Overview of Grid

The scope of emerging Grid research within public health, and specifically within NCPHI at CDC, is to translate research knowledge to enable better health outcomes within communities and to develop information that "health decision makers" (i.e., individuals, epidemiologists, medical practitioners, payers and administrators) can use.  Providing software as a service requires attention to data, workflow and outcomes. Data management is an essential component that often requires physical experimentation and measurement to enable the digitization of medical data for standard network transit, storage, analysis and visualization. Three classes of applications have proven to benefit from Grid computing. COMPUTE INTENSIVE applications, including interactive simulation (surgical simulation and modeling), very large-scale simulation and analysis (Public Health Information Networks, systems biology frameworks, NeuroCommons), and engineering (parameter studies and linked component models). DATA INTENSIVE applications, including experimental data analysis (computational biology), imaging (Radiology PACS) and sensor analysis (biosurveillance, situational awareness).  DISTRIBUTED COLLABORATION applications, including online instrumentation (microscopes, mass spectrometry, medical devices), remote visualization (advanced distributed learning (anatomy curriculum, public health population studies), and engineering (large-scale structural testing of distributed systems, biochemical engineering) The widespread use of grid computing may be the next logical step on the public health development path from computation, to shared datasets, and ultimately to knowledge-based environments. Grid computing offers functionality and methods to build an interaction framework for public health application environments. A primary benefit is the ability to re-use Grid services without having to rebuild each time as new user requirement arises.