Close proximity risk assessment for SARS-CoV-2 infection

Architectural interventions to mitigate the spread of SARS-CoV-2 in emergency departments

INTRODUCTION

Emergency departments (EDs) are a critical entry gate for infectious agents into hospitals. In this interdisciplinary study, we explore how infection prevention and control (IPC) architectural interventions mitigate the spread of emerging respiratory pathogens using the example of SARS-CoV-2 in a prototypical ED.

METHODS

Using an agent-based approach, we integrated data on patients' and healthcare workers' (HCWs) routines and the architectural characteristics of key ED areas. We estimated the number of transmissions in the ED by modelling the interactions between and among patients and HCWs. Architectural interventions were guided towards the gradual separation of pathogen carriers, compliance with a minimum interpersonal distance, and deconcentrating airborne pathogens (higher air exchange rates (AERs)). Interventions were epidemiologically evaluated for their mitigation effects on diverse endpoints.

RESULTS

Simulation results indicated that higher AERs in the ED (compared with baseline) may provide a moderate level of infection mitigation (incidence rate ratio (IRR) of 0.95 (95% confidence interval (CI) 0.93-0.98)) while the overall burden decreased more when rooms in examination areas were separated (IRR of 0.78 (95% CI 0.76-0.81)) or when the size of the ED base was increased (IRR of 0.79 (95% CI 0.78-0.81)). The reduction in SARS-CoV-2-associated nosocomial transmissions was largest when architectural interventions were combined (IRR of 0.61 (95% CI 0.59-0.63)).

CONCLUSIONS

These modelling results highlight the importance of IPC architectural interventions; they can be devised independently of profound knowledge of an emerging pathogen, focusing on technical, constructive, and functional components. These results may inform public health decision-makers and hospital architects on how IPC architectural interventions can be optimally used in healthcare premises.

Comprehensive Review of COVID-19: Epidemiology, Pathogenesis, Advancement in Diagnostic and Detection Techniques, and Post-Pandemic Treatment Strategies

At present, COVID-19 remains a public health concern due to the ongoing evolution of SARS-CoV-2 and its prevalence in particular countries. This paper provides an updated overview of the epidemiology and pathogenesis of COVID-19, with a focus on the emergence of SARS-CoV-2 variants and the phenomenon known as 'long COVID'. Meanwhile, diagnostic and detection advances will be mentioned. Though many inventions have been made to combat the COVID-19 pandemic, some outstanding ones include multiplex RT-PCR, which can be used for accurate diagnosis of SARS-CoV-2 infection. ELISA-based antigen tests also appear to be potential diagnostic tools to be available in the future. This paper also discusses current treatments, vaccination strategies, as well as emerging cell-based therapies for SARS-CoV-2 infection. The ongoing evolution of SARS-CoV-2 underscores the necessity for us to continuously update scientific understanding and treatments for it.

Applications of Quantitative Microbial Risk Assessment to Respiratory Pathogens and Implications for Uptake in Policy: A State-of-the-Science Review

BACKGROUND

Respiratory tract infections are major contributors to the global disease burden. Quantitative microbial risk assessment (QMRA) holds potential as a rapidly deployable framework to understand respiratory pathogen transmission and inform policy on infection control.

OBJECTIVES

The goal of this paper was to evaluate, motivate, and inform further development of the use of QMRA as a rapid tool to understand the transmission of respiratory pathogens and improve the evidence base for infection control policies.

METHODS

We conducted a literature review to identify peer-reviewed studies of complete QMRA frameworks on aerosol inhalation or contact transmission of respiratory pathogens. From each of the identified studies, we extracted and summarized information on the applied exposure model approaches, dose-response models, and parameter values, including risk characterization. Finally, we reviewed linkages between model outcomes and policy.

RESULTS

We identified 93 studies conducted in 16 different countries with complete QMRA frameworks for diverse respiratory pathogens, including SARS-CoV-2, Legionella spp., Staphylococcus aureus, influenza, and Bacillus anthracis. Six distinct exposure models were identified across diverse and complex transmission pathways. In 57 studies, exposure model frameworks were informed by their ability to model the efficacy of potential interventions. Among interventions, masking, ventilation, social distancing, and other environmental source controls were commonly assessed. Pathogen concentration, aerosol concentration, and partitioning coefficient were influential exposure parameters as identified by sensitivity analysis. Most (84%, n = 78 ) studies presented policy-relevant content including a) determining disease burden to call for policy intervention, b) determining risk-based threshold values for regulations, c) informing intervention and control strategies, and d) making recommendations and suggestions for QMRA application in policy.

CONCLUSIONS

We identified needs to further the development of QMRA frameworks for respiratory pathogens that prioritize appropriate aerosol exposure modeling approaches, consider trade-offs between model validity and complexity, and incorporate research that strengthens confidence in QMRA results. https://doi.org/10.1289/EHP12695.

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 %.

