An interaction-oriented multi-agent SIR model to assess the spread of SARS-CoV-2

Abstract

It is important to recognize that the dynamics of each country are different. Therefore, the SARS-CoV-2 (COVID-19) pandemic necessitates each country to act locally, but keep thinking globally. Governments have a responsibility to manage their limited resources optimally while struggling with this pandemic. Managing the trade-offs regarding these dynamics requires some sophisticated models. Agent-based simulation is a powerful tool to create such kind of models. Correspondingly, this study addresses the spread of COVID-19 employing an interaction-oriented multi-agent SIR (Susceptible Infected-Recovered) model. This model is based on the scale-free networks (incorporating 10,000 nodes) and it runs some experimental scenarios to analyze the main effects and the interactions of average-node-degree, initial-outbreak-size, spread-chance, recovery-chance, and gain-resistance factors on average-duration (of the pandemic last), average-percentage of infected, maximum-percentage of infected, and the expected peak-time. Obtained results from this work can assist determining the correct tactical responses of partial lockdown.