Wells-Riley Equation Research Articles

Intelligent generation method of infection risk map and management system in hospital waiting room for respiratory infectious diseases

Respiratory infectious diseases, such as seasonal influenza, pose significant risks to public health safety through airborne transmission. This study aims to explore methods for controlling airborne infection risk in waiting rooms, a hotspot for cross-infection. A database of 990 samples was established by integrating the results of computational fluid dynamics and social force model simulations into the Wells-Riley equation. We introduced two indicators, Pr (Particle Removal Efficiency) and Hr (High-risk Area Ratio), to evaluate the cleanliness performance of waiting room spaces. Additionally, the Sci (Space Cleanliness Index) was proposed as a criterion for evaluating architectural designs and fine-tuning fresh air system operational parameters. We also unveiled the mapping relationship between spatial morphology parameters and ventilation design parameters in relation to the cleanliness performance of waiting rooms. An infection-risk prediction proxy model was established based on machine learning methods, suitable for rapid prediction of spatial infection risks in waiting rooms and optimization of fresh air system operational parameters. The findings can be applied to the selection of hospital design schemes and the optimization of ventilation operational control strategies, aiding in strengthening infectious disease control and reducing the public health safety risks of cross-infection from respiratory infections during peak seasons in hospitals.

Opening windows as a supplementary ventilation strategy for large classrooms in a pandemic situation: Field measurements and simulation

The coronavirus disease 2019 (COVID-19) has led to significant global health emergencies in recent years, prompting widespread recommendations across various regions for opening windows in building codes. However, official guidelines have not adequately addressed the details of window opening arrangements related to the supporting evidence from existing literature. This study investigated the risk of disease outbreaks via respiratory transmission in large university studio classrooms. It examined the effectiveness of window-door opening strategies combined with mechanical ventilation in mitigating the infection risk of respiratory diseases in such spaces. A full-scale school ventilation performance test bed was used to measure indoor airflow and CO2 distribution patterns. Comparisons were made with the numerical simulation of exhaled viral emissions. A modified Wells-Riley equation was employed to calculate inhalation exposure and disease infection risks at the room level. Measurements showed that fresh air ventilation from the mechanical system was relatively low compared to existing standards, especially in pandemic situations. The results show that opening windows can lead to a decrease to low probabilities (below 5 %) of infection risk in 85.02 % area of a classroom over an 8-h period, which demonstrates that window-opening behavior is an effective supplementary strategy for large educational spaces with mechanically ventilated systems during emergencies when active measures are insufficient for diluting diseases. Furthermore, the study reveals that maximizing outdoor air rates by opening all windows may occasionally result in adverse indoor airflow redistribution, increasing virus load from 0.0382 to 0.8926 quanta/m3 near the virus source locations.

Airborne transmission risks of tuberculosis and COVID-19 in schools in South Africa, Switzerland, and Tanzania: Modeling of environmental data.

The COVID-19 pandemic renewed interest in airborne transmission of respiratory infections, particularly in congregate indoor settings, such as schools. We modeled transmission risks of tuberculosis (caused by Mycobacterium tuberculosis, Mtb) and COVID-19 (caused by SARS-CoV-2) in South African, Swiss and Tanzanian secondary schools. We estimated the risks of infection with the Wells-Riley equation, expressed as the median with 2.5% and 97.5% quantiles (credible interval [CrI]), based on the ventilation rate and the duration of exposure to infectious doses (so-called quanta). We computed the air change rate (ventilation) using carbon dioxide (CO2) as a tracer gas and modeled the quanta generation rate based on reported estimates from the literature. The share of infectious students in the classroom is determined by country-specific estimates of pulmonary TB. For SARS-CoV-2, the number of infectious students was estimated based on excess mortality to mitigate the bias from country-specific reporting and testing. Average CO2 concentration (parts per million [ppm]) was 1,610 ppm in South Africa, 1,757 ppm in Switzerland, and 648 ppm in Tanzania. The annual risk of infection for Mtb was 22.1% (interquartile range [IQR] 2.7%-89.5%) in South Africa, 0.7% (IQR 0.1%-6.4%) in Switzerland, and 0.5% (IQR 0.0%-3.9%) in Tanzania. For SARS-CoV-2, the monthly risk of infection was 6.8% (IQR 0.8%-43.8%) in South Africa, 1.2% (IQR 0.1%-8.8%) in Switzerland, and 0.9% (IQR 0.1%-6.6%) in Tanzania. The differences in transmission risks primarily reflect a higher incidence of SARS-CoV-2 and particularly prevalence of TB in South Africa, but also higher air change rates due to better natural ventilation of the classrooms in Tanzania. Global comparisons of the modeled risk of infectious disease transmission in classrooms can provide high-level information for policy-making regarding appropriate infection control strategies.

