Model Calculations of Aerosol Transmission and Infection Risk of COVID-19 in Indoor Environments

Comparative assessment of airborne infection risk tools in enclosed spaces: Implications for disease control

The COVID-19 pandemic, caused by SARS-CoV-2, highlighted the importance of understanding transmission modes and implementing effective mitigation strategies. Recognizing airborne transmission as a primary route has reshaped public health measures, emphasizing the need to optimize indoor environments to reduce risks. Numerous tools have emerged to assess airborne infection risks in enclosed spaces, providing valuable resources for public health authorities, researchers, and the general public. However, comparing the outputs of these tools is challenging because of variations in assumptions, mathematical models, and data sources. We conducted a comprehensive review, comparing digital airborne infection risk calculators using standardized building-specific input parameters. These tools generally produce similar and consistent outputs with identical inputs. Variations mainly stem from model selection and the handling of unsteady viral load conditions. Differences in source term calculations, including particle emission concentrations and respiratory activity, also contribute to disparities. These differences are minor compared to the inherent uncertainties in risk assessment. Consistency in results increases with higher ventilation rates, showing a robust trend across models. However, inconsistencies arose in the inclusion of face masks, often due to the lack of detailed efficiency values. Despite some differences, the overall consistency underscores the value of these tools in public health strategy and infectious disease control. We also compared some of the model's efforts to conduct retrospective assessments against reported transmission events by assuming input parameters to the models so that the calculated risk would closely fit the original outbreak infection rate. Thus, validating these models against past outbreaks remains challenging because of the lack of essential input information from observed events. This comparative analysis demonstrates the importance of transparent data sources and justifiable model assumptions to enhance the reliability and precision of risk assessments.

Analysis of inoculation strategies during COVID-19 pandemic with an agent-based simulation approach

BACKGROUND

The severity of recent Coronavirus (COVID-19) pandemics has revealed the importance of development of inoculation strategies in case of limited vaccine availability. Authorities have implemented inoculation strategies based on perceived risk factors such as age and existence of other chronic health conditions for survivability from the disease. However, various other factors can be considered for identifying the preferred inoculation strategies depending on the vaccine availability and disease spread levels. This study explores the effectiveness of inoculating different groups of population in case of various vaccine availabilities and disease spread levels by means of some performance metrics namely: Attack Rate (AR), Death Rate (DR) and Hospitalization Rate (HR).

METHOD

In this study we have implemented a highly detailed Agent-Based Simulation (ABS) model that extends classical SEIR Model by including five more additional states: Asymptomatic (A), Quarantine (Q), Hospitalized (H), Dead (D) and Immune (M) which can be used as a decision support tool to prioritize the groups of the population inoculated. The approach employs the modelling of daily mobility of individuals, their interactions and transmission of virus among individuals. The population is heterogeneously clustered according to age, family size, work status, transportation and leisure preferences with 17 different groups in order to find the most appropriate one to inoculate. Three different Disease Spread Levels (DSL) (low, mid, high) are experimented with four different Vaccine Available Percentages (VAP) (25%, 50%, 75% and 85%) with a total of 84 scenarios.

RESULTS

As the benchmark, under the No Vaccine case Attack Rate, Hospitalization Rate, and Death Rate goes as high as 99.53%, 16.96%, and 1.38%, respectively. Corresponding highest performance metrics (rates) are 72.33%, 15.95%, and 1.35% for VAP = 25%; 50.25%, 9.55%, and 0.94% for VAP = 50%; 24.53%, 2.62%, and 0.25% for VAP = 75%; and 11.51%, 0.002%, and 0.08% for VAP = 85%. The results of our study shows that the common practice of inoculation based on the age of individual does not yield the best outcome in terms of performance metrics across all DSL and VAP values. The groups containing workers and students that represent highly interactive individuals, i.e. Group (9, 10), Group (9, 11, 10‾) and Group (9, 10, 11, 12‾) emerge as a commonly recommended choice for inoculation in the majority of cases. As expected, we observe that the higher is the VAP levels the more is the number of alternative inoculation groups.

CONCLUSIONS

Findings of this study present that: (i) inoculation considerably decreases the number of infected individuals, the number of deaths and the number of hospitalized individuals due to the disease, (ii) the best inoculation group/groups with respect to performance metrics varies depending on the vaccine availability percentages and disease spread levels, (iii) simultaneous implementation of both inoculation and precautions like lock-down, social distances and quarantines, yields a stronger impact on disease spread and its consequences.

Assessing the Effect of a Pandemic on Emergency Medical Service Response Times and Interventions for Out-of-Hospital Cardiac Arrest

OBJECTIVES

The coronavirus disease 2019 (COVID-19) pandemic has significantly disrupted emergency medical service (EMS) prehospital care for patients with out-of-hospital cardiac arrest (OHCA), necessitating a thorough assessment of its effects on prehospital time and emergency interventions. Therefore, we aimed to analyze the changes in EMS operations before and after the onset of the pandemic and their potential effects on patient care.

METHODS

We retrospectively reviewed OHCA cases between January 2017 and December 2022, categorizing them into pre-pandemic and pandemic phases. We examined the prehospital time from call intake to hospital arrival, analyzing time segments in detail (on-scene arrival, patient contact, loading, and departure) and procedural frequency/location. Changes in prehospital time, requests for hospital admission, laryngeal tube insertion, and venous line establishment were assessed using a multivariate analysis.

RESULTS

Among the 925 OHCA cases, the pandemic phase (n = 467) experienced a 3-minute average prehospital delay compared with the pre-pandemic phase (n = 458) (P < 0.0001). Specifically, on-scene arrival time (adjusted odds ratio (aOR): 2.06; 95% confidence interval (CI): 1.36-3.11), laryngeal tube insertions (aOR: 3.2; 95% CI: 2.1-4.9), and post-transport venous access placements (aOR: 1.67; 95% CI: 1.06-2.63) increased. Hospital admission requests also increased significantly (aOR: 9.5; 95% CI: 2.78-32.7).

CONCLUSION

These findings indicate that pandemic conditions delayed EMS responses and altered clinical practices, highlighting the urgent need for EMS system enhancements to improve on-site interventions. Therefore, addressing these challenges, particularly through strategies that expedite early adrenaline administration, is essential for optimizing patient outcomes.

An Easy-to-Use Tool to Predict SARS-CoV-2 Risk of Infection in Closed Settings: Validation with the Use of an Individual-Based Monte Carlo Simulation

The dynamics of the SARS-CoV-2 pandemic showed that closed environments, such as hospitals and schools, are more likely to host infection clusters due to environmental variables like humidity, ventilation, and overcrowding. This study aimed to validate our local transmission model by reproducing the data on SARS-CoV-2 diffusion in a hospital ward. We implemented our model in a Monte Carlo procedure that simulates the contacts between patients and healthcare workers in Trieste's geriatric ward and calculates the number of infected individuals. We found the median number of infected workers to be 38.98 (IQR = 7.75), while all patients were infected in most of the simulation runs. More infections occurred in rooms with lower volumes. Higher ventilation and mask-wearing contribute to reduced infections; in particular, we obtained a median value of 35.06 (IQR = 9.21) for the simulation in which we doubled room ventilation and 26.12 (IQR = 10.33) in the simulation run in which workers wore surgical masks. We managed to reproduce the data on infections in the ward; using a sensitivity analysis, we identified the parameters that had the greatest impact on the probability of transmission and the size of the outbreak.

