Airborne Infection Risk Research Articles

Comparing strategies for the mitigation of SARS-CoV-2 airborne infection risk in tiered auditorium venues

The COVID-19 pandemic demonstrated that reliable risk assessment of venues is still challenging and resulted in the indiscriminate closure of many venues worldwide. Therefore, this study used an experimental, numerical and analytical approach to investigate the airborne transmission risk potential of differently ventilated, sized and shaped venues. The data were used to assess the magnitude of effect of various mitigation measures and to develop recommendations. Here we show that, in general, positions in the near field of an emission source were at high risk, while the risk of infection from positions in the far field varied depending on the ventilation strategy. Occupancy, airflow rate, residence time, virus variants, activity level and face masks affected the individual and global infection risk in all venues. The global infection risk was lowest for the displacement ventilation case, making it the most effective ventilation strategy for keeping airborne transmission and the number of secondary cases low, compared to mixing or natural ventilation.

Evaluation of Ventilation Strategies to Mitigate Airborne Infection Risk in a Dental School: A Three-Dimensional CFD Analysis of Airflow Patterns and Ventilation Efficiency

Infection prevention and control is a crucial element in providing a safe environment for dental clinics and reducing airborne infections risks during dental procedures. In response to the prevailing COVID-19 situations, the clinical space in the dental school was operated with ventilation strategies, increasing air exchanges and incorporating supply and return air arrangement based on seating positions. This study evaluated airflow patterns to examine personal exposure to airborne infection risk under these strategies. The three-dimensional computational fluid dynamics technique using computational fluid dynamics (CFD) analysis was performed in 50 multi-units of the dental school of the university in Bangkok, Thailand. The results revealed substantial improvements in indoor ventilation. Improvement of airflow patterns and directions surpassed conventional design of the pre-existing building’s system and helped reduce airborne contaminant concentrations. The further discussion of occupant-based design in dental schools is needed to optimize ventilation systems and engineering controls concerning indoor airborne infections.

Conceptual model of electrostatic non-ionizing air filter

Abstract Many infectious diseases such as SARS-CoV-2, rhinovirus, adenovirus, and influenza are transmitted through small respiratory droplets and various filtration systems are used to reduce the risk of airborne infection. Here, we develop a conceptual model of a filter, which is based on the electrostatic deposition of air droplets potentially containing pathogens. Within the model, we explore the effect of polarization of neutral spherical droplets moving in an air flow past cylindrical electrodes. The strength of electric field they produce is low enough to eliminate corona discharge and minimize air ionization, but high enough to capture the droplets. Based on the proposed model, we calculate the filtration efficiency in a quasi-continuous approximation. Also, we conduct numerical simulations of the deposition of respiratory droplets on a compact periodic array of cylindrical electrodes located at the nodes of a square lattice. The droplets with a radius of 10-20 μm pose the greatest threat in spreading the respiratory infections, and as we demonstrate, the proposed system is very effective in capturing droplets of this size, specifically, for a filter element with a width of about 5 mm, the filtration efficiency exceeds 90%.

Uncertainties in exposure predictions arising from point measurements of carbon dioxide in classroom environments.

Predictions of airborne infection risk can be made based on the fraction of rebreathed air inferred from point measurements of carbon dioxide (CO[Formula: see text]). We investigate the extent to which environmental factors, particularly spatial variations due to the ventilation provision, affect the uncertainty in these predictions. Spatial variations are expected to be especially problematic in naturally ventilated spaces, which include the majority of classrooms in the UK. An idealized classroom, broadly representative of the physics of (buoyancy-driven) displacement ventilation, is examined using computational fluid dynamics, with different ventilation configurations. Passive tracers are used to model both the CO[Formula: see text] generated by all 32 occupants and the breath of a single infectious individual (located in nine different regions). The distribution of infected breath is shown to depend strongly on the distance from the release location but is also affected by the pattern of the ventilating flow, including the presence of stagnating regions. However, far-field exposure predictions based on single point measurements of CO[Formula: see text] within the breathing zone are shown to rarely differ from the actual exposure to infected breath by more than a factor of two-we argue this uncertainty is small compared with other uncertainties inherent in modelling airborne infection risk.

