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Compartmental models (written as $CM$) and agent-based models (written as$AM$) are dominant methods in the field of epidemic simulation. But in theliterature there lacks discussion on how to build the \textbf{quantitativerelationship} between them. In this paper, we propose an agent-based $SIR$model: $ASIR$. $ASIR$ can robustly reproduce the infection curve predicted by agiven SIR model (the simplest $CM$.) Notably, one can deduce any parameter of$ASIR$ from parameters of $SIR$ without manual tuning. $ASIR$ offersepidemiologists a method to transform a calibrated $SIR$ model into anagent-based model that inherit $SIR$'s performance without another round ofcalibration. The design $ASIR$ is inspirational for building a generalquantitative relationship between $CM$ and $AM$.

ASIR: Robust Agent-based Representation Of SIR Model