Antiseptics: An expeditious third force in the prevention and management of coronavirus diseases

Notably, severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS) and coronavirus disease 2019 (COVID-19) have all had significant negative impact on global health and economy. COVID-19 alone, has resulted to millions of deaths with new cases and mortality still being reported in its various waves. The development and use of vaccines have not stopped the transmission of SARS coronavirus 2 (SARS-CoV-2), the etiological agent of COVID-19, even among vaccinated individuals. The use of vaccines and curative drugs should be supplemented with adoption of simple hygiene preventive measures in the fight against the spread of the virus, especially for healthcare workers. Several virucidal topical antiseptics, such as povidone-iodine (PVP-I), citrox, cyclodextrins among others, have been demonstrated to be efficacious in the inactivation of SARS-CoV-2 and other coronaviruses in both in vitro and in vivo studies. The strategic application of these virucidal formulations could provide the additional impetus needed to effectively control the spread of the virus. We have here presented a simple dimension towards curtailing the dissemination of COVID-19, and other coronaviruses, through the application of effective oral, nasal and eye antiseptics among patients and medical personnel. We have further discussed the mechanism of action of some of these commonly available virucidal solutions while also highlighting some essential controversies in their use.

Identification of key potential risk areas and key potential failure modes in hemodialysis rooms by the FMEA method following routine prevention and control of the COVID-19 pandemic

Hemodialysis is an important part of nosocomial infection prevention and control (IPC). This study aimed to identify the key potential risk areas and failure modes in hemodialysis rooms in hospitals and put forward a series of improvement measures to prevent and control the spread of the coronavirus disease 2019 (COVID-19). Hemodialysis patients are highly susceptible to COVID-19 and usually have a high incidence of severe illness and mortality after infection with COVID-19. Therefore, IPC in hemodialysis patients is of crucial strategic significance. Based on 30 domain experts' interviews and careful analysis of prevention and control documents, we constructed a comprehensive failure system for a model that identifies the potential risks for nosocomial COVID-19 infection in the hemodialysis room. Subsequently, a thorough risk assessment of the potential failure factors identified in our model was conducted. The failure key factors corresponding to the human element in medical waste (garbage) disposal (C2) are verified to be the highest risk factors. They are as follows: The cleaning staff did not dispose of different types of medical waste (garbage) (C21), did not wear masks according to the regulations (C22), and lacked knowledge and norms of nosocomial IPC (C23). This study provides valuable insights for hospital decision-makers on the potential failure factors related to COVID-19 infections in hemodialysis rooms. By working with hospital infection specialists, the suggested improvement measures can help reduce the risk of virus exposure among hospital medical staff, patients, and cleaning staff.

Effects of recirculation and air change per hour on COVID-19 transmission in indoor settings: A CFD study with varying HVAC parameters

COVID-19 has already claimed over 7 million lives and has infected over 775 million people globally [1]. SARS-CoV-2, the virus that causes Covid-19, spreads primarily through droplets from infected people's airways, rendering Heating, Ventilation, and Air Conditioning (HVAC) systems critical in controlling infection risk levels in the indoor environment. To understand the dynamics of exhaled droplets and aerosols and the percentage of particles that are inhaled, escaped, recirculated, or trapped on different surfaces for a variety of environmental settings, we have presented our findings from the Computational Fluid Dynamics (CFD) modeling to investigate the impact of changing HVAC parameters in this paper. When combined with the spatial and temporal distribution of droplets, this method can be used to assess the potential risk and strengthen resilience. This finding demonstrates the viability and usefulness of CFD for modeling the distribution and dynamics of droplets and aerosols in confined environments. Our study demonstrates that raising the Air Change per Hour (ACH) from 2 to 8 reduces the risk of particle inhalation by nearly 70 %. Additionally, limiting the amount of air recirculation or increasing the amount of fresh air helps to reduce the number of airborne particles in an indoor space. To reduce the potential for respiratory droplet-related transmission and to provide relevant recommendations to the appropriate authority, the same computational approach could be applied to a wide range of ventilated indoor environments such as public buses, restaurants, exhibitions, and theaters.

Parametric analysis of SARS-CoV-2 dose-response models in transportation scenarios

Transportation systems involve high-density crowds of geographically diverse people with variations in susceptibility; therefore, they play a large role in the spread of infectious diseases like SARS-CoV-2. Dose-response models are widely used to model the relationship between the trigger of a disease and the level of exposure in transmission scenarios. In this study, we quantified and bounded viral exposure-related parameters using empirical data from five transportation-related events of SARS-CoV-2 transmission. Dose-response models were then applied to parametrically analyze the infection spread in generic transportation systems, including a single-aisle airplane, bus, and railway coach, and then examined the mitigating efficiency of masks by performing a sensitivity analysis of the related factors. We found that dose level significantly affected the number of secondary infections. In general, we observed that mask usage reduced infection rates at all dose levels and that high-quality masks equivalent to FFP2/N95 masks are effective for all dose levels. In comparison, we found that lower-quality masks exhibit limited mitigation efficiency, especially in the presence of high dosage. The sensitivity analysis indicated that a reduction in the infection distance threshold is a critical factor in mask usage.

Estimated relative potential for airborne SARS-CoV-2 transmission in a day care centre

We estimated the hourly probability of airborne severe acute respiratory coronavirus 2 (SARS-CoV-2) transmission and further the estimated number of persons at transmission risk in a day care centre by calculating the inhaled dose for airborne pathogens based on their concentration, exposure time and activity. Information about the occupancy and activity of the rooms was collected from day care centre personnel and building characteristics were obtained from the design values. The generation rate of pathogens was calculated as a product of viral load of the respiratory fluids and the emission of the exhaled airborne particles, considering the prevalence of the disease and the activity of the individuals. A well-mixed model was used in the estimation of the concentration of pathogens in the air. The Wells-Riley model was used for infection probability. The approach presented in this study was utilised in the identification of hot spots and critical events in the day care centre. Large variation in the infection probabilities and estimated number of persons at transmission risk was observed when modelling a normal day at the centre. The estimated hourly infection probabilities between the worst hour in the worst room and the best hour in the best room varied in the ratio of 100:1. Similarly, the number of persons at transmission risk between the worst and best cases varied in the ratio 1000:1. Although there are uncertainties in the input values affecting the absolute risk estimates the model proved to be useful in ranking and identifying the hot spots and events in the building and implementing effective control measures.

Viral infection transmission and indoor air quality: A systematic review

Respiratory disease transmission in indoor environments presents persistent challenges for health authorities, as exemplified by the recent COVID-19 pandemic. This underscores the urgent necessity to investigate the dynamics of viral infection transmission within indoor environments. This systematic review delves into the methodologies of respiratory infection transmission in indoor settings and explores how the quality of indoor air (IAQ) can be controlled to alleviate this risk while considering the imperative of sustainability. Among the 2722 articles reviewed, 178 were retained based on their focus on respiratory viral infection transmission and IAQ. Fifty eight articles delved into SARS-CoV-2 transmission, 21 papers evaluated IAQ in contexts of other pandemics, 53 papers assessed IAQ during the SARS-CoV-2 pandemic, and 46 papers examined control strategies to mitigate infectious transmission. Furthermore, of the 46 papers investigating control strategies, only nine considered energy consumption. These findings highlight clear gaps in current research, such as analyzing indoor air and surface samples for specific indoor environments, oversight of indoor and outdoor parameters (e.g., temperature, relative humidity (RH), and building orientation), neglect of occupancy schedules, and the absence of considerations for energy consumption while enhancing IAQ. This study distinctly identifies the indoor environmental conditions conducive to the thriving of each respiratory virus, offering IAQ trade-offs to mitigate the risk of dominant viruses at any given time. This study argues that future research should involve digital twins in conjunction with machine learning (ML) techniques. This approach aims to enhance IAQ by analyzing the transmission patterns of various respiratory viruses while considering energy consumption.

