Improving process algebra model structure and parameters in infectious disease epidemiology through data mining

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.

Keywords
epidemiological modeling; mumps infection; process algebras; Bio-PEPA formalism; data mining; association rules; clustering; time series

Journal
Journal of Intelligent Information Systems: Volume 52, Issue 3

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