Evaluation of the AEGIS System: Is It a Grid?

Introduction: Significant challenges exist within biosurveillance domain due to needed data being in silos. These challenges may be potentially solved by emerging technologies, such as grid computing, based on the philosophy of efficiently and effectively coordinating distributed resources (data, computational power, etc). We took an incremental approach, the goal of the first phase being the identification of a biosurveillance system aligned with grid-computing architecture. This presentation will describe the first phase, in which we evaluated Harvard-based Automated Epidemiological Geotemporal Integrated Surveillance (AEGIS) System, for early detection of symptomatic manifestations of diseases, deployed at the Massachusetts Department of Public Health, USA.

Methods: We took a qualitative approach and conducted interviews, observations, and literature review for AEGIS system study, grid attributes descriptions, and development of an evaluation framework. The evaluation framework comprised of: Core principle, evaluation checklist, and comparative analysis. We based the evaluation framework on Ian Foster’s core principle, “Ultimately the Grid must be evaluated in terms of the applications, business value, and scientific results that it delivers, not its architecture.” We expanded the three-point checklist proposed by Ian Foster to a 12-point checklist for evaluating distributed information network architecture for the presence of grid attributes. We then comparatively analyzed the AEGIS system attributes using the expanded grid checklist.

Results: The investigation revealed that AEGIS architecture does in fact map to the key functional attributes defined within a standard grid computing architecture. AEGIS was also found to exhibit other attributes such as timeliness, robustness, and quality-of-data assurance that can potentially overcome biosurveillance domain challenges. However, technically AEGIS does not use classical grid middleware.

Conclusion: AEGIS system is functionally aligned with grid computing architecture. We plan to compare AEGIS with centralized biosurveillance systems in terms of outcome-based performance measures, to study the realized benefits of the grid-based architectural approach.