Impact of respiratory aerosol size and number distribution on the relative importance of different routes in SARS-CoV-2 transmission

Exploring the relative importance of different routes in SARS-CoV-2 transmission is crucial in infection prevention. However, even in the same environmental setting, the relative importance of different routes has varied in different studies. We hypothesize that respiratory aerosol size and number distribution might play a key role. In this study, size and number distribution of respiratory droplets emitted from breathing, talking, and coughing were identified from PubMed and Web of Science. The infection risk of SARS-CoV-2 via airborne, droplet, and fomite transmission routes was modeled in a household and a healthcare setting. The relative importance of three routes varied with different size distributions in both settings. Generally, the contribution of the airborne route increased with the volume percentage of respirable droplets emitted. And the increase of the total number of emitted droplets leads to an increase in the contribution of tdroplet route. In the healthcare setting, as the total number of emitted droplets increased from 110 to 4,973, the contribution of droplet route increased from 62.24% to 98.11%. Next, by considering the combination of breathing, coughing, and talking when the infected person was asymptomatic, the airborne route predominated over the droplet and contact routes. When the infected person had developed symptoms, that is, cough, the droplet route played a dominant role in SARS-CoV-2 transmission. In conclusion, risk analyses will be improved with improved sampling methods that enable characterization of viruses within respiratory droplets of different sizes.

Quantifying Exhaled Particles in Healthy Humans During Various Respiratory Activities Under Realistic Conditions

Background: Quantitatively collecting and characterizing exhaled aerosols is vital for infection risk assessment, but the entire droplet size spectrum has often been neglected. We analyzed particle number and size distribution of healthy participants in various respiratory activities, considering inter-individual variability, and deployed a simplified far-field model to inform on infection risks. Methods: Participants repeated the same respiratory activities on two visits. Particles were collected using an airtight extraction helmet supplied with High Efficiency Particulate Air (HEPA) filtered air. The sampling volume flow was transported to two particle counters covering the small and large particle spectrum. The applied simple mass balance model included respiratory activity, viral load, room size, and air exchange rates. Results: Thirty participants completed the study. The major fraction of the number-based size distribution was <5 μm in all respiratory activities. In contrast, the major fraction of the volume-based size distribution was 2-12 μm in tidal breathing, but >60 μm in all other activities. Aerosol volume flow was lowest in tidal breathing, 10-fold higher in quiet/normal speaking, deep breathing, coughing, and 100-fold higher in loud speaking/singing. Intra-individual reproducibility was high. Between participants, aerosol volume flow varied by two orders of magnitude in droplets <80 μm, and three orders of magnitude in droplets >80 μm. Simple model calculations not accounting for potential particle size-dependent differences in viral load and infection-related differences were used to model airborne pathogen concentrations. Conclusions: Quantitative analysis of exhaled aerosols for the entire droplet size spectrum as well as the variability in aerosol emission between individuals provides information that can support infection research. Clinical Trial Registration number: NCT04771585.

Analysis of SARS-CoV-2 transmission in a university classroom based on real human close contact behaviors

Due to high-population density, frequent close contact, possible poor ventilation, university classrooms are vulnerable for transmission of respiratory infectious diseases. Close contact and long-range airborne are possibly main routes for SARS-CoV-2 transmission. In this study, taking a university classroom in Beijing for example, close contact behaviors of students were collected through a depth-detection device, which could detect depth to each pixel of the image, based on semi-supervised learning. Finally, >23 h of video data were obtained. Using Computational Fluid Dynamics, the relationship between viral exposure and close contact behaviors (e.g. interpersonal distance, relative facial orientations, and relative positions) was established. A multi-route transmission model (short-range airborne, mucous deposition, and long-range airborne) of infectious diseases considering real close contact behaviors was developed. In the case of Omicron, the risk of infection in university classrooms and the efficacy of different interventions were assessed based on dose-response model. The average interpersonal distance in university classrooms is 0.9 m (95 % CI, 0.5 m-1.4 m), with the highest proportion of face-to-back contact at 87.0 %. The risk of infection of susceptible students per 45-min lesson was 1 %. The relative contributions of short-range airborne and long-range airborne transmission were 40.5 % and 59.5 %, respectively, and the mucous deposition was basically negligible. When all students are wearing N95 respirators, the infection risk could be reduced by 96 %, the relative contribution of long-range airborne transmission increases to 95.6 %. When the fresh air per capita in the classroom is 24 m3/h/person, the virus exposure could be decreased by 81.1 % compared to the real situation with 1.02 m3/h/person. In a classroom with an occupancy rate of 50 %, after optimized arrangement of student distribution, the infection risk could be decreased by 62 %.

COVID-19: An overview on possible transmission ways, sampling matrices and diagnosis

COVID-19 is an RNA virus belonging to the SARS family of viruses and includes a wide range of symptoms along with effects on other body organs in addition to the respiratory system. The high speed of transmission, severe complications, and high death rate caused scientists to focus on this disease. Today, many different investigation types are performed on COVID-19 from various points of view in the literature. This review summarizes most of them to provide a useful guideline for researchers in this field. After a general introduction, this review is divided into three parts. In the first one, various transmission ways COVID-19 are classified and explained in detail. The second part reviews the used biological samples for the detection of virus and the final section describes the various methods reported for the diagnosis of COVID-19 in various biological matrices.

Risk assessment of airborne virus transmission in an intensive care unit due to single and sequential coughing

The virus causing COVID-19 has constantly been mutating into new variants. Some of them are more transmissive and resistant to antibiotics. The current research article aims to examine the airborne transmission of the virus expelled by coughing action in a typical intensive care unit. Both single and sequential coughing actions have been considered to get closer to practical scenarios. The objective is to assess the effectiveness of air change per hour (ACH) on the risk of infection to a healthcare person and how the air change rate influences the dispersion of droplets. Such a study is seldom reported and has significant relevance. A total of four cases were analyzed, of which two were of sequential cough. When the ACH is changed from 6 to 12, the average particle residence time is reduced by ∼7 s. It is found that the risk of infection in the case of sequential cough will be relatively low compared to a single cough if the outlet of the indoor environment is placed above the patient's head. This arrangement also eliminates the requirement of higher ACH, which has significance from an energy conservation perspective.

On-site airborne pathogen detection for infection risk mitigation

Human-infecting pathogens that transmit through the air pose a significant threat to public health. As a prominent instance, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that caused the COVID-19 pandemic has affected the world in an unprecedented manner over the past few years. Despite the dissipating pandemic gloom, the lessons we have learned in dealing with pathogen-laden aerosols should be thoroughly reviewed because the airborne transmission risk may have been grossly underestimated. From a bioanalytical chemistry perspective, on-site airborne pathogen detection can be an effective non-pharmaceutic intervention (NPI) strategy, with on-site airborne pathogen detection and early-stage infection risk evaluation reducing the spread of disease and enabling life-saving decisions to be made. In light of this, we summarize the recent advances in highly efficient pathogen-laden aerosol sampling approaches, bioanalytical sensing technologies, and the prospects for airborne pathogen exposure measurement and evidence-based transmission interventions. We also discuss open challenges facing general bioaerosols detection, such as handling complex aerosol samples, improving sensitivity for airborne pathogen quantification, and establishing a risk assessment system with high spatiotemporal resolution for mitigating airborne transmission risks. This review provides a multidisciplinary outlook for future opportunities to improve the on-site airborne pathogen detection techniques, thereby enhancing the preparedness for more on-site bioaerosols measurement scenarios, such as monitoring high-risk pathogens on airplanes, weaponized pathogen aerosols, influenza variants at the workplace, and pollutant correlated with sick building syndromes.

A Web-Based COVID-19 Tool for Testing Residents in Retirement Homes: Development Study

BACKGROUND

Long-term care facilities have been widely affected by the COVID-19 pandemic. Empirical evidence demonstrated that older people are the most impacted and are at higher risk of mortality after being infected. Regularly testing care facility residents is a practical approach to detecting infections proactively. In many cases, the care staff must perform the tests on the residents while also providing essential care, which in turn causes imbalances in their working time. Once an outbreak occurs, suppressing the spread of the virus in retirement homes (RHs) is challenging because the residents are in contact with each other, and isolation measures cannot be widely enforced. Regular testing strategies, on the other hand, have been shown to effectively prevent outbreaks in RHs. However, high-frequency testing may consume substantial staff working time, which results in a trade-off between the time invested in testing and the time spent providing essential care to residents.