Superspreading of SARS-CoV-2 at a choir rehearsal in Finland-A computational fluid dynamics view on aerosol transmission and patient interviews.

COVID-19 pandemic has highlighted the role of aerosol transmission and the importance of superspreading events. We analyzed a choir rehearsal in November 2020, where all participants, except one who had recently earlier recovered from COVID-19, were infected. We explore the risk factors for severe disease in this event and model the aerosol dispersion in the rehearsal room. Characteristics of participants were collected by interviews and supplemented with patient records. A computational simulation of aerosol distribution in the rehearsal room and the efficacy of potential safety measures was conducted using the Large-Eddy Simulation approach. Infection risk was studied by analyzing quanta emission and exposure with the Wells-Riley equation. The simulation showed that airborne transmission likely explains this mass contagion event. Every singer was exposed to the virus in only 5 min from the beginning of the rehearsal, and maximum concentration levels were reached at 20 min the concentration levels started to approach a steady state after 20 min. Although concentration differences existed in the room, risk levels near (1 m) and far (5 m) from the aerosol source were similar for certain singers. Modeling indicated infection risk levels of 70-100% after one hour; the risk would have been considerably reduced by wearing high-filtration respirators. Age and pre-existing comorbidities predicted more severe disease. The high incidence of illness may be partly attributed to the relatively high median age of individuals. Additionally, those admitted to the hospital had multiple underlying health conditions that predispose them to more severe disease. Airborne transmission and indoor space can explain this mass exposure event. High-filtration respirators could have prevented some infections. The importance of safety distances diminishes the longer the indoor event. The concept of safety distance is challenging, as our study suggests that long range airborne transmission may occur in indoor events with extended duration. We encourage informing the public, especially persons at risk, of safety measures during epidemics.

Assessment of tuberculosis transmission probability in three Thai prisons based on five dynamic models.

This study aimed to assess and compare the probability of tuberculosis (TB) transmission based on five dynamic models: the Wells-Riley equation, two Rudnick & Milton-proposed models based on air changes per hour (ACH) and liters per second per person (L/s/p), the model proposed by Issarow et al, and the Applied Susceptible-Exposed-Infected-Recovered (SEIR) TB transmission model. This study also aimed to determine the impact of model parameters on such probabilities in three Thai prisons. A cross-sectional study was conducted using data from 985 prison cells. The TB transmission probability for each cell was calculated using parameters relevant to the specific model formula, and the magnitude of the model agreement was examined by Spearman's rank correlation and Bland-Altman plot. Subsequently, a multiple linear regression analysis was conducted to investigate the influence of each model parameter on the estimated probability. Results revealed that the median (Quartiles 1 and 3) of TB transmission probability among these cells was 0.052 (0.017, 0.180). Compared with the pioneered Wells-Riley's model, the remaining models projected discrepant TB transmission probability from less to more commensurate to the degree of model modification from the pioneered model as follows: Rudnick & Milton (ACH), Issarow et al., and Rudnick & Milton (L/s/p), and the applied SEIR models. The ventilation rate and number of infectious TB patients in each cell or zone had the greatest impact on the estimated TB transmission probability in most models. Additionally, the number of inmates in each cell, the area per person in square meters, and the inmate turnover rate were identified as high-impact parameters in the applied SEIR model. All stakeholders must urgently address these influential parameters to reduce TB transmission in prisons. Moreover, further studies are required to determine their relative validity in accurately predicting TB incidence in prison settings.

Prediction of Risk of Airborne Transmitted Diseases based on the Wells-Riley Equation: Applying in the Metro Hall Environments

The recent years have witnessed the increasing attention aroused by airborne transmitted diseases. Increasing evidence has shown that indoor air quality has a significant impact on people’s health, as well as the transmission of infectious diseases. As a result, more and more experts pay attention to the topic of airborne transmitted diseases. To improve the understanding of the risk of airborne transmitted diseases, in this paper we have reviewed the previous risk prediction model and tried to develop an improved model. The model, which has taken the size of space, the ventilation of the space, and the time of co-location, are also applied in a realistic model based on the system dynamic method, simulating the condition of passengers’ going into the station hall. According to the model and simulation outcoming, expanding the volume of room and ventilation can reduce the risk of infection. However, the co-location has a positive correlation with the infectious risk.

CO2 concentration as an indicator of indoor ventilation performance to control airborne transmission of SARS-CoV-2

BackgroundThe Wells-Riley equation has been extensively used to quantify the infection risk of airborne transmission indoors. This equation is difficult to apply to actual conditions because it requires measurement of the outdoor air supply rate, which vary with time and are difficult to quantify. The method of determining the fraction of inhaled air that has been exhaled previously by someone in a building using a CO2 concentration measurement can solve the limitations of the existing method. Using this method, the indoor CO2 concentration threshold can be determined to keep the risk of infection below certain conditions. MethodsBased on the calculation of the rebreathed fraction, an appropriate mean indoor CO2 concentration and required air exchange rate to control SARS-CoV-2 airborne transmission was calculated. The number of indoor occupants, ventilation rate, and the deposition and inactivation rates of the virus-laden aerosols were considered. The application of the proposed indoor CO2 concentration-based infection rate control was investigated through case studies in school classrooms and restaurants. ResultsIn a typical school classroom environment with 20–25 occupants and an exposure time of 6–8 h, the average indoor CO2 concentration should be kept below 700 ppm to control the risk of airborne infection indoors. The ASHRAE recommended ventilation rate is sufficient when wearing a mask in classrooms. For a typical restaurant with 50–100 occupants and an exposure time of 2–3 h, the average indoor CO2 concentration should be kept below about 900 ppm. Residence time in the restaurant had a significant effect on the acceptable CO2 concentration. ConclusionGiven the conditions of the occupancy environment, it is possible to determine an indoor CO2 concentration threshold, and keeping the CO2 concentration lower than a certain threshold could help reduce the risk of COVID-19 infection.

Effectiveness of personalized ventilation in reducing airborne infection risk for long-term care facilities

Throughout history, the human population has experienced major outbreaks of infectious diseases. In December 2019 the previously unknown SARS-CoV-2 virus emerged, which had a huge impact globally. Residents of long- term care facilities (LTCFs) showed to be highly susceptible to infection due to their frailty. Respiratory infectious diseases, such as COVID-19, can spread among others via the airborne transmission route. This is caused by sharing the same indoor environment. To reduce the risk of infection via the airborne route, it is important to consider ventilation and other building services system measures, including personalized ventilation (PV). PV has the potential of being a suitable solution for LTCFs, as it could still allow interaction between residents and visitors in the common rooms, which is regarded very important from a mental health perspective. To identify the potential of PV in the context of infection risk, a laboratory experiment was conducted to investigate its effectiveness on the infection risk reduction. The research was performed in a controlled climate chamber. In the experiment a person was mimicked and positioned close to a PV system that provided filtered recirculated air. A particle source maintained a constant particle concentration in the room. The performance of the PV system was measured through the particle concentration near the breathing zone as compared to the room concentration. Several design parameters were investigated. Translating the outcomes to a fictive (equivalent) ventilation rate, the Wells-Riley equation was applied to determine the infection risk. The outcomes indicated that, in this laboratory setting, the PV system can reduce the risk of an infection up to 50%. The performance is affected by the distance of the supply head to the breathing zone, the angle of the supply head, airflows in the room and the location of the particle source. To further optimize the system and allow its application in LTCFs, several aspects still need further attention, such as mobility/placing the person, the breathing pattern of the user and factors influencing the comfort and use.

Aerosol transmission in queuing and dining scenarios in canteens and the effectiveness of control measures

This study investigates the aerosol transmission in queuing and dining scenarios in canteens and explores the effectiveness of control measures. An improved Wells-Riley equation is adopted to calculate the infection risk. The dilution of exhaled aerosols is difficult in the crowded queuing scenario, where the replacement of queuing positions increases the cross-infection risk. The highest infection risk is 1.16% and 1.08% for the linear-queue and cross-queue condition, respectively. Shortening the queuing duration, increasing the separation distance, and wearing masks can considerably reduce the infection risk. In the dining scenario, the effect of increasing ACH is limited on reducing the local concentration. An exhaust vent installed close to the top of the partition can effectively remove the local high-concentration aerosols. Intermittent occupation of a seat can considerably reduce the transmission risk between the consecutive dinners taking that seat. These findings should contribute to improved control of infectious transmission in canteens.