Assessment of airborne transmitted infection risk in classrooms using computational fluid dynamics and machine learning-based surrogate modeling

In recent years, the assessment of airborne transmitted infection risk has been extensively performed using Computational Fluid Dynamics (CFD) simulations, especially in response to the COVID-19 pandemic. Nevertheless, the high computational demands and time-intensive nature of CFD simulations highlight the need for fast or real-time infection risk predictions. This capability is crucial for swift decision-making in dynamic environments where timely health interventions are critical. This paper presents a thorough analysis of airborne infection risks in classroom environments based on CFD simulations to understand key factors such as ventilation strategies, air change rates, occupant arrangements, source locations, and particle sizes. This study also employs data-driven supervised learning methods—specifically, Long Short-Term Memory (LSTM) and Artificial Neural Networks (ANN)—to generate surrogate models for predicting airborne infection risk. Key findings reveal that different ventilation strategies significantly affect airborne infection risk, reducing it by 49 %–77 %. Moreover, the conventional Wells-Riley model was identified as lacking in its ability to accurately predict local infection risks. The study further challenges the assumption that higher air change rates are universally beneficial, considering that occupants seated in the back rows of a classroom experienced up to a 166 % increased risk, despite elevating air change rates from 1.1 h−1 to 11 h−1. These results suggest that physical distancing alone may be insufficient and highlight the importance of considering other factors such as occupant arrangements. Regarding the model performance, the ANN-based surrogate model demonstrated varying prediction accuracy. For inhalable particle concentration predictions for susceptible occupants, R2 values ranged from 0.31 to 0.65 with CVRMSE values between 100 % and 180 %. In contrast, the model achieved an R2 of 0.79 and a CVRMSE of 34 % for infectors. The insights and methodologies from this study can inform HVAC system design and operation strategies to better mitigate infectious disease transmission in densely occupied indoor environments.

A better approach to mitigate the risk of airborne infections in workplaces.

Although the worst of the coronavirus disease 2019 pandemic appears to be over, the burden of airborne infection in workplaces remains unacceptably high, leaving society vulnerable, and harming workers and others. A better approach is needed to mitigate the risk of infection as actively as other multifactorial ubiquitous risks such as workplace stress. Sources and pathways of transmission need much improved control, with special emphasis on ensuring the protection of the vulnerable and susceptible.

Enhancing healthcare facility resilience: utilizing machine learning model for airborne disease infection prediction

During this pandemic, advanced epidemiological models have been widely used to determine intervention strategies for controlling the spread of the disease in public and healthcare settings. These models played a crucial role by providing predictive insights into disease transmission dynamics, informing resource allocation and guiding policy decisions. However, the accuracy of these predictions depends on the substantial amount of input data, which was not readily available at the onset of the pandemic. Another concern with the existing models is their inability to adequately account for the complex indoor built environments, which has been shown to significantly impact infection risk. To tackle these issues, this paper discusses the potential of developing a joint modelling technique that integrates machine learning models, building information models and agent-based models to assess the risk of nosocomial airborne infections. With limited available data, machine learning models can determine infection risk with high confidence.

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.

Relative Health Risk Reduction from an Advanced Multi-Modal Air Purification System: Evaluation in a Post-Surgical Healthcare Setting.

Advanced air treatment systems have the potential to reduce airborne infection risk, improve indoor air quality (IAQ) and reduce energy consumption, but few studies reported practical implementation and performance. PlasmaShield®, an advanced multi-modal HVAC-integrated system, was directly compared with a standard MERV-13 system in a post-surgical paediatric healthcare setting. The evaluation entailed monitoring of multi-size airborne particles, bioaerosols and key IAQ parameters. Measurements were taken for outside air, supply air and air in the occupied space for 3 days prior to, and after, the installation of the PlasmaShield system. Compared with the existing arrangement, very significant reductions in particle number concentrations were observed in the occupied space, especially with virus-like submicron particles. Significant reductions in airborne culturable bacteria and fungi were observed in the supply air, with more modest reductions in the occupied space. In the case of virus-like particles, there was an eight-fold improvement in equivalent clean air, suggesting a five-fold infection risk reduction for long-range exposure. The data suggest multiple benefits of airborne particle and bioaerosol reduction, with applications beyond healthcare. Long-term studies are recommended to confirm the combined IAQ, health and energy benefits.