OBJECTIVE

We developed a web application (Retirement Home Testing Optimizer) to assist RH managers in identifying effective testing schedules for residents. The outcome of the app, called the "testing strategy," is based on dividing facility residents into groups and then testing no more than 1 group per day.

METHODS

We created the web application by incorporating influential factors such as the number of residents and staff, the average rate of contacts, the amount of time spent to test, and constraints on the test interval and size of groups. We developed mixed integer nonlinear programming models for balancing staff workload in long-term care facilities while minimizing the expected detection time of a probable infection inside the facility. Additionally, by leveraging symmetries in the problem, we proposed a fast and efficient local search method to find the optimal solution.

RESULTS

Considering the number of residents and staff and other practical constraints of the facilities, the proposed application computes the optimal trade-off testing strategy and suggests the corresponding grouping and testing schedule for residents. The current version of the application is deployed on the server of the Where2Test project and is accessible on their website. The application is open source, and all contents are offered in English and German. We provide comprehensive instructions and guidelines for easy use and understanding of the application's functionalities. The application was launched in July 2022, and it is currently being tested in RHs in Saxony, Germany.

CONCLUSIONS

Recommended testing strategies by our application are tailored to each RH and the goals set by the managers. We advise the users of the application that the proposed model and approach focus on the expected scenarios, that is, the expected risk of infection, and they do not guarantee the avoidance of worst-case scenarios.

At the crossroads of epidemiology and biology: Bridging the gap between SARS-CoV-2 viral strain properties and epidemic wave characteristics

The COVID-19 pandemic has given rise to numerous articles from different scientific fields (epidemiology, virology, immunology, airflow physics …) without any effort to link these different insights. In this review, we aim to establish relationships between epidemiological data and the characteristics of the virus strain responsible for the epidemic wave concerned. We have carried out this study on the Wuhan, Alpha, Delta and Omicron strains allowing us to illustrate the evolution of the relationships we have highlighted according to these different viral strains. We addressed the following questions. 1) How can the mean infectious dose (one quantum, by definition in epidemiology) be measured and expressed as an amount of viral RNA molecules (in genome units, GU) or as a number of replicative viral particles (in plaque-forming units, PFU)? 2) How many infectious quanta are exhaled by an infected person per unit of time? 3) How many infectious quanta are exhaled, on average, integrated over the whole contagious period? 4) How do these quantities relate to the epidemic reproduction rate R as measured in epidemiology, and to the viral load, as measured by molecular biological methods? 5) How has the infectious dose evolved with the different strains of SARS-CoV-2? We make use of state-of-the-art modelling, reviewed and explained in the appendix of the article (Supplemental Information, SI), to answer these questions using data from the literature in both epidemiology and virology. We have considered the modification of these relationships according to the vaccination status of the population.

COVID-19 activity risk calculator as a gamified public health intervention tool

The Coronavirus disease 2019 (COVID-19) pandemic, caused by the virus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has impacted over 200 countries leading to hospitalizations and deaths of millions of people. Public health interventions, such as risk estimators, can reduce the spread of pandemics and epidemics through influencing behavior, which impacts risk of exposure and infection. Current publicly available COVID-19 risk estimation tools have had variable effectiveness during the pandemic due to their dependency on rapidly evolving factors such as community transmission levels and variants. There has also been confusion surrounding certain personal protective strategies such as risk reduction by mask-wearing and vaccination. In order to create a simple easy-to-use tool for estimating different individual risks associated with carrying out daily-life activity, we developed COVID-19 Activity Risk Calculator (CovARC). CovARC is a gamified public health intervention as users can "play with" how different risks associated with COVID-19 can change depending on several different factors when carrying out routine daily activities. Empowering the public to make informed, data-driven decisions about safely engaging in activities may help to reduce COVID-19 levels in the community. In this study, we demonstrate a streamlined, scalable and accurate COVID-19 risk calculation system. Our study also demonstrates the quantitative impact of vaccination and mask-wearing during periods of high case counts. Validation of this impact could inform and support policy decisions regarding case thresholds for mask mandates, and other public health interventions.

A coupled experimental and statistical approach for an assessment of SARS-CoV-2 infection risk at indoor event locations

Indoor event locations are particularly affected by the SARS-CoV-2 pandemic. At large venues, only incomplete risk assessments exist, whereby no suitable measures can be derived. In this study, a physical and data-driven statistical model for a comprehensive infection risk assessment has been developed. At venues displacement ventilation concepts are often implemented. Here simplified theoretical assumptions fail for the prediction of relevant airflows for airborne transmission processes. Thus, with locally resolving trace gas measurements infection risks are computed more detailed. Coupled with epidemiological data such as incidences, vaccination rates, test sensitivities, and audience characteristics such as masks and age distribution, predictions of new infections (mean), situational R-values (mean), and individual risks on- and off-seat can be achieved for the first time. Using the Stuttgart State Opera as an example, the functioning of the model and its plausibility are tested and a sensitivity analysis is performed with regard to masks and tests. Besides a reference scenario on 2022-11-29, a maximum safety scenario with an obligation of FFP2 masks and rapid antigen tests as well as a minimum safety scenario without masks and tests are investigated. For these scenarios the new infections (mean) are 10.6, 0.25 and 13.0, respectively. The situational R-values (mean) - number of new infections caused by a single infectious person in a certain situation - are 2.75, 0.32 and 3.39, respectively. Besides these results a clustered consideration divided by age, masks and whether infections occur on-seat or off-seat are presented. In conclusion this provides an instrument that can enable policymakers and operators to take appropriate measures to control pandemics despite ongoing mass gathering events.

Positive feedback loops exacerbate the influence of superspreaders in disease transmission

Superspreaders are recognized as being important drivers of disease spread. However, models to date have assumed random occurrence of superspreaders, irrespective of whom they were infected by. Evidence suggests though that those individuals infected by superspreaders may be more likely to become superspreaders themselves. Here, we begin to explore, theoretically, the effects of such a positive feedback loop on (1) the final epidemic size, (2) the herd immunity threshold, (3) the basic reproduction number, R₀, and (4) the peak prevalence of superspreaders, using a generic model for a hypothetical acute viral infection and illustrative parameter values. We show that positive feedback loops can have a profound effect on our chosen epidemic outcomes, even when the transmission advantage of superspreaders is moderate, and despite peak prevalence of superspreaders remaining low. We argue that positive superspreader feedback loops in different infectious diseases, including SARS-CoV-2, should be investigated further, both theoretically and empirically.

Calabrese J. On the optimal presence strategies for workplace during pandemics: A COVID-19 inspired probabilistic model

During pandemics like COVID-19, both the quality and quantity of services offered by businesses and organizations have been severely impacted. They often have applied a hybrid home office setup to overcome this problem, although in some situations, working from home lowers employee productivity. So, increasing the rate of presence in the office is frequently desired from the manager's standpoint. On the other hand, as the virus spreads through interpersonal contact, the risk of infection increases when workplace occupancy rises. Motivated by this trade-off, in this paper, we model this problem as a bi-objective optimization problem and propose a practical approach to find the trade-off solutions. We present a new probabilistic framework to compute the expected number of infected employees for a setting of the influential parameters, such as the incidence level in the neighborhood of the company, transmission rate of the virus, number of employees, rate of vaccination, testing frequency, and rate of contacts among the employees. The results show a wide range of trade-offs between the expected number of infections and productivity, for example, from 1 to 6 weekly infections in 100 employees and a productivity level of 65% to 85%. This depends on the configuration of influential parameters and the occupancy level. We implement the model and the algorithm and perform several experiments with different settings of the parameters. Moreover, we developed an online application based on the result in this paper which can be used as a recommender for the optimal rate of occupancy in companies/workplaces.

