The efficacy of social distance and ventilation effectiveness in preventing COVID-19 transmission

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

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

Prospects of Innovative Therapeutics in Combating the COVID-19 Pandemic

The sudden global crisis of COVID-19, driven by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), demands swift containment measures due to its rapid spread and numerous problematic mutations, which complicate the establishment of herd immunity. With escalating fatalities across various nations no foreseeable end in sight, there is a pressing need to create swiftly deployable, rapid, cost-effective detection, and treatment methods. While various steps are taken to mitigate the transmission and severity of the disease, vaccination is proven throughout mankind history as the best method to acquire immunity and circumvent the spread of infectious diseases. Nonetheless, relying solely on vaccination might not be adequate to match the relentless viral mutations observed in emerging variants of SARS-CoV-2, including alterations to their RBD domain, acquisition of escape mutations, and potential resistance to antibody binding. Beyond the immune system activation achieved through vaccination, it is crucial to develop new medications or treatment methods to either impede the infection or enhance existing treatment modalities. This review emphasizes innovative treatment strategies that aim to directly disrupt the virus's ability to replicate and spread, which could play a role in ending the SARS-CoV-2 pandemic.

Risk assessment of infection of COVID-19 contacts based on scenario simulation

We constructed a rapid infection risk assessment model for contacts of COVID-19. The improved Wells-Riley model was used to estimate the probability of infection for contacts of COVID-19 in the same place and evaluate their risk grades. We used COVID-19 outbreaks that were documented to validate the accuracy of the model. We analyzed the relationship between controllable factors and infection probability and constructed common scenarios to analyze the infection risk of contacts in different scenarios. The model showed the robustness of the fitting (mean relative error = 5.89%, mean absolute error = 2.03%, root mean squared error = 2.03%, R2 = 0.991). We found that improving ventilation from poorly ventilated to naturally ventilated and wearing masks can reduce the probability of infection by about two times. Contacts in places of light activity, loud talking or singing, and heavy exercise, oral breathing (e.g., gyms, KTV, choirs) were at higher risk of infection. The model constructed in this study can quickly and accurately assess the infection risk grades of COVID-19 contacts. Simply opening doors and windows for ventilation can significantly reduce the risk of infection in certain places. The places of light activity, loud talking or singing, and heavy exercise, oral breathing, should pay more attention to prevent and control transmission of the epidemic.

Mental Health and Other Factors Associated with COVID-19 Vaccination Intention toward Children of Military Parents in Lambayeque, Peru

There is evidence that vaccine acceptability is strongly associated with mental health. However, no studies assessing intention to vaccinate (ITV) intention toward children of military parents have been documented. The current research aimed to establish the prevalence and factors of ITV children against COVID-19 in military parents in Lambayeque-Peru, 2021. Analysis was conducted with the dependent variable ITV children reported by military parents. The independent variables were history of mental health, searching for mental health support, food insecurity, resilience, anxiety, depression, burnout, posttraumatic stress, and suicidal risk. Prevalence ratios and 95% confidence intervals were estimated. Of 201 military personnel evaluated, 92.5% were male, 82.5% were of the Catholic faith, and the median age was 40.9% of respondents reported seeking mental health help during the COVID-19 pandemic. It was reported anxiety (20.3%), depression (6.5%), and posttraumatic stress disorder (6.5%). Most reported ITV in children against COVID-19 (93%). In the multiple models, we found that Catholics had a 23% higher prevalence of ITV in the children where PR = prevalence ratios and CI = confidence intervals (PR = 1.23; 95% CI: 1.01-1.50). Likewise, seeking mental health support increased the prevalence of ITV by 8% (PR = 1.08; 95% CI: 1.00-1.15). Seeking mental health support and belonging to the Catholic faith had a higher ITV of children of Peruvian military personnel. Finding mental health support, experiencing burnout syndrome, having a relative who suffers from mental health problems, and being part of the Catholic religion were associated with a higher willingness to immunize the children of Peruvian military members.

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.

Protecting children from COVID-19: examining U.S. parents motivation and behaviour using an integrated model of self-determination theory and the theory of planned behaviour

OBJECTIVES

This longitudinal study applied the integrated model of self-determination theory (SDT) and the theory of planned behaviour (TPB) to explain COVID-19 preventive behaviours among parents of young children in the United States.

DESIGN

The study adopted a two-wave longitudinal study design. Parents (N = 681) completed self-report questionnaires related to measures of SDT and the TPB constructs and behavioural adherence at baseline and after one month. We used standardised residual change scores to test the structural relationships of the integrated model.

RESULTS

The parameter estimates of the model (CFI > .96, TLI > .86, RMSEA = .05, SRMR = .03) fit acceptably well to the data. Psychological need support was positively and significantly linked to autonomous and controlled motivation and amotivation. Autonomous motivation was positively and significantly correlated with TPB factors, and intention. Intention was a significant and positive predictor of behavioural adherence.

CONCLUSION

The integrated model of SDT and the TPB appeared to be applicable to the explanation of COVID-19 prevention among the U.S. parents. Longitudinal data showed that a psychological need supportive social environment was related to favourable motivation, social cognition beliefs, intention and behavioural adherence to the preventive behaviours of parents protecting their young children from COVID-19.

Cognitive influences on biosecurity measure compliance during a global pandemic

Introduction

During the first years of the pandemic, COVID-19 forced governments worldwide to take drastic measures to reduce the spread of the virus. Some of these measures included mandatory confinements, constant use of masks, and social distancing. Despite these measures being mandatory in many countries and the abundance of evidence on their effectiveness at slowing the spread of the virus, many people failed to comply with them.

Methods

This research explored the role of cognitive factors in predicting compliance with COVID-19 safety measures across two separate studies. Building on earlier work demonstrating the relevance of cognitive processes in health behaviour, this study aimed to identify key predictors of adherence to safety guidelines during the pandemic. Utilising hierarchical regression models, we investigated the influence of age, sex, cognitive control, cognitive flexibility (Study 1), working memory, psychological health, and beliefs about COVID-19 (Study 2) on compliance to biosafety measures.

Results

Demographic variables and cognitive control were significant predictors of compliance in both studies. However, cognitive flexibility and working memory did not improve the models' predictive capacities. In Study 2, integrating measures of psychological health and beliefs regarding COVID-19 severity significantly improved the model. Further, interaction effects between age and other variables also enhanced the predictive value.

Discussion

The findings emphasise the significant role cognitive control, age, psychological health, and perceptions about COVID-19 play in shaping compliance behaviour, highlighting avenues for targeted interventions to improve public health outcomes during a pandemic.

Rain, rain, go away, come again another day: do climate variations enhance the spread of COVID-19?

The spread of infectious diseases was further promoted due to busy cities, increased travel, and climate change, which led to outbreaks, epidemics, and even pandemics. The world experienced the severity of the 125 nm virus called the coronavirus disease 2019 (COVID-19), a pandemic declared by the World Health Organization (WHO) in 2019. Many investigations revealed a strong correlation between humidity and temperature relative to the kinetics of the virus's spread into the hosts. This study aimed to solve the riddle of the correlation between environmental factors and COVID-19 by applying RepOrting standards for Systematic Evidence Syntheses (ROSES) with the designed research question. Five temperature and humidity-related themes were deduced via the review processes, namely 1) The link between solar activity and pandemic outbreaks, 2) Regional area, 3) Climate and weather, 4) Relationship between temperature and humidity, and 5) the Governmental disinfection actions and guidelines. A significant relationship between solar activities and pandemic outbreaks was reported throughout the review of past studies. The grand solar minima (1450-1830) and solar minima (1975-2020) coincided with the global pandemic. Meanwhile, the cooler, lower humidity, and low wind movement environment reported higher severity of cases. Moreover, COVID-19 confirmed cases and death cases were higher in countries located within the Northern Hemisphere. The Blackbox of COVID-19 was revealed through the work conducted in this paper that the virus thrives in cooler and low-humidity environments, with emphasis on potential treatments and government measures relative to temperature and humidity. HIGHLIGHTS: • The coronavirus disease 2019 (COIVD-19) is spreading faster in low temperatures and humid area. • Weather and climate serve as environmental drivers in propagating COVID-19. • Solar radiation influences the spreading of COVID-19. • The correlation between weather and population as the factor in spreading of COVID-19.

COVID-19 transmission and control in land public transport: A literature review

Land public transport is an important link within and between cities, and how to control the transmission of COVID-19 in land public transport is a critical issue in our daily lives. However, there are still many inconsistent opinions and views about the spread of SARS-CoV-2 in land public transport, which limits our ability to implement effective interventions. The purpose of this review is to overview the literature on transmission characteristics and routes of the epidemic in land public transport, as well as to investigate factors affecting its spread and provide feasible measures to mitigate the infection risk of passengers. We obtained 898 papers by searching the Web of Science, Pubmed, and WHO global COVID database by keywords, and finally selected 45 papers that can address the purpose of this review. Land public transport is a high outbreak area for COVID-19 due to characteristics like crowding, inadequate ventilation, long exposure time, and environmental closure. Different from surface touch transmission and drop spray transmission, aerosol inhalation transmission can occur not only in short distances but also in long distances. Insufficient ventilation is the most important factor influencing long-distance aerosol transmission. Other transmission factors (e.g., interpersonal distance, relative orientation, and ambient conditions) should be noticed as well, which have been summarized in this paper. To address various influencing factors, it is essential to suggest practical and efficient preventive measures. Among these, increased ventilation, particularly the fresh air (i.e., natural ventilation), has proven to effectively reduce indoor infection risk. Many preventive measures are also effective, such as enlarging social distance, avoiding face-to-face orientation, setting up physical partitions, disinfection, avoiding talking, and so on. As research on the epidemic has intensified, people have broken down many perceived barriers, but more comprehensive studies on monitoring systems and prevention measures in land public transport are still needed.

An overview for monitoring and prediction of pathogenic microorganisms in the atmosphere

Corona virus disease 2019 (COVID-19) has exerted a profound adverse impact on human health. Studies have demonstrated that aerosol transmission is one of the major transmission routes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Pathogenic microorganisms such as SARS-CoV-2 can survive in the air and cause widespread infection among people. Early monitoring of pathogenic microorganism transmission in the atmosphere and accurate epidemic prediction are the frontier guarantee for preventing large-scale epidemic outbreaks. Monitoring of pathogenic microorganisms in the air, especially in densely populated areas, may raise the possibility to detect viruses before people are widely infected and contain the epidemic at an earlier stage. The multi-scale coupled accurate epidemic prediction system can provide support for governments to analyze the epidemic situation, allocate health resources, and formulate epidemic response policies. This review first elaborates on the effects of the atmospheric environment on pathogenic microorganism transmission, which lays a theoretical foundation for the monitoring and prediction of epidemic development. Secondly, the monitoring technique development and the necessity of monitoring pathogenic microorganisms in the atmosphere are summarized and emphasized. Subsequently, this review introduces the major epidemic prediction methods and highlights the significance to realize a multi-scale coupled epidemic prediction system by strengthening the multidisciplinary cooperation of epidemiology, atmospheric sciences, environmental sciences, sociology, demography, etc. By summarizing the achievements and challenges in monitoring and prediction of pathogenic microorganism transmission in the atmosphere, this review proposes suggestions for epidemic response, namely, the establishment of an integrated monitoring and prediction platform for pathogenic microorganism transmission in the atmosphere.

Zonal demand-controlled ventilation strategy to minimize infection probability and energy consumption: A coordinated control based on occupant detection

Due to the outbreak of COVID-19, an increased risk of airborne transmission has been experienced in buildings, particularly in confined public places. The need for ventilation as a means of infection prevention has become more pronounced given that some basic precautions (like wearing masks) are no longer mandatory. However, ventilating the space as a whole (e.g., using a unified ventilation rate) may lead to situations where there is either insufficient or excessive ventilation in localized areas, potentially resulting in localized virus accumulation or large energy consumption. It is of urgent need to investigate real-time control of ventilation systems based on local demands of the occupants to strike a balance between infection risk and energy saving. In this work, a zonal demand-controlled ventilation (ZDCV) strategy was proposed to optimize the ventilation rates in sub-zones. A camera-based occupant detection method was developed to detect occupants (with eight possible locations in sub-zones denoted as 'A' to 'H'). Linear ventilation model (LVM), dimension reduction, and artificial neural network (ANN) were integrated for rapid prediction of pollutant concentrations in sub-zones with the identified occupants and ventilation rates as inputs. Coordinated ventilation effects between sub-zones were optimized to improve infection prevention and energy savings. Results showed that rapid prediction models achieved an average prediction error of 6 ppm for CO2 concentration fields compared with the simulation under different occupant scenarios (i.e., occupant locations at ABH, ABCFH, and ABCDEFH). ZDCV largely reduced the infection risk to 2.8% while improved energy-saving efficiency by 34% compared with the system using constant ventilation rate. This work can contribute to the development of building environmental control systems in terms of pollutant removal, infection prevention, and energy sustainability.

Predictors of Depression in Elderly According to Gender during COVID-19: Using the Data of 2020 Community Health Survey

BACKGROUND

This study aimed to examine factors influencing depression according to gender in people during COVID-19.

METHODS

This study was conducted on 61,147 elderly individuals over the age of 65 who participated in the 2020 Community Health Survey (CHS). Data analysis was conducted using SAS 9.4.

RESULTS

Elderly females had a higher perceived fear of COVID-19 than males. The common factors affecting depression in elderly individuals were age, monthly income, economic activity, stress, subjective health status, and social support. Among elderly women, changes in residential areas and daily life due to COVID-19 were identified as factors affecting depression.

CONCLUSIONS

Therefore, during COVID-19, it was necessary to provide customized depression relief programs for the elderly, and it was necessary to find ways for them to positively perceive their health status and increase healthcare efficacy. In the future, it is necessary to pay attention to elderly women living in rural areas and make efforts to ensure that their daily lives are not interrupted by infectious diseases.

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.

How much natural ventilation rate can suppress COVID-19 transmission in occupancy zones?

Background

Previous research has emphasized the importance of efficient ventilation in suppressing COVID-19 transmission in indoor spaces, yet suitable ventilation rates have not been suggested.

Materials and Methods

This study investigated the impacts of mechanical, natural, single-sided, cross-ventilation, and three mask types (homemade, surgical, N95) on COVID-19 spread across eight common indoor settings. Viral exposure was quantified using a mass balance calculation of inhaled viral particles, accounting for initial viral load, removal via ventilation, and mask filtration efficiency.

Results

Results demonstrated that natural cross-ventilation significantly reduced viral load, decreasing from 10,000 to 0 viruses over 15 minutes in a 100 m2 space by providing ~1325 m3/h of outdoor air via two 0.6 m2 openings at 1.5 m/s wind speed. In contrast, single-sided ventilation only halved viral load at best.

Conclusion

Natural cross-ventilation with masks effectively suppressed airborne viruses, lowering potential infections and disease transmission. The study recommends suitable ventilation rates to reduce COVID-19 infection risks in indoor spaces.

Reduction of acute respiratory infections in day-care by non-pharmaceutical interventions: a narrative review

Objective

Children who start in day-care have 2-4 times as many respiratory infections compared to children who are cared for at home, and day-care staff are among the employees with the highest absenteeism. The extensive new knowledge that has been generated in the COVID-19 era should be used in the prevention measures we prioritize. The purpose of this narrative review is to answer the questions: Which respiratory viruses are the most significant in day-care centers and similar indoor environments? What do we know about the transmission route of these viruses? What evidence is there for the effectiveness of different non-pharmaceutical prevention measures?

Design

Literature searches with different terms related to respiratory infections in humans, mitigation strategies, viral transmission mechanisms, and with special focus on day-care, kindergarten or child nurseries, were conducted in PubMed database and Web of Science. Searches with each of the main viruses in combination with transmission, infectivity, and infectious spread were conducted separately supplemented through the references of articles that were retrieved.

Results

Five viruses were found to be responsible for ≈95% of respiratory infections: rhinovirus, (RV), influenza virus (IV), respiratory syncytial virus (RSV), coronavirus (CoV), and adenovirus (AdV). Novel research, emerged during the COVID-19 pandemic, suggests that most respiratory viruses are primarily transmitted in an airborne manner carried by aerosols (microdroplets).

Conclusion

Since airborne transmission is dominant for the most common respiratory viruses, the most important preventive measures consist of better indoor air quality that reduces viral concentrations and viability by appropriate ventilation strategies. Furthermore, control of the relative humidity and temperature, which ensures optimal respiratory functionality and, together with low resident density (or mask use) and increased time outdoors, can reduce the occurrence of respiratory infections.

Direct numerical simulation of contaminant removal in presence of underfloor air distribution system

Indoor contaminant removal over 0.5 ≤ FrT ≤ 5.0, 0.5 ≤ N ≤ 5.0, and 50 ≤ Re ≤ 500 was investigated numerically, wherein FrT refers to the Froude number, N refers to the buoyancy ratio, and Re refers to the Reynolds number. As demonstrated, the ventilation effectiveness increased with increasing contaminant source intensity and air supply intensity at a constant air temperature, indicating that increase the fresh air can effectively eliminate contaminants in this case. At high air supply temperatures, the heat retention time and contaminant transport was extremely short, and the fresh air induced by strong natural convection floating lift was rapidly discharged. Additioanlly, the air supply intensity had significant effects on contaminant removal. Quantification of the ventilation effectiveness under the combined effects of air supply intensity, air supply temperature and contaminant source intensity was determined based on the results of direct numerical simulations.

An assessment of the airborne longevity of group A Streptococcus

Group A streptococcus (GAS) infections result in more than 500 000 deaths annually. Despite mounting evidence for airborne transmission of GAS, little is known about its stability in aerosol. Measurements of GAS airborne stability were carried out using the Controlled Electrodynamic Levitation and Extraction of Bioaerosols onto a Substrate (CELEBS) instrument. CELEBS measurements with two different isolates of GAS suggest that it is aerostable, with approximately 70 % of bacteria remaining viable after 20 min of levitation at 50 % relative humidity (RH), with lower survival as RH was reduced. GAS airborne viability loss was driven primarily by desiccation and efflorescence (i.e. salt crystallization), with high pH also potentially playing a role, given reduced survival in bicarbonate containing droplet compositions. At low enough RH for efflorescence to occur, a greater proportion of organic components in the droplet appeared to protect the bacteria from efflorescence. These first insights into the aerosol stability of GAS indicate that airborne transmission of these respiratory tract bacteria may occur, and that both the composition of the droplet containing the bacteria, and the RH of the air affect the duration of bacterial survival in this environment. Future studies will explore a broader range of droplet and air compositions and include a larger selection of GAS strains.

Mapping the pandemic: a review of Geographical Information Systems-based spatial modeling of Covid-19

According to the World Health Organization (WHO), COVID-19 has caused more than 6.5 million deaths, while over 600 million people are infected. With regard to the tools and techniques of disease analysis, spatial analysis is increasingly being used to analyze the impact of COVID-19. The present review offers an assessment of research that used regional data systems to study the COVID-19 epidemic published between 2020 and 2022. The research focuses on: categories of the area, authors, methods, and procedures used by the authors and the results of their findings. This input will enable the contrast of different spatial models used for regional data systems with COVID-19. Our outcomes showed increased use of geographically weighted regression and Moran I spatial statistical tools applied to better spatial and time-based gauges. We have also found an increase in the use of local models compared to other spatial statistics models/methods.

COVID-19 transmission in U.S. transit buses: A scenario-based approach with agent-based simulation modeling (ABSM)

The transit bus environment is considered one of the primary sources of transmission of the COVID-19 (SARS-CoV-2) virus. Modeling disease transmission in public buses remains a challenge, especially with uncertainties in passenger boarding, alighting, and onboard movements. Although there are initial findings on the effectiveness of some of the mitigation policies (such as face-covering and ventilation), evidence is scarce on how these policies could affect the onboard transmission risk under a realistic bus setting considering different headways, boarding and alighting patterns, and seating capacity control. This study examines the specific policy regimes that transit agencies implemented during early phases of the COVID-19 pandemic in USA, in which it brings crucial insights on combating current and future epidemics. We use an agent-based simulation model (ABSM) based on standard design characteristics for urban buses in USA and two different service frequency settings (10-min and 20-min headways). We find that wearing face-coverings (surgical masks) significantly reduces onboard transmission rates, from no mitigation rates of 85% in higher-frequency buses and 75% in lower-frequency buses to 12.5%. The most effective prevention outcome is the combination of KN-95 masks, open window policies, and half-capacity seating control during higher-frequency bus services, with an outcome of nearly 0% onboard infection rate. Our results advance understanding of COVID-19 risks in the urban bus environment and contribute to effective mitigation policy design, which is crucial to ensuring passenger safety. The findings of this study provide important policy implications for operational adjustment and safety protocols as transit agencies seek to plan for future emergencies.

Building parameters linked with indoor transmission of SARS-CoV-2

The rapid spread of Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has emphasized the importance of understanding and adapting to the indoor remediation of transmissible diseases to decrease the risk for future pandemic threats. While there were many precautions in place to hinder the spread of COVID-19, there has also been a substantial increase of new research on SARS-CoV-2 that can be utilized to further mitigate the transmission risk of this novel virus. This review paper aims to identify the building parameters of indoor spaces that could have considerable influence on the transmission of SARS-CoV-2. The following building parameters have been identified and analyzed, emphasizing their link with the indoor transmission of SARS-CoV-2: temperature and relative humidity, temperature differences between rooms, ventilation rate and access to natural ventilation, occupant density, surface type and finish, airflow direction and speed, air stability, indoor air pollution, central air conditioning systems, capacity of air handling system and HVAC filter efficiency, edge sealing of air filters, room layout and interior design, and compartmentalization of interior space. This paper also explains the interactions of SARS-CoV-2 with indoor environments and its persistence. Furthermore, the modifications of the key building parameters have been discussed for controlling the transmission of SARS-CoV-2 in indoor spaces. Understanding the information provided in this paper is crucial to develop effective health and safety measures that will aid in infection prevention.

Temporal and Probabilistic Comparisons of Epidemic Interventions

Forecasting disease spread is a critical tool to help public health officials design and plan public health interventions. However, the expected future state of an epidemic is not necessarily well defined as disease spread is inherently stochastic, contact patterns within a population are heterogeneous, and behaviors change. In this work, we use time-dependent probability generating functions (PGFs) to capture these characteristics by modeling a stochastic branching process of the spread of a disease over a network of contacts in which public health interventions are introduced over time. To achieve this, we define a general transmissibility equation to account for varying transmission rates (e.g. masking), recovery rates (e.g. treatment), contact patterns (e.g. social distancing) and percentage of the population immunized (e.g. vaccination). The resulting framework allows for a temporal and probabilistic analysis of an intervention's impact on disease spread, which match continuous-time stochastic simulations that are much more computationally expensive. To aid policy making, we then define several metrics over which temporal and probabilistic intervention forecasts can be compared: Looking at the expected number of cases and the worst-case scenario over time, as well as the probability of reaching a critical level of cases and of not seeing any improvement following an intervention. Given that epidemics do not always follow their average expected trajectories and that the underlying dynamics can change over time, our work paves the way for more detailed short-term forecasts of disease spread and more informed comparison of intervention strategies.

Facility design and worker justice: COVID-19 transmission in meatpacking plants

BACKGROUND

Meatpacking plants were major sources of COVID-19 outbreaks, posing unprecedented risks to employees, family members, and local communities. The effect on food availability during outbreaks was immediate and staggering: within 2 months, the price of beef increased by almost 7% with documented evidence of significant meat shortages. Meatpacking plant designs, in general, optimize on production; this design approach constrains the ability to enhance worker respiratory protection without reducing output.

METHODS

Using agent-based modeling, we simulate the spread of COVID-19 within a typical meatpacking plant design under varying levels of mitigation measures, including combinations of social distancing and masking interventions.

RESULTS

Simulations show an average infection rate of close to 99% with no mitigation, 99% with the policies that US companies ultimately adopted, 81% infected with the combination of surgical masks and distancing policies, and 71% infected with N95 masks and distancing. Estimated infection rates were high, reflecting the duration and exertion of the processing activities and lack of fresh airflow in an enclosed space.

CONCLUSION

Our results are consistent with anecdotal findings in a recent congressional report, and are much higher than US industry has reported. Our results suggest current processing plant designs made rapid transmission of the virus during the pandemic's early days almost inevitable, and implemented worker protections during COVID-19 did not significantly affect the spread of the virus. We argue current federal policies and regulations are insufficient to ensure the health and safety of workers, creating a justice issue, and jeopardizing food availability in a future pandemic.

HRL4EC: Hierarchical reinforcement learning for multi-mode epidemic control

Infectious diseases, such as Black Death, Spanish Flu, and COVID-19, have accompanied human history and threatened public health, resulting in enormous infections and even deaths among citizens. Because of their rapid development and huge impact, laying out interventions becomes one of the most critical paths for policymakers to respond to the epidemic. However, the existing studies mainly focus on epidemic control with a single intervention, which makes the epidemic control effectiveness severely compromised. In view of this, we propose a Hierarchical Reinforcement Learning decision framework for multi-mode Epidemic Control with multiple interventions called HRL4EC. We devise an epidemiological model, referred to as MID-SEIR, to describe multiple interventions' impact on transmission explicitly, and use it as the environment for HRL4EC. Besides, to address the complexity introduced by multiple interventions, this work transforms the multi-mode intervention decision problem into a multi-level control problem, and employs hierarchical reinforcement learning to find the optimal strategies. Finally, extensive experiments are conducted with real and simulated epidemic data to validate the effectiveness of our proposed method. We further analyze the experiment data in-depth, conclude a series of findings on epidemic intervention strategies, and make a visualization accordingly, which can provide heuristic support for policymakers' pandemic response.

Crowding on public transport using smart card data during the COVID-19 pandemic: New methodology and case study in Chile

Most crowding measures in public transportation are usually aggregated at a service level. This type of aggregation does not help to analyze microscopic behavior such as exposure risk to viruses. To bridge such a gap, our paper proposes four novel crowding measures that might be well suited to proxy virus exposure risk at public transport. In addition, we conduct a case study in Santiago, Chile, using smart card data of the buses system to compute the proposed measures for three different and relevant periods of the COVID-19 pandemic: before, during, and after Santiago's lockdown. We find that the governmental policies diminished public transport crowding considerably for the lockdown phase. The average exposure time when social distancing is not possible passes from 6.39 min before lockdown to 0.03 min during the lockdown, while the average number of encountered persons passes from 43.33 to 5.89. We shed light on how the pandemic impacts differ across various population groups in society. Our findings suggest that poorer municipalities returned faster to crowding levels similar to those before the pandemic.

Multi-scale risk assessment and mitigations comparison for COVID-19 in urban public transport: A combined field measurement and modeling approach

The outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has caused an unparalleled disruption to daily life. Given that COVID-19 primarily spreads in densely populated indoor areas, urban public transport (UPT) systems pose significant risks. This study presents an analysis of the air change rate in buses, subways, and high speed trains based on measured CO2 concentrations and passenger behaviors. The resulting values were used as inputs for an infection risk assessment model, which was used to quantitatively evaluate the effects of various factors, including ventilation rates, respiratory activities, and viral variants, on the infection risk. The findings demonstrate that ventilation has a negligible impact on reducing average risks (less than 10.0%) for short-range scales, but can result in a reduction of average risks by 32.1%-57.4% for room scales. When all passengers wear masks, the average risk reduction ranges from 4.5-folds to 7.5-folds. Based on our analysis, the average total reproduction numbers (R) of subways are 1.4-folds higher than buses, and 2-folds higher than high speed trains. Additionally, it is important to note that the Omicron variant may result in a much higher R value, estimated to be approximately 4.9-folds higher than the Delta variant. To reduce disease transmission, it is important to keep the R value below 1. Thus, two indices have been proposed: time-scale based exposure thresholds and spatial-scale based upper limit warnings. Mask wearing provides the greatest protection against infection in the face of long exposure duration to the omicron epidemic.

New ventilation design criteria for energy sustainability and indoor air quality in a post Covid-19 scenario

The Covid-19 outbreak raised great attention to the importance of indoor air quality in buildings. Even if the Covid-19 epidemic is nearing an end, all stakeholders agree that increasing outside air flow rates is beneficial for decreasing the likelihood of contagion, lowering the risk of future pandemics, and enhancing the general safety of the interior environment. Indeed, diverse concerns raised about whether the ventilation standards in place are still adequate. In this context, this research intends to assess the suitability of current ventilation standards in addressing the current pandemic scenario and to offer novel criteria and guidelines for the design and operation of HVAC systems, as well as useful guidance for the creation of future ventilation standards in a post-Covid-19 scenario. To that end, a comprehensive analysis of the ANSI/ASHRAE 62.1 is carried out, with an emphasis on its effectiveness in reducing the risk of infection. Furthermore, the efficacy of various ventilation strategies in reducing the likelihood of contagion has been investigated. Finally, because building ventilation is inextricably linked to energy consumption, the energy and economic implications of the proposed enhancements have been assessed. To carry out the described analysis, a novel method was developed that combines Building Energy Modelling (BEM) and virus contagion risk assessment. The analyses conducted produced interesting insights and criteria for ventilation system design and operation, as well as recommendations for the development of future standards.

Risk perception of compound emergencies: A household survey on flood evacuation and sheltering behavior during the COVID-19 pandemic

Compound hazards are derived from independent disasters that occur simultaneously. Since the outbreak of COVID-19, the coupling of low-probability high-impact climate events has introduced a novel form of conflicting stressors that inhibits the operation of traditional logistics developed for single-hazard emergencies. The competing goals of hindering virus contagion and expediting massive evacuation have posed unique challenges for community safety. Yet, how a community perceives associated risks has been debated. This research utilized a web-based survey to explore the relationship between residents' perceptions of conflicting risks and emergency choices made during a historic compound event, the flooding in 2020 in Michigan, US that coincided with the pandemic. After the event, postal mail was randomly sent to 5,000 households living in the flooded area, collecting 556 responses. We developed two choice models for predicting survivors' evacuation options and sheltering length. The impact of sociodemographic factors on perceptions of COVID-19 risks was also examined. The results revealed greater levels of concern among females, democrats, and the economically inactive population. The relationship between evacuation choice and concern about virus exposure was dependent upon the number of seniors in the household. Concern about a lack of mask enforcement particularly discouraged evacuees from extended sheltering.

Roboethics of tourism and hospitality industry: A systematic review

This study aims to give a comprehensive analysis of customers' acceptance and use of AI gadgets and its relevant ethical issues in the tourism and hospitality business in the era of the Internet of Things. Adopting a PRISMA methodology for Systematic Reviews and Meta-Analyses, the present research reviews how tourism and hospitality scholars have conducted research on AI technology in the field of tourism and the hospitality industry. Most of the journal articles related to AI issues published in Web of Science, ScienceDirect.com and the journal websites were considered in this review. The results of this research offer a better understanding of AI implementation with roboethics to investigate AI-related issues in the tourism and hospitality industry. In addition, it provides decision-makers in the hotel industry with practical references on service innovation, participation in the design of AI devices and AI device applications, meeting customer needs, and optimising customer experience. The theoretical implications and practical interpretations are further identified.

The effects of city morphology on airborne transmission of COVID-19. Case study: Port Said City, Egypt

Looking beyond COVID-19 outbreak, Scholars continue to develop innovative approaches to bring the city on to health and safety. Recent studies have indicated that urban spaces could produce or propagate pathogens, which is an urgent topic at the city level. However, there is a dearth of studies investigating the interrelationship between urban morphology and pandemics outbreak at the neighborhood level. Accordingly, this research will trace the effect of cities morphologies on the rate of spread of COVID-19 through a simulation study held on five areas that form the urban morphology of Port Said City, using Envi-met software. Results are explored based on the degree of concentration and rate of diffusion of coronavirus particles. It was observed on a regular basis that wind speed has a directly proportional relationship with the diffusion of the particles and an inversely proportional relationship with the concentration of the particles. However, certain urban characteristics led to inconsistent and opposing results like wind tunnels, