Modelling aerosol transport and virus exposure with numerical simulations in relation to SARS-CoV-2 transmission by inhalation indoors

SARS-CoV-2 indoor environment contamination with epidemiological and experimental investigations

SARS-CoV-2 has been detected both in air and on surfaces, but questions remain about the patient-specific and environmental factors affecting virus transmission. Additionally, more detailed information on viral sampling of the air is needed. This prospective cohort study (N = 56) presents results from 258 air and 252 surface samples from the surroundings of 23 hospitalized and eight home-treated COVID-19 index patients between July 2020 and March 2021 and compares the results between the measured environments and patient factors. Additionally, epidemiological and experimental investigations were performed. The proportions of qRT-PCR-positive air (10.7% hospital/17.6% homes) and surface samples (8.8%/12.9%) showed statistical similarity in hospital and homes. Significant SARS-CoV-2 air contamination was observed in a large (655.25 m³ ) mechanically ventilated (1.67 air changes per hour, 32.4-421 L/s/patient) patient hall even with only two patients present. All positive air samples were obtained in the absence of aerosol-generating procedures. In four cases, positive environmental samples were detected after the patients had developed a neutralizing IgG response. SARS-CoV-2 RNA was detected in the following particle sizes: 0.65-4.7 μm, 7.0-12.0 μm, >10 μm, and <100 μm. Appropriate infection control against airborne and surface transmission routes is needed in both environments, even after antibody production has begun.

Impacts of human movement and ventilation mode on the indoor environment, droplet evaporation, and aerosol transmission risk at airport terminals

The dispersion of the coronavirus pandemic has caused immense damage worldwide, and people have begun to ruminate epidemic prevention strategies for public places. Airport terminals with a high number of occupied passengers have become potentially high-risk regions for aerosol transmission of coronavirus disease 2019 (COVID-19). In this study, the Eulerian-Lagrangian approach and realizable k-ε turbulence model were used to numerically simulate airflow organization and aerosol transmission when passengers are moving slowly in a line. During the aerosol transmission period, evaporation was considered a key factor influencing the particle size distribution at the beginning of aerosol transmission from humans. Moreover, passenger movement at the airport terminal was attained by employing dynamic mesh algorithms. Based on the relative direction of passengers and air vents when queuing in the terminal building, we studied three conditions: windward walking, leeward walking, and crosswind walking. The results of this study showed that the walking has an important influence on droplet distribution. Droplet distribution indicates that individuals standing behind patients during queuing movements have a higher risk of infection than those standing in front of them. A significant aerosol accumulation was discovered at 0.5 m behind the patient when passengers moved simultaneously. An aerosol transmission distance of 15 s aligned with the passenger's walking direction could reach up to 9.32 m. Furthermore, although the evaporation time of the large droplets was longer than that of the small droplets, both large and small droplets evaporated rapidly after exhalation. The crosswind influence caused the droplets to travel farther away in a direction perpendicular to human movement, which increased the distance by approximately 1.26 m compared to the absence of the crosswind influence.

Overview of the Role of Spatial Factors in Indoor SARS-CoV-2 Transmission: A Space-Based Framework for Assessing the Multi-Route Infection Risk

The COVID-19 pandemic has lasted from 2019 to 2022, severely disrupting human health and daily life. The combined effects of spatial, environmental, and behavioral factors on indoor COVID-19 spread and their interactions are usually ignored. Especially, there is a lack of discussion on the role of spatial factors in reducing the risk of virus transmission in complex and diverse indoor environments. This paper endeavours to summarize the spatial factors and their effects involved in indoor virus transmission. The process of release, transport, and intake of SARS-CoV-2 was reviewed, and six transmission routes according to spatial distance and exposure way were classified. The triangular relationship between spatial, environmental and occupant behavioral parameters during virus transmission was discussed. The detailed effects of spatial parameters on droplet-based, surface-based and air-based transmission processes and virus viability were summarized. We found that spatial layout, public-facility design and openings have a significant indirect impact on the indoor virus distribution and transmission by affecting occupant behavior, indoor airflow field and virus stability. We proposed a space-based indoor multi-route infection risk assessment framework, in which the 3D building model containing detailed spatial information, occupant behavior model, virus-spread model and infection-risk calculation model are linked together. It is also applicable to other, similar, respiratory infectious diseases such as SARS, influenza, etc. This study contributes to developing building-level, infection-risk assessment models, which could help building practitioners make better decisions to improve the building's epidemic-resistance performance.

Assessment of the COVID-19 infection risk at a workplace through stochastic microexposure modeling

BACKGROUND

The COVID-19 pandemic has a significant impact on economy. Decisions regarding the reopening of businesses should account for infection risks.

OBJECTIVE

This paper describes a novel model for COVID-19 infection risks and policy evaluations.

METHODS

The model combines the best principles of the agent-based, microexposure, and probabilistic modeling approaches. It takes into account specifics of a workplace, mask efficiency, and daily routines of employees, but does not require specific inter-agent rules for simulations. Likewise, it does not require knowledge of microscopic disease related parameters. Instead, the risk of infection is aggregated into the probability of infection, which depends on the duration and distance of every contact. The probability of infection at the end of a workday is found using rigorous probabilistic rules. Unlike previous models, this approach requires only a few reference data points for calibration, which are more easily collected via empirical studies.

RESULTS

The application of the model is demonstrated for a typical office environment and for a real-world case.

CONCLUSION

The proposed model allows for effective risk assessment and policy evaluation when there are large uncertainties about the disease, making it particularly suitable for COVID-19 risk assessments.

Assessment of airborne transmission from coughing processes with thermal plume adjacent to body and radiators on effectiveness of social distancing

The new coronavirus disease COVID-19 has caused a worldwide pandemic to be declared in a very short period of time. The complexity of the infection lies in asymptomatic carriers that can inadvertently transmit the virus through airborne droplets. This kind of viral disease can infect the human body with tiny particles that carry various bacteria that are generated by the respiratory system of infected patients. In this study, numerical results are proposed that demonstrate the effect of human body temperature and temperature from radiators in a room on the spread of the smallest droplets and particles in an enclosed space. The numerical model proposed in this work takes into account the sedimentation of particles and droplets under the action of gravitational sedimentation and transport in a closed room during the processes of breathing, sneezing or coughing. Various cases were considered, taking into account normal human breathing, coughing or sneezing, as well as three different values of the rate of emission of particles from the human mouth. The heat plume, which affects the concentration of particles in the breathing zone, spreads the particle up to a distance of 4.29 m in the direction of the air flow. It can also be seen from the results obtained that the presence of radiators strongly affects the propagation of particles of various sizes in a closed room. From the obtained results, it should be noted that in order to recommend the optimal social distance, it is necessary to take into account many factors, especially momentum, gravity, human body temperature, as well as the process of natural convection, which greatly affect the propagation of particles in a closed room. The conclusions drawn from the results of this work show that, given the environmental conditions, the social distance of 2 m may not be enough.

Modelling airborne transmission of SARS-CoV-2 at a local scale

The coronavirus disease (COVID-19) pandemic has changed our lives and still poses a challenge to science. Numerous studies have contributed to a better understanding of the pandemic. In particular, inhalation of aerosolised pathogens has been identified as essential for transmission. This information is crucial to slow the spread, but the individual likelihood of becoming infected in everyday situations remains uncertain. Mathematical models help estimate such risks. In this study, we propose how to model airborne transmission of SARS-CoV-2 at a local scale. In this regard, we combine microscopic crowd simulation with a new model for disease transmission. Inspired by compartmental models, we describe virtual persons as infectious or susceptible. Infectious persons exhale pathogens bound to persistent aerosols, whereas susceptible ones absorb pathogens when moving through an aerosol cloud left by the infectious person. The transmission depends on the pathogen load of the aerosol cloud, which changes over time. We propose a ‘high risk’ benchmark scenario to distinguish critical from non-critical situations. A parameter study of a queue shows that the new model is suitable to evaluate the risk of exposure qualitatively and, thus, enables scientists or decision-makers to better assess the spread of COVID-19 and similar diseases.

