|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|
Pollock, Kevin G
|Citation:||Hamami D, Atmani B, Cameron R, Pollock KG & Shankland C (2019) Improving process algebra model structure and parameters in infectious disease epidemiology through data mining. Journal of Intelligent Information Systems, 52 (3), pp. 477-499. https://doi.org/10.1007/s10844-017-0476-1|
|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.|
|Rights:||This item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study. Publisher policy allows this work to be made available in this repository; The final publication is available at Springer via https://doi.org/10.1007/s10844-017-0476-1|
|HamaniEtAl.pdf||Fulltext - Accepted Version||1.13 MB||Adobe PDF||View/Open|
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