High performance computing for 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. Space-time detection techniques 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 techniques. Our work focuses on efficient parallelization of these computations on a Linux Beowolf cluster in order to attempt to meet these real time needs.