The dose-response model has been widely used for quantifying the risk of infection of airborne diseases like COVID-19. The model has been used in the room-average analysis of infection risk and analysis using passive scalars as a proxy for aerosol transport. However, it has not been employed for risk estimation in numerical simulations of droplet dispersion. In this work, we develop a framework for the evaluation of the probability of infection in droplet dispersion simulations using the dose-response model. We introduce a version of the model that can incorporate the higher transmissibility of variant strains of SARS-CoV2 and the effect of vaccination in evaluating the probability of infection. Numerical simulations of droplet dispersion during speech are carried out to investigate the infection risk over space and time using the model. The advantage of droplet dispersion simulations for risk evaluation is demonstrated through the analysis of the effect of ambient wind, humidity on infection risk, and through a comparison with risk evaluation based on passive scalars as a proxy for aerosol transport.

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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.

In this study, the flow field around face masks was visualized and evaluated using computational fluid dynamics. The protective efficiency of face masks suppressing droplet infection owing to differences in the shape, medium, and doubling usage is predicted. Under the ongoing COVID-19 pandemic condition, many studies have been conducted to highlight that airborne transmission is the possible transmission route. However, the virus infection prevention effect of face masks has not been sufficiently discussed and, thus, remains as a controversial issue. Therefore, we aimed to provide a beneficial index for the society. The topology-free immersed boundary method, which is advantageous for complex shapes, was used to model the flow in the constriction area, including the contact surface between the face and mask. The jet formed from the oral cavity flow out through the surface of the mask and leaks from the gap between the face and mask. A Darcy-type model of porous media was used to model the flow resistance of masks. A random variable stochastic model was used to measure particle transmittance. We evaluated the differences in the amount of leakage and deposition of the droplets during exhalation and inhalation, depending on the differences in the conditions between the surgical and cloth masks owing to coughing and breathing. The obtained results could be useful for epidemiological measures by numerically showing the particle suppression effect of the face mask. This includes both exhalation and inhalation.

Transmission of infectious respiratory diseases through airborne dispersion of viruses poses a great risk to public health. In several major diseases, one of the main modes of transmission is through respiratory droplets. Virus laden respiratory droplets and aerosols can be generated during coughing, sneezing and speaking. These droplets and aerosols can remain suspended in air and be transported by airflow posing a risk of infection in individuals who might come in contact with them. With this background, in this work, we present a numerical framework for simulation of dispersion of respiratory sputum droplets using implicit large-eddy simulations. A combination of discrete Lagrangian droplet model and fully compressible Navier-Stokes flow solver is employed in this study. The method is applied to analyze cases such as droplet dispersion during speech and cough under different environmental settings. Furthermore, the performance of the numerical framework is evaluated through strong and weak scaling analysis. CCS CONCEPTS• Applied computing → Engineering; • Computing methodologies → Continuous models.