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Zhang Ying

Syndromic surveillance has been widely adopted as a real-time monitoring tool in early response to disease outbreaks. In order to provide real-time information on the impact of 2009 H1N1 during the Fall 2009 semester, Georgetown University (GU) and George Washington University (GWU) employed syndromic surveillance systems incorporating a variety of data sources. 

 

Objective

To describe the 2009 H1N1 outbreak at GU and GWU in Fall 2009. Identify the datasets that most accurately depict 2009 H1N1 disease in real time.

Referenced File
Submitted by hparton on

Infectious disease surveillance is a process, the product of which reflects both real illness and public awareness of the disease (Figure 1). According to our previous research studies [1,2], decisions made by patients, healthcare providers, and public health professionals about seeking and providing healthcare and about reporting cases to health authorities are all influenced by the information environment, which changes constantly. Biases are therefore imbedded in each surveillance systems, and need to be assessed to provide better situational awareness for decision-making.

Objective

Our goal is to develop a statistical framework to characterize influenza surveillance systems and their sensitivity to information environment.

Submitted by knowledge_repo… on

Syndromic surveillance has been widely used in influenza surveillance worldwide. However, despite the potential benefits created by the large volume of data, biases due to the changes in healthcare seeking behavior and physicians’ reporting behavior, as well as the background noise caused by seasonal flu epidemics, contribute to the complexity of the surveillance system and may limit its utility as a tool for early detection. Since most current analysis methods are developed for outbreak detection, there are few tools to characterize influenza surveillance data for situational awareness purposes in a quantitative manner. Hong Kong Centre for Health Protection has a comprehensive influenza surveillance system based on healthcare providers, laboratories, schools, daycare centers and residential care homes for the elderly. Hong Kong usually experiences a summer peak in July and August, which potentially doubles the data volume and constitutes a natural experiment to assess the effect of school-age children in the influenza transmission dynamics. The richness of the available data and the unique epidemiological characteristics make Hong Kong an ideal study object to develop and evaluate our model.

Objective

Our goal is to develop a statistical model for characterizing influenza surveillance systems that will be helpful in interpreting multiple streams of influenza surveillance data in future outbreaks.

Submitted by rmathes on
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