Hospital infections and antimicrobial resistance are scourges of modern society, affecting several million Americans annually and wasting billions of dollars. Timely, accurate data analysis is critical to preventing the spread of infection and resistance. Preliminary results show that treating clinical microbiology data as signals and applying various analytical techniques to them has tremendous potential to improve the detection of outbreaks and shifts in resistance early on, while the possibility for effective intervention still exists. Building on these results, this research aims to fully characterize at least five years of historical microbiology data from three prominent hospitals. This will be used as training data to optimize early detection techniques through the use of genetic algorithms (GAs) using a Linux supercomputing cluster. Surveys will be conducted of the infection control personnel before and after a one year trial of an early prototype real-time monitoring system to determine the impact such a system has on their ability to prevent the spread of infectious disease. Success will be measured by the survey results as well as by the number of outbreaks detected in a timely manner. This research will extend the scientific understanding of GAs, infectious disease surveillance, and cluster detection.

Proposed Commercial Applications

Hospital infections and antimicrobial resistance are enormous problems for hospitals. Every hospital has infection control program, and every program must review microbiology data. A surveillance system built upon the research proposed herein will enable healthcare facilities to detect outbreaks and trends in a timely manner and actively intervene to prevent the spread of disease.

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Small Business Innovation Research Grants (SBIR) - Phase II (R44)
Project #
5R44AI048332-04
Application #
6730539
Study Section
Special Emphasis Panel (ZRG1-SSS-9 (12))
Program Officer
Peters, Kent
Project Start
2000-08-15
Project End
2006-03-31
Budget Start
2004-04-01
Budget End
2006-03-31
Support Year
4
Fiscal Year
2004
Total Cost
$806,636
Indirect Cost
Name
Vecna Technologies, Inc.
Department
Type
DUNS #
094078958
City
Greenbelt
State
MD
Country
United States
Zip Code
20770