Effects of purifiers on the airborne transmission of droplets inside a bus

Prediction of COVID-19 Infection in Dental Clinic by CFD and Wells-Riley Model, Identifying Safe Area and Proper Ventilation Velocity: Prédiction de l'infection au COVID-19 dans une clinique dentaire par CFD et modèle Wells-Riley, identification de la zone de sécurité et de la vitesse de ventilation appropriée

The COVID-19 virus is recognized worldwide as a significant public health threat. A dental clinic is one of the most dangerous places in the COVID-19 epidemic, and disease transmission is rapid. Planning is essential to create the right conditions in the dental clinic. In this study, the cough of an infected person is examined in a 9 × 6 × 3 m³ area. Computational fluid dynamic (CFD) is applied to simulate the flow field and to determine the dispersion path. The innovation of this research is checking the risk of infection for each person in the designated dental clinic, choosing the suitable velocity for ventilation, and identifying safe areas. In the first step, the effects of different ventilation velocities on the dispersion of virus-infected droplets are investigated, and the most appropriate ventilation flow velocity has been identified. Then, the results of the presence or absence of a dental clinic separator shield on the spread of respiratory droplets have been identified. Finally, the risk of infection (by the Wells-Riley equation) is assessed, and safe areas are identified. The effect of RH on droplet evaporation in this dental clinic is assumed to be 50%. The N_Tn values in an area with a separator shield are less than 1%. When there is a separator shield, the infection risk of people in A3 and A7 (the other side of the separator shield) is reduced from 23% to 4%, and 21% to 2%, respectively.

Effectiveness of N95 Mask in Preventing COVID-19 Transmission

N95 mask has emerged as a potential measure to mitigate the airborne transmission of respiratory disease such as COVID-19. Herein, we experimentally investigated the impact and interaction of pure water droplets as surrogate to respiratory droplets with the different layers of a commercially available N95 mask to demonstrate the penetration and passage-capability of respiratory fluids through the different layers. The penetration of an impacting droplet through the mask layers was characterized by elucidating the ejection of secondary droplets from the rear-side surface of the target mask material. In addition, the passage of respiratory fluids through the mask layers was characterized by capillary imbibition of the droplet liquid through the pores, as a function of wettability of the mask material. Droplet impact at Weber numbers We = 208 and 416 has been considered in the present study; the chosen We range corresponds to that of cough droplets realized in real respiratory events. Each layer of the N95 mask is hydrophobic that prevents capillary imbibition through the pores: a sessile droplet placed over the surface exhibits classical diffusion-limited evaporation. Droplet impact experiments on N95 mask layer surfaces reveal that a single layer allows liquid penetration at We = 416; while a combination of five layers, as is the case of a commercially available N95 mask, blocks the penetration completely, consistent with the widely known effectiveness of N95 masks. Herein, we devote special attention to compare the so-obtained efficiency of N95 masks to that of a recently designed two-layer cloth mask containing an intermediate High-Efficiency Particulate Air (HEPA) filter layer (Narayan et al. in Phys Fluids 34:061703, 2022). We conclusively show that the performance of the designed cloth mask is identical to that of a commercially available N95 mask. The assessment of mask effectiveness further includes examination of breathability and comfort by means of passage of air through them. A comparative study has been presented herein for a clear demonstration of effectiveness of different masks in preventing air-borne transmission of COVID-19.

Characteristics of collection and inactivation of virus in air flowing inside a winding conduit equipped with 280 nm deep UV-LEDs

A general-purpose virus inactivation unit that can inactivate viruses was developed using deep ultraviolet (DUV) LEDs that emit DUV rays with a wavelength of 280 nm. The inside of the virus inactivation unit is a rectangular conduit with a sharp turn of 180° (sharp-turned rectangular conduit). Virus inactivation is attempted by directly irradiating the air passing through the conduit with DUV rays. The flow characteristics of air and virus particles inside the virus inactivation unit were investigated using numerical simulations. The air was locally accelerated at the sharp turn parts and flowed along the partition plate in the sharp-turned rectangular conduit. The aerosol particles moving in the sharp-turned rectangular conduit were greatly bent in orbit at the sharp turn parts, and then rapidly approached the partition plate at the lower part of the conduit. Consequently, many particles collided with the partition plates behind the sharp-turn parts. SARS-CoV-2 virus was nebulized in the virus inactivation unit, and the RNA concentration and virus inactivation rate with and without the emission of DUV-LEDs were measured in the experiment. The concentration of SARS-CoV-2 RNA was reduced to 60% through DUV-LED irradiation. In addition, SARS-CoV-2 passing through the virus inactivation unit was inactivated below the detection limit by the emission of DUV-LEDs. The virus inactivation rate and the value of the detection limit corresponded to 99.38% and 35.36 TCID₅₀/mL, respectively.