An empirical analysis of American Passenger's willingness to fly in commercial airplanes after vaccination against COVID-19

Although several studies have explored the effects of the pandemic on aviation, little remains known about whether members of the public are willing to fly again after they have been vaccinated. The current study uses the Health Belief Model (HBM) to fill this missing gap by manipulating the following variables: 1) whether or not the participant is vaccinated; 2) whether or not airlines require that all passengers and crew receive vaccinations; 3) length of flight; 4) destination; and 5) the number of passengers. The data from 678 participants revealed that willingness to fly is much higher if the participants themselves have been vaccinated, if the airlines require all passengers to be vaccinated, if the flight is short, if the destination is domestic, and if the number of passengers is low. These findings did not appear to differ as a function of flying business versus pleasure. We discuss the practical implications of these data as airlines struggle to bring back their customer base.

Safe CO2 threshold limits for indoor long-range airborne transmission control of COVID-19

CO2-based infection risk monitoring is highly recommended during the current COVID-19 pandemic. However, the CO2 monitoring thresholds proposed in the literature are mainly for spaces with fixed occupants. Determining CO2 threshold is challenging in spaces with changing occupancy due to the co-existence of quanta and CO2 remaining from previous occupants. Here, we propose a new calculation framework for deriving safe excess CO2 thresholds (above outdoor level), C t, for various spaces with fixed/changing occupancy and analyze the uncertainty involved. We categorized common indoor spaces into three scenarios based on their occupancy conditions, e.g., fixed or varying infection ratios (infectors/occupants). We proved that the rebreathed fraction-based model can be applied directly for deriving C t in the case of a fixed infection ratio (Scenario 1 and Scenario 2). In the case of varying infection ratios (Scenario 3), C t derivation must follow the general calculation framework due to the existence of initial quanta/excess CO2. Otherwise, C t can be significantly biased (e.g., 260 ppm) when the infection ratio varies greatly. C t can vary significantly based on specific space factors such as occupant number, physical activity, and community prevalence, e.g., 7 ppm for gym and 890 ppm for lecture hall, indicating C t must be determined on a case-by-case basis. An uncertainty of up to 6 orders of magnitude for C t was found for all cases due to uncertainty in emissions of quanta and CO2, thus emphasizing the role of accurate emissions data in determining C t.

A review on indoor airborne transmission of COVID-19– modelling and mitigation approaches

In the past few years, significant efforts have been made to investigate the transmission of COVID-19. This paper provides a review of the COVID-19 airborne transmission modeling and mitigation strategies. The simulation models here are classified into airborne transmission infectious risk models and numerical approaches for spatiotemporal airborne transmissions. Mathematical descriptions and assumptions on which these models have been based are discussed. Input data used in previous simulation studies to assess the dispersion of COVID-19 are extracted and reported. Moreover, measurements performed to study the COVID-19 airborne transmission within indoor environments are introduced to support validations for anticipated future modeling studies. Transmission mitigation strategies recommended in recent studies have been classified to include modifying occupancy and ventilation operations, using filters and air purifiers, installing ultraviolet (UV) air disinfection systems, and personal protection compliance, such as wearing masks and social distancing. The application of mitigation strategies to various building types, such as educational, office, public, residential, and hospital, is reviewed. Recommendations for future works are also discussed based on the current apparent knowledge gaps covering both modeling and mitigation approaches. Our findings show that different transmission mitigation measures were recommended for various indoor environments; however, there is no conclusive work reporting their combined effects on the level of mitigation that may be achieved. Moreover, further studies should be conducted to understand better the balance between approaches to mitigating the viral transmissions in buildings and building energy consumption.

The Skagit County choir COVID-19 outbreak - have we got it wrong?

OBJECTIVES

Over time, papers or reports may come to be taken for granted as evidence for some phenomenon. Researchers cite them without critically re-examining findings in the light of subsequent work. This can give rise to misleading or erroneous results and conclusions. We explore whether this has occurred in the widely reported outbreak of SARS-CoV-2 at a rehearsal of the Skagit Valley Chorale in March 2020, where it was assumed, and subsequently asserted uncritically, that the outbreak was due to a single infected person.

STUDY DESIGN

Review of original report and subsequent modelling and interpretations.

METHODS

We reviewed and analysed original outbreak data in relation to published data on incubation period, subsequent modelling drawing on the data, and interpretations of transmission characteristics of this incident.

RESULTS

We show it is vanishingly unlikely that this was a single point source outbreak as has been widely claimed and on which modelling has been based.

CONCLUSION

An unexamined assumption has led to erroneous policy conclusions about the risks of singing, and indoor spaces more generally, and the benefits of increased levels of ventilation. Although never publicly identified, one individual bears the moral burden of knowing what health outcomes have been attributed to their actions. We call for these claims to be re-examined and for greater ethical responsibility in the assumption of a point source in outbreak investigations.

Agent-Based Simulation for Infectious Disease Modelling over a Period of Multiple Days, with Application to an Airport Scenario

With the COVID-19 pandemic, the role of infectious disease spreading in public places has been brought into focus more than ever. Places that are of particular interest regarding the spread of infectious diseases are international airport terminals, not only for the protection of staff and ground crew members but also to help minimize the risk of the spread of infectious entities such as COVID-19 around the globe. Computational modelling and simulation can help in understanding and predicting the spreading of infectious diseases in any such scenario. In this paper, we propose a model, which combines a simulation of high geometric detail regarding virus spreading with an account of the temporal progress of infection dynamics. We, thus, introduce an agent-based social force model for tracking the spread of infectious diseases by modelling aerosol traces and concentration of virus load in the air. We complement this agent-based model to have consistency over a period of several days. We then apply this model to investigate simulations in a realistic airport setting with multiple virus variants of varying contagiousness. According to our experiments, a virus variant has to be at least twelve times more contagious than the respective control to result in a level of infection of more than 30%. Combinations of agent-based models with temporal components can be valuable tools in an attempt to assess the risk of infection attributable to a particular virus and its variants.

Visual Analytics Platform for Centralized Covid-19 Digital Contact Tracing

The Covid-19 pandemic and its dramatic worldwide impact have required global multidisciplinary actions to mitigate its effects. Mobile phone activity-based digital contact tracing (DCT) via Bluetooth Low Energy (BLE) technology has been considered a powerful pandemic monitoring tool, yet it sparked a controversial debate about privacy risks for people. In order to explore the potential benefits of a DCT system in the context of Occupational Risk Prevention, this paper presents the potential of Visual Analytics methods to summarize and extract relevant information from complex DCT data collected during a long-term experiment at our research centre. Visual tools were combined with quantitative metrics to provide insights into contact patterns among volunteers. Results showed that crucial actors such as participants acting as bridges between groups could be easily identified - ultimately allowing for making more informed management decisions aimed at containing the potential spread of a disease.

What is the role of aerosol transmission in SARS-Cov-2 Omicron spread in Shanghai?

The Omicron transmission has infected nearly 600,000 people in Shanghai from March 26 to May 31, 2022. Combined with different control measures taken by the government in different periods, a dynamic model was constructed to investigate the impact of medical resources, shelter hospitals and aerosol transmission generated by clustered nucleic acid testing on the spread of Omicron. The parameters of the model were estimated by least square method and MCMC method, and the accuracy of the model was verified by the cumulative number of asymptomatic infected persons and confirmed cases in Shanghai from March 26 to May 31, 2022. The result of numerical simulation demonstrated that the aerosol transmission figured prominently in the transmission of Omicron in Shanghai from March 28 to April 30. Without aerosol transmission, the number of asymptomatic subjects and symptomatic cases would be reduced to 130,000 and 11,730 by May 31, respectively. Without the expansion of shelter hospitals in the second phase, the final size of asymptomatic subjects and symptomatic cases might reach 23.2 million and 4.88 million by May 31, respectively. Our results also revealed that expanded vaccination played a vital role in controlling the spread of Omicron. However, even if the vaccination rate were 100%, the transmission of Omicron should not be completely blocked. Therefore, other control measures should be taken to curb the spread of Omicron, such as widespread antiviral therapies, enhanced testing and strict tracking quarantine measures. This perspective could be utilized as a reference for the transmission and prevention of Omicron in other large cities with a population of 10 million like Shanghai.