Does the exponential Wells-Riley model provide a good fit for human coronavirus and rhinovirus? A comparison of four dose-response models based on human challenge data

The risk assessments during the COVID-19 pandemic were primarily based on dose-response models derived from the pooled datasets for infection of animals susceptible to SARS-CoV. Despite similarities, differences in susceptibility between animals and humans exist for respiratory viruses. The two most commonly used dose-response models for calculating the infection risk of respiratory viruses are the exponential and the Stirling approximated β-Poisson (BP) models. The modified version of the one-parameter exponential model or the Wells-Riley model was almost solely used for infection risk assessments during the pandemic. Still, the two-parameter (α and β) Stirling approximated BP model is often recommended compared to the exponential dose-response model due to its flexibility. However, the Stirling approximation restricts this model to the general rules of β ≫ 1 and α ≪ β, and these conditions are very often violated. To refrain from these requirements, we tested a novel BP model by using the Laplace approximation of the Kummer hypergeometric function instead of the conservative Stirling approximation. The datasets of human respiratory airborne viruses available in the literature for human coronavirus (HCoV-229E) and human rhinovirus (HRV-16 and HRV-39) are used to compare the four dose-response models. Based on goodness-of-fit criteria, the exponential model was the best fitting model for the HCoV-229E (k = 0.054) and for HRV-39 datasets (k = 1.0), whereas the Laplace approximated BP model followed by the exact and Stirling approximated BP models are preferred for both the HRV-16 (α = 0.152 and β = 0.021 for Laplace BP) and the HRV-16 and HRV-39 pooled datasets (α = 0.2247 and β = 0.0215 for Laplace BP).

Perception of interpersonal distance and social distancing before and during COVID-19 pandemic

Since COVID-19 is easily transmitted among people in close physical proximity, the focus of epidemiological policy during the COVID-19 crisis included major restrictions on interpersonal distance. However, the way in which distance restrictions affected spatial perception is unclear. In the current study, we examined interpersonal distance preferences and perceptions at three time points: pre-pandemic, early post-pandemic, and late post-pandemic. The results indicate that following the pandemic outbreak, people perceived others as farther away than they actually were, suggesting that the distance restrictions were associated with an enlargement of perceived interpersonal distance. Interestingly, however, people maintained the same distance from one another as before the outbreak, indicating no change in actual distance behavior due to the risk of infection. These findings suggest that COVID-19 was associated with a change in the way distance is perceived, while in practice, people maintain the same distance as before. In contrast, COVID-related anxiety predicted both a preference for maintaining a greater distance and a bias toward underestimating perceived distance from others. Thus, individuals who were highly fearful of COVID-19 perceived other people to be closer than they actually were and preferred to maintain a larger distance from them. The results suggest that subjective risk can lead to an increased perception of danger and a subsequent change in behavior. Taken together, even when behaviors should logically change, the decision-making process can be based on distorted perceptions. This insight may be used to predict public compliance.

Epidemic exposure risk assessment in digital contact tracing: A fuzzy logic approach

Background

Bluetooth low energy (BLE)-based contact-tracing applications were widely used during the COVID-19 pandemic. However, the use of only the received signal strength feature for proximity calculations may not be adaptable to different virus variants or scalable for other potential epidemic diseases.

Objective

This study presents a novel framework in regard to evaluating and classifying personal exposure risk that considers both contact features, which include distance and length of contact, and environment features, which include crowd size and the number of recently infected cases in the environment. The framework utilizes a fuzzy expert system that is adaptable to different virus variants.

Methods

The proposed method was tested on two viruses with different close contact features, which used four membership functions and 256 fuzzy rule sets.

Results

The proposed framework classified personal exposure risks into four classes, which include low, medium, high, and too high risk. The empirical results showed that the fuzzy logic-based approach reduced the number of false positive cases and demonstrated better accuracy and precision than the current BLE-only approaches.

Conclusions

The proposed framework provides a more practical and adaptable method in regard to assessing exposure risks in real-world scenarios. It has the potential to be scalable and adaptable to different virus variants and other potential epidemic diseases by considering both contact and environment features. These findings may be useful in order to develop more effective digital contact-tracing applications and policies.

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.

Respiratory particle emission rates from children during speaking

The number of respiratory particles emitted during different respiratory activities is one of the main parameters affecting the airborne transmission of respiratory pathogens. Information on respiratory particle emission rates is mostly available for adults (few studies have investigated adolescents and children) and generally involves a limited number of subjects. In the present paper we attempted to reduce this knowledge gap by conducting an extensive experimental campaign to measure the emission of respiratory particles of more than 400 children aged 6 to 12 years while they pronounced a phonetically balanced word list at two different voice intensity levels ("speaking" and "loudly speaking"). Respiratory particle concentrations, particle distributions, and exhaled air flow rates were measured to estimate the respiratory particle emission rate. Sound pressure levels were also simultaneously measured. We found out that median respiratory particle emission rates for speaking and loudly speaking were 26 particles s-1 (range 7.1-93 particles s-1) and 41 particles s-1 (range 10-146 particles s-1), respectively. Children sex was significant for emission rates, with higher emission rates for males during both speaking and loudly speaking. No effect of age on the emission rates was identified. Concerning particle size distributions, for both respiratory activities, a main mode at approximately 0.6 µm and a second minor mode at < 2 µm were observed, and no differences were found between males and females. This information provides important input parameters in predictive models adopted to estimate the transmission risk of airborne pathogens in indoor spaces.

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.

Close contact behaviors of university and school students in 10 indoor environments

Close contact routes, including short-range airborne and large-droplet routes, play an important role in the transmission of SARS-CoV-2 in indoor environments. However, the exposure risk of such routes is difficult to quantify due to the lack of data on the close contact behavior of individuals. In this study, a digital wearable device, based on semi-supervised learning, was developed to automatically record human close contact behavior. We collected 337,056 s of indoor close contact of school and university students from 194.5 h of depth video recordings in 10 types of indoor environments. The correlation between aerosol exposure and close contact behaviors was then evaluated. Individuals in restaurants had the highest close contact ratio (64%), as well as the highest probability of face-to-face pattern (78%) during close contact. Accordingly, university students showed greater exposure potential in dormitories than school students in homes, however, a lower exposure was observed in classrooms and postgraduate student offices in comparison with school students in classrooms. In addition, restaurants had the highest aerosol exposure volume for both short-range inhalation and direct deposition on the facial mucosa. Thus, the classroom was established as the primary indoor environment where school students are exposed to aerosols.

