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

How to Cite

Zhang, Y., Arab, A., Stoto, M. A., & Cowling, B. J. (2014). From Noise to Characterization Tool: Assessing Biases in Influenza Surveillance Methods Using a Bayesian Hierarchical Model. Online Journal of Public Health Informatics, 6(1). https://doi.org/10.5210/ojphi.v6i1.5106

Abstract

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.
https://doi.org/10.5210/ojphi.v6i1.5106
PDF
Authors own copyright of their articles appearing in the Online Journal of Public Health Informatics. Readers may copy articles without permission of the copyright owner(s), as long as the author and OJPHI are acknowledged in the copy and the copy is used for educational, not-for-profit purposes. Share-alike: when posting copies or adaptations of the work, release the work under the same license as the original. For any other use of articles, please contact the copyright owner. The journal/publisher is not responsible for subsequent uses of the work, including uses infringing the above license. It is the author's responsibility to bring an infringement action if so desired by the author.