Newsflash

August 2013 - The NIH awarded Dr. Wagner R01 funding for a project entitled “Probabilistic Disease Surveillance.”  

 
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History of the RODS Laboratory

RODS Milestones

  • September 1999 - Formation of the RODS Laboratory by Drs. Jeremy Espino, Fu-Chiang Tsui and Michael Wagner.  Prototype of RODS Surveillance System begins to monitor emergency departments of University of Pittsburgh Medical Center.
  • October 2001 - Established 13 county surveillance of emergency departments for Western Pennsylvania using RODS.
  • November 2001 - Dr. Michael Wagner delivers a report on biosurveillance before the US Congress
  • January 2002 - Deployment of Utah RODS for 2002 Winter Olympics
  • February 2002 - President George W. Bush visits the RODS lab and calls RODS the "modern DEW line" 
  • December 2002 - RODS Software is made publicly available as free software
  • November 2002 - The National Retail Data Monitor is created
  • August 2003 - The RODS Software is made open source
  • May 2006 - The Handbook of Biosurveillance is published
  • September 2009 - The University of Pittsburgh becomes a CDC Center of Excellence in Public Health Informatics
  • June 2012 - Deployed the Influenza Monitoring System in Allegheny County, PA
  • April 2013 - First release of the Apollo Web Services
  • November 2013 - 1.3.1 release of Apollo Web Services

Research on Disease Surveillance (1999-2005)

In August 1999, Dr. Michael Wagner saw how his work on decision-theoretic clinical decision support was the basis for a new paradigm of disease surveillance and control. He requested and received permission from the National Library of Medicine (NLM) to change the focus of his current research and founded the Real-time Outbreak and Disease Surveillance (RODS) laboratory.   With Drs. Espino and Tsui, he began to develop the RODS system. 

In 2001, Drs. Wagner, Tsui and Espino published papers on case detection and outbreak detection from routinely collected ICD-9-coded chief complaints, which helped define what was possible and metrics and experimental designs for research in case and outbreak detection. They also published more general papers about the paradigm and its mathematical foundations.

In early 2002, the RODS Laboratory deployed the RODS system for real-time surveillance at the Salt Lake City, Utah Winter 2002 Olympics.  The significance of the research was indicated by a visit to Pittsburgh on Feb. 5, 2002 by President Bush, Secretary of DHHS Tommy Thompson and soon-to-be Secretary of the Department of Homeland Security, Tom Ridge.  

From 2000-2013, the RODS Laboratory worked on projects funded by AHRQ, CDC, DARPA, DHS, the Commonwealth of PA, and the Sloan Foundation.  These projects developed real-time data collection systems involving hundreds of healthcare systems and 30,000 stores that sell over-the-counter healthcare products.   The research informed many dimensions of practice and analysis including the value of different kinds of secondary data for outbreak detection, algorithmic analytic methods, evaluations and the pragmatics of deployment and operation, natural language processing algorithms for case detection, and outbreak-detection algorithms.  The projects identified and addressed a range of privacy and legal issues. 

The projects at the RODS Laboratory also spawned the Bayesian Aerosol Release Detector (BARD) and PANDA algorithms, whose development continued under an NSF grant to develop algorithms for Bayesian biosurveillance, and a CDC grant to further develop BARD.

In 2005, the RODS Laboratory and its partners wrote a book to explain the new paradigm of biosurveillance so as to accelerate its adoption.  The Handbook of Biosurveillance was published in 2006.  

After the RODS system was deployed at the 2002 Olympics, the basic approach became a requirement for local and state health departments to receive CDC funding; hospital participation was later incentivized by Medicare “meaningful use” requirements.  The system has been operated by a private company since 2006, servicing 12 states. Thus, the RODS project has joined the small fraternity of biomedical informatics innovations that have completed the full translation-to-practice cycle—from concept to capitalized product as described by the NLM’s Biomedical Study Section in its 1994 paper on applied informatics projects.

The significance of this body of work on real-time disease surveillance was that it helped set in motion a paradigm shift in outbreak detection and decision making that is still unfolding. 

Research on Decision Making in Biosurveillance (2005-2013)

In 2005, the RODS Laboratory decided to attack the problem of optimal decision-making directly by building decision models. and decision-theoretic systems.  The laboratory developed a prototype program called BioEcon that automatically constructed decision (theoretic) trees for high risk, time critical decisions about how to respond when faced with uncertainty about the characteristics or even existence of an epidemic.  

In 2008, the NLM awarded the RODS Laboratory funding to further develop BioEcon and to perform additional decision analyses.  The project published the mathematical basis of BioEcon and has made the BioEcon program and tutorial available at http://research.rods.pitt.edu/bioecon/).  Many decision analyses were published by co-Investigator Bruce Lee’s team.   

Research on Probabilistic, Decision-theoretic Disease Surveillance and Control (2009-2016) 

In 2009, the RODS Laboratory returned the focus to disease surveillance to meet a need of the decision models identified by the BioEcon research, which use epidemic simulators as deterministic nodes in its decision models. The problem was that epidemic simulators had to be initialized with the incidence of disease and level of immunity of a population on the day of decision.  Disease surveillance systems simply did not have this capability. 

In 2009, the CDC awarded funded the RODS Laboratory to create a Center of Excellence to develop the requisite disease surveillance capability and thoroughly evaluate it.  By year 2 of that project, the Center had developed and deployed an operational Influenza Monitoring System in Allegheny County with the requisite capability and evaluated its performance using real data from the 2009 H1N1 outbreak with promising results. However, further development was abruptly put on hold when the CDC cut funding to all four Centers of Excellence in Public Health Informatics.

In 2013, the NLM awarded the RODS Laboratory to continue to pursue research on "Probabilistic Disease Surveillance," rekindling the laboratory's research in probabilistic, decision-theoretic disease surveillance.  The goal of this project is to install and evaluate an extended version of the Allegheny County system in both Allegheny County, PA and Salt Lake County, Utah.

Research on Improving Access to Epidemic Simulators and on Representing Epidemic Scenarios and Ecosystems for Simulation (2012-2019)

In 2010, the Lockheed Martin Corporation provided the RODS Laboratory with seed funding to work on improving access to epidemic simulators.  The result of this 1-year effort was the first version of the Apollo Web Services, which are a set of application programming interfaces that define how programs such as disease surveillance systems, end-user applications and other systems will access epidemic simulators. 

In 2012, the National Institute of General Medical Sciences (NIGMS) awarded R01 funding to the RODS Laboratory to develop a standard computable language for representing infectious disease scenarios for simulation. The significance of a standard representation is that it will allow analysts to run the same infectious disease scenario on more than one epidemic simulator, which was a highly desired form of sensitivity analysis during the 2009 H1N1 pandemic.  The project has published the Apollo standard and the Apollo Web Services, made three epidemic simulators available, and created three end-user applications powered by Apollo. 

The Apollo project has also implemented a ‘library’ of standardized computable knowledge about infections, treatments and control strategies that analysts can use to build new models and store the parameters of their existing models.  The library includes an ontology-based catalog of published disease models that enables the analyst to query for computable representations suitable for further use/reuse. 

 

 

 

 
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