Has COVID-19 changed our loyalty towards public transport? Understanding the moderating role of the pandemic in the relationship between service quality, customer satisfaction and loyalty

COVID-19 has been associated with a decline in public transport ridership in many cities. However, the impact of the pandemic on passenger perceptions of public transport, particularly loyalty, has remained largely unexplored. Using a case study of Tehran's bus system, this paper aims to assess the moderating effect of COVID-19 on passenger loyalty. A cross-sectional survey of bus users was conducted in two similar periods, before and during COVID-19. The data was analysed using structural equation modelling, with the effect of observed heterogeneity evaluated using multiple indicators multiple causes models and multi-group analysis. The results indicate that during COVID-19, perceptions of service quality have improved, particularly in relation to comfort (including crowding), safety, reliability and information. While this has led to increased customer satisfaction, loyalty to the system has decreased. An increase in the perceived attractiveness of using private cars was found to be the primary factor for reducing passenger loyalty with the perceived monetary and psychological costs of using cars being less effective in encouraging people to use the bus. Taking into account the significant utility of private vehicles in urban transportation during COVID-19, the present study identifies feasible areas for improving the performance of bus services to increase satisfaction with this form of public transport among car owners.

Molecular detection of SARS-COV-2 in exhaled breath at the point-of-need

The SARS-CoV-2 pandemic has highlighted the need for improved technologies to help control the spread of contagious pathogens. While rapid point-of-need testing plays a key role in strategies to rapidly identify and isolate infectious patients, current test approaches have significant shortcomings related to assay limitations and sample type. Direct quantification of viral shedding in exhaled particles may offer a better rapid testing approach, since SARS-CoV-2 is believed to spread mainly by aerosols. It assesses contagiousness directly, the sample is easy and comfortable to obtain, sampling can be standardized, and the limited sample volume lends itself to a fast and sensitive analysis. In view of these benefits, we developed and tested an approach where exhaled particles are efficiently sampled using inertial impaction in a micromachined silicon chip, followed by an RT-qPCR molecular assay to detect SARS-CoV-2 shedding. Our portable, silicon impactor allowed for the efficient capture (>85%) of respiratory particles down to 300 nm without the need for additional equipment. We demonstrate using both conventional off-chip and in-situ PCR directly on the silicon chip that sampling subjects’ breath in less than a minute yields sufficient viral RNA to detect infections as early as standard sampling methods. A longitudinal study revealed clear differences in the temporal dynamics of viral load for nasopharyngeal swab, saliva, breath, and antigen tests. Overall, after an infection, the breath-based test remains positive during the first week but is the first to consistently report a negative result, putatively signalling the end of contagiousness and further emphasizing the potential of this tool to help manage the spread of airborne respiratory infections.

Numerical study of when and who will get infected by coronavirus in passenger car

In light of the COVID-19 pandemic, it is becoming extremely necessary to assess respiratory disease transmission in passenger cars. This study numerically investigated the human respiration activities' effects, such as breathing and speaking, on the transport characteristics of respiratory-induced contaminants in passenger car. The main objective of the present study is to accurately predict when and who will get infected by coronavirus while sharing a passenger car with a patient of COVID-19 or similar viruses. To achieve this goal, transient simulations were conducted in passenger car. We conducted a 3D computational fluid dynamics (CFD)-based investigation of indoor airflow and the associated aerosol transport in a passenger car. The Eulerian-Eulerian flow model coupled with k-ε turbulence approach was used to track respiratory contaminants with diameter ≥ 1 μm that were released by different passengers within the passenger car. The results showed that around 6.38 min, this is all that you need to get infected with COVID-19 when sharing a poorly ventilated car with a driver who got coronavirus. It also has been found that enhancing the ventilation system of the passenger car will reduce the risk of contracting Coronavirus. The predicted results could be useful for future engineering studies aimed at designing public transport and passenger cars to face the spread of droplets that may be contaminated with pathogens.

Evaluation of Airflow Sensitivity to the Truncation Level of a Realistic Human Airway Model in an Accurate Numerical Simulation

BACKGROUND

The truncation level of human airways is an influential factor in the analysis of respiratory flow in numerical simulations. Due to computational limitations and limited resolution of diagnostic medical imaging equipment, a truncated geometry of airways is always investigated.

OBJECTIVE

This study aimed to employ image-based geometries with zero generation and 5^th-generation truncation levels and assess bronchial airways truncation's effect on tracheal airflow characteristics.

MATERIAL AND METHODS

In this numerical study, computational fluid dynamics was employed to solve the respiratory flow in a realistic human airway model using the large eddy simulation technique coupling with the wall-adapting local eddy-viscosity (WALE) sub-grid scale model. The accuracy of numerical simulations was ensured by examining the large eddy simulation index of quality and Kolmogorov's K^-5/3 law.

RESULTS

The turbulent kinetic energy along the trachea has increased abnormally in the geometry with the zero-generation truncation level, and more severe fluctuations occurred in the velocity field of this geometry, which increased the tendency of each point to rotate. Compared to the extended model, the airflow's more chaotic behavior prevented larger-scale vortices from forming in the geometry with the zero-generation truncation level. Larger-scale vortices in the extended model caused the primary flow passing next to the vortices to accelerate more intensely, increasing the wall shear stress peaks in this geometry.

CONCLUSION

Eliminating the bronchial airways caused changes in tracheal airflow characteristics.

Airborne bacterial and PM characterization in intensive care units: correlations with physical control parameters

In this study, the spatial variation of airborne bacteria in intensive care units (ICUs) was characterized. Fine particulate matter and several physical parameters were also monitored including temperature and relative humidity. The results showed that the total bacterial load ranged between 20.4 and 134.3 CFU/m³ across the ICUs. Bacterial cultures of the collected samples did not isolate any multi-drug-resistant Gram-negative bacilli indicating the absence of such aerosolized pathogens in the ICUs. Meanwhile, particulate matter levels in several ICUs were found to exceed the international guidelines set for 24-h PM exposure. Moreover, examining bacterial load contribution by size suggested that bacteria with sizes less than 0.65 µm contributed the least to the total bacterial loads, while those with sizes between 0.65 and 1.1 µm contributed the most. A multiple linear regression model was also built to predict the bacterial loads in the ICUs. The regression analysis explained 77% of the variability observed in the measured bacterial concentrations. The model showed that the level of activity in the ICU rooms as well as its occupancy level had strong positive correlations with bacterial loads, while distance away from the patient had a non-linear relationship with measured loads. No statistically significant correlation was found between bacterial load and particulate matter concentrations.

Airborne infection risk assessment of COVID-19 in an inpatient department through on-site occupant behavior surveys

Airborne transmission is a possible infection route of the coronavirus disease 2019 (COVID-19). This investigation focuses on the airborne infection risk of COVID-19 in a nursing unit in an inpatient building in Shenzhen, China. On-site measurements and questionnaire surveys were conducted to obtain the air change rates and occupant trajectories, respectively. The aerosol transport and dose–response models were applied to evaluate the infection risk. The average outdoor air change rate measured in the wards was 1.1 h⁻¹, which is below the minimum limit of 2.0 h⁻¹ required by ASHRAE 170–2021. Considering the surveyed occupant behavior during one week, the patients and their attendants spent an average of 19.4 h/d and 15.1 h/d, respectively, in the wards, whereas the nurses primarily worked in the nurse station (3.0 h/d) and wards (2.4 h/d). The doctors primarily worked in their offices (2.6 h/d) and wards (1.1 h/d). Assuming one undetected COVID-19 infector emitting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the nursing unit, we calculated the accumulated viral dose and infection probabilities of the occupants. After one week, the cumulative infection risks of the patients and attendants were almost equal (0.002), and were higher than those of the nurses (0.0013) and doctors (0.0004). Proper protection measures, such as reducing the number of attendants, increasing the air change rate, and wearing masks, were found to reduce the infection risk. It should be noted that the reported results are based on several assumptions, such as the speculated virological properties of SARS-CoV-2 and the particular trajectories of occupants. Moreover, only second generations of transmission were taken into consideration, whereas in reality, the week-long exposure may cause third generation of transmission or worse.

