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A novel approach to preventing SARS-CoV-2 transmission in classrooms: An OpenFOAM based CFD Study

Computers and Society 2022-11-14 v1 Fluid Dynamics Populations and Evolution Quantitative Methods

Abstract

The education sector has suffered a catastrophic setback due to ongoing COVID-pandemic, with classrooms being closed indefinitely. The current study aims to solve the existing dilemma by examining COVID transmission inside a classroom and providing long-term sustainable solutions. In this work, a standard 5m x 3m x 5m classroom is considered where 24 students are seated, accompanied by a teacher. A computational fluid dynamics simulation based on OpenFOAM is performed using a Eulerian-Lagrangian framework. Based on the stochastic dose response framework, we have evaluated the infection risk in the classroom for two distinct cases: (i) certain students are infected (ii) the teacher is infected. If the teacher is infected, the probability of infection could reach 100% for certain students. When certain students are infected, the maximum infection risk for a susceptible person reaches 30%. The commonly used cloth mask proves to be ineffective in providing protection against infection transmission reducing the maximum infection probability by approximately 26% only. Another commonly used solution in the form of shields installed on desks have also failed to provide adequate protection against infection reducing the infection risk only by 50%. Furthermore, the shields serves as a source of fomite mode of infection. Screens suspended from the ceiling, which entrap droplets, have been proposed as a novel solution that reduces the infection risk by 90% and 95% compared to the no screen scenario besides being completely devoid of fomite infection mode. As a result of the screens, the class-time can be extended by 55 minutes.

Keywords

Cite

@article{arxiv.2211.06295,
  title  = {A novel approach to preventing SARS-CoV-2 transmission in classrooms: An OpenFOAM based CFD Study},
  author = {Anish Pal and Riddhideep Biswas and Ritam Pal and Sourav Sarkar and Achintya Mukhopadhyay},
  journal= {arXiv preprint arXiv:2211.06295},
  year   = {2022}
}