A Bayesian Approach to Characterize Hong Kong Influenza Surveillance Systems

Authors

  • Ying Zhang Georgetown University
  • Ali Arab Georgetown University
  • Michael A. Stoto Georgetown University

DOI:

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.

Author Biography

Ying Zhang, Georgetown University

Ying Zhang is a PhD candidate for Global Infectious Diseases program at Georgetown University. She graduated from Fudan University Medical School in China with a bachelor degree in Medicine. She has worked on various influenza related projects, including influenza surveillance systems design and implementation, pH1N1 outbreak analysis, and flu surveillance modeling as her thesis topic, under the supervision of Dr. Michael Stoto and Dr. Ali Arab.

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Published

2013-03-24

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

Issue

Section

Poster Presentations