Determining the filtration effectiveness of non-standard respiratory protective devices by an ad-hoc laboratory methodology

The recent pandemic caused by COVID-19 profoundly changed people's habits. Wearing a face mask has become usual in everyday life to reduce the risk of infection from airborne diseases. At the beginning of the pandemic, the massive request of surgical or filtering face piece (FFP) masks resulted in a global shortage of these devices for the most exposed people, such as healthcare workers. Due to this high demand for respiratory protective devices, many industrial plants have partly converted to the production of face masks using adapted materials and not complying with any specific regulation (non-standard respiratory protective devices or community masks). In this work, an ad-hoc laboratory methodology has been developed to evaluate the filtration efficiency of the materials that compose the community masks using specific instrumentation. The instrumentation consists of three main tools: an aerosol generator, a specifically designed measuring chamber, and an optical particle sizer (OPS) for the measurement of aerosol concentration. The generated aerosol was sent into the measuring chamber, divided into two separate sections by the respiratory mask. The OPS measured the aerosol mass concentration upstream and downstream of the respiratory mask, and from the concentration difference the filtration efficiency was evaluated. The proposed methodology has been validated by evaluating the particle filtration efficiency (PFE) of certified respiratory masks and was then applied for the evaluation of the filtration efficiency of different types of non-standard or community masks to analyze their effectiveness in protecting from the risk of infection of airborne diseases.

Modelling the impact of non-pharmaceutical interventions on workplace transmission of SARS-CoV-2 in the home-delivery sector

OBJECTIVE

We aimed to use mathematical models of SARS-COV-2 to assess the potential efficacy of non-pharmaceutical interventions on transmission in the parcel delivery and logistics sector.

METHODS

We devloped a network-based model of workplace contacts based on data and consultations from companies in the parcel delivery and logistics sectors. We used these in stochastic simulations of disease transmission to predict the probability of workplace outbreaks in this settings. Individuals in the model have different viral load trajectories based on SARS-CoV-2 in-host dynamics, which couple to their infectiousness and test positive probability over time, in order to determine the impact of testing and isolation measures.

RESULTS

The baseline model (without any interventions) showed different workplace infection rates for staff in different job roles. Based on our assumptions of contact patterns in the parcel delivery work setting we found that when a delivery driver was the index case, on average they infect only 0.14 other employees, while for warehouse and office workers this went up to 0.65 and 2.24 respectively. In the LIDD setting this was predicted to be 1.40, 0.98, and 1.34 respectively. Nonetheless, the vast majority of simulations resulted in 0 secondary cases among customers (even without contact-free delivery). Our results showed that a combination of social distancing, office staff working from home, and fixed driver pairings (all interventions carried out by the companies we consulted) reduce the risk of workplace outbreaks by 3-4 times.

CONCLUSION

This work suggests that, without interventions, significant transmission could have occured in these workplaces, but that these posed minimal risk to customers. We found that identifying and isolating regular close-contacts of infectious individuals (i.e. house-share, carpools, or delivery pairs) is an efficient measure for stopping workplace outbreaks. Regular testing can make these isolation measures even more effective but also increases the number of staff isolating at one time. It is therefore more efficient to use these isolation measures in addition to social distancing and contact reduction interventions, rather than instead of, as these reduce both transmission and the number of people needing to isolate at one time.

A CFD-based framework to assess airborne infection risk in buildings

The COVID-19 pandemic has prompted huge efforts to further the scientific knowledge of indoor ventilation and its relationship to airborne infection risk. Exhaled infectious aerosols are spread and inhaled as a result of room airflow characteristics. Many calculation methods and assertions on risk assume 'well-mixed' flow conditions. However, ventilation in buildings is complex and often not showing well-mixed conditions. Ventilation guidance is typically based on the provision of generic minimum ventilation flow rates for a given space, irrespective of the effectiveness in the delivery of the supply air. Furthermore, the airflow might be heavily affected by the season, the HVAC ventilation, or the opening of windows, which would potentially generate draughts and non-uniform conditions. As a result, fresh air concentration would be variable depending upon a susceptible receptor's position in a room and, therefore, associated airborne infection risk. A computational fluid dynamics (CFD) and dynamic thermal modelling (DTM) framework is proposed to assess the influence of internal airflow characteristics on airborne infection risk. A simple metric is proposed, the hourly airborne infection rate (HAI) which can easily help designers to stress-test the ventilation within a building under several conditions. A case study is presented, and the results clearly demonstrate the importance of understanding detailed indoor airflow characteristics and associated concentration patterns in order to provide detailed design guidance, e.g. occupancy, supply air diffusers and furniture layouts, to reduce airborne infection risk.

