):

From Noise to Characterization Tool: Assessing Biases in Influenza Surveillance Methods Using a Bayesian Hierarchical Model

From Noise to Characterization Tool: Assessing Biases in Influenza Surveillance Methods Using a Bayesian Hierarchical Model

From Noise to Characterization Tool: Assessing Biases in Influenza Surveillance Methods Using a Bayesian Hierarchical Model

Authors of this article:

Ying Zhang1 ;   Ali Arab1 ;   Michael A. Stoto1 ;   Bejamin J. Cowling2
The full text of this article is available as a PDF download by clicking here.
Article Authors Cited by Tweetations Metrics

    Infectious disease surveillance is a process, the product of which reflects both real illness and public awareness of the disease. To develop a statistical framework to characterize influenza surveillance systems, Bayesian hierarchical model was applied to estimate the statistical relationships between influenza surveillance data and information environment (e.g. HealthMap, Google search volume,etc.) The model identified characteristics of surveillance systems that are more resistant to the information environment (percentage data, broad case definition and the senior population). General practitioner (%ILI-visit) and Laboratory (%positive) seem to capture the true infection at a constant proportion, and are less influenced by information environment.