Dataset Information

Computational investigation of prolonged airborne dispersion of novel coronavirus-laden droplets.

ABSTRACT: We have performed highly accurate numerical simulations to investigate prolonged dispersion of novel coronavirus-laden droplets in classroom air. Approximately 10,900 virus-laden droplets were released into the air by a teacher coughing and tracked for 90 min by numerical simulations. The teacher was standing in front of multiple students in a classroom. To estimate viral transmission to the students, we considered the features of the novel coronavirus, such as the virus half-life. The simulation results revealed that there was a high risk of prolonged airborne transmission of virus-laden droplets when the outlet flow of the classroom ventilation was low (i.e., 4.3 and 8.6 cm/s). The rates of remaining airborne virus-laden droplets produced by the teacher coughing were 40% and 15% after 45 and 90 min, respectively. The results revealed that students can avoid exposure to the virus-laden droplets by keeping a large distance from the teacher (5.5 m), which is more than two times farther than the currently suggested social distancing rules. The results of this study provide guidelines to set a new protection plan in the classroom to prevent airborne transmission of virus-laden droplets to students.

SUBMITTER: Yamakawa M

PROVIDER: S-EPMC7869630 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Computational investigation of prolonged airborne dispersion of novel coronavirus-laden droplets.

Yamakawa Masashi M Kitagawa Atsuhide A Ogura Kiyota K Chung Yongmann M YM Kim Minsuok M

Journal of aerosol science 20210208

We have performed highly accurate numerical simulations to investigate prolonged dispersion of novel coronavirus-laden droplets in classroom air. Approximately 10,900 virus-laden droplets were released into the air by a teacher coughing and tracked for 90 min by numerical simulations. The teacher was standing in front of multiple students in a classroom. To estimate viral transmission to the students, we considered the features of the novel coronavirus, such as the virus half-life. The simulatio ...[more]

PMID: 33583950

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Project description:Influenza A Virus (IAV) replications start from the deposition of inhaled virus-laden droplets on the epithelial cells in the pulmonary tracts. In order to understand the local deposition patterns and within-host dynamics of infectious aerosols, accurate information of high-resolution imaging capabilities, as well as real-time flow cytometry analysis, are required for tracking infected cells, virus agents, and immune system responses. However, clinical and animal studies are in deficit to meet the above-mentioned demands, due to their limited operational flexibility and imaging resolution. Therefore, this study developed an experimentally validated multiscale epidemiological computational model, i.e., the Computational Fluid-Particle Dynamics (CFPD) plus Host Cell Dynamics (HCD) model, to predict the transport and deposition of the low-strain IAV-laden droplets, as well as the resultant regional immune system responses. The hygroscopic growth and shrinkage of IAV-laden droplets were accurately modeled. The subject-specific respiratory system was discretized by generating the new polyhedral-core mesh. By simulating both mouth and nasal breathing scenarios, the inhalations of isotonic IAV-laden droplets with three different compositions were achieved. It is the first time that parametric analysis was performed using the multiscale model on how different exposure conditions can influence the virus aerodynamics in the lung and the subsequent immune system responses. Numerical results show a higher viral accretion followed by a faster immune system response in the supraglottic region when droplets with the higher salt concentration were inhaled. Consequently, more severe symptoms and longer recovery are expected at the pharynx. Furthermore, local deposition maps of IAV-laden droplets and post-deposition infection dynamics provide informative and direct evidence which significantly enhance the fundamental understanding of the underlying mechanisms for upper airway and lower airway infections.

Dataset Information

Computational investigation of prolonged airborne dispersion of novel coronavirus-laden droplets.

Publications

Computational investigation of prolonged airborne dispersion of novel coronavirus-laden droplets.

Similar Datasets

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