Predicting Levels of Influenza Incidence | Zendy

Anna L. Buczak | Zendy; Liane Ramac-Thomas | Zendy; Erhan Guven | Zendy; Yevgeniy Elbert | Zendy; Steven M. Babin | Zendy; Benjamin Baugher | Zendy; Sheri Lewis | Zendy

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Predicting Levels of Influenza Incidence

Author(s) -

Anna L. Buczak,

Liane Ramac-Thomas,

Erhan Guven,

Yevgeniy Elbert,

Steven M. Babin,

Benjamin Baugher,

Sheri Lewis

Publication year - 2014

Publication title -

online journal of public health informatics

Language(s) - English

Resource type - Journals

ISSN - 1947-2579

DOI - 10.5210/ojphi.v6i1.5149

Subject(s) - incidence (geometry) , disease , exploit , data mining , computer science , medicine , statistics , data science , environmental health , pathology , mathematics , computer security , geometry

Advanced techniques in fuzzy association rule data mining and integrating evidence from multiple sources are used to predict levels of influenza incidence several weeks in advance and display results on a map in order to help public health professionals prepare mitigation measures. This approach exploits the complicated relationships between disease incidence and measurable environmental, biological, and sociological variables that were found to have associations with the disease in other studies. Predictions were compared with data not used in model development in order to avoid exaggerated values of performance. The positive and negative predictive values were 0.941 and 0.935, respectively.

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