  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.
