论文标题
ASIR：SIR模型的基于强大的代理代表
ASIR: Robust Agent-based Representation Of SIR Model
论文作者
论文摘要
隔间模型（以$ cm $为单位编写）和基于代理的模型（以$ AM $的形式写成）是流行模拟领域的主要方法。但是在文献中，缺乏关于如何在它们之间建立\ textbf {定量关系}的讨论。在本文中，我们提出了一个基于代理的$ SIR $ MODEL：$ ASIR $。 $ asir $可以强稳定地重现给定的SIR模型预测的感染曲线（最简单的$ cm $。）尤其是，可以从$ $ sir $的参数中推导出任何参数，而无需手动调整。 $ ASIR $为流行病学家提供了一种将校准的$ SIR $模型转换为基于代理的模型，该模型继承了$ Sir $ $ $的性能而没有其他校准。 Design $ asir $是为建立$ cm $和$ am $之间建立一般定量关系的鼓舞人心。
Compartmental models (written as $CM$) and agent-based models (written as $AM$) are dominant methods in the field of epidemic simulation. But in the literature there lacks discussion on how to build the \textbf{quantitative relationship} between them. In this paper, we propose an agent-based $SIR$ model: $ASIR$. $ASIR$ can robustly reproduce the infection curve predicted by a given SIR model (the simplest $CM$.) Notably, one can deduce any parameter of $ASIR$ from parameters of $SIR$ without manual tuning. $ASIR$ offers epidemiologists a method to transform a calibrated $SIR$ model into an agent-based model that inherit $SIR$'s performance without another round of calibration. The design $ASIR$ is inspirational for building a general quantitative relationship between $CM$ and $AM$.
