传染病SIR模型的系统动力学方差与Agent建模
Variance in System Dynamics and Agent Based Modelling Using the SIR Model of Infectious Disease
摘要：
流行病学过程的经典确定性模拟，例如基于系统动力学的模拟，基于一组固定的输入参数产生单一结果，模拟之间没有变化。随后使用蒙特卡罗方法对这些模拟修改输入参数，以了解输入参数的变化如何影响模拟结果的传播。基于代理的模拟能够基于底层代理的本地交互的知识在每次运行时产生不同的输出结果，并且不对输入参数进行任何更改。在本文中，我们比较了这两个不同的模拟范式内的变化的影响和效果，并表明基于代理的模拟流行病SIR（易感，传染，和adverted）模型是更有效地捕捉自然变化SIR内相比，一个等效的模型，使用系统动力学与蒙特卡洛模拟。为了证明这一效果，SIR模型的实施使用系统动力学（蒙特卡洛模拟）和基于代理的建模的基础上先前公布的经验数据。
英文摘要：
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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