Large eddy simulation of droplet transport and deposition in the human respiratory tract to evaluate inhalation risk

As evidenced by the worldwide pandemic, respiratory infectious diseases and their airborne transmission must be studied to safeguard public health. This study focuses on the emission and transport of speech-generated droplets, which can pose risk of infection depending on the loudness of the speech, its duration and the initial angle of exhalation. We have numerically investigated the transport of these droplets into the human respiratory tract by way of a natural breathing cycle in order to predict the infection probability of three strains of SARS-CoV-2 on a person who is listening at a one-meter distance. Numerical methods were used to set the boundary conditions of the speaking and breathing models and large eddy simulation (LES) was used for the unsteady simulation of approximately 10 breathing cycles. Four different mouth angles when speaking were contrasted to evaluate real conditions of human communication and the possibility of infection. Breathed virions were counted using two different approaches: the breathing zone of influence and direction deposition on the tissue. Our results show that infection probability drastically changes based on the mouth angle and the breathing zone of influence overpredicts the inhalation risk in all cases. We conclude that to portray real conditions, the probability of infection should be based on direct tissue deposition results to avoid overprediction and that several mouth angles must be considered in future analyses.

Effect of indoor temperature on the velocity fields and airborne transmission of sneeze droplets: An experimental study and transient CFD modeling

The spread of the COVID-19 pandemic through the airborne transmission of coronavirus-containing droplets emitted during coughing, sneezing, and speaking has now been well recognized. This study presented the effect of indoor temperature (T∞) on the airflow dynamics, velocity fields, size distribution, and airborne transmission of sneeze droplets in a confined space through experimental investigation and computational fluid dynamic (CFD) modeling. The CFD simulations were performed using the renormalization group k-ε turbulence model. The experimental shadowgraph imaging and CFD simulations showed the time evolution of sneeze droplet concentrations into the turbulent expanded puff, droplet cloud, and fully-dispersed droplets. Also, the predicted mean velocity of droplets was compared with the obtained experimental data to assess the accuracy of the results. In addition, the validated computational model was used to study the sneeze complex airflow behavior and airborne transmission of small, medium, and large respiratory droplets in confined spaces at different temperatures. The warm room showed more than ∼14 % increase in airborne aerosols than the room with a mild temperature. The study provides information on the effect of room temperature on the evaporation of respiratory droplets during sneezing. The findings of this fundamental study may be used in developing exposure guidelines by controlling the temperature level in indoor environments to reduce the exposure risk of COVID-19.

In vitro model for investigating aerosol dispersion in a simulated COVID-19 patient during high-flow nasal cannula treatment

The use of high-flow nasal cannula in the treatment of COVID-19 infected patients has proven to be a valuable treatment option to improve oxygenation. Early in the pandemic, there were concerns for the degree of risk of disease transmission to health care workers utilizing these treatments that are considered aerosol generating procedures. This study developed an in vitro model to examine the release of simulated patient-derived bioaerosol with and without high-flow nasal cannula at gas flow rates of 30 and 50 L/min. Aerosol dispersion was evaluated at 30 and 90 cm distances. Reduction of transmission risk was assessed using a surgical facemask on the manikin. Results indicated that the use of a facemask facilitated a 94-95% reduction in exhaled aerosol concentration at 30 cm and 22-60% reduction for 90 cm distance across both gas flow rates. This bench study confirms that this in vitro model can be used as a tool to assess the risk of disease transmission during aerosol generating procedures in a simulated patient and to test factors to mitigate the risk.

Assessment of SARS-CoV-2 airborne infection transmission risk in public buses

Public transport environments are thought to play a key role in the spread of SARS-CoV-2 worldwide. Indeed, high crowding indexes (i.e. high numbers of people relative to the vehicle size), inadequate clean air supply, and frequent extended exposure durations make transport environments potential hotspots for transmission of respiratory infections. During the COVID-19 pandemic, generic mitigation measures (e.g. physical distancing) have been applied without also considering the airborne transmission route. This is due to the lack of quantified data about airborne contagion risk in transport environments. In this study, we apply a novel combination of close proximity and room-scale risk assessment approaches for people sharing public transport environments to predict their contagion risk due to SARS-CoV-2 respiratory infection. In particular, the individual infection risk of susceptible subjects and the transmissibility of SARS-CoV-2 (expressed through the reproduction number) are evaluated for two types of buses, differing in terms of exposure time and crowding index: urban and long-distance buses. Infection risk and reproduction number are calculated for different scenarios as a function of the ventilation rates (both measured and estimated according to standards), crowding indexes, and travel times. The results show that for urban buses, the close proximity contribution significantly affects the maximum occupancy to maintain a reproductive number of <1. In particular, full occupancy of the bus would be permitted only for an infected subject breathing, whereas for an infected subject speaking, masking would be required. For long-distance buses, full occupancy of the bus can be maintained only if specific mitigation solutions are simultaneously applied. For example, for an infected person speaking for 1 h, appropriate filtration of the recirculated air and simultaneous use of FFP2 masks would permit full occupancy of the bus for a period of almost 8 h. Otherwise, a high percentage of immunized persons (>80%) would be needed.

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

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

Airborne Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in Hospitals: Effects of Aerosol-Generating Procedures, HEPA-Filtration Units, Patient Viral Load, and Physical Distance

BACKGROUND

Transmission of coronavirus disease 2019 (COVID-19) can occur through inhalation of fine droplets or aerosols containing infectious virus. The objective of this study was to identify situations, patient characteristics, environmental parameters, and aerosol-generating procedures (AGPs) associated with airborne severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus.

METHODS

Air samples were collected near hospitalized COVID-19 patients and analyzed by RT-qPCR. Results were related to distance to the patient, most recent patient diagnostic PCR cycle threshold (Ct) value, room ventilation, and ongoing potential AGPs.

