Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/25688
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Improving process algebra model structure and parameters in infectious disease epidemiology through data mining (Forthcoming/Available Online)
Authors: Hamami, Dalila
Baghdad, Atmani
Cameron, Ross
Pollock, Kevin G
Shankland, Carron
Contact Email: carron.shankland@stir.ac.uk
Keywords: epidemiological modeling
mumps infection
process algebras
Bio-PEPA formalism
data mining
association rules
clustering
time series
Issue Date: 22-Jul-2017
Citation: Hamami D, Baghdad A, Cameron R, Pollock KG & Shankland C (2017) Improving process algebra model structure and parameters in infectious disease epidemiology through data mining (Forthcoming/Available Online), Journal of Intelligent Information Systems.
Abstract: Computational models are increasingly used to assist decision-making in public health epidemiology, but achieving the best model is a complex task due to the interaction of many components and variability of parameter values causing radically different dynamics. The modelling process can be enhanced through the use of data mining techniques. Here, we demonstrate this by applying association rules and clustering techniques to two stages of mod- elling: identifying pertinent structures in the initial model creation stage, and choosing optimal parameters to match that model to observed data. This is illustrated through application to the study of the circulating mumps virus in Scotland, 2004-2015.
DOI Link: http://dx.doi.org/10.1007/s10844-017-0476-1
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