Smart 3D Simulation of Covid-19 for Evaluating the Social Distance Measures

Abstract

The aim of this research is to model and simulate the recent and ongoing COVID19 pandemic in terms of virus contagiousness among mixed groups of patients, carriers and unaffected individuals when taking into consideration closed environments (such as malls or schools) which are ideal environments for the spread of COVID19. Machine learning techniques are utilized to model, analysis and predicate the behavior of COVID virus when spreading among human clusters. This prediction model will be used to develop a simulation environment for viewing the propagation of the COVID19 virus under different circumstances related to the type and size of the human gatherings while taking into consideration the spatiotemporal aspects of the crowd. Reinforcement learning techniques is used to train and deploy intelligent human agents that mimic the behavior of humans in real-world setting. By using 3D graphics technology, we are hoping to add a visualization aspect to the simulation to further enhance the usability and engagement level of the simulation, and to provide authorities and non-specialist people with a beneficial experience that aids them in terms of decision-making regarding future spreading of the virus under customizable lockdown scenarios. 2021, Springer Nature Switzerland AG.