Increased airborne transmission of COVID-19 with new variants, implications for health policies

New COVID-19 variants, either of higher viral load such as delta or higher contagiousness like omicron, can lead to higher airborne transmission than historical strains. This paper highlights their implications for health policies, based on a clear analytical understanding and modeling of the airborne contamination paths, of the dose following exposure, and the importance of the counting unit for pathogens, itself linked to the dose-response law. Using the counting unit of Wells, i.e. the quantum of contagium, we develop the conservation equation of quanta which allows deriving the value of the quantum concentration at steady state for a well-mixed room. The link with the monitoring concentration of carbon dioxide is made and used for a risk analysis of a variety of situations for which we collected CO2 time-series observations. The main conclusions of these observations are that 1) the present norms of ventilation, are both insufficient and not respected, especially in a variety of public premises, leading to high risk of contamination and that 2) air can often be considered well-mixed. Finally, we insist that public health policy in the field of airborne transmission should be based on a multi parameter analysis such as the time of exposure, the quantum production rate, mask wearing and the infector proportion in the population in order to evaluate the risk, considering the whole complexity of dose evaluation. Recognizing airborne transmission requires thinking in terms of time of exposure rather than in terms of proximal distance.

Influence of indoor airflow on particle spread of a single breath and cough in enclosures: Does opening a window really 'help'?

The spread of respiratory diseases via aerosol particles in indoor settings is of significant concern. The SARS-CoV-2 virus has been found to spread widely in confined enclosures like hotels, hospitals, cruise ships, prisons, and churches. Particles exhaled from a person indoors can remain suspended long enough for increasing the opportunity for particles to spread spatially. Careful consideration of the ventilation system is essential to minimise the spread of particles containing infectious pathogens. Previous studies have shown that indoor airflow induced by opened windows would minimise the spread of particles. However, how outdoor airflow through an open window influences the indoor airflow has not been considered. The aim of this study is to provide a clear understanding of the indoor particle spread across multiple rooms, in a situation similar to what is found in quarantine hotels and cruise ships, using a combination of HVAC (Heating, Ventilation and Air-Conditioning) ventilation and an opening window. Using a previously validated mathematical model, we used 3D CFD (computational fluid dynamics) simulations to investigate to what extent different indoor airflow scenarios contribute to the transport of a single injection of particles ( 1 . 3 μ m ) in a basic 3D multi-room indoor environment. Although this study is limited to short times, we demonstrate that in certain conditions approximately 80% of the particles move from one room to the corridor and over 60% move to the nearby room within 5 to 15 s. Our results provide additional information to help identifying relevant recommendations to limit particles from spreading in enclosures.

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.

CFD Numerical Simulation in Building Drainage Stacks as an Infection Pathway of COVID-19

Being aware of global pandemics, this research focused on the potential infection routes in building drainage systems. Case studies have found that dysfunctional building drainage systems not only failed to block contaminants but also potentially became a route for the spreading of viruses. Several fluid simulations in pipelines were conducted in this research using COMSOL Multiphysics. In particular, virus transmission from one patient's room to other uninfected residential units through pipelines was visualized. A 12-story building, which is commonly seen in the local area, was designed as a simulation model to visualize the transmission and analyze its hazards. Furthermore, five environmental factors were filtered out for discussion: distance, time span, pressure, initial concentration, and environment temperature. By manipulating these factors, the relationship between the factors and the behavior of the contaminant could be explored. In addition, a simulation with a different pipeline arrangement was included to observe the virus diffusion behavior under different scenarios. The visualized simulation concluded that the contaminant would spread through the drainage system and arrive at the neighboring four floors within an hour under the circumstances of a 12-story building with broken seals and constant pressure and contaminant supply on the seventh floor. Meanwhile, the whole building would be exposed to infection risks by the continuous virus spreading through a drainage system. Distance, time span, and pressure were considered critical factors that affected indoor contamination in the system. On the other hand, initial concentration and environmental temperature did not have significant roles. Visualizing the behavior of viruses provides a glimpse of what happens behind walls, paving the way for recognizing the importance of maintaining functional drainage systems for individuals' health.

Definition of an Indoor Air Sampling Strategy for SARS-CoV-2 Detection and Risk Management: Case Study in Kindergartens

In the last two years, the world has been overwhelmed by SARS-CoV-2. One of the most important ways to prevent the spread of the virus is the control of indoor conditions: from surface hygiene to ventilation. Regarding the indoor environments, monitoring the presence of the virus in the indoor air seems to be promising, since there is strong evidence that airborne transmission through infected droplets and aerosols is its dominant transmission route. So far, few studies report the successful detection of SARS-CoV-2 in the air; moreover, the lack of a standard guideline for air monitoring reduces the uniformity of the results and their usefulness in the management of the risk of virus transmission. In this work, starting from a critical analysis of the existing standards and guidelines for indoor air quality, we define a strategy to set-up indoor air sampling plans for the detection of SARS-CoV-2. The strategy is then tested through a case study conducted in two kindergartens in the metropolitan city of Milan, in Italy, involving a total of 290 children and 47 teachers from 19 classrooms. The results proved its completeness, effectiveness, and suitability as a key tool in the airborne SARS-CoV-2 infection risk management process. Future research directions are then identified and discussed.

An agent-based study on the airborne transmission risk of infectious disease in a fever clinic during COVID-19 pandemic

Prevention of nosocomial infections is particularly important for the control of COVID-19 pandemic. We conducted a field study and performed extensive numerical simulations of infection transmission in a fever clinic during pandemic through an agent-based model with pedestrian dynamic and an infection transmission model. Furthermore, we evaluated the cross-infection risk of the patients influenced by the patient inject flow, medical service capability and plane layout. The service capability of fever clinic is determined by the least efficient medical session. When patient inject flow exceeded the service capability, the average dwell time, contact time, exposure dose, and risk of infection of patients all increased dramatically. With the patient inject flow exceeding the service capability, the growth rate of the contact time between patients and the cross-infection risk increased by 11.5-fold and 29.5-fold, respectively. The plane layout of the fever clinic affected the exposure dose and risk of infection. The waiting areas in the fever clinic had the highest risk, where the cumulative exposure dose of virus occupied up to 66.5% of the total. Our research will help to evaluate the biosafety of hospital buildings used for the diagnosis and treatment of infectious diseases.

Ventilation strategies and design impacts on indoor airborne transmission: A review

The COVID-19 outbreak has brought the indoor airborne transmission issue to the forefront. Although ventilation systems provide clean air and dilute indoor contaminated air, there is strong evidence that airborne transmission is the main route for contamination spread. This review paper aims to critically investigate ventilation impacts on particle spread and identify efficient ventilation strategies in controlling aerosol distribution in clinical and non-clinical environments. This article also examines influential ventilation design features (i.e., exhaust location) affecting ventilation performance in preventing aerosols spread. This paper shortlisted published documents for a review based on identification (keywords), pre-processing, screening, and eligibility of these articles. The literature review emphasizes the importance of ventilation systems' design and demonstrates all strategies (i.e., mechanical ventilation) could efficiently remove particles if appropriately designed. The study highlights the need for occupant-based ventilation systems, such as personalized ventilation instead of central systems, to reduce cross-infections. The literature underlines critical impacts of design features like ventilation rates and the number and location of exhausts and suggests designing systems considering airborne transmission. This review underpins that a higher ventilation rate should not be regarded as a sole indicator for designing ventilation systems because it cannot guarantee reducing risks. Using filtration and decontamination devices based on building functionalities and particle sizes can also increase ventilation performance. This paper suggests future research on optimizing ventilation systems, particularly in high infection risk spaces such as multi-storey hotel quarantine facilities. This review contributes to adjusting ventilation facilities to control indoor aerosol transmission.

