Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/3111

Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: A rigorous approach to investigating common assumptions about disease transmission: Process algebra as an emerging modelling methodology for epidemiology
Authors: McCaig, Chris
Begon, Mike
Norman, Rachel
Shankland, Carron
Contact Email: ran@maths.stir.ac.uk
Keywords: epidemiology
multiscale modelling
theoretical computer science
changing scale
Issue Date: Mar-2011
Publisher: Springer
Citation: McCaig C, Begon M, Norman R & Shankland C (2011) A rigorous approach to investigating common assumptions about disease transmission: Process algebra as an emerging modelling methodology for epidemiology, Theory in Biosciences, 130 (1), pp. 19-29.
Abstract: Changing scale, for example the ability to move seamlessly from an individual-based model to a population-based model, is an important problem in many fields. In this paper we introduce process algebra as a novel solution to this problem in the context of models of infectious disease spread. Process algebra allows us to describe a system in terms of the stochastic behaviour of individuals, and is a technique from computer science. We review the use of process algebra in biological systems, and the variety of quantitative and qualitative analysis techniques available. The analysis illustrated here solves the changing scale problem: from the individual behaviour we can rigorously derive equations to describe the mean behaviour of the system at the level of the population. The biological problem investigated is the transmission of infection, and how this relates to individual interactions
Type: Journal Article
URI: http://hdl.handle.net/1893/3111
URL: http://www.springerlink.com/content/1431-7613/
DOI Link: http://dx.doi.org/10.1007/s12064-010-0106-8
Rights: Published in Theory in Biosciences by Springer.; The final publication is available at www.springerlink.com. http://www.springerlink.com/content/9t33247x507v4378/
Notes: Theory in Biosciences, 130(1):19-29, Special Issue on Emerging Modelling Methodologies in Medicine, 2011, DOI 10.1007/s12064-010-0106-8
Affiliation: University of Stirling
University of Liverpool
Mathematics - CSM Dept
Computing Science - CSM Dept

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