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

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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.