RESULTS

In total, 310 air samples were collected; of these, 26 (8%) were positive for SARS-CoV-2. Of the 231 samples from patient rooms, 22 (10%) were positive for SARS-CoV-2. Positive air samples were associated with a low patient Ct value (OR, 5.0 for Ct <25 vs >25; P = .01; 95% CI: 1.18-29.5) and a shorter physical distance to the patient (OR, 2.0 for every meter closer to the patient; P = .05; 95% CI: 1.0-3.8). A mobile HEPA-filtration unit in the room decreased the proportion of positive samples (OR, .3; P = .02; 95% CI: .12-.98). No association was observed between SARS-CoV-2-positive air samples and mechanical ventilation, high-flow nasal cannula, nebulizer treatment, or noninvasive ventilation. An association was found with positive expiratory pressure training (P < .01) and a trend towards an association for airway manipulation, including bronchoscopies and in- and extubations.

CONCLUSIONS

Our results show that major risk factors for airborne SARS-CoV-2 include short physical distance, high patient viral load, and poor room ventilation. AGPs, as traditionally defined, seem to be of secondary importance.

Close contact behavior-based COVID-19 transmission and interventions in a subway system

During COVID-19 pandemic, analysis on virus exposure and intervention efficiency in public transports based on real passenger's close contact behaviors is critical to curb infectious disease transmission. A monitoring device was developed to gather a total of 145,821 close contact data in subways based on semi-supervision learning. A virus transmission model considering both short- and long-range inhalation and deposition was established to calculate the virus exposure. During rush-hour, short-range inhalation exposure is 3.2 times higher than deposition exposure and 7.5 times higher than long-range inhalation exposure of all passengers in the subway. The close contact rate was 56.1 % and the average interpersonal distance was 0.8 m. Face-to-back was the main pattern during close contact. Comparing with random distribution, if all passengers stand facing in the same direction, personal virus exposure through inhalation (deposition) can be reduced by 74.1 % (98.5 %). If the talk rate was decreased from 20 % to 5 %, the inhalation (deposition) exposure can be reduced by 69.3 % (73.8 %). In addition, we found that virus exposure could be reduced by 82.0 % if all passengers wear surgical masks. This study provides scientific support for COVID-19 prevention and control in subways based on real human close contact behaviors.

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.

The COVID-19 pandemic: Virus transmission and risk assessment

The coronaviruses are the largest known RNA viruses of which SASR-CoV-2 has been spreading continuously due to its repeated mutation triggered by several environmental factors. Multiple human interventions and lessons learned from the SARS 2002 outbreak helped reduce its spread considerably, and thus, the virus was contained but the emerging mutations burdened the medical facility leading to many deaths in the world. As per the world health organization (WHO) droplet mode transmission is the most common mode of SASR-CoV-2 transmission to which environmental factors including temperature and humidity play a major role. This article highlights the responsibility of environmental causes that would affect the distribution and fate of the virus. Recent development in the risk assessment models is also covered in this article.

Prevention of SARS-CoV-2 and respiratory viral infections in healthcare settings: current and emerging concepts

PURPOSE OF REVIEW

COVID-19 has catalyzed a wealth of new data on the science of respiratory pathogen transmission and revealed opportunities to enhance infection prevention practices in healthcare settings.

RECENT FINDINGS

New data refute the traditional division between droplet vs airborne transmission and clarify the central role of aerosols in spreading all respiratory viruses, including Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), even in the absence of so-called 'aerosol-generating procedures' (AGPs). Indeed, most AGPs generate fewer aerosols than talking, labored breathing, or coughing. Risk factors for transmission include high viral loads, symptoms, proximity, prolonged exposure, lack of masking, and poor ventilation. Testing all patients on admission and thereafter can identify early occult infections and prevent hospital-based clusters. Additional prevention strategies include universal masking, encouraging universal vaccination, preferential use of N95 respirators when community rates are high, improving native ventilation, utilizing portable high-efficiency particulate air filters when ventilation is limited, and minimizing room sharing when possible.

SUMMARY

Multifaceted infection prevention programs that include universal testing, masking, vaccination, and enhanced ventilation can minimize nosocomial SARS-CoV-2 infections in patients and workplace infections in healthcare personnel. Extending these insights to other respiratory viruses may further increase the safety of healthcare and ready hospitals for novel respiratory viruses that may emerge in the future.

Exposure and respiratory infection risk via the short-range airborne route

Leading health authorities have suggested short-range airborne transmission as a major route of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). However, there is no simple method to assess the short-range airborne infection risk or identify its governing parameters. We proposed a short-range airborne infection risk assessment model based on the continuum model and two-stage jet model. The effects of ventilation, physical distance and activity intensity on the short-range airborne exposure were studied systematically. The results suggested that increasing physical distance and ventilation reduced short-range airborne exposure and infection risk. However, a diminishing return phenomenon was observed when the ventilation rate or physical distance was beyond a certain threshold. When the infectious quantum concentration was less than 1 quantum/L at the mouth, our newly defined threshold distance and threshold ventilation rate were independent of quantum concentration. We estimated threshold distances of 0.59, 1.1, 1.7 and 2.6 m for sedentary/passive, light, moderate and intense activities, respectively. At these distances, the threshold ventilation was estimated to be 8, 20, 43, and 83 L/s per person, respectively. The findings show that both physical distancing and adequate ventilation are essential for minimising infection risk, especially in high-intensity activity or densely populated spaces.

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

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

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

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

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.

