Facelli Julio

Surveillance of deaths due to influenza and pneumonia using death records has the potential to be a relatively inexpensive and quick approach to tracking and detecting influenza and respiratory illness outbreaks; however, presently such a system does not exist because of the time delays in processing death records: in Utah, as is similar elsewhere in the United States, coded death certificate data are typically not available for at least 1–3 months after the date of death, and coded national vital statistics data are not available until after 2–3 years.

Objective

This poster presents the rationale for designing a real-time surveillance system, based on mortality data, using grid and natural language processing tools that will address the current barrier that coded death certificate data not being available for several months. To develop a public health tool that delivers a timely surveillance system for influenza and pneumonia, we integrated death certificates from the Utah Department of Health, analytical grid services, and natural language processing tools to monitor levels of mortality. This example demonstrates how local, state, and national authorities can automate their influenza and pneumonia surveillance system, and expand the number of reporting cities.

Currently, there’s little effective communication and collaboration among public health departments. The lack of collaboration has resulted in more than 300 separate biosurveillance systems, which are disease specific, not integrated or interoperable, and may be duplicative. Grid architecture is a promising methodology to aid in building a decentralized health surveillance infrastructure because it encourages an ecosystem development culture, which has the potential to increase collaboration and decrease duplications.

Objective

This poster describes an approach which leverages grid technology for the epidemiological analysis of public health data. Through a virtual environment, users, particularly epidemiologists, and others unfamiliar with the application, can perform on-demand powerful statistical analyses.