Aerosol reduction of two dental extraoral scavenger devices in vitro

Objective

Since the outbreak of SARS-CoV-2, aerosol control in the operatory has become a key safety issue in dentistry. The utilisation of extraoral scavenger devices (EOSs) is one of the various approaches to in-treatment aerosol reduction in dentistry. The use and efficacy of EOSs in dental settings, however, are still a matter of debate in the literature and there are still open questions about their proper use. Thus, research into this area is essential to inform dental practice. The objective of this study was to examine the aerosol reduction efficacy of two different EOS in vitro.

Methods

Two commercially available EOSs were tested during modeled dental treatment in a setup that previously proved to generate high aerosol load. Measurements were done in two particle size ranges: 5.6–560 nm (the full range of the spectrometer) and 60.4–392.4 nm (a range that is especially relevant to the spread of SARS-CoV-2 with aerosol).

Results

Both devices managed to reduce the aerosol load to a statistically significant extent as compared to the scenario when only a high-volume evacuator and a saliva ejector (and no EOS) were used.

Conclusions

Within the limitations of the study, the results support the assumption that EOSs for aerosol reduction increase in-treatment safety in the dental operatory.

Airborne transmission of COVID-19 virus in enclosed spaces: An overview of research methods

Since the outbreak of COVID-19 in December 2019, the severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) has spread worldwide. This study summarized the transmission mechanisms of COVID-19 and their main influencing factors, such as airflow patterns, air temperature, relative humidity, and social distancing. The transmission characteristics in existing cases are providing more and more evidence that SARS CoV-2 can be transmitted through the air. This investigation reviewed probabilistic and deterministic research methods, such as the Wells-Riley equation, the dose-response model, the Monte-Carlo model, computational fluid dynamics (CFD) with the Eulerian method, CFD with the Lagrangian method, and the experimental approach, that have been used for studying the airborne transmission mechanism. The Wells-Riley equation and dose-response model are typically used for the assessment of the average infection risk. Only in combination with the Eulerian method or the Lagrangian method can these two methods obtain the spatial distribution of airborne particles' concentration and infection risk. In contrast with the Eulerian and Lagrangian methods, the Monte-Carlo model is suitable for studying the infection risk when the behavior of individuals is highly random. Although researchers tend to use numerical methods to study the airborne transmission mechanism of COVID-19, an experimental approach could often provide stronger evidence to prove the possibility of airborne transmission than a simple numerical model. All in all, the reviewed methods are helpful in the study of the airborne transmission mechanism of COVID-19 and epidemic prevention and control.

Mobile human ad hoc networks: A communication engineering viewpoint on interhuman airborne pathogen transmission

A number of transmission models for airborne pathogens transmission, as required to understand airborne infectious diseases such as COVID-19, have been proposed independently from each other, at different scales, and by researchers from various disciplines. We propose a communication engineering approach that blends different disciplines such as epidemiology, biology, medicine, and fluid dynamics. The aim is to present a unified framework using communication engineering, and to highlight future research directions for modeling the spread of infectious diseases through airborne transmission. We introduce the concept of mobile human ad hoc networks (MoHANETs), which exploits the similarity of airborne transmission-driven human groups with mobile ad hoc networks and uses molecular communication as the enabling paradigm. In the MoHANET architecture, a layered structure is employed where the infectious human emitting pathogen-laden droplets and the exposed human to these droplets are considered as the transmitter and receiver, respectively. Our proof-of-concept results, which we validated using empirical COVID-19 data, clearly demonstrate the ability of our MoHANET architecture to predict the dynamics of infectious diseases by considering the propagation of pathogen-laden droplets, their reception and mobility of humans.

Simulating COVID-19 classroom transmission on a university campus

We study the airborne transmission risk associated with holding in-person classes on university campuses for the original strain and a more contagious variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We adopt a model for airborne transmission risk in an enclosed room that considers room properties, mask efficiency, and initial infection probability of the occupants. Additionally, we study the effect of vaccination on the spread of the virus. The presented model has been evaluated in simulations using fall 2019 (prepandemic) and fall 2020 (hybrid instruction) course registration data of a large US university, allowing for assessing the difference in transmission risk between in-person and hybrid programs and the impact of occupancy reduction, mask-wearing, and vaccination. The simulations indicate that without vaccination, moving 90% of the classes online leads to a 17 to 18× reduction in new cases, and universal mask usage results in an ∼2.7 to 3.6× reduction in new infections through classroom interactions. Furthermore, the results indicate that for the original variant and using vaccines with efficacy greater than 90%, at least 23% (64%) of students need to be vaccinated with (without) mask usage in order to operate the university at full occupancy while preventing an increase in cases due to classroom interactions. For the more contagious variant, even with universal mask usage, at least 93% of the students need to be vaccinated to ensure the same conditions. We show that the model is able to predict trends observed in weekly infection rates for fall 2021.

Indoor transmission of airborne viral aerosol with a simplistic reaction-diffusion model

A simplistic reaction-diffusion model is undertaken in the present work to mathematically explore the spatio-temporal development of concentration of indoor aerosols containing infectious COVID-19 respiratory virus nuclei. Extracting exact solutions of concentration field under the influence of several physical parameters is preferred rather than adopting a more realistic complex model requiring time-consuming numerical simulations. Even though the proposed model is not sophisticated, the analytical solutions can provide quick prediction of the probability of contracting the virus in a ventilated closed room. Moreover, from the obtained elementary solutions of the viral concentration field, it is easy to analyze its spatio-temporal evolution and final equilibrium state. Formulae enable us to estimate the time to get infected and the risk of getting infected within an elapsed time under various physical operative situations involving a uniform infectious particle mixture ejection into the medium, wearing a face mask with a well-defined efficiency parameter and taking into account a localized source of infection. One of the essential conclusion from the current research is that less aerosols carrying COVID-19 particles are as a result of good indoor ventilation conditions, of removing the medium air through windows (or other exits) and of wearing masks of high efficiency. Moreover, the risk and probability of being caught by the indoor COVID-19 disease increases in time, particularly in the downstream of a localized infectious person. The results can be beneficial to understand and take necessary safety considerations against the infection risk in closed public or governmental environments.

Aerodynamic Prediction of Time Duration to Becoming Infected with Coronavirus in a Public Place

The COVID-19 pandemic has caused panic and chaos that modern society has never seen before. Despite their paramount importance, the transmission routes of coronavirus SARS-CoV-2 remain unclear and a point of contention between the various sectors. Recent studies strongly suggest that COVID-19 could be transmitted via air in inadequately ventilated environments. The present study investigates the possibility of the aerosol transmission of coronavirus SARS-CoV-2 and illustrates the associated environmental conditions. The main objective of the current work is to accurately predict the time duration of getting an infection while sharing an indoor space with a patient of COVID-19 or similar viruses. We conducted a 3D computational fluid dynamics (CFD)-based investigation of indoor airflow and the associated aerosol transport in a restaurant setting, where likely cases of airflow-induced infection of COVID-19 caused by asymptomatic individuals were reported in Guangzhou, China. The Eulerian–Eulerian flow model coupled with the k-Ɛ turbulence approach was employed to resolve complex indoor processes, including human respiration activities, such as breathing, speaking, and sneezing. The predicted results suggest that 10 minutes are enough to become infected with COVID-19 when sharing a Table with coronavirus patients. The results also showed that although changing the ventilation rate will improve the quality of air within closed spaces, it will not be enough to protect a person from COVID-19. This model may be suitable for future engineering analyses aimed at reshaping public spaces and indoor common areas to face the spread of aerosols and droplets that may contain pathogens.

