A Bayesian Approach to Characterize Hong Kong Influenza Surveillance Systems
PDF
HTML

How to Cite

Zhang, Y., Arab, A., & Stoto, M. A. (2013). A Bayesian Approach to Characterize Hong Kong Influenza Surveillance Systems. Online Journal of Public Health Informatics, 5(1). https://doi.org/10.5210/ojphi.v5i1.4567

Abstract

To develop a statistical tool for characterizing multiple influenza surveillance data for situational awareness, we used Bayesian statistical model incorporating factors such as disease transmission, behavior patterns in healthcare seeking and provision, biases and errors embedded in the reporting process, with the observed data from Hong Kong. The patterns in the ratios of paired data streams help to characterize influenza surveillance systems. To better interpret influenza surveillance data, behavior data related to healthcare resources utilization need to be collected in real-time.
https://doi.org/10.5210/ojphi.v5i1.4567
PDF
HTML
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.