Prediction of COVID-19 infection in dental clinic by CFD and Wells-Riley model, identifying safe area and proper ventilation velocity

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 NTn 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.

Modeling the infection risk and emergency evacuation from bioaerosol leakage around an urban vaccine factory

Mounting interest in modeling outdoor diffusion and transmission of bioaerosols due to the prevalence of COVID-19 in the urban environment has led to better knowledge of the issues concerning exposure risk and evacuation planning. In this study, the dispersion and deposition dynamics of bioaerosols around a vaccine factory were numerically investigated under various thermal conditions and leakage rates. To assess infection risk at the pedestrian level, the improved Wells-Riley equation was used. To predict the evacuation path, Dijkstra’s algorithm, a derived greedy algorithm based on the improved Wells-Riley equation, was applied. The results show that, driven by buoyancy force, the deposition of bioaerosols can reach 80 m on the windward sidewall of high-rise buildings. Compared with stable thermal stratification, the infection risk of unstable thermal stratification in the upstream portion of the study area can increase by 5.53% and 9.92% under a low and high leakage rate, respectively. A greater leakage rate leads to higher infection risk but a similar distribution of high-risk regions. The present work provides a promising approach for infection risk assessment and evacuation planning for the emergency response to urban bioaerosol leakage.

PANDEMIC: Occupancy driven predictive ventilation control to minimize energy consumption and infection risk

During the SARS-CoV-2 (COVID-19) pandemic, governments around the world have formulated policies requiring ventilation systems to operate at a higher outdoor fresh air flow rate for a sufficient time, which has led to a sharp increase in building energy consumption. Therefore, it is necessary to identify an energy-efficient ventilation strategy to reduce the risk of infection. In this study, we developed an occupant-number-based model predictive control (OBMPC) algorithm for building ventilation systems. First, we collected the occupancy and Heating, ventilation, and air conditioning system (HVAC) data from March to July 2021. Then, four different models (Auto regression moving average-based multilayer perceptron (ARMA_MLP), Recurrent neural networks (RNN), Long short-term memory networks (LSTM), and Nonhomogeneous Markov with change points detection (NH_Markov)) were used to predict the number of room occupants from 15 min to 24 h ahead with an interval output. We found that each model could predict the number of occupants with 85 % accuracy using a one-person offset. The accuracy of 15 min of the ahead prediction could reach 95 % with a one-person offset, but none of them could track abrupt changes. The occupancy prediction results were used to calculate the ventilation demand using the Wells-Riley equation, and the upper bound can maintain an infection risk lower than 2 % for 93 % of the day. This OBMPC model could reduce the coil load by 52.44 % and shift the peak load by 3 h up to 5 kW compared with 24 × 7 h full outdoor air (OA) system when people wear masks in the space. The occupancy prediction uncertainty could cause a 9 % to 26 % difference in demand ventilation, a 0.3 °C to 2.4 °C difference in zone temperature, a 28.5 % to 44.5 % difference in outdoor airflow rate, and a 10.7 % to 28.2 % difference in coil load.

Evaluating vacant middle seats and masks as Coronavirus exposure reduction strategies in aircraft cabins using particle tracer experiments and computational fluid dynamics simulations.

Aircraft cabins have high-performance ventilation systems, yet typically hold many persons in close proximity for long durations. The current study estimated airborne virus exposure and infection reductions when middle seats are vacant compared to full occupancy and when passengers wear surgical masks in aircraft. Tracer particle data reported by U.S. Transportation Command (TRANSCOM) and CFD simulations reported by Boeing were used along with NIOSH data, to build nonlinear regression models with particle exposure and distance from particle source as variables. These models that estimate exposure at given distances from the viral source were applied to evaluate exposure reductions from vacant middle seats. Reductions averaged 54% for the seat row where an infectious passenger is located and 36% for a 24-row cabin containing one infectious passenger, with middle seats vacant. Analysis of the TRANSCOM data showed that universal masking (surgical masks) reduced exposures by 62% and showed masking and physical distancing provide further reductions when practiced together. For a notional scenario involving 10 infectious passengers, compared with no intervention, masking, distancing, and both would prevent 6.2, 3.8, and 7.6 secondary infections, respectively, using the Wells-Riley equation. These results suggest distancing alone, masking alone, and these practiced together reduce SARS CoV-2 exposure risk in increasing order of effectiveness, when an infectious passenger is present.

