A Bayesian Approach to Characterize Hong Kong Influenza Surveillance Systems

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.