Agent-based epidemiological modeling of COVID-19 in localized environments

Epidemiological modeling is used, under certain assumptions, to represent the spread of a disease within a population. Information generated by these models can then be applied to inform public health practices and mitigate risk. To provide useful and actionable preparedness information to administrators and policy makers, epidemiological models must be formulated to model highly localized environments such as office buildings, campuses, or long-term care facilities. In this paper, a highly configurable agent-based simulation (ABS) framework designed for localized environments is proposed. This ABS provides information about risk and the effects of both pharmacological and non-pharmacological interventions, as well as detailed control over the rapidly evolving epidemiological characteristics of COVID-19. Simulation results can inform decisions made by facility administrators and be used as inputs for a complementary decision support system. The application of our ABS to our research lab environment as a proof of concept demonstrates the configurability and insights achievable with this form of modeling, with future work focused on extensibility and integration with decision support.

Ten questions concerning the paradox of minimizing airborne transmission of infectious aerosols in densely occupied spaces via sustainable ventilation and other strategies in hot and humid climates

Airborne disease transmission in indoor spaces and resulting cross-contamination has been a topic of broad concern for years - especially recently with the outbreak of COVID-19. Global recommendations on this matter consist of increasing the outdoor air supply in the aim of diluting the indoor air. Nonetheless, a paradoxical relationship has risen between increasing amount of outdoor air and its impact on increased energy consumption - especially densely occupied spaces. The paradox is more critical in hot and humid climates, where large amounts of energy are required for the conditioning of the outdoor air. Therefore, many literature studies investigated new strategies for the mitigation of cross-contamination with little-to-no additional cost of energy. These strategies mainly consist of the dilution and/or the capture and removal of contaminants at the levels of macroenvironment room air and occupant-adjacent microenvironment. On the macroenvironment level, the dilution occurs by the supply of large amounts of outdoor air in a sustainable way using passive cooling systems, and the removal of contaminants happens via filtering. Similarly, the microenvironment of the occupant can be diluted using localized ventilation techniques, and contaminants can be captured and removed by direct exhaust near the source of contamination. Thus, this work answers ten questions that explore the most prevailing technologies from the above-mentioned fronts that are used to mitigate cross-contamination in densely occupied spaces located in hot and humid climates at minimal energy consumption. The paper establishes a basis for future work and insights for new research directives for macro and microenvironment approaches.

Investigation of airborne particle exposure in an office with mixing and displacement ventilation

Effective ventilation could reduce COVID-19 infection in buildings. By using a computational fluid dynamics technique and advanced experimental measurement methods, this investigation studied the air velocity, air temperature, and particle number concentration in an office under a mixing ventilation (MV) system and a displacement ventilation (DV) system with different ventilation rates. The results show reasonably good agreement between the computed results and measured data. The air temperature and particle number concentration under the MV system were uniform, while the DV system generated a vertical stratification of the air temperature and particle number concentration. Because of the vertical stratification of the particle number concentration, the DV system provided better indoor air quality than the MV system. An increase in ventilation rate can reduce the particle concentration under the two systems. However, the improvement was not proportional to the ventilation rate. The increase in ventilation rate from 2 ACH to 4 ACH and 6 ACH for MV system reduced the particle concentration by 20% and 60%, respectively. While for the DV system, increasing the ventilation rate from 2 ACH to 4 ACH and 6 ACH reduced the particle concentration by only 10% and 40%, respectively. The ventilation effectiveness of the MV system was close to 1.0, but it was much higher for the DV system. Therefore, the DV system was better than the MV system.

Airborne and aerosol pathogen transmission modeling of respiratory events in buildings: An overview of computational fluid dynamics

Pathogen droplets released from respiratory events are the primary means of dispersion and transmission of the recent pandemic of COVID-19. Computational fluid dynamics (CFD) has been widely employed as a fast, reliable, and inexpensive technique to support decision-making and to envisage mitigatory protocols. Nonetheless, the airborne pathogen droplet CFD modeling encounters limitations due to the oversimplification of involved physics and the intensive computational demand. Moreover, uncertainties in the collected clinical data required to simulate airborne and aerosol transport such as droplets' initial velocities, tempo-spatial profiles, release angle, and size distributions are broadly reported in the literature. There is a noticeable inconsistency around these collected data amongst many reported studies. This study aims to review the capabilities and limitations associated with CFD modeling. Setting the CFD models needs experimental data of respiratory flows such as velocity, particle size, and number distribution. Therefore, this paper briefly reviews the experimental techniques used to measure the characteristics of airborne pathogen droplet transmissions together with their limitations and reported uncertainties. The relevant clinical data related to pathogen transmission needed for postprocessing of CFD data and translating them to safety measures are also reviewed. Eventually, the uncertainty and inconsistency of the existing clinical data available for airborne pathogen CFD analysis are scurtinized to pave a pathway toward future studies ensuing these identified gaps and limitations.

Visualization of the infection risk assessment of SARS-CoV-2 through aerosol and surface transmission in a negative-pressure ward

Since December 2019, coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a great challenge to the world's public health system. Nosocomial infections have occurred frequently in medical institutions worldwide during this pandemic. Thus, there is an urgent need to construct an effective surveillance and early warning system for pathogen exposure and infection to prevent nosocomial infections in negative-pressure wards. In this study, visualization and construction of an infection risk assessment of SARS-CoV-2 through aerosol and surface transmission in a negative-pressure ward were performed to describe the distribution regularity and infection risk of SARS-CoV-2, the critical factors of infection, the air changes per hour (ACHs) and the viral variation that affect infection risk. The SARS-CoV-2 distribution data from this model were verified by field test data from the Wuhan Huoshenshan Hospital ICU ward. ACHs have a great impact on the infection risk from airborne exposure, while they have little effect on the infection risk from surface exposure. The variant strains demonstrated significantly increased viral loads and risks of infection. The level of protection for nurses and surgeons should be increased when treating patients infected with variant strains, and new disinfection methods, electrostatic adsorption and other air purification methods should be used in all human environments. The results of this study may provide a theoretical reference and technical support for reducing the occurrence of nosocomial infections.

Impact of natural ventilation on exposure to SARS-CoV 2 in indoor/semi-indoor terraces using CO2 concentrations as a proxy

Nowadays, it is necessary a better airborne transmission understanding of respiratory diseases in shared indoor and semi-indoor environments with natural ventilation in order to adopt effective people's health protection measures. The aim of this work is to evaluate the relative exposure to SARS-CoV 2 in a set of virtual scenarios representing enclosed and semi-enclosed terraces under different outdoor meteorological conditions. For this purpose, indoor CO₂ concentration is used as a proxy for the risk assessment. Airflow and people exhaled CO₂ in different scenarios are simulated through Computational Fluid Dynamics (CFD) modelling with Unsteady Reynolds-Averaged Navier-Stokes (URANS) approach. Both spatial average concentrations and local concentrations are analyzed. In general, spatial average concentrations decrease as ventilation increases, however, depending on the people arrangement inside the terrace, spatial average concentrations and local concentrations can be very different. Therefore, for assessing the relative exposure to SARS-CoV 2 it is necessary to consider the indoor flow patterns between infectors and susceptibles. This research provides detailed information about CO₂ dispersion in enclosed/semi-enclosed scenarios, which can be very useful for reducing the transmission risk through better natural ventilation designs and improving the classic risk models since it allows to check their hypotheses in real-world scenarios. Although CFD ventilation studies in indoor/semi-indoor environments have been already addressed in the literature, this research is focused on restaurant terraces, scenarios scarcely investigated. Likewise, one of the novelties of this study is to take into account the outdoor meteorological conditions to appropriately simulate natural ventilation.

