A compositional approach to performance modelling
A compositional approach to performance modelling
The Mathematics of Infectious Diseases
SIAM Review
Information Processing Letters
Verifying Continuous Time Markov Chains
CAV '96 Proceedings of the 8th International Conference on Computer Aided Verification
Modelling biochemical pathways through enhanced π-calculus
Theoretical Computer Science - Special issue: Computational systems biology
BioAmbients: an abstraction for biological compartments
Theoretical Computer Science - Special issue: Computational systems biology
Journal of Computer and System Sciences
Modelling Biological Compartments in Bio-PEPA
Electronic Notes in Theoretical Computer Science (ENTCS)
Improved Continuous Approximation of PEPA Models through Epidemiological Examples
Electronic Notes in Theoretical Computer Science (ENTCS)
Some Investigations Concerning the CTMC and the ODE Model Derived From Bio-PEPA
Electronic Notes in Theoretical Computer Science (ENTCS)
PRISM: probabilistic model checking for performance and reliability analysis
ACM SIGMETRICS Performance Evaluation Review
Bio-PEPA: A framework for the modelling and analysis of biological systems
Theoretical Computer Science
QEST '09 Proceedings of the 2009 Sixth International Conference on the Quantitative Evaluation of Systems
CMSB'06 Proceedings of the 2006 international conference on Computational Methods in Systems Biology
Beta binders for biological interactions
CMSB'04 Proceedings of the 20 international conference on Computational Methods in Systems Biology
Transactions on Computational Systems Biology VII
Comparison of the mean-field approach and simulation in a peer-to-peer botnet case study
EPEW'11 Proceedings of the 8th European conference on Computer Performance Engineering
Conservation of Mass Analysis for Bio-PEPA
Electronic Notes in Theoretical Computer Science (ENTCS)
Electronic Notes in Theoretical Computer Science (ENTCS)
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Many models have been defined in order to describe the evolution of a disease in a population. The modelling of diseases is helpful to understand the mechanisms for their spread and to predict their future evolution. Most of the models in the literature are defined in terms of systems of differential equations and only a few of them propose stochastic simulation for the analysis. The main aim of this work is to apply the process algebra Bio-PEPA for the modelling and analysis of epidemiological models. As Bio-PEPA has been originally defined for biochemical networks, we define a variant of it suitable for representing epidemiological models. Some features of Bio-PEPA are useful in the context of epidemiology as well: location can abstract spatial structure and event can describe the introduction of prophylaxis in a population infected by a disease at a given day. Concerning the analysis, we can take advantage of the various kinds of analysis supported by Bio-PEPA, such as, for instance, stochastic simulation, model checking and ODE-based analyses. In particular, the modeller can select the most appropriate approach for the study of the model and analysis techniques can be used together for a better understanding of the behaviour of the system. In this paper we apply Bio-PEPA to the study of epidemiological models of avian influenza, based on different assumptions about the spatial structure and the possible kind of treatment. These models demonstrate that Bio-PEPA has several features that facilitate epidemiological modelling.