Agent-Based Modeling to Simulate Aerosolized Transmission of SARS-CoV-2 inside Small Ventilated Spaces

Abstract

Deterministic and stochastic models for aerosol virus spread have become aplenty in the last several years. We believe it is important to explore all avenues of models and look to expand the current repertoire of models in this domain using a simple stochastic agent-based method. The goal is to understand if this type of agent model is applicable to real-life scenarios and to discuss possible policy implications of our findings on disease spread through aerosols in small spaces with ventilation using our developed model. We apply our agent model to see how different spatial organizations of an infected individual impact infections and their distributions. We also perform some sensitivity analysis with regard to both how different vectors of infection change overall infectivity rates but also how different levels of ventilation and filtration can impact infectivity as well. Our findings show that the simple stochastic movement of particles should be explored further with regard to agent-based disease spread models, and that filtration plays a large role in determining the overall infection rate of people in small spaces with an infector. We also found that placement of the index infector with regard to other susceptible people and ventilation play an impactful role in how a disease may spread in a short time frame within small confines.