The RODS Open Source Project
Open Source Outbreak and Disease Surveillance Software

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RODS is available as open source software under the terms of the Affero Public License. You can use the software freely, but if you improve it you must contribute your code to the RODS project.

Please be aware that the links to files below may direct your browser to another Web page offering a number of mirror sites. Do not use "Save as..." on these links. BTW: Latest, development versions of the code can be found at in our subversion source code repository. If you want to become a committer ask Jeremy.

File Size, Bytes Description 454,111 SyCo Version 1.0 Source Code 52,383,942 (49.9M) RODS Version 4.2.5 Source Code 24,069,516 (24M)

RODS Version 4.1 Source Code 19,094,501 (19M) RODS Version 4.0.6 User Interface Source Code 25,741,216 (25M) RODS Version 3.0 Source Code 19640 CoCo Version 3.0 Source Code and Binary 6682517 (6M) RODS Version 2.0.3a Source Code 6674834 (6M) RODS Version 2.0.3 Source Code
rods-1.6.tar.gz 656260 RODS 1.6 Source Code

To compile the source code and use the entire RODS system you'll need to:

  • Compile the source code using Sun's JDK1.5+ compiler, Ant, and Xdoclet.
  • Install Tomcat (open source)
  • Install Apache (open source) and jk (for HTTPS)
  • Install Oracle or Microsoft SQL Server(commercial license)
  • Use ArcIMS and ArcSDE for GIS functions (prior to Version 5)

The RODS Laboratory runs this code in a production setting using Linux on Sun hardware. Compiling and installing RODS and its associated software packages requires the following skills: Java, Ant, J2EE, Tomcat administration, database administration, Apache administration and ArcIMS expertise. Connecting hospitals requires network administration and HL7 expertise.

Compiled Software Packages

Package Site Description
RODS 3.0 Demo for Windows Sourceforge RODS 3.0 Demo for Windows
WSARE Auton Lab WSARE: What's Strange About Recent Events (outbreak detection algorithm from Auton Labs)
Scan Statistics Auton Lab A fast implementation of scan statistic search for spatial overdensities. Our goal is to find rectangular regions where the count (e.g. number of disease cases) is higher than expected, given the underlying population distribution.

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