Impact of pre-pandemic travel mobility patterns on the spatial diffusion of COVID-19 in South Korea

Introduction

Physical mobility is critical for the spread of infectious diseases in humans. However, few studies have conducted empirical investigations on the impact of pre-pandemic travel mobility patterns on the diffusion of coronavirus disease 2019 (COVID-19). Therefore, this study examines its impact at the city-county level on the diffusion by the wave period during the two-year pandemic in South Korea.

Methods

This study first employs factor analysis by using the travel origin-destination data by travel mode at the county level as of 2019 to derive pre-pandemic travel mobility patterns. Next, the study identifies how they had affected the diffusion of COVID-19 over time by employing the negative binomial regression models on confirmed COVID-19 cases for each wave, including the entire pandemic period.

Results

The study derived five pre-pandemic mobility patterns: 1) rail-oriented mobility, 2) intra-county bus-oriented mobility, 3) road-oriented mobility, 4) high-speed rail-oriented mobility, and 5) inter-county bus-oriented mobility. Among them, the biggest risk to the diffusion of COVID-19 was the rail-oriented mobility before the pandemic if controlling such measures as accessibility, physical environment, and demographic and socioeconomic indicators. In addition, the order of the magnitudes for the impact of pre-pandemic travel mobility factors on its spatial diffusion had not changed during experiencing the three different wave periods during the two-year pandemic in South Korea.

Conclusions

The study concludes that the rail-oriented travel mobility pattern before the pandemic could pose the greatest threat factor to the spatial spread of COVID-19 at any scale and time. Policymakers should develop strategies to prevent the spatial spread of COVID-19 by reducing human mobility for daily living in areas with strong rail mobility patterns formed before the pandemic.

The effect of relative air humidity on the evaporation timescales of a human sneeze

The present paper investigates droplet and aerosol emission from the human respiratory function by numerical and experimental methods, which is analyzed at the worst-case scenario, a violent sneeze without a face covering. The research findings develop the understanding of airborne disease transmission relevant to COVID-19, its recent variants, and other airborne pathogens. A human sneeze is studied using a multiphase Computational Fluid Dynamics (CFD) model using detached eddy simulation coupled to the emission of droplets that break up, evaporate, and disperse. The model provides one of the first experimental benchmarks of CFD predictions of a human sneeze event. The experiments optically capture aerosols and droplets and are processed to provide spatiotemporal data to validate the CFD model. Under the context of large random uncertainty, the studies indicate the reasonable correlation of CFD prediction with experimental measurements using velocity profiles and exposure levels, indicating that the model captures the salient details relevant to pathogen dispersion. Second, the CFD model was extended to study the effect of relative humidity with respect to the Wells curve, providing additional insight into the complexities of evaporation and sedimentation characteristics in the context of turbulent and elevated humidity conditions associated with the sneeze. The CFD results indicated correlation with the Wells curve with additional insight into features, leading to non-conservative aspects associated with increased suspension time. These factors are found to be associated with the combination of evaporation and fluid-structure-induced suspension. This effect is studied for various ambient air humidity levels and peaks for lower humidity levels, indicating that the Wells curve may need a buffer in dry climates. Specifically, we find that the increased risk in dry climates may be up to 50% higher than would be predicted using the underlying assumptions in Wells' model.

Numerical Investigation on the Droplet Dispersion inside a Bus and the Infection Risk Prediction

COVID-19 can be easily transmitted to passengers by inhaling exhaled droplets from the infected person in a bus. Therefore, studying droplet dispersion would provide further insight into the mechanism of virus transmission and predict the risk of infection among passengers on a bus. In this research, a bus equipped with air-conditioning was employed as the research object. To determine the dispersion path, concentration distribution, and escape time of the droplets, computational fluid dynamic (CFD) was applied to simulate the flow field and the droplets’ dispersion. The effect of the air supply rate, the location of vents, and the location of infected persons on the dispersion were discussed. Based on the distribution of droplets in the cabin calculated by CFD, a superposition method was used to determine the number of virus particles inhaled by every individual passenger over a four-hour journey. Then, infection risk was assessed by the Wells-Riley equation for all the passengers in the cabin after the whole journey. The results show that the distribution of droplets in the cabin is greatly influenced by the location of the infected person, and the airflow pattern is highly associated with the air supply rate and the location of vents. The infection risk of passengers located at the droplet dispersion path and the distance from the infected persons less than 2.2 m is over 10%. The increase in the air supply rate could speed up the spread of the droplets but at the same time, it could reduce the infection risk.