Major challenges in syndromic surveillance today include lack of standardization in syndrome definitions and limited ability to detect outbreaks of specific and rare diseases. To generate situational awareness surveillance results across various regions must be comparable and epidemiologically well defined. In addition, the high cost of obtaining and maintaining powerful computing resources (e.g., parallel computers) needed for data processing and analysis, and absence of a protocol for data sharing, highlight some of the obstacles to achieving situational awareness.
Cloud computing is an enabling technology that can overcome these challenges and facilitate new and novel approaches to surveillance.
Objective
We present a Cloud Computing based approach to disease surveillance that facilitates efficient data collection, processing and storage, as well as new concepts for data sharing and data fusion, disease search and situational awareness.