Direct estimates of absolute ventilation and estimated Mycobacterium tuberculosis transmission risk in clinics in South Africa.

Healthcare facilities are important sites for the transmission of pathogens spread via bioaerosols, such as Mycobacterium tuberculosis. Natural ventilation can play an important role in reducing this transmission. We aimed to measure rates of natural ventilation in clinics in KwaZulu-Natal and Western Cape provinces, South Africa, then use these measurements to estimate Mycobacterium tuberculosis transmission risk. We measured ventilation in clinic spaces using a tracer-gas release method. In spaces where this was not possible, we estimated ventilation using data on indoor and outdoor carbon dioxide levels. Ventilation was measured i) under usual conditions and ii) with all windows and doors fully open. Under various assumptions about infectiousness and duration of exposure, measured absolute ventilation rates were related to risk of Mycobacterium tuberculosis transmission using the Wells-Riley Equation. In 2019, we obtained ventilation measurements in 33 clinical spaces in 10 clinics: 13 consultation rooms, 16 waiting areas and 4 other clinical spaces. Under usual conditions, the absolute ventilation rate was much higher in waiting rooms (median 1769 m3/hr, range 338-4815 m3/hr) than in consultation rooms (median 197 m3/hr, range 0-1451 m3/hr). When compared with usual conditions, fully opening existing doors and windows resulted in a median two-fold increase in ventilation. Using standard assumptions about infectiousness, we estimated that a health worker would have a 24.8% annual risk of becoming infected with Mycobacterium tuberculosis, and that a patient would have an 0.1% risk of becoming infected per visit. Opening existing doors and windows and rearranging patient pathways to preferentially use better ventilated clinic spaces result in important reductions in Mycobacterium tuberculosis transmission risk. However, unless combined with other tuberculosis infection prevention and control interventions, these changes are insufficient to reduce risk to health workers, and other highly exposed individuals, to acceptable levels.

Association between the infection probability of COVID-19 and ventilation rates: An update for SARS-CoV-2 variants.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of the current coronavirus disease 2019 (COVID-19) pandemic, is evolving. Thus, the risk of airborne transmission in confined spaces may be higher, and corresponding precautions should be re-appraised. Here, we obtained the quantum generation rate (q) value of three SARS-CoV-2 variants (Alpha, Delta, and Omicron) for the Wells-Riley equation with a reproductive number-based fitted approach and estimated the association between the infection probability and ventilation rates. The q value was 89–165 h−1 for Alpha variant, 312–935 h−1 for Delta variant, and 725–2,345 h−1 for Omicron variant. The ventilation rates increased to ensure an infection probability of less than 1%, and were 8,000–14,000 m3 h−1, 26,000–80,000 m3 h−1, and 64,000–250,000 m3 h−1 per infector for the Alpha, Delta, and Omicron variants, respectively. If the infector and susceptible person wore N95 masks, the required ventilation rates decreased to about 1/100 of the values required without masks, which can be achieved in most typical scenarios. An air purifier was ineffective for reducing transmission when used in scenarios without masks. Preventing prolonged exposure time in confined spaces remains critical in reducing the risk of airborne transmission for highly contagious SARS-CoV-2 variants.

Individual heterogeneity and airborne infection: Effect of non-uniform air distribution

The classical Wells–Riley equation assumes homogeneity of susceptible individuals and environments to airborne exposure. However, individual susceptibility to infection is mostly heterogeneous, and exposure variability could arise from differences in inhalation rate, spatiotemporal non-uniformity of infectious aerosol concentrations, and the exposure trajectory and time. Non-uniform air distribution results in spatial non-uniformity of infectious aerosol concentrations. The non-uniformity effect is essentially a problem of individual infection probability. Here, we derived a general dose-response equation and a heterogeneous Wells–Riley equation accounting for individual variability in infection probability. The heterogeneous Wells-Riley equation shows the potential of the zone air distribution effectiveness to consider spatial non-uniformity under steady-state conditions. An existing quanta generation rate formula was theoretically justified. The new equation was then applied to a restaurant reporting an outbreak of coronavirus disease 2019, with spatial and/or temporal heterogeneity of infectious aerosol concentrations. Our results show the need to include spatial non-uniformity in outbreak investigations. A hypothetical two-zone setup was used to demonstrate how the inter-zonal distribution of clean air and the inter-zonal exchange flow affect airborne infections. An infector in a poorly diluted zone with the greatest number of susceptible individuals would result in the most secondary infections, whereas an infector in a well-ventilated zone with few susceptible individuals would result in the least secondary infections.