Aerosol emission from playing wind instruments and related COVID-19 infection risk during music performance

The pandemic of COVID-19 led to restrictions in all kinds of music activities. Airborne transmission of SARS-CoV-2 requires risk assessment of wind instrument playing in various situations. Previous studies focused on short-range transmission, whereas long-range transmission risk has not been assessed. The latter requires knowledge of aerosol emission rates from wind instrument playing. We measured aerosol concentrations in a hermetically closed chamber of 20 m3 in an operating theatre as resulting from 20 min standardized wind instrument playing (19 flute, 11 oboe, 1 clarinet, 1 trumpet players). We calculated aerosol emission rates showing uniform distribution for both instrument groups. Aerosol emission from wind instrument playing ranged from 11 ± 288 particles/second (P/s) up to 2535 ± 195 P/s, expectation value ± uncertainty standard deviation. The analysis of aerosol particle size distributions shows that 70-80% of emitted particles had a size of 0.25-0.8 µm and thus are alveolar. Masking the bell with a surgical mask did not reduce aerosol emission. Aerosol emission rates were higher from wind instrument playing than from speaking or breathing. Differences between instrumental groups could not be found but high interindividual variance, as expressed by uniform distribution of aerosol emission rates. Our findings indicate that aerosol emission depends on physiological factors and playing techniques rather than on the type of instrument, in contrast to some previous studies. Based on our results, we present transmission risk calculations for long-range transmission of COVID-19 for three typical woodwind playing situations.

SARS-CoV-2 Droplet and Airborne Transmission Heterogeneity

The spread dynamics of the SARS-CoV-2 virus have not yet been fully understood after two years of the pandemic. The virus's global spread represented a unique scenario for advancing infectious disease research. Consequently, mechanistic epidemiological theories were quickly dismissed, and more attention was paid to other approaches that considered heterogeneity in the spread. One of the most critical advances in aerial pathogens transmission was the global acceptance of the airborne model, where the airway is presented as the epicenter of the spread of the disease. Although the aerodynamics and persistence of the SARS-CoV-2 virus in the air have been extensively studied, the actual probability of contagion is still unknown. In this work, the individual heterogeneity in the transmission of 22 patients infected with COVID-19 was analyzed by close contact (cough samples) and air (environmental samples). Viral RNA was detected in 2/19 cough samples from patient subgroups, with a mean Ct (Cycle Threshold in Quantitative Polymerase Chain Reaction analysis) of 25.7 ± 7.0. Nevertheless, viral RNA was only detected in air samples from 1/8 patients, with an average Ct of 25.0 ± 4.0. Viral load in cough samples ranged from 7.3 × 105 to 8.7 × 108 copies/mL among patients, while concentrations between 1.1-4.8 copies/m3 were found in air, consistent with other reports in the literature. In patients undergoing follow-up, no viral load was found (neither in coughs nor in the air) after the third day of symptoms, which could help define quarantine periods in infected individuals. In addition, it was found that the patient's Ct should not be considered an indicator of infectiousness, since it could not be correlated with the viral load disseminated. The results of this work are in line with proposed hypotheses of superspreaders, which can attribute part of the heterogeneity of the spread to the oversized emission of a small percentage of infected people.

Modeling Clothing as a Vector for Transporting Airborne Particles and Pathogens across Indoor Microenvironments

Evidence suggests that human exposure to airborne particles and associated contaminants, including respiratory pathogens, can persist beyond a single microenvironment. By accumulating such contaminants from air, clothing may function as a transport vector and source of "secondary exposure". To investigate this function, a novel microenvironmental exposure modeling framework (ABICAM) was developed. This framework was applied to a para-occupational exposure scenario involving the deposition of viable SARS-CoV-2 in respiratory particles (0.5-20 μm) from a primary source onto clothing in a nonhealthcare setting and subsequent resuspension and secondary exposure in a car and home. Variability was assessed through Monte Carlo simulations. The total volume of infectious particles on the occupant's clothing immediately after work was 4800 μm3 (5th-95th percentiles: 870-32 000 μm3). This value was 61% (5-95%: 17-300%) of the occupant's primary inhalation exposure in the workplace while unmasked. By arrival at the occupant's home after a car commute, relatively rapid viral inactivation on cotton clothing had reduced the infectious volume on clothing by 80% (5-95%: 26-99%). Secondary inhalation exposure (after work) was low in the absence of close proximity and physical contact with contaminated clothing. In comparison, the average primary inhalation exposure in the workplace was higher by about 2-3 orders of magnitude. It remains theoretically possible that resuspension and physical contact with contaminated clothing can occasionally transmit SARS-CoV-2 between humans.

Link between SARS-CoV-2 emissions and airborne concentrations: Closing the gap in understanding

The airborne transmission of SARS-CoV-2 remains surprisingly controversial; indeed, health and regulatory authorities still require direct proof of this mode of transmission. To close this gap, we measured the viral load of SARS-CoV-2 of an infected subject in a hospital room (through an oral and nasopharyngeal swab), as well as the airborne SARS-CoV-2 concentration in the room resulting from the person breathing and speaking. Moreover, we simulated the same scenarios to estimate the concentration of RNA copies in the air through a novel theoretical approach and conducted a comparative analysis between experimental and theoretical results. Results showed that for an infected subject's viral load ranging between 2.4 × 106 and 5.5 × 106 RNA copies mL-1, the corresponding airborne SARS-CoV-2 concentration was below the minimum detection threshold when the person was breathing, and 16.1 (expanded uncertainty of 32.8) RNA copies m-3 when speaking. The application of the predictive approach provided concentrations metrologically compatible with the available experimental data (i.e. for speaking activity). Thus, the study presented significant evidence to close the gap in understanding airborne transmission, given that the airborne SARS-CoV-2 concentration was shown to be directly related to the SARS-CoV-2 emitted. Moreover, the theoretical analysis was shown to be able to quantitatively link the airborne concentration to the emission.

Increased close proximity airborne transmission of the SARS-CoV-2 Delta variant

The Delta variant of SARS-CoV-2 causes higher viral loads in infected hosts, increasing the risk of close proximity airborne transmission through breathing, speaking and coughing. We performed a Monte Carlo simulation using a social contact network and exponential dose-response model to quantify the close proximity reproduction number of both wild-type SARS-CoV-2 and the Delta variant. We estimate more than twice as many Delta variant cases will reproduce infection in their close proximity contacts (64%) versus the wild-type SARS-CoV-2 (29%). Occupational health guidelines must consider close proximity airborne transmission and recommend improved personal respiratory protection for high-risk workers.

COVID-19: the case for aerosol transmission

The COVID-19 pandemic is the most severe pandemic caused by a respiratory virus since the 1918 influenza pandemic. As is the case with other respiratory viruses, three modes of transmission have been invoked: contact (direct and through fomites), large droplets and aerosols. This narrative review makes the case that aerosol transmission is an important mode for COVID-19, through reviewing studies about bioaerosol physiology, detection of infectious SARS-CoV-2 in exhaled bioaerosols, prolonged SARS-CoV-2 infectivity persistence in aerosols created in the laboratory, detection of SARS-CoV-2 in air samples, investigation of outbreaks with manifest involvement of aerosols, and animal model experiments. SARS-CoV-2 joins influenza A virus as a virus with proven pandemic capacity that can be spread by the aerosol route. This has profound implications for the control of the current pandemic and for future pandemic preparedness.

Infection risk of SARS-CoV-2 in a dining setting: Deposited droplets and aerosols

Considering that safe-distancing and mask-wearing measures are not strictly enforced in dining settings in the context of SARS-CoV-2, the infection risks of patrons in a dining outlet (e.g., a cafe) is assessed in this study. The size-resolved aerosol emission rate (AER) and droplets deposition rate (DDR) on dining plates from speaking were obtained through chamber measurements and droplet deposition visualization via fluorescent imaging technique (FIT), respectively. The AER from speaking was 24698 #/min in the size range of 0.3-5.5 μm, while the DDR was 365 #/min in the size range of 43-2847 μm. Furthermore, an infection risk model was adopted and revised to evaluate the infection risk of 120 diners for a "3-h event" in the cafe. In a four-person dining setting around a rectangular table, a diner seated diagonally across an infected person posed the least infection risk due to the deposited droplets on dining plates. The deposited droplets on a dining plate were dominant in possible viral transmission as compared to the long-range airborne route when a diner shared a table with the infected person. Yet, long-range airborne transmission had the potential to infect other diners in the cafe, even resulting in super-spreading events. A fresh air supply of 12.1-17.0 L/s per person is recommended for the cafe to serve 4-20 diners concurrently to minimize infection risks due to aerosols. Current ventilation standards (e.g., 8-10 L/s per person) for a cafe are not enough to avoid the airborne transmission of SARS-CoV-2.

Data Gathering Techniques in WSN: A Cross-Layer View

Wireless sensor networks (WSNs) have taken a giant leap in scale, expanding their applicability to a large variety of technological domains and applications, ranging from the Internet of things (IoT) for smart cities and smart homes to wearable technology healthcare applications, underwater, agricultural and environmental monitoring and many more. This expansion is rapidly growing every passing day in terms of the variety, heterogeneity and the number of devices which such applications support. Data collection is commonly the core application in WSN and IoT networks, which are typically composed of a large variety of devices, some constrained by their resources (e.g., processing, storage, energy) and some by highly diverse demands. Many challenges span all the conceptual communication layers, from the Physical to the Applicational. Many novel solutions devised in the past do not scale well with the exponential growth in the population of the devices and need to be adapted, revised, or new innovative solutions are required to comply with this massive growth. Furthermore, recent technological advances present new opportunities which can be leveraged in this context. This paper provides a cross-layer perspective and review of data gathering in WSN and IoT networks. We provide some background and essential milestones that have laid the foundation of many subsequent solutions suggested over the years. We mainly concentrate on recent state-of-the-art research, which facilitates the scalable, energy-efficient, cost-effective, and human-friendly functionality of WSNs and the novel applications in the years to come.

Risk of SARS-CoV-2 in a car cabin assessed through 3D CFD simulations

In this study, the risk of infection from SARS-CoV-2 Delta variant of passengers sharing a car cabin with an infected subject for a 30-min journey is estimated through an integrated approach combining a recently developed predictive emission-to-risk approach and a validated CFD numerical model numerically solved using the open-source OpenFOAM software. Different scenarios were investigated to evaluate the effect of the infected subject position within the car cabin, the airflow rate of the HVAC system, the HVAC ventilation mode, and the expiratory activity (breathing vs. speaking). The numerical simulations here performed reveal that the risk of infection is strongly influenced by several key parameters: As an example, under the same ventilation mode and emitting scenario, the risk of infection ranges from zero to roughly 50% as a function of the HVAC flow rate. The results obtained also demonstrate that (i) simplified zero-dimensional approaches limit proper evaluation of the risk in such confined spaces, conversely, (ii) CFD approaches are needed to investigate the complex fluid dynamics in similar indoor environments, and, thus, (iii) the risk of infection in indoor environments characterized by fixed seats can be in principle controlled by properly designing the flow patterns of the environment.