Respiratory pandemic and indoor aeraulics of classrooms

Respiratory pandemics, such as COVID19, may be transmitted by several modes. The present work focuses on the transmission through small droplets released by people from their mouth by breathing, speaking, coughing, sneering, and possibly aspirated by other people around through their respiration. An analysis of droplet evolution in simplified situations shows that the droplets reach very quickly a quasi-equilibrium temperature before encompassing an isothermal evaporation process. The removal of droplets from suspension is thus piloted by balance between evaporation and sedimentation. It is shown that ambient relative humidity is a major factor influencing the lifetime of droplets and the distance they may travel. As a consequence, and independently of any other health consideration linked to ambient humidity, it is seen that a dry air is a favourable factor for limiting risk of contamination from COVID19. Further investigation is made using computational fluid dynamics (CFD) in a classroom geometry. Several ventilation strategies are investigated: classical regulatory mechanical ventilation, open window natural ventilation and displacement natural ventilation. Ventilation has several effects which influence contamination risk: by introducing fresh air, it reduces droplet concentration; humidity released by human occupants is also limited. However, these effects are not uniform in space, and depend on ventilation strategy. Application of a dose-effect model calibrated for COVID19 to CFD results allows to estimate contamination risk. It is shown that contamination risk is higher for regulatory mechanical ventilation, and may be reduced, using natural ventilation in the absence of wind, by a factor 2.3 to nearly 3 when the teacher is sick, and by a factor 6 to 500 when a student is sick. In the presence of wind, the reduction factor is as high as 13 when the teacher is sick and 17 when a student is sick.

Direct capture and smartphone quantification of airborne SARS-CoV-2 on a paper microfluidic chip

SARS, a new type of respiratory disease caused by SARS-CoV, was identified in 2003 with significant levels of morbidity and mortality. The recent pandemic of COVID-19, caused by SARS-CoV-2, has generated even greater extents of morbidity and mortality across the entire world. Both SARS-CoV and SARS-CoV-2 spreads through the air in the form of droplets and potentially smaller droplets (aerosols) via exhaling, coughing, and sneezing. Direct detection from such airborne droplets would be ideal for protecting general public from potential exposure before they infect individuals. However, the number of viruses in such droplets and aerosols is too low to be detected directly. A separate air sampler and enough collection time (several hours) are necessary to capture a sufficient number of viruses. In this work, we have demonstrated the direct capture of the airborne droplets on the paper microfluidic chip without the need for any other equipment. 10% human saliva samples were spiked with the known concentration of SARS-CoV-2 and sprayed to generate liquid droplets and aerosols into the air. Antibody-conjugated submicron particle suspension is then added to the paper channel, and a smartphone-based fluorescence microscope isolated and counted the immunoagglutinated particles on the paper chip. The total capture-to-assay time was <30 min, compared to several hours with the other methods. In this manner, SARS-CoV-2 could be detected directly from the air in a handheld and low-cost manner, contributing to slowing the spread of SARS-CoV-2. We can presumably adapt this technology to a wide range of other respiratory viruses.

Exposure Risk to Medical Staff in a Nasopharyngeal Swab Sampling Cabin under Four Different Ventilation Strategies

Medical staff working in a nasopharyngeal swab sampling cabin are exposed to a higher exposure risk of COVID-19. In this study, computational fluid dynamics (CFD) are used to evaluate the exposure risk to medical staff in a nasopharyngeal swab sampling cabin of Chinese customs under four different ventilation strategies, i.e., multiple supply fans ventilation (MSFV), multiple exhaust fans ventilation (MEFV), single exhaust fan and outer windows closed ventilation (SEFV), and single exhaust fan and outer windows opened ventilation (SEFV-W). The impact of physical partitions on exposure risk is also discussed. The results show that MSFV performed best in reducing exposure risk. No significant difference was found between MEFV and SEFV. SEFV-W performed better than SEFV with a ventilation rate of 10–50 L/(s∙Person), while it performed worse with a ventilation rate of 50–90 L/(s∙Person). The exposure risk to medical staff did not decrease linearly with the increase in the ventilation flow rate under the four ventilation strategies. For MSFV, the installation of partitions is conducive to the reduction in the exposure risk. This study is expected to provide some guidance for ventilation designs in sampling cabins.

3D modelling and simulation of the dispersion of droplets and drops carrying the SARS-CoV-2 virus in a railway transport coach

Computational fluid dynamics (CFD) modelling and 3D simulations of the air flow and dispersion of droplets or drops in semi-confined ventilated spaces have found topical applications with the unfortunate development of the Covid-19 pandemic. As an illustration of this scenario, we have considered the specific situation of a railroad coach containing a seated passenger infected with the SARS-CoV-2 virus (and not wearing a face mask) who, by breathing and coughing, releases droplets and drops that contain the virus and that present aerodynamic diameters between 1 and 1000 µm. The air flow is generated by the ventilation in the rail coach. While essentially 3D, the flow is directed from the bottom to the top of the carriage and comprises large to small eddies visualised by means of streamlines. The space and time distribution of the droplets and drops is computed using both an Eulerian model and a Lagrangian model. The results of the two modelling approaches are fully consistent and clearly illustrate the different behaviours of the drops, which fall down close to the infected passenger, and the droplets, which are carried along with the air flow and invade a large portion of the rail coach. This outcome is physically sound and demonstrates the relevance of CFD for simulating the transport and dispersion of droplets and drops with any diameter in enclosed ventilated spaces. As coughing produces drops and breathing produces droplets, both modes of transmission of the SARS-CoV-2 virus in human secretions have been accounted for in our 3D numerical study. Beyond the specific, practical application of the rail coach, this study offers a much broader scope by demonstrating the feasibility and usefulness of 3D numerical simulations based on CFD. As a matter of fact, the same computational approach that has been implemented in our study can be applied to a huge variety of ventilated indoor environments such as restaurants, performance halls, classrooms and open-plan offices in order to evaluate if their occupation could be critical with respect to the transmission of the SARS-CoV-2 virus or to other airborne respiratory infectious agents, thereby enabling relevant recommendations to be made.

A coupled Computational Fluid Dynamics and Wells-Riley model to predict COVID-19 infection probability for passengers on long-distance trains

Coupled Wells-Riley (WR) and Computational Fluid Dynamics (CFD) modelling (WR-CFD) facilitates a detailed analysis of COVID-19 infection probability (IP). This approach overcomes issues associated with the WR 'well-mixed' assumption. The WR-CFD model, which makes uses of a scalar approach to simulate quanta dispersal, is applied to Chinese long-distance trains (G-train). Predicted IPs, at multiple locations, are validated using statistically derived (SD) IPs from reported infections on G-trains. This is the first known attempt to validate a coupled WR-CFD approach using reported COVID-19 infections derived from the rail environment. There is reasonable agreement between trends in predicted and SD IPs, with the maximum SD IP being 10.3% while maximum predicted IP was 14.8%. Additionally, predicted locations of highest and lowest IP, agree with those identified in the statistical analysis. Furthermore, the study demonstrates that the distribution of infectious aerosols is non-uniform and dependent on the nature of the ventilation. This suggests that modelling techniques neglecting these differences are inappropriate for assessing mitigation measures such as physical distancing. A range of mitigation strategies were analysed; the most effective being the majority (90%) of passengers correctly wearing high efficiency masks (e.g. N95). Compared to the base case (40% of passengers wearing low efficiency masks) there was a 95% reduction in average IP. Surprisingly, HEPA filtration was only effective for passengers distant from an index patient, having almost no effect for those in close proximity. Finally, as the approach is based on CFD it can be applied to a range of other indoor environments.

"SARS-CoV-2 is transmitted by particulate air pollution": Misinterpretations of statistical data, skewed citation practices, and misuse of specific terminology spreading the misconception

In epidemiology, there are still outdated myths associated with the spread of respiratory infections. Recently, we have witnessed the origination of a new misconception, to the effect that SARS-CoV-2 is transmitted in the open air by way of particulate air pollution (atmospheric particulate matter (PM)). There is no evidence to support the idea behind this misconception. Nevertheless, more and more people are involved in animated debate and the number of studies concerning atmospheric PM as a carrier of SARS-CoV-2 is growing rapidly. In this work, the origin of the misconception was investigated, and the published papers which have contributed to the spread of this myth were analyzed. The results show that the following factors lie behind the origin and spread of the misconception: a) The specific terminology is not always clearly defined or consistently used by scientists. In particular, the terms 'particulate matter', 'atmospheric aerosol particles', 'air pollutants', and 'atmospheric aerosols' need to be clarified, and besides they are often equated to 'infectious aerosols', 'virus-bearing aerosols', 'bio-aerosols', 'virus-laden particles', 'respiratory aerosol/droplets', and 'droplet nuclei'. b) Authors misinterpret statistical data and information from other sources. Interpretation of the correlation between PM levels and the increasing incidence and severity of COVID-19 infection, is often changed from "PM may reflect the indirect action of certain atmospheric conditions that maintain infectious nuclei suspended for prolonged periods, parameters that also act on atmospheric pollutants" to "PM could cause an increase in infectious droplets/aerosols containing SARS-CoV-2." This is a dramatic change to the meaning. Moreover, it is often not taken into account that PM may reflect activities in areas with high population density and this population density at the same time contributes to the spread COVID-19. c) Skewed citation practices. Many authors cite a hypothetical conclusion from an original study, then other authors cite the papers of these authors as primary sources. This practice leads to the effect that there are many witnesses to a 'phenomenon' that did not ever occur. Thus, the terminology used in interdisciplinary communications should be more nuanced and defined precisely. Authors should be more careful when citing unconfirmed data (and hypotheses) as well as in interpreting statistical data so as to avoid confusion and spreading false information. This is especially important now in the era of the COVID-19 pandemic.

