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APHA Scientific Session and Event Listing

Recorded presentation

Al Ozonoff, PhD, Suporn Sukpraprut, MS, and Paola Sebastiani, PhD. Biostatistics, Boston University School of Public Health, 715 Albany Street, T4E, Boston, MA 02118, 617-638-5866, aozonoff@bu.edu

The spread of SARS during 2002-2003, the shortage of flu vaccine in 2004, and the looming threat of an influenza pandemic caused by the H5N1 virus motivate the urgent need for surveillance systems able to provide early quantitative predictions of acute respiratory infections.

Influenza is a leading cause of death for all age groups, but disproportionately affects the very old and very young. Because influenza viruses continuously undergo genetic changes, surveillance efforts are in place to detect changes in the viruses and to monitor their effects on morbidity and mortality. Influenza surveillance in the U.S. is managed by the Centers for Disease Control and Prevention, which monitors the effect of influenza on mortality through weekly reports filed by the vital statistics offices of 122 cities that contain the counts of deaths for which pneumonia or influenza were listed as a contributing cause of death.

The current influenza surveillance system collects these data streams individually but monitors them in the aggregate (e.g. at the national level). Detection methods currently use an epidemic threshold that is calculated using a seasonal baseline defined by a cyclic regression model.

In this work we discuss novel methods for integration and aggregation of mortality data with the objectives to derive local thresholds for detection of influenza epidemics. We demonstrate a novel Bayesian clustering procedure that suggests how to aggregate data from the 122 cities, and dynamic models built on aggregate data that can be used for prediction of epidemic trends.

Learning Objectives: At the conclusion of the session, the participant (learner) in this session will be able to

• Describe and discuss basic principles of infectious disease surveillance, and influenza surveillance in particular.
• Analyze influenza surveillance data using basic modeling techniques such as ordinary regression.
• Apply Bayesian methodology to influenza surveillance data.

Keywords: Surveillance, Infectious Diseases

Presenting author's disclosure statement:

Any relevant financial relationships? No

Recorded presentation