Effects of face masks and ventilation on the risk of SARS-CoV-2 respiratory transmission in public toilets: a quantitative microbial risk assessment

Public toilets may increase the risk of COVID-19 infection via airborne transmission; however, related research is limited. We aimed to estimate SARS-CoV-2 infection risk through respiratory transmission using a quantitative microbial risk assessment framework by retrieving SARS-CoV-2 concentrations from the swab tests of 251 Thai patients. Three virus-generating scenarios were investigated: an infector breathing, breathing with a cough, and breathing with a sneeze. The infection risk (95th percentile) was as high as 10^-1 with breathing and increased to 1 with a cough or a sneeze. No significant gender differences for toilet users (receptors) were noted. The highest risk scenario, namely breathing with a sneeze, was further evaluated for risk mitigation measures. Mitigation to a lower risk under 10^-3 succeeded only when the infector and the receptor both wore N95 respirators or surgical masks. Ventilation of up to 20 air changes per hour (ACH) did not decrease the risk. However, an extended waiting time of 10 min between an infector and a receptor resulted in approximately 1.0-log₁₀ further risk reduction when both wore masks with the WHO-recommended 12 ACH. The volume of expelled droplets, virus concentrations, and receptor dwell time were identified as the main contributors to transmission risk.

An integrated simulation framework for the prevention and mitigation of pandemics caused by airborne pathogens

In this work, we developed an integrated simulation framework for pandemic prevention and mitigation of pandemics caused by airborne pathogens, incorporating three sub-models, namely the spatial model, the mobility model, and the propagation model, to create a realistic simulation environment for the evaluation of the effectiveness of different countermeasures on the epidemic dynamics. The spatial model converts images of real cities obtained from Google Maps into undirected weighted graphs that capture the spatial arrangement of the streets utilized next for the mobility of individuals. The mobility model implements a stochastic agent-based approach, developed to assign specific routes to individuals moving in the city, through the use of stochastic processes, utilizing the weights of the underlying graph to deploy shortest path algorithms. The propagation model implements both the epidemiological model and the physical substance of the transmission of an airborne pathogen (in our approach, we investigate the transmission parameters of SARS-CoV-2). The deployment of a set of countermeasures was investigated in reducing the spread of the pathogen, where, through a series of repetitive simulation experiments, we evaluated the effectiveness of each countermeasure in pandemic prevention.

Evaluation of biochemical characteristics of 183 COVID-19 patients: A retrospective study

Introduction and aim

Coronavirus disease 2019 (COVID-19), with a high mortality rate, has caught the eyes of researchers worldwide and placed a heavy burden on the health care system. Accordingly, this study aimed to evaluate the values of biochemical parameters on the outcomes of COVID-19 patients in Golestan, Iran.

Materials and methods

This retrospective study was conducted on 183 COVID-19 patients (i.e., 94 males and 89 females) between March and September 2020. The biochemical parameters and demographic data of the patients (including age, sex, urea, creatinine [Cr], lactate dehydrogenase [LDH], and creatine kinase [CK]) were obtained from electrical medical records. According to the outcome of COVID-19, the patients were categorized into two groups (i.e., death [n = 63] and survival [n = 120] groups), and the biochemical parameters and outcomes of COVID-19 were analyzed.

Results

Of the 183 patients, 120 (65.5%) had a non-severe type and recovered from COVID-19, and 63 (34.4%) developed into a critically severe type and died. The mean age of all patients was 56.5 years old. The highest mortality was observed in patients with LDH ≥280. The data obtained by the one-sample t-test showed that there were significantly higher mean values of urea, Cr, CK, and LDH in COVID-19 patients when compared to their reference intervals (P˂0.0001 for all).

Conclusions

Some biochemical parameters are effective in the evaluation of dynamic variations in COVID-19 patients. It can be concluded from the results that biochemical parameters and reinforce LDH may be useful for the evaluation of the COVID-19 outcome.

The vaccination threshold for SARS-CoV-2 depends on the indoor setting and room ventilation

BACKGROUND

Effective vaccines are now available for SARS-CoV-2 in the 2nd year of the COVID-19 pandemic, but there remains significant uncertainty surrounding the necessary vaccination rate to safely lift occupancy controls in public buildings and return to pre-pandemic norms. The aim of this paper is to estimate setting-specific vaccination thresholds for SARS-CoV-2 to prevent sustained community transmission using classical principles of airborne contagion modeling. We calculated the airborne infection risk in three settings, a classroom, prison cell block, and restaurant, at typical ventilation rates, and then the expected number of infections resulting from this risk at varying percentages of occupant immunity.

RESULTS

We estimate the setting-specific immunity threshold for control of wild-type SARS-CoV-2 to range from a low of 40% for a mechanically ventilation classroom to a high of 85% for a naturally ventilated restaurant.

CONCLUSIONS

If vaccination rates are limited to a theoretical minimum of approximately two-thirds of the population, enhanced ventilation above minimum standards for acceptable air quality is needed to reduce the frequency and severity of SARS-CoV-2 superspreading events in high-risk indoor environments.

A Paradigm Shift to Align Transmission Routes With Mechanisms

Current infection-control guidelines subscribe to a contact/droplet/airborne paradigm that is based on outdated understanding. Here, we propose to modify and align existing guidelines with a more accurate description of the different transmission routes. This will improve the effectiveness of control measures as more transmissible variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerge.