My research interests are in the use of process algebra to develop epidemiological models. Using process algebra to describe a population in terms of individual behaviour we can scale up to the population level by deriving deterministic equations for the mean behaviour of the system.

This work is part of the EPSRC funded project System Dynamics from Individual Interactions: a Process Algebra Approach to Epidemiology

Publications

C. McCaig, R. Norman and C. Shankland. From individuals to populations: A mean field semantics for process algebra . Theoretical Computer Science. In Press, 2010.

C. McCaig, M. Begon, R. Norman and C. Shankland. A rigorous approach to investigating common assumptions about disease transmission. Theory in Biociences. In Press, Special Issue on Emerging Modelling Methodologies. 2010.

C. McCaig, R. Norman and C. Shankland. From Individuals to Populations: A Symbolic Process Algebra Approach to Epidemiology. Mathematics in Computer Science Vol 2, No. 3, Special Issue on Symbolic Computation in Biology. 2009.

S. Benkirane, J. Hillston, C. McCaig, R. Norman and C. Shankland. Improved Continuous Approximation of PEPA Models through Epidemiological Examples. In Proceedings of the Second Workshop From Biology to Concurrency and Back, FBTC 2008. Vol 229, Issue 1 of Electronic Notes in Theoretical Computer Science, 2009.

C. McCaig, R Norman, and C. Shankland. Process algebra models of population dynamics. In Algebraic Biology, AB2008, Vol 5147 of Lecture Notes in Computer Science, 2008.

C. McCaig, R Norman, and C. Shankland. Deriving mean field equations from large process algebra models. Technical Report CSM-175, Department of Computing Science and Mathematics, University of Stirling, March 2008.

C. McCaig From individuals to populations: changing scale in process algebra models of biological systems. PhD Thesis, University of Stirling, 2007