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

Authors

  • Ying Zhang Georgetown University, Reston, VA, United States
  • Ali Arab Georgetown University, Reston, VA, United States
  • Michael A. Stoto Georgetown University, Reston, VA, United States
  • Bejamin J. Cowling The University of Hong Kong, Hong Kong, Hong Kong.

DOI:

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.

Author Biography

Ying Zhang, Georgetown University, Reston, VA, United States

Dr. Ying Zhang is a recent PhD graduate from 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 surveillance research projects using both qualitative and quantitative approaches, including syndromic systems design and implementation, pH1N1 outbreak analysis, organizational response to pH1N1 outbreak at health departments, influenza surveillance modelling and so forth.

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Published

2014-03-09

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

Issue

Section

Poster Presentations