Infectious disease informatics

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This June 2009 map shows number of swine flu cases per U.S. county. Collecting such data and clearly representing it is just one of the various challenges facing infectious disease informaticians.

Infectious disease informatics (IDI) is a multidisciplinary field of science that focuses on "the development of the science and technologies needed for collecting, sharing, reporting, analyzing, and visualizing infectious disease data and for providing data and decision-making support for infectious disease prevention, detection, and management."[1] The field has expanded over time from analyzing public health laboratory data for potential disease vectors to a more robust syndromic surveillance of epidemiological factors[2] and and to more advanced bioinformatic approaches towards microbial, biomarker, and computational research.[3]

History

In the late 1990s and early 2000s, infectious disease data was increasingly being collected and stored in an electronic format by laboratories of all types at local, state, national, and international levels. As this data format became more common and data transmission requirements to entities like the Centers for Disease Control and Prevention (CDC) became more stringent, custom in-house infectious disease information systems were developed to better access, analyze, and report the associated data. Despite the increase in the number of these systems, some challenges remained for them in the mid-2000s: they required better interoperability among other such systems; improvements in system scalability, user interfaces, model sophistication; a more efficient reporting and alert system across organization boundaries; and a more scrutinous approach to legal issues surrounding data sharing and transmittal to and from those systems.[1]

As these challenges were overcome, IDI was clearly becoming more than an application of informatics to disease tracking and reporting. Biomedical research into and biosurveillance of infectious diseases became more sophisticated, requiring equally sophisticated informatics tools to manage, analyze, report, and share the deluge of data. Microbial genome sequence analysis, biomarker discovery, and bacterial computational models and simulations have brought new data-driven approaches to infectious disease research.[3] And the extraction, analysis, and interpretation of biosurveillance data and information from multiple sources requires the development of automated algorithms and improved analytical methodologies.[4]

Application

Infectious disease informatics can help tackle problems and tasks such as the following[3]:

  • the optimization of developed antimicrobials
  • the improvement of vaccines
  • the discovery of biomarkers for transmissibility and clinical outcomes of infectious diseases
  • the development of research into host-pathogen interactions
  • the sequencing and comparative study of the genomes of pathogens
  • the development of computational models for and simulations of bacteria and other microbials
  • the computational analysis and mining of collected disease data
  • the improvement of antibiotic prescribing decisions
  • the automation and facilitation of surveillance data collection and processing

Informatics

Most any infectious disease informatics tool will require some sort of security for "checking the integrity and authenticity of data feeds from the underlying information sources."[1] A few unique considerations must be made in IDI informatics applications, including the confidentiality of any included personal health information (PHI) and the non-binary nature of user access privileges. A public health director of a certain region may be able to contribute a dataset for analysis, but they'll have to ensure the right balance of PHI to meet local, state, and federal regulations; some data may need to be anonymized or pseudonymized. Additionally, that director may not be permitted the same granular level of access to the datasets from other regions, necessitating special stipulations in the security structure of informatics systems.[1]

Another important aspect of an infectious disease information management system is its evaluation for utility to the end user:

"Most previous infectious disease informatics research emphasizes the construction of novel or better solutions for users’ information searches or analysis tasks as means to create advanced artifacts, such as models, methods, techniques, algorithms, or systems (i.e., instantiations). Again, system evaluation receives far less attention and usually takes place in an ad hoc manner. Technical advancements and novelty cannot guarantee the success of an infectious disease informatics or biodefense system; users often define its success, because a system’s utilities demand both system design/functionality and user behaviors and assessments. Any infectious disease informatics or biodefense system therefore must be thoroughly examined, and its effectiveness or utilities must be systematically evaluated."[5]

Aside from information management systems, IDI draws on numerous other information technologies, including but not limited to geographic information systems (GIS), visualization software, data mining tools, and biostatistical applications.[1] Open-source software projects such as Framework for Reconstructing Epidemiological Dynamics (FRED)[6] and Spatiotemporal Epidemiological Modeler (STEM)[7] are helping extend the available informatics tools available to epidemiologists, public health personnel, and other scientists working with infectious disease research.

See also

External links

References

  1. 1.0 1.1 1.2 1.3 1.4 Chen, Hsinchun; Fuller, Sherrilynne S.; Friedman, Carol; Hersh, William (2006). "Chapter 13: Infectious Disease Informatics and Outbreak Detection". Medical Informatics: Knowledge Management and Data Mining in Biomedicine. Springer. pp. 359–397. ISBN 9780387257396. http://books.google.com/books?id=ku0ubWDKFZgC&pg=PA360. Retrieved 11 June 2014. 
  2. Chen, Hsinchun; Zeng, Daniel; Yan, Ping (2010). "Chapter 1: Infectious Disease Informatics: An Introduction and An Analysis Framework". Infectious Disease Informatics: Syndromic Surveillance for Public Health and Bio-Defense. Springer. pp. 3–8. ISBN 9781441912787. http://books.google.com/books?id=5BdCfSxtNJMC&pg=2. Retrieved 11 June 2014. 
  3. 3.0 3.1 3.2 Sintchenko, Vitali (2009). "Chapter 1: Informatics for Infectious Disease Research and Control". Infectious Disease Informatics. Springer. pp. 1–26. ISBN 9781441913272. http://books.google.com/books?id=H794Ej9GPFcC&pg=PA1. Retrieved 11 June 2014. 
  4. Rolka, Henry; O'Connor, Jean (2011). "Chapter 1: Real-Time Public Health Biosurveillance: Systems and Policy Considerations". Infectious Disease Informatics and Biosurveillance. Springer. pp. 3–22. ISBN 9781441968920. http://books.google.com/books?id=SC9nEO_13pEC&pg=PA3. Retrieved 11 June 2014. 
  5. Hu, Paul Jen-Hwa; Zeng, Daniel; Chen, Hsinchun (2011). "Chapter 19: System Evaluation and User Technology Adoption: A Case Study of BioPortal". Infectious Disease Informatics and Biosurveillance. Springer. pp. 439–457. ISBN 9781441968920. http://books.google.com/books?id=SC9nEO_13pEC&pg=PA439. Retrieved 11 June 2014. 
  6. "FRED: Framework for Reconstructing Epidemiological Dynamics". Public Health Dynamics Laboratory, University of Pittsburgh. http://fred.publichealth.pitt.edu/. 
  7. "The Spatiotemporal Epidemiological Modeler (STEM) Project". The Eclipse Foundation. http://www.eclipse.org/stem/.