Predicting the spatio-temporal infection risk in indoor spaces using an efficient airborne transmission model

We develop a spatially dependent generalization to the Wells-Riley model, which determines the infection risk due to airborne transmission of viruses. We assume that the infectious aerosol concentration is governed by an advection-diffusion-reaction equation with the aerosols advected by airflow, diffused due to turbulence, emitted by infected people, and removed due to ventilation, inactivation of the virus and gravitational settling. We consider one asymptomatic or presymptomatic infectious person breathing or talking, with or without a mask, and model a quasi-three-dimensional set-up that incorporates a recirculating air-conditioning flow. We derive a semi-analytic solution that enables fast simulations and compare our predictions to three real-life case studies-a courtroom, a restaurant, and a hospital ward-demonstrating good agreement. We then generate predictions for the concentration and the infection risk in a classroom, for four different ventilation settings. We quantify the significant reduction in the concentration and the infection risk as ventilation improves, and derive appropriate power laws. The model can be easily updated for different parameter values and can be used to make predictions on the expected time taken to become infected, for any location, emission rate, and ventilation level. The results have direct applicability in mitigating the spread of the COVID-19 pandemic.

Beyond well-mixed: A simple probabilistic model of airborne disease transmission in indoor spaces

We develop a simple model for assessing risk of airborne disease transmission that accounts for non-uniform mixing in indoor spaces and is compatible with existing epidemiological models. A database containing 174 high-resolution simulations of airflow in classrooms, lecture halls, and buses is generated and used to quantify the spatial distribution of expiratory droplet nuclei for a wide range of ventilation rates, exposure times, and room configurations. Imperfect mixing due to obstructions, buoyancy, and turbulent dispersion results in concentration fields with significant variance. The spatial non-uniformity is found to be accurately described by a shifted lognormal distribution. A well-mixed mass balance model is used to predict the mean, and the standard deviation is parameterized based on ventilation rate and room geometry. When employed in a dose-response function risk model, infection probability can be estimated considering spatial heterogeneity that contributes to both short- and long-range transmission.

Electrospun membranes filtering 100 nm particles from air flow by means of the van der Waals and Coulomb forces

Nonwoven fibrous filter membranes are widely used in filtration because of their low cost. They are less effective in intercepting airborne particles of the order of 100 nm, which is of the SARS-CoV-2 (COVID-19) virus's size. Many diseases, including COVID-19, predominantly spread by droplets released by breathing, coughing, sneezing, or medical procedures. It was shown that the smallest droplets can evaporate in air before settling, thus, making viruses airborne and easily penetrating even the best masks and filters. As a result, air-filtering membranes, which are capable of effective interception of ∼100 nm nanoparticles are highly desirable. A traditional way to improve filtration efficiency by overlapping several layers of nonwoven fabrics increases the required pressure drop, and thus, should be avoided as much as possible. Here, we propose and demonstrate an innovative approach to enhance performance of filtration membranes based on (i) a dramatic reduction in the fiber size, and (ii) metal coating of the fibers. The first component of this approach allows one to incorporate a novel physical mechanism of filtration, the short-range van der Waals forces, whereas the second one adds the long-range electric Coulomb forces if the oncoming nanoparticles are pre-charged and the metal-plated membrane grounded. In the present work, the ∼100 nm aluminum nanoparticles are filtered as a model of commensurate airborne single COVID-19 viruses, and Platinum is used as the sputter-coated material for the fiber coating. The resulting filtration efficiency enhanced by the electric Coulomb forces alone is increased by the factor of 1.77, while the filtration efficiency additionally facilitated by the van der Waals forces increased by the factor of 2.44. In comparison to the filter membranes with ∼500 nm fibers without the electric forces involved, the van-der-Waals-electric filter membrane with fibers ∼90 nm is 2.24 × 1.77 = 3.96 times more effective. The quality factor of a membrane which combines the van der Waals and Coulomb forces is 10.6 psi^-1, which is almost three times that of a comparable membrane without the electric Coulomb force (with only van der Waals forces being used).

Investigation on the evaporation and dispersion of human respiratory droplets with COVID-19 virus

On March 11, 2020, COVID-19 was declared as a pandemic by World Health Organization (WHO). Effective prevention is indispensable for defeating the ongoing COVID-19 pandemic. The evaporation and diffusion characteristics of the droplet in the air are the critical factors for the virus transmission by droplets. To better understand transmission routes of COVID-19 through respiratory droplets, a new evaporation and dispersion model for respiratory droplets is proposed to estimate droplet lifetime and the size of spreading zone in air. The importance of respiratory activities and environmental factors on the transmission of respiratory viruses are further discussed. The predictive results demonstrate initial particle size, ambient temperature and relative humidity all have significant effect on the survival time and infection distance of respiratory droplets. Decreasing droplet initial size always shortens the lifetime and the transmission distance of respiratory droplets. The 100 μm droplets expelled by talking or coughing can be carried more than 2 m away. Increasing ambient temperature and decreasing ambient humidity can effectively reduce the lifetime and propagation distance of respiratory droplets, thus reducing the risk of viral infection. These findings could contribute to developing effective prevention measures for controlling infectious disease transmission via droplets.

Biosensor detection of airborne respiratory viruses such as SARS-CoV-2

Airborne SARS-CoV-2 transmission represents a significant route for possible human infection that is not yet fully understood. Viruses in droplets and aerosols are difficult to detect because they are typically present in low amounts. In addition, the current techniques used, such as RT-PCR and virus culturing, require large amounts of time to get results. Biosensor technology can provide rapid, handheld, and point-of-care systems that can identify virus presence quickly and accurately. This paper reviews the background of airborne virus transmission and the characteristics of SARS-CoV-2, its relative risk for transmission even at distances greater than the currently suggested 6 feet (or 2 m) physical distancing. Publications on biosensor technology that may be applied to the detection of airborne SARS-CoV-2 and other respiratory viruses are also summarized. Based on the current research we believe that there is a pressing need for continued research into handheld and rapid methods for sensitive collection and detection of airborne viruses. We propose a paper-based microfluidic chip and immunofluorescence assay as one method that could be investigated as a low-cost and portable option.

Energy Analysis of Control Measures for Reducing Aerosol Transmission of COVID-19 in the Tourism Sector of the “Costa Daurada” Spain

In this paper, the use of HVAC systems and non-HVAC control measures to reduce virus-laden bioaerosol exposure in a highly occupied indoor space is investigated. A simulation tool was used to model the fate and transport of bioaerosols in an indoor space in the hotel industry (bar or pub) with three types of HVAC system (central air handling system (CAHS), dedicated outdoor air system (DOAS), and wall unit system (WUS)). Non-HVAC control measures such as portable air cleaners (PAC) and local exhaust fans were considered. Occupant exposure was evaluated for 1 μm bioaerosols, which transport SARS-CoV-2, for 3 h/day of continuous source and exposure. The combined effects of ventilation (400 l/s of outdoor air), recirculated air filtration (90% efficacy), and a PAC with a capacity up to 900 m3/h mitigated the (normalized) integrated exposure of the occupant by 0.66 to 0.51 (CAHS) and 0.43 to 0.36 (DOAS). In the case of WUS, the normalized integrated exposure was reduced by up to 0.2 when the PAC with a capacity of up to 900 m3/h was used. The corresponding electricity consumed increased by 297.4 kWh/year (CAHS) and 482.7 kWh/year (DOAS), while for the WUS it increased by 197.1 kWh/year.