Aerosol formation during processing of potentially infectious samples on Roche immunochemistry analyzers (cobas e analyzers) and in an end-to-end laboratory workflow to model SARS-CoV-2 infection risk for laboratory operators

Objectives

To assess aerosol formation during processing of model samples in a simulated real-world laboratory setting, then apply these findings to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to assess the risk of infection to laboratory operators.

Design

This study assessed aerosol formation when using cobas e analyzers only and in an end-to-end laboratory workflow. Recombinant hepatitis B surface antigen (HBsAg) was used as a surrogate marker for infectious SARS-CoV-2 viral particles. Using the HBsAg model, air sampling was performed at different positions around the cobas e analyzers and in four scenarios reflecting critical handling and/or transport locations in an end-to-end laboratory workflow. Aerosol formation of HBsAg was quantified using the Elecsys® HBsAg II quant II immunoassay. The model was then applied to SARS-CoV-2.

Results

Following application to SARS-CoV-2, mean HBsAg uptake/hour was 1.9 viral particles across the cobas e analyzers and 0.87 viral particles across all tested scenarios in an end-to-end laboratory workflow, corresponding to a maximum inhalation rate of <16 viral particles during an 8-hour shift.

Conclusion

Low production of marker-containing aerosol when using cobas e analyzers and in an end-to-end laboratory workflow is consistent with a remote risk of laboratory-acquired SARS-CoV-2 infection for laboratory operators.

A Simulation-Based Cost-Effectiveness Analysis of Severe Acute Respiratory Syndrome Coronavirus 2 Infection Prevention Strategies for Visitors of Healthcare Institutions

OBJECTIVES

The aim is to quantitatively evaluate different infection prevention strategies in the context of hospital visitor management during pandemics and to provide a decision support system for strategic and operational decisions based on this evaluation.

METHODS

A simulation-based cost-effectiveness analysis is applied to the data of a university hospital in Southern Germany and published COVID-19 research. The performance of different hospital visitor management strategies is evaluated by several decision-theoretic methods with varying objective functions.

RESULTS

Appropriate visitor restrictions and infection prevention measures can reduce additional infections and costs caused by visitors of healthcare institutions by >90%. The risk of transmission of severe acute respiratory syndrome coronavirus 2 by visitors of terminal care (ie, palliative care) patients can be reduced almost to 0 if appropriate infection prevention measures are implemented. Antigen tests do not seem to be beneficial from both a cost and an effectiveness perspective.

CONCLUSIONS

Hospital visitor management is crucial and effectively prevents infections while maintaining cost-effectiveness. For terminal care patients, visitor restrictions can be omitted if appropriate infection prevention measures are taken. Antigen testing plays a subordinate role, except in the case of a pure focus on additional infections caused by visitors of healthcare institutions. We provide decision support to authorities and hospital visitor managers to identify appropriate visitor restriction and infection prevention strategies for specific local conditions, incidence rates, and objectives.

Best practice approaches to outpatient management of people living with Parkinson's disease during the COVID-19 pandemic

The prevalence of Parkinson's disease (PD) is rising, rendering it one of the most common neurodegenerative diseases. Treatment and monitoring of patients require regular specialized in- and outpatient care. Patients with PD are more likely to have a complicated disease course if they become infected with severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2). Regular in-hospital appointments place these patients at risk of exposure to SARS-CoV-2 due to travel and contact with other patients and staff. However, guidelines for the management of outpatients with PD during times of increased risk of infection are currently lacking. These are urgently needed to conduct risk-benefit evaluations to recommend the best medical treatment. This article discusses best practice approaches based on the current literature, as suggested by the multidisciplinary Network of University Medicine (NUM) in Germany. These include measures such as mask-wearing, hand hygiene, social distancing measures, and appropriate testing strategies in outpatient settings, which can minimize the risk of exposure. Furthermore, the urgency of appointments should be considered. Visits of low urgency may be conducted by general practitioners or via telemedicine consultations, whereas in-person presentation is required in case of moderate and high urgency visits. Classification of urgency should be carried out by skilled medical staff, and telemedicine (telephone or video consultations) may be a useful tool in this situation. The currently approved vaccines against SARS-CoV-2 are safe and effective for patients with PD and play a key role in minimizing infection risk for patients with PD.

Risk assessment for long- and short-range airborne transmission of SARS-CoV-2, indoors and outdoors

Preventive measures to reduce infection are needed to combat the COVID-19 pandemic and prepare for a possible endemic phase. Current prophylactic vaccines are highly effective to prevent disease but lose their ability to reduce viral transmission as viral evolution leads to increasing immune escape. Long-term proactive public health policies must therefore complement vaccination with available nonpharmaceutical interventions aiming to reduce the viral transmission risk in public spaces. Here, we revisit the quantitative assessment of airborne transmission risk, considering asymptotic limits that considerably simplify its expression. We show that the aerosol transmission risk is the product of three factors: a biological factor that depends on the viral strain, a hydrodynamical factor defined as the ratio of concentration in viral particles between inhaled and exhaled air, and a face mask filtering factor. The short-range contribution to the risk, present both indoors and outdoors, is related to the turbulent dispersion of exhaled aerosols by air drafts and by convection (indoors), or by the wind (outdoors). We show experimentally that airborne droplets and CO2 molecules present the same dispersion. As a consequence, the dilution factor, and therefore the risk, can be measured quantitatively using the CO2 concentration, regardless of the room volume, the flow rate of fresh air, and the occupancy. We show that the dispersion cone leads to a concentration in viral particles, and therefore a short-range transmission risk, inversely proportional to the squared distance to an infected person and to the flow velocity. The aerosolization criterion derived as an intermediate result, which compares the Stokes relaxation time to the Lagrangian time-scale, may find application for a broad class of aerosol-borne pathogens and pollutants.

How long and effective does a mask protect you from an infected person who emits virus-laden particles: By implementing one-dimensional physics-based modeling

SARS-CoV-2 spreads via droplets, aerosols, and smear infection. From the beginning of the COVID-19 pandemic, using a facemask in different locations was recommended to slow down the spread of the virus. To evaluate facemasks' performance, masks' filtration efficiency is tested for a range of particle sizes. Although such tests quantify the blockage of the mask for a range of particle sizes, the test does not quantify the cumulative amount of virus-laden particles inhaled or exhaled by its wearer. In this study, we quantify the accumulated viruses that the healthy person inhales as a function of time, activity level, type of mask, and room condition using a physics-based model. We considered different types of masks, such as surgical masks and filtering facepieces (FFPs), and different characteristics of public places such as office rooms, buses, trains, and airplanes. To do such quantification, we implemented a physics-based model of the mask. Our results confirm the importance of both people wearing a mask compared to when only one wears the mask. The protection time for light activity in an office room decreases from 7.8 to 1.4 h with surgical mask IIR. The protection time is further reduced by 85 and 99% if the infected person starts to cough or increases the activity level, respectively. Results show the leakage of the mask can considerably affect the performance of the mask. For the surgical mask, the apparent filtration efficiency reduces by 75% with such a leakage, which cannot provide sufficient protection despite the high filtration efficiency of the mask. The facemask model presented provides key input in order to evaluate the protection of masks for different conditions in public places. The physics-based model of the facemask is provided as an online application.

Efficient Energy Saving Scenarios for Indoor PM2.5 Management in an Apartment of South Korea

Indoor PM2.5 must be effectively controlled to minimize adverse impacts on public health. Cooking is one of the main sources of PM2.5 in residential areas, and indoor air quality (IAQ) management methods such as natural and mechanical ventilation, range hood, and air purifier are typically used to reduce cooking-generated PM2.5 concentrations. However, studies on the combined effects of various IAQ management methods on indoor PM2.5 reduction and energy consumption are limited. In this study, a theoretical model was established to estimate the performance of various IAQ management methods for controlling indoor PM2.5 concentrations and energy consumption. The model was verified by comparative experiments in which, various IAQ management methods were operated individually or combined. Seasonal energy consumption was calculated through the verified model, and energy consumption saving scenarios were derived for maintaining indoor PM2.5 concentrations less than 10 μg/m3, a World Health Organization annual guideline, under fair and poor outdoor PM2.5 concentrations of 15 and 50 μg/m3, respectively. Based on our results, we found that energy consumption could be reduced significantly by applying natural ventilation in spring, autumn, and summer and mechanical ventilation in winter. Our study identified efficient energy saving PM2.5 management scenarios using various IAQ management methods by predicting indoor PM2.5 concentration and energy consumption according to the annual life patterns of typical residents in South Korea.

