AEGIS: A Robust and Scalable Real-time Public Health Surveillance System
- Ben Y Reis,
- Chaim Kirby,
- Lucy E Hadden,
- Karen Olson,
- Andrew J McMurry,
- James B Daniel,
- Kenneth D Mandl
- Affiliations of the authors: Children’s Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology (BYR, CK, LEH, KO, AJM, KDM), Harvard Medical School (BYR, KDM), Department of Public Health (JBD), Boston, MA
- Correspondence and reprints: Ben Y. Reis, PhD, Children’s Hospital Informatics Program at the Harvard-MIT Division of Health Sciences and Technology, 1 Autumn Street, Room 540.1, Boston, MA 02115;.e-mail: < >
- Received 2 December 2006
- Accepted 11 June 2007
In this report, we describe the Automated Epidemiological Geotemporal Integrated Surveillance system (AEGIS), developed for real-time population health monitoring in the state of Massachusetts. AEGIS provides public health personnel with automated near-real-time situational awareness of utilization patterns at participating healthcare institutions, supporting surveillance of bioterrorism and naturally occurring outbreaks. As real-time public health surveillance systems become integrated into regional and national surveillance initiatives, the challenges of scalability, robustness, and data security become increasingly prominent. A modular and fault tolerant design helps AEGIS achieve scalability and robustness, while a distributed storage model with local autonomy helps to minimize risk of unauthorized disclosure. The report includes a description of the evolution of the design over time in response to the challenges of a regional and national integration environment.
This work was supported by grants N01-LM-3-3515 and 5 R01 LM007677-04 from the National Library of Medicine, National Institutes of Health, and by contract number 5225 3 338CHI from the Massachusetts Department of Public Health, and by grant 1 R01 PH000040-01 from the Centers for Disease Control and Prevention.
The following people not mentioned above contributed significantly to making the AEGIS system a reality: John Brownstein, Lucas Jordan, Emmet Sprecher, and Albert Hong.