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Digital Library

of the European Council for Modelling and Simulation

 

Title:

Variance In System Dynamics And Agent Based Modelling Using

The SIR Model Of Infectious Disease

Authors:

Aslam Ahmed, Julie Greensmith, Uwe Aickelin

Published in:

 

(2012).ECMS 2012 Proceedings edited by: K. G. Troitzsch, M. Moehring, U. Lotzmann. European Council for Modeling and Simulation. doi:10.7148/2012 

 

ISBN: 978-0-9564944-4-3

 

26th European Conference on Modelling and Simulation,

Shaping reality through simulation

Koblenz, Germany, May 29 – June 1 2012

 

Citation format:

Ahmed, A., Greensmith, J., & Aickelin, U. (2012). Variance In System Dynamics And Agent Based Modelling Using The SIR Model Of Infectious Disease. ECMS 2012 Proceedings edited by: K. G. Troitzsch, M. Moehring, U. Lotzmann (pp. 9-15). European Council for Modeling and Simulation. doi:10.7148/2012-0009-0015

DOI:

http://dx.doi.org/10.7148/2012-0009-0015

Abstract:

Classical deterministic simulations of epidemiological processes, such as those based on System Dynamics, produce a single result based on a fixed set of input parameters with no variance between simulations. Input parameters are subsequently modified on these simulations using Monte-Carlo methods, to understand how changes in the input parameters affect the spread of results for the simulation. Agent Based simulations are able to produce different output results on each run based on knowledge of the local interactions of the underlying agents and without making any changes to the input parameters. In this paper we compare the influence and effect of variation within these two distinct simulation paradigms and show that the Agent Based simulation of the epidemiological SIR (Susceptible, Infectious, and Recovered) model is more effective at capturing the natural variation within SIR compared to an equivalent model using System Dynamics with Monte-Carlo simulation. To demonstrate this effect, the SIR model is implemented using both System Dynamics (with Monte-Carlo simulation) and Agent Based Modelling based on previously published empirical data.

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