Case studies using a simple airborne infection risk calculator to minimize COVID-19 infection risk: Common approaches and challenges

Following the recognition of the role of airborne transmission in the spread of SARS-CoV-2, the role of building ventilation in minimising indoor respiratory events has become of significant interest, with numerous risk assessment tools developed to understand infection risk in different indoor environments. To date there has been limited retrospective analysis of how such tools were applied to assess indoor infection risk and inform building occupancy during the COVID-19 pandemic. In this paper we document case studies from Australia and New Zealand using one such risk assessment tool, the Airborne Infection Risk Calculator (AIRC), and describe how the AIRC was used to assess COVID-19 risk in different indoor settings and how users customized the tool for their own purposes. While inherent uncertainties mean the AIRC model could not calculate the exact risk in any of the case studies, the model's framework enabled objective discussion about the role of ventilation in infection risk reduction and public health. Future use of tools such as the AIRC would be improved with development of policies or regulations that promote or require a standardized approach for this assessment, translation of improvement in ventilation in a room or building scale into overall public health benefits, and endorsement of such tools by Global and/or National health authorities.

Mitigating airborne infection risks in public transportation: A systematic review

Airborne infections pose significant challenges to public transportation systems which can result in significant decline in ridership levels and financial stress for operators. This systematic review presents a comprehensive overview of measures and strategies employed by ground public transportation agencies to protect passengers and staff while ensuring the uninterrupted operation. This study also conducted a bibliometric analysis to provide insights into key topics, publication patterns, and major contributors in the field of airborne transmission research in public transportation. We have included studies published from January 2003 to June 2024, which reported measures and recommendations for managing public transportation to reduce virus transmission. Of the 2848 initially identified studies, 69 met our eligibility criteria. Our review identified four key strategies to prevent virus transmission in public transportation, including air quality improvement, cleaning, mask-wearing, and social distancing in vehicles and stations. While social distancing poses a significant challenge to public transportation, the integration of crowd management techniques and technology-driven information dissemination can provide effective strategies for managing capacity. The adoption of technology-driven solutions, such as efficient filtration systems, automated mask detection mechanisms, ultraviolet disinfection devices, and real-time passenger information, is required to implement these strategies effectively. Transportation agencies can utilize an airborne infection risk calculator during pandemics and beyond to assess and mitigate the risk of airborne transmission in various modes of transportation. Lessons from the Covid-19 pandemic underscored the need for developing advanced technologies to enhance passenger and staff safety in public transportation vehicles and stations.

On modelling airborne infection risk.

Airborne infection risk analysis is usually performed for enclosed spaces where susceptible individuals are exposed to infectious airborne respiratory droplets by inhalation. It is usually based on exponential, dose-response models of which a widely used variant is the Wells-Riley (WR) model. We revisit this infection-risk estimate and extend it to the population level. We use an epidemiological model where the mode of pathogen transmission, airborne or contact, is explicitly considered. We illustrate the link between epidemiological models and the WR and the Gammaitoni and Nucci models. We argue that airborne infection quanta are, up to an overall density, airborne infectious respiratory droplets modified by a parameter that depends on biological properties of the pathogen, physical properties of the droplet and behavioural properties of the individual. We calculate the time-dependent risk of being infected for two scenarios. We show how the epidemic infection risk depends on the viral latent period and the event time, the time infection occurs. Infection risk follows the dynamics of the infected population. As the latent period decreases, infection risk increases. The longer a susceptible is present in the epidemic, the higher its risk of infection for equal exposure time to the pathogen is.

Effectiveness of Negative Pressure Booths in Mitigating Airborne Infection Risk During Transesophageal Echocardiography

Effectiveness of Negative Pressure Booths in Mitigating Airborne Infection Risk During Transesophageal Echocardiography

Strategic ventilation design for reducing airborne infection transmission in a two-story building: A numerical approach

