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has been cited by the following article:

• TITLE: Modelling Influenza Vaccination Outcomes

  AUTHORS: Ljiljana Trtica-Majnaric, Natasa Sarlija, Branko Vitale

  KEYWORDS: Influenza Vaccination Outcome; Modeling; The Health-Status; Systems Biology; Logistic Regression; Vaccination Protocols

  JOURNAL NAME: World Journal of Vaccines, Vol.2 No.1, February 17, 2012

  ABSTRACT: Modelling response to influenza vaccination can improve our understanding of how proposed factors, older age, past exposure to influenza viruses, and health disorders, used together, affect antibody production after influenza vaccination. Knowledge about this may be important when planning influenza vaccination protocols. This problem will be emphasized especially in the future, when many alternative vaccines and vaccination approaches are likely to be allowed for a routine use. A major difficulty, in modelling response to influenza vaccination, is how to identify health parameters, suitable for general use. To deal with the complexity of this task, we reached out for the concept of a systems biology and machine learning methods. Based on this approach, we showed that it is possible to construct useful models of influenza vaccination outcomes. In addition, by varying criteria for definition of the model’s outcome measure, that is, of low antibody response to influenza vaccination, we showed that a set of health parameters, albeit limited, are necessary for model to achieve a wider practical use.