Prerequisite for COVID-19 Prediction: A Review on Factors Affecting the Infection Rate

Since the year 2020, coronavirus disease 2019 (COVID-19) has emerged as the dominant topic of discussion in the public and research domains. Intensive research has been carried out on several aspects of COVID-19, including vaccines, its transmission mechanism, detection of COVID-19 infection, and its infection rate and factors. The awareness of the public related to the COVID-19 infection factors enables the public to adhere to the standard operating procedures, while a full elucidation on the correlation of different factors to the infection rate facilitates effective measures to minimize the risk of COVID-19 infection by policy makers and enforcers. Hence, this paper aims to provide a comprehensive and analytical review of different factors affecting the COVID-19 infection rate. Furthermore, this review analyses factors which directly and indirectly affect the COVID-19 infection risk, such as physical distance, ventilation, face masks, meteorological factor, socioeconomic factor, vaccination, host factor, SARS-CoV-2 variants, and the availability of COVID-19 testing. Critical analysis was performed for the different factors by providing quantitative and qualitative studies. Lastly, the challenges of correlating each infection risk factor to the predicted risk of COVID-19 infection are discussed, and recommendations for further research works and interventions are outlined.

A multi-layered strategy for COVID-19 infection prophylaxis in schools: A review of the evidence for masks, distancing, and ventilation

Implications for the academic and interpersonal development of children and adolescents underpin a global political consensus to maintain in-classroom teaching during the ongoing COVID-19 pandemic. In support of this aim, the WHO and UNICEF have called for schools around the globe to be made safer from the risk of COVID-19 transmission. Detailed guidance is needed on how this goal can be successfully implemented in a wide variety of educational settings in order to effectively mitigate impacts on the health of students, staff, their families, and society. This review provides a comprehensive synthesis of current scientific evidence and emerging standards in relation to the use of layered prevention strategies (involving masks, distancing, and ventilation), setting out the basis for their implementation in the school environment. In the presence of increasingly infectious SARS-Cov-2 variants, in-classroom teaching can only be safely maintained through a layered strategy combining multiple protective measures. The precise measures that are needed at any point in time depend upon a number of dynamic factors, including the specific threat-level posed by the circulating variant, the level of community infection, and the political acceptability of the resultant risk. By consistently implementing appropriate prophylaxis measures, evidence shows that the risk of infection from in-classroom teaching can be dramatically reduced. Current studies indicate that wearing high-quality masks and regular testing are amongst the most important measures in preventing infection transmission; whilst effective natural and mechanical ventilation systems have been shown to reduce infection risks in classrooms by over 80%.

A critical review on the role of leakages in the facemask protection against SARS-CoV-2 infection with consideration of vaccination and virus variants

The protection provided by facemasks has been extensively investigated since the beginning of the SARS-CoV-2 outbreak, focusing mostly on the filtration efficiency of filter media for filtering face pieces (FFP), surgical masks, and cloth masks. However, faceseal leakage is a major contributor to the number of potentially infectious airborne droplets entering the respiratory system of a susceptible individual. The identification of leaking spots and the quantification of leaking flows are crucial to estimate the protection provided by facemasks. This study presents a critical review on the measurement and calculation of facemask leakages and a quantitative analysis of their role in the risk of SARS-CoV-2 infection. It shows that the pairing between the mask dimensions and the wearer's face is essential to improve protection efficiency, especially for FFP2 masks, and summarizes the most common leaking spots at the interface between the mask and the wearer's face. Leakage is a crucial factor in the calculation of the protection provided by facemasks and outweighs the filtration performances. The fit factors measured among mask users were summarized for different types of face protection. The reviewed data were integrated into a computational model to compare the mitigation impact of facemasks with vaccination with consideration of new variants of SARS-CoV-2. Combining a high adoption rate of facemasks and a high vaccination rate is crucial to efficiently control the spread of highly infectious variants.

Social Efficiency of Public Transportation Policy in Response to COVID-19: Model Development and Application to Intercity Buses in Seoul Metropolitan Area

Although more than two years have passed since the appearance of the coronavirus disease 2019 (COVID-19), few policies on public transportation have been implemented to reduce its spread. It is common knowledge that public transportation is vulnerable to COVID-19, but it has not been easy to formulate an appropriate public transportation policy based on a valid rationale. In this study, a modified SEIHR model was developed to evaluate the socioeconomic effects of public transportation policies. By applying the developed model to intercity buses in the Seoul metropolitan area, the socioeconomic efficiency of the policy of reducing the number of passengers was evaluated. The analysis showed that the optimal number of passengers decreased as the number of initially infected people increased; in addition, the basic reproduction number R₀, illness cost per person, and probability of infection with a single virus were higher. However, depending on these variable conditions, the policy to reduce the number of passengers in a vehicle may not be required, so it is necessary to make an appropriate judgment according to the situation. In particular, the emergence of a new mutant COVID-19 will necessitate the development of appropriate countermeasures by comprehensively examining the change in the number of infected individuals and the fatality rate. This study can guide the development of such countermeasures.

Experimental Methods of Investigating Airborne Indoor Virus-Transmissions Adapted to Several Ventilation Measures

This study introduces a principle that unifies two experimental methods for evaluating airborne indoor virus-transmissions adapted to several ventilation measures. A first-time comparison of mechanical/natural ventilation and air purification with regard to infection risks is enabled. Effortful computational fluid dynamics demand detailed boundary conditions for accurate calculations of indoor airflows, which are often unknown. Hence, a suitable, simple and generalized experimental set up for identifying the spatial and temporal infection risk for different ventilation measures is more qualified even with unknown boundary conditions. A trace gas method is suitable for mechanical and natural ventilation with outdoor air exchange. For an accurate assessment of air purifiers based on filtration, a surrogate particle method is appropriate. The release of a controlled rate of either trace gas or particles simulates an infectious person releasing virus material. Surrounding substance concentration measurements identify the neighborhood exposure. One key aspect of the study is to prove that the requirement of concordant results of both methods is fulfilled. This is the only way to ensure that the comparison of different ventilation measures described above is reliable. Two examples (a two-person office and a classroom) show how practical both methods are and how the principle is applicable for different types and sizes of rooms.

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.

Reading the Score of the Air-Change in Airborne Microbial Load in Contrast to Particulate Matter during Music Making

The potential impact of music-making on air quality around musicians was inferred at the outset of the SARS-CoV-2 pandemic from measurements on individual musical instruments and from theoretical considerations. However, it is unclear to what extent playing together in an orchestra under optimal ventilation conditions really increases infection risks for individual musicians. In this study, changes in indoor air quality were assessed by measuring common parameters, i.e., temperature, relative humidity, and carbon dioxide, along with particle counting and determining the presence of airborne pharyngeal bacteria under different seating arrangements. The study was conducted in cooperation with a professional orchestra on a stage ventilated by high volume displacement ventilation. Even with a full line-up, the particle load was only slightly influenced by the presence of the musicians on stage. At the same time, however, a clear increase in pharyngeal flora could be measured in front of individual instrument groups, but independent of seat spacing. Simultaneous measurement of various air parameters and, above all, the determination of relevant indicator bacteria in the air, enables site-specific risk assessment and safe music-making even during a pandemic.