Rapid estimation of viral emission source location via genetic algorithm

Indoor spread of infectious diseases is well-studied as a common transmission route. For highly infectious diseases, like Sars-CoV-2, considering poorly or semi ventilated areas outdoors is increasingly important. This is important in communities with high proportions of infected people, highly infectious variants, or where spread is difficult to manage. This work develops a simulation framework based on probabilistic distributions of viral particles, decay, and infection. The methodology reduces the computational cost of generating rapid estimations of a wide variety of scenarios compared to other simulation methods with high computational cost and more fidelity. Outdoor predictions are provided in example applications for a gathering of five people with oscillating wind and a public speaking event. The results indicate that infection is sensitive to population density and outdoor transmission is plausible and likely locations of a virtual super-spreader are identified. Outdoor gatherings should consider precautions to reduce infection spread.

Airborne particle dispersion by high flow nasal oxygen: An experimental and CFD analysis

High Flow Nasal Oxygen (HFNO) therapy offers a proven means of delivering respiratory support to critically ill patients suffering from viral illness such as COVID-19. However, the therapy has the potential to modify aerosol generation and dispersion patterns during exhalation and thereby put healthcare workers at increased risk of disease transmission. Fundamentally, a gap exists in the literature with regards to the effect of the therapy on the fluid dynamics of the exhalation jet which is essential in understanding the dispersion of aerosols and hence quantifying the disease transmission risk posed by the therapy. In this paper, a multi-faceted approach was taken to studying the aerosol-laden exhalation jet. Schlieren imaging was used to visualise the flow field for a range of expiratory activities for three healthy human volunteers receiving HFNO therapy at flow rates of 0-60 L/min. A RANS turbulence model was implemented using the CFD software OpenFOAM and used to perform a parametric study on the influence of exhalation velocity and duration on the dispersion patterns of non-evaporating droplets in a room environment. A dramatic increase in the turbulence of the exhalation jet was observed when HFNO was applied. Quantitative analysis indicated that the mean exhalation velocity was increased by 2.2-3.9 and 2.3-3 times that for unassisted breathing and coughing, respectively. A 1-2 second increase was found in the exhalation duration. The CFD model showed that small droplets (10-40 μm) were most greatly affected, where a 1 m/s increase in velocity and 1 s increase in duration caused an 80% increase in axial travel distance.

A simplified tempo-spatial model to predict airborne pathogen release risk in enclosed spaces: An Eulerian-Lagrangian CFD approach

COVID19 pathogens are primarily transmitted via airborne respiratory droplets expelled from infected bio-sources. However, there is a lack of simplified accurate source models that can represent the airborne release to be utilized in the safe-social distancing measures and ventilation design of buildings. Although computational fluid dynamics (CFD) can provide accurate models of airborne disease transmissions, they are computationally expensive. Thus, this study proposes an innovative framework that benefits from a series of relatively accurate CFD simulations to first generate a dataset of respiratory events and then to develop a simplified source model. The dataset has been generated based on key clinical parameters (i.e., the velocity of droplet release) and environmental factors (i.e., room temperature and relative humidity) in the droplet release modes. An Eulerian CFD model is first validated against experimental data and then interlinked with a Lagrangian CFD model to simulate trajectory and evaporation of numerous droplets in various sizes (0.1 μm-700 μm). A risk assessment model previously developed by the authors is then applied to the simulation cases to identify the horizontal and vertical spread lengths (risk cloud) of viruses in each case within an exposure time. Eventually, an artificial neural network-based model is fitted to the spread lengths to develop the simplified predictive source model. The results identify three main regimes of risk clouds, which can be fairly predicted by the ANN model.

High-resolution large-eddy simulation of indoor turbulence and its effect on airborne transmission of respiratory pathogens-Model validation and infection probability analysis

High-resolution large-eddy simulation (LES) is exploited to study indoor air turbulence and its effect on the dispersion of respiratory virus-laden aerosols and subsequent transmission risks. The LES modeling is carried out with unprecedented accuracy and subsequent analysis with novel mathematical robustness. To substantiate the physical relevance of the LES model under realistic ventilation conditions, a set of experimental aerosol concentration measurements are carried out, and their results are used to successfully validate the LES model results. The obtained LES dispersion results are subjected to pathogen exposure and infection probability analysis in accordance with the Wells-Riley model, which is here mathematically extended to rely on LES-based space- and time-dependent concentration fields. The methodology is applied to assess two dissimilar approaches to reduce transmission risks: a strategy to augment the indoor ventilation capacity with portable air purifiers and a strategy to utilize partitioning by exploiting portable space dividers. The LES results show that use of air purifiers leads to greater reduction in absolute risks compared to the analytical Wells-Riley model, which fails to predict the original risk level. However, the two models do agree on the relative risk reduction. The spatial partitioning strategy is demonstrated to have an undesirable effect when employed without other measures, but may yield desirable outcomes with targeted air purifier units. The study highlights the importance of employing accurate indoor turbulence modeling when evaluating different risk-reduction strategies.

Prediction and control of aerosol transmission of SARS-CoV-2 in ventilated context: from source to receptor

Global spread of COVID-19 has seriously threatened human life and health. The aerosol transmission route of SARS-CoV-2 is observed often associated with infection clusters under poorly ventilated environment. In the context of COVID-19 pandemic, significant transformation and optimization of traditional ventilation systems are needed. This paper is aimed to offer better understanding and insights into effective ventilation design to maximize its ability in airborne risk control, for particularly the COVID-19. Comprehensive reviews of each phase of aerosol transmission of SARS-CoV-2 from source to receptor are conducted, so as to provide a theoretical basis for risk prediction and control. Infection risk models and their key parameters for risk assessment of SARS-CoV-2 are analyzed. Special focus is given on the efficacy of different ventilation strategies in mitigating airborne transmission. Ventilation interventions are found mainly impacting on the dispersion and inhalation phases of aerosol transmission. The airflow patterns become a key factor in controlling the aerosol diffusion and distribution. Novel and personalized ventilation design, effective integration with other environmental control techniques and resilient HVAC system design to adapt both common and epidemic conditions are still remaining challenging, which need to be solved with the aid of multidisciplinary research and intelligent technologies.

A short review of vapour droplet dispersion models used in CFD to study the airborne spread of COVID19

The use of computational fluid dynamics (CFD) to simulate the spread of COVID19 and many other airborne diseases, especially in an indoor environment needs accurate understanding of dispersion models. Modelling the transport/dispersion of vapour droplets within the atmosphere is a complex problem, as it involves the motion of more than one phase, as well as the interphase interactions between the phases. This paper reviews the current canon of research on dispersion modelling of vapour droplets by looking at three specific aspects: (i) physical definition/specification of the initial droplet size distribution; (ii) physics of evaporation/condensation models and (iii) transport equations (with molecular/turbulent dispersion models) to describe the movement of the vapour droplets as they propagate through the air. This review found that the state of modelling implements a wide range of models which shows variances in results thus leading to a state where it is difficult to know which model is most accurate. The authors suggest that further studies in this direction should focus on developing a principle set of equations by benchmarking the previously developed models to establish model uncertainty of the previously developed models with reference to a fixed theoretical model and be compared under identical conditions. However, it must be noted that due to the complex nature of microdroplet evaporation and dispersion coupled with the unpredictable way droplet size distributions are produced, current experimental methodologies that are available to validate such simulations, such as particle image velocimetry, are still not robust enough to provide detailed data to verify minute aspects of the simulations.