Modeling impacts of ventilation and filtration methods on energy use and airborne disease transmission in classrooms

Lowering the potential of airborne disease transmission in school buildings is especially important in the wake of the COVID-19 pandemic. The benefits of increased ventilation and filtration for reducing disease transmission compared to drawbacks of reduced thermal comfort and increased energy consumption and electricity demand are not well described. A comprehensive simulation of outdoor air ventilation rates and filtration methods was performed with a modified Wells-Riley equation and EnergyPlus building simulation to understand the trade-offs between infection probability and energy consumption for a simulated classroom in 13 cities across the US. A packaged heating, ventilation, and air conditioning unit was configured, sized, and simulated for each city to understand the impact of five ventilation flow rates and three filtration systems. Higher ventilation rates increased energy consumption and resulted in a high number of unmet heating and cooling hours in most cities (excluding Los Angeles and San Francisco). On average, across the 13 cities simulated, annual energy consumed by an improved filtration system was 31% lower than the energy consumed by 100% outdoor air ventilation. In addition, the infection probability was 29% lower with improved filtration. An economizer, which activates cooling based on an outdoor temperature setpoint, increased ventilation and reduced both energy consumption and infection probability. It was also concluded that ventilation and filtration measures better reduced absolute infection probability when the quanta generation rate for an infectious disease was higher. Dynamic outdoor airflow rate controls and filtration technologies that consider both health and energy consumption are an important area for further research.

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.

Airborne transmission of the Delta variant of SARS-CoV-2 in an auditorium

The Delta variant of SARS-CoV-2 has inflicted heavy burdens on healthcare systems globally, although direct evidence on the quantity of exhaled viral shedding from Delta cases is lacking. The literature remains inconclusive on whether existing public health guidance, based on earlier evidence of COVID-19, should respond differently to more infectious viral strains. This paper describes a study on an outbreak of the Delta variant of COVID-19 in an auditorium, where one person contracted the virus from three asymptomatic index cases sitting in a different row. Field inspections were conducted on the configuration of seating, building and ventilation systems. Numerical simulation was conducted to retrospectively assess the exhaled viral emission, decay, airborne dispersion, with a modified Wells-Riley equation used to calculate the inhalation exposure and disease infection risks at the seat level. Results support the airborne disease transmission. The viral emission rate for Delta cases was estimated at 31 quanta per hour, 30 times higher than those of the original variant. The high quantity of viral plume exhaled by delta cases can create a high risk zone nearby, which, for a mixing ventilation system, cannot be easily mitigated by raising mixing rates or introducing fresh air supply. Such risks can be reduced by wearing an N95 respirator, less so for social distancing. A displacement ventilation system, through which the air is supplied at the floor and returned from the ceiling, can reduce risks compared with a mixing system. The study has implications for ventilation guidelines and hygiene practices in light of more infectious viral strains of COVID-19.

Tradeoffs between ventilation, air mixing, and passenger density for the airborne transmission risk in airport transportation vehicles

Airport transportation vehicles, such as buses, aerotrains, and shuttles, provide important passenger transfer services in airports. This study quantitatively investigated COVID-19 aerosol infection risk and identified acceptable operational conditions, such as passenger occupancy rates and duration of rides, given the performance of vehicle ventilation. The greatest risk to the largest number of passengers is from an index case whose exhaled breath would take the longest time to exit the vehicle. The study identified such a case based on ventilation patterns, and it tracked the spread of viral aerosols (5 μm) by using the Wells-Riley equation to predict aerosol infection risk distribution. These distributions allowed a definition of an imperfect mixing degree (δ) as the ratio of actual risk and the calculated risk under a perfect mixing condition, and further derived regression equations to predict δ in the far-field (FF) and near-field (NF) of each passenger. These results revealed an order of magnitude higher aerosol infection risk in NF than in FF. For example, with a ventilation rate of 58 ACH (air changes per hour) and a 45% occupancy rate, unmasked passengers should stay up to 15 min in the bus and 35 min in the shuttle to limit infection risk in NF within 10%. These also indicate that masking is an important and effective risk reduction measure in transportation vehicles, especially important in NF. Overall, the analysis of imperfect air mixing allows direct comparison of risks in different transportation vehicles and a structured approach to development of policy recommendations.