ASIR: Robust Agent-based Representation Of SIR Model

TL;DR

This paper introduces ASIR, an agent-based model that accurately replicates SIR model predictions, enabling seamless parameter translation and bridging the gap between compartmental and agent-based epidemic models.

Contribution

The paper presents ASIR, a novel agent-based SIR model that directly derives parameters from the traditional SIR model, eliminating manual tuning and enhancing model interoperability.

Findings

ASIR accurately reproduces SIR infection curves.

Parameters of ASIR can be deduced from SIR model parameters.

ASIR facilitates transforming calibrated SIR models into agent-based models.

Abstract

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 $AM$.