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WSC
2008

An application of parallel Monte Carlo modeling for real-time disease surveillance

14 years 9 days ago
An application of parallel Monte Carlo modeling for real-time disease surveillance
The global health, threatened by emerging infectious diseases, pandemic influenza, and biological warfare, is becoming increasingly dependent on the rapid acquisition, processing, integration and interpretation of massive amounts of data. In response to these pressing needs, new information infrastructures are needed to support active, real time surveillance. Detection algorithms may have a high computational cost in both the time and space domains. High performance computing platforms may be the best approach for efficiently computing these algorithms. Unfortunately, these platforms are unavailable to many health care agencies. Our work focuses on efficient parallelization of outbreak detection algorithms within the context of cloud computing as a high throughput computing platform. Cloud computing is investigated as an approach to meet real time constraints and reduce or eliminate costs associated with real time disease surveillance systems.
David W. Bauer, Mojdeh Mohtashemi
Added 02 Oct 2010
Updated 02 Oct 2010
Type Conference
Year 2008
Where WSC
Authors David W. Bauer, Mojdeh Mohtashemi
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