Sustainable solutions for indoor pollution abatement during COVID phase: A critical study on current technologies & challenges

The appearance of the contagious virus COVID-19, several revelations and environmental health experts punctually predicted the possibly disastrous public health complications of coexisting catching and airborne contamination-arbitrated disease. But much attention has been given on the outdoor-mediated interactions. Almost 3.8 million premature deaths occur every year globally due to the illness from indoor air pollution. Considering the human staying longer span indoors due to restricted human activities or work from home, the indoor air quality (IAQ) might show prominent role for individual health life. Currently, the Environmental Protection Agency (EPA) ensures no regulation of indoor airborne pollution. Herein, the paper underlines the common bases of indoor air pollution, poor IAQ, and impacts of the aerosolized airborne particles on the human health. In order to address these challenges and collective contagion events in indoor environment, several emerging control techniques and preventive sustainable solutions are suggested. By this, more innovations need to be investigated in future to measure the impact of indoor air pollution on individual health.

Effectiveness of air-purifying devices and measures to reduce the exposure to bioaerosols in school classrooms

The SARS-CoV-2 pandemic, which suddenly appeared at the beginning of 2020, revealed our knowledge deficits in terms of ventilation and air pollution control. It took many weeks to realize that aerosols are the main route of transmission. The initial attempt to hold back these aerosols through textile masks seemed almost helpless, although there is sufficient knowledge about the retention capacity of fabric filters for aerosols. In the absence of a sufficient number of permanently installed heating, ventilation, and air conditioning systems, three main approaches are pursued: (a) increasing the air exchange rate by supplying fresh air, (b) using mobile air purifiers, and (c) disinfection by introducing active substances into the room air. This article discusses the feasibility of these different approaches critically. It also provides experimental results of air exchange measurements in a school classroom that is equipped with a built-in fan for supplying fresh air. With such a fan and a window tilted at the appropriate distance, an air exchange rate of 5/h can be set at a low power level and without any significant noise pollution. Heat balance calculations show that no additional heat exchanger is necessary in a normal classroom with outside temperatures above 10°C. Furthermore, a commercial mobile air purifier is studied in a chamber and a test room setup in order to examine and evaluate the efficiency of such devices against viable viruses under controlled and realistic conditions. For this purpose, bacteriophages of the type MS2 are used. Both window ventilation and air purifiers were found to be suitable to reduce the concentration of phages in the room.

A population framework for predicting the proportion of people infected by the far-field airborne transmission of SARS-CoV-2 indoors

The number of occupants in a space influences the risk of far-field airborne transmission of SARS-CoV-2 because the likelihood of having infectious and susceptible people both correlate with the number of occupants. This paper explores the relationship between occupancy and the probability of infection, and how this affects an individual person and a population of people. Mass-balance and dose-response models determine far-field transmission risks for an individual person and a population of people after sub-dividing a large reference space into 10 identical comparator spaces. For a single infected person, the dose received by an individual person in the comparator space is 10 times higher because the equivalent ventilation rate per infected person is lower when the per capita ventilation rate is preserved. However, accounting for population dispersion, such as the community prevalence of the virus, the probability of an infected person being present and uncertainty in their viral load, shows the transmission probability increases with occupancy and the reference space has a higher transmission risk. Also, far-field transmission is likely to be a rare event that requires a high emission rate, and there are a set of Goldilocks conditions that are just right when equivalent ventilation is effective at mitigating against transmission. These conditions depend on the viral load, because when they are very high or low, equivalent ventilation has little effect on transmission risk. Nevertheless, resilient buildings should deliver the equivalent ventilation rate required by standards as minimum.

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.

SARS-CoV-2 Surveillance in Indoor Air Using Electrochemical Sensor for Continuous Monitoring and Real-Time Alerts

The severe acute respiratory syndrome related coronavirus 2 (SARS-CoV-2) has spread globally and there is still a lack of rapid detection techniques for SARS-CoV-2 surveillance in indoor air. In this work, two test rigs were developed that enable continuous air monitoring for the detection of SARS-CoV-2 by sample collection and testing. The collected samples from simulated SARS-CoV-2 contaminated air were analyzed using an ultra-fast COVID-19 diagnostic sensor (UFC-19). The test rigs utilized two air sampling methods: cyclone-based collection and internal impaction. The former achieved a limit of detection (LoD) of 0.004 cp/L in the air (which translates to 0.5 cp/mL when tested in aqueous solution), lower than the latter with a limit of 0.029 cp/L in the air. The LoD of 0.5 cp/mL using the UFC-19 sensor in aqueous solution is significantly lower than the best-in-class assays (100 cp/mL) and FDA EUA RT-PCR test (6250 cp/mL). In addition, the developed test rig provides an ultra-fast method to detect airborne SARS-CoV-2. The required time to test 250 L air is less than 5 min. While most of the time is consumed by the air collection process, the sensing is completed in less than 2 s using the UFC-19 sensor. This method is much faster than both the rapid antigen (<20 min) and RT-PCR test (<90 min).

Water-to-Air Transfer of Nano/Microsized Particulates: Enrichment Effect in Bubble Bursting Jet Drops

Bubbles dispersed in liquids are widely present in many natural and industrial processes and play a key role in mediating mass transfer during their lifetime from formation to rising to bursting. In particular, nano/microsized particulates and organisms present in the bulk water can be highly enriched in the jet drops ejected during bubble bursting, impacting global climate and public health. However, the detailed mechanism of this enrichment remains obscure with the enrichment factor being difficult to predict. Here, we experimentally investigate the enrichment of nano/microsized particles in bubble bursting jet drops and highlight the underlying hydrodynamic mechanism, combining the effects of bubble scavenge and bursting on the transport of particles. Scaling laws for the enrichment factor are subsequently proposed that describe both our and prior experimental results reasonably well. Our study may provide new insights for water-to-air transfer of bulk particulates such as microbes related to bubble bursting.

TU Delft COVID-app: A tool to democratize CFD simulations for SARS-CoV-2 infection risk analysis

This work describes a modelling approach to SARS-CoV-2 dispersion based on experiments. The main goal is the development of an application integrated in Ansys Fluent to enable computational fluid dynamics (CFD) users to set up, in a relatively short time, complex simulations of virion-laden droplet dispersion for calculating the probability of SARS-CoV-2 infection in real life scenarios. The software application, referred to as TU Delft COVID-app, includes the modelling of human expiratory activities, unsteady and turbulent convection, droplet evaporation and thermal coupling. Data describing human expiratory activities have been obtained from selected studies involving measurements of the expelled droplets and the air flow during coughing, sneezing and breathing. Particle Image Velocimetry (PIV) measurements of the transient air flow expelled by a person while reciting a speech have been conducted with and without a surgical mask. The instantaneous velocity fields from PIV are used to determine the velocity flow rates used in the numerical simulations, while the average velocity fields are used for validation. Furthermore, the effect of surgical masks and N95 respirators on particle filtration and the probability of SARS-CoV-2 infection from a dose-response model have also been implemented in the application. Finally, the work includes a case-study of SARS-CoV-2 infection risk analysis during a conversation across a dining/meeting table that demonstrates the capability of the newly developed application.

Model and Validation Study for Optimizing Students’ Positions in Classrooms to Limit the Spread of Infectious Diseases Such as COVID

Classrooms at any educational institution have become high-risk sites for contagion during past and present pandemic periods caused by the SARS-CoV-2 (COVID-19) viral siege, given the prolonged time educators and students spend in joint activity. Among the several strategies employed by educational institutions to minimize the outbreak of contagion are regulating classroom capacity and studying the optimal spatial arrangement of students. The architectural features of each classroom, which include corridors, ventilation components, total volume, and maximum capacity, among other factors, have a direct impact on the risk of human contagion. This work is a proposal to optimize the spatial arrangement of students to minimize the risk of contagion, considering not only the distance between them, but also the different architectural features in the classrooms. The analyses conducted in the different scenarios conclude with a comparison of risk in terms of the arrangement of students that various educators would have used at different education levels in their classrooms based solely on intuitive criteria. The results indicate that in some situations, the locations chosen by educators can double the risk of infection compared to optimal arrangements.