This study employs Computational Fluid Dynamics (CFD) to assess the impact of various ventilation configurations on respiratory droplet dispersion in a two-story restaurant building, aiming to reduce airborne infection transmission. A total of 24 scenarios, involving three ventilation configurations and eight individuals as potential droplet sources, are studied. The configurations—Case A, Case B, and Case C—are analyzed for their effectiveness in controlling droplet dispersion and improving air exchange effectiveness (AEE). Case B, with an outlet near occupants and an inlet above the zone separation, proved most effective in minimizing the droplet transmission between floors, reducing suspended airborne droplets on each floor, and preventing surface contamination, thereby enhancing indoor air quality. Despite the positioning inlet and outlet vents near crowded areas, Case A falls short in effectively removing droplets due to the intricate airflow patterns caused by air diffusers. Case C showed larger AEE but was less effective in droplet dispersion control, indicating a crucial balance between optimizing air circulation and limiting pathogen dispersion is needed. The findings underscore the importance of strategic ventilation design in mitigating airborne infection transmission in multi-level buildings. An optimal configuration, as demonstrated by Case B, combines efficient air distribution with strategic placement of ventilation components to create barriers against interzonal droplet transmission. This study highlights the potential of targeted ventilation strategies to significantly reduce the risk of airborne infection in enclosed, populated spaces.

A New Tailored Approach to Calculate the Optimal Number of Outdoor Air Changes in School Building HVAC Systems in the Post-COVID-19 Era

Air conditioning systems can play a positive or negative role in the spread of COVID-19 infection. The importance of sufficient outdoor air changes in buildings was highlighted by the World Health Organization, therefore these should be guaranteed by mechanical ventilation systems or adequate air conditioning systems. The proposed case study concerns the optimal number of outdoor air changes to limit COVID-19 contagion for a school building in Central Italy. The Wells–Riley model is used to assess the risk of airborne infection, while energy consumption is calculated by a dynamic energy simulation software. The scope of the paper offers an innovative method to define the optimal ventilation strategy for the building’s HVAC system design to reduce the risk of infection with limited increases in energy consumption and greenhouse gas emissions. Results show that the desirable approach is the one in which the same low value of contagion risk is set in all rooms. This new approach results in significant energy savings, compared to the most common ones (setting the same high outdoor air rates for all rooms) to counteract the risk of infection. Finally, the zero-emission building target is verified by introducing a suitable photovoltaic system to offset pollutant emissions.

Integrated infection and crowd behaviour model for COVID-19 infection risk assessment onboard large passenger vessels

The development of the global COVID-19 pandemic from 2020 onward has had significant impact on the world and specifically the maritime industry. Striking examples were COVID-19 outbreaks onboard the Diamond Princess cruise vessel and the U.S.S. Theodore Roosevelt aircraft carrier at the start of the pandemic. Contagious disease management onboard large passenger ships remains a complex issue, amplified by the international character of the industry, confined environment and shared facilities. This paper therefore presents an integrated infection and crowd behavior model used to calculate agent-specific infection risk, incorporating guest and crew circulation through a passenger ship layout. The integrated model is used to investigate the effect of ship layout design, capacity reduction and mask wearing on COVID-19 airborne infection risk onboard large passenger vessels.

Conversion of a regular patient room to negative pressure at Ancona university hospital (Italy). A combined experimental and numerical investigation

Negative pressure isolation rooms are employed to prevent the spread of infectious diseases to surrounding spaces. This study reports a combined experimental and numerical analysis aimed at reliably assessing the performance of hospital ventilation systems and effectively reducing the risk of airborne infections. The study appraises the enhancement of the safety conditions by retrofitting an existing, positive pressure room ventilation system with a negative pressure and recirculation one. The assessment consisted in experimental measurements of airborne particle concentration and Computational Fluid Dynamics (CFD) simulations with Lagrangian particle tracking of respiratory droplets. The ventilation performance improvement was quantified in terms of recovery rate, contaminant removal effectiveness, particle transfer toward adjacent spaces and probability of infection.Two experimental campaigns were carried out: an ISO standard recovery assessment and a test in real operational conditions. The negative pressure configuration significantly improved the safety inside the patient ward: the recovery rate for droplets with diameter larger than 5 μm increased by a factor three, and the contaminant removal effectiveness after 15 min increased from 45% to 81%. The numerical model for Computational Fluid Dynamics simulation was successfully validated, confirming the ability of the approach to reproduce the undergoing Physics. The negative pressure design eliminated the particle transmission to surrounding spaces, while 2.5% of the emitted respiratory droplets escaped the room in the original existing scenario. The probability of infection after 15 min exposure was reduced by a factor three and peak values reaching 15% were limited to the immediate vicinity of the patients.

Reducing the contaminant dispersion and infection risks in the train cabins by adjusting the inlet turbulence intensity: A study based on turbulence simulation

Existing studies on ventilation in closed spaces mainly considered the average inlet velocity and ignored the influence of inlet turbulent fluctuation. However, the variation in inlet turbulence intensity (TI) is considerable and significantly affects the dispersion of contaminants. This study conducts numerical simulations verified by experiments to investigate the effect of the inlet TI on train contaminants dispersion and analyze infection probability variation. Firstly, the unsteady Reynolds-averaged Navier-Stokes (URANS) method and improved delayed detached eddy simulation (IDDES) method are compared in simulating the internal airflow characteristics based on the on-site measurement. The results indicate that the latter dominates in capturing airflow pulsations more than the former, although the mean airflow results obtained from both methods agree well with experimental results. Furthermore, the IDDES method is employed to investigate the effect of the inlet TI on contaminant dispersion, and the infection risks are also assessed using the improved probability model. The results show that, with the increase of TI from 5 % to 30 %, the contaminant removal grows considerably, with the removal index rising from 0.23 to 1.86. The increased TI leads to the overall and local infection risks of occupants descending significantly, wherein the former decreases from 1.53 % to 0.88 % with a reduction rate of 42 %, and the latter drops from 3.30 % to 2.16 % with a mitigation rate of 35 %. The findings can serve as solid guidelines for numerical method selection in accurately capturing the indoor dynamic airflow distribution and for the ventilation parameters design regarding TI inside trains to mitigate the airborne infection risk.

A quanta-independent approach for the assessment of strategies to reduce the risk of airborne infection

The Wells-Riley model is extensively used for retrospective and prospective modelling of the risk of airborne transmission of infection in indoor spaces. It is also used when examining the efficacy of various removal and deactivation methods for airborne infectious aerosols in the indoor environment, which is crucial when selecting the most effective infection control technologies. The problem is that the large variation in viral load between individuals makes the Wells-Riley model output very sensitive to the input parameters and may yield a flawed prediction of risk. The absolute infection risk estimated with this model can range from nearly 0 % to 100 % depending on the viral load, even when all other factors, such as removal mechanisms and room geometry, remain unchanged. We therefore propose a novel method that removes this sensitivity to viral load. We define a quanta-independent maximum absolute before-after difference in infection risk that is independent of quanta factors like viral load, physical activity, or the dose-response relationships. The input data needed for a non-steady-state calculation are just the removal rates, room volume, and occupancy duration. Under steady-state conditions the approach provides an elegant solution that is only dependent on removal mechanisms before and after applying infection control measures. We applied this method to compare the impact of relative humidity, ventilation rate and its effectiveness, filtering efficiency, and the use of ultraviolet germicidal irradiation on the infection risk. The results demonstrate that the method provides a comprehensive understanding of the impact of infection control strategies on the risk of airborne infection, enabling rational decisions to be made regarding the most effective strategies in a specific context. The proposed method thus provides a practical tool for mitigation of airborne infection risk.

The Wells-Riley model revisited: Randomness, heterogeneity, and transient behaviours.

The Wells-Riley model has been widely used to estimate airborne infection risk, typically from a deterministic point of view (i.e., focusing on the average number of infections) or in terms of a per capita probability of infection. Some of its main limitations relate to considering well-mixed air, steady-state concentration of pathogen in the air, a particular amount of time for the indoor interaction, and that all individuals are homogeneous and behave equally. Here, we revisit the Wells-Riley model, providing a mathematical formalism for its stochastic version, where the number of infected individuals follows a Binomial distribution. Then, we extend the Wells-Riley methodology to consider transient behaviours, randomness, and population heterogeneity. In particular, we provide analytical solutions for the number of infections and the per capita probability of infection when: (i) susceptible individuals remain in the room after the infector leaves, (ii) the duration of the indoor interaction is random/unknown, and (iii) infectors have heterogeneous quanta production rates (or the quanta production rate of the infector is random/unknown). We illustrate the applicability of our new formulations through two case studies: infection risk due to an infectious healthcare worker (HCW) visiting a patient, and exposure during lunch for uncertain meal times in different dining settings. Our results highlight that infection risk to a susceptible who remains in the space after the infector leaves can be nonnegligible, and highlight the importance of incorporating uncertainty in the duration of the indoor interaction and the infectivity of the infector when estimatingrisk.