2013/07/08 by Ahmed Aslam, Ahmed, Aslam, Julie Greensmith +3
Decision Sciences · Mathematics · #COVID-19 epidemiological studies #Computational Engineering #FOS: Computer and information sciences #Finance #Multiagent Systems (cs.MA) #and Science (cs.CE) #demographic modeling and climate adaptation
paper · pdf · doi:10.48550/arxiv.1307.2001
openalex publication_date 2013/07/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Classical deterministic simulations of epidemiological processes, such as\nthose based on System Dynamics, produce a single result based on a fixed set of\ninput parameters with no variance between simulations. Input parameters are\nsubsequently modified on these simulations using Monte-Carlo methods, to\nunderstand how changes in the input parameters affect the spread of results for\nthe simulation. Agent Based simulations are able to produce different output\nresults on each run based on knowledge of the local interactions of the\nunderlying agents and without making any changes to the input parameters. In\nthis paper we compare the influence and effect of variation within these two\ndistinct simulation paradigms and show that the Agent Based simulation of the\nepidemiological SIR (Susceptible, Infectious, and Recovered) model is more\neffective at capturing the natural variation within SIR compared to an\nequivalent model using System Dynamics with Monte-Carlo simulation. To\ndemonstrate this effect, the SIR model is implemented using both System\nDynamics (with Monte-Carlo simulation) and Agent Based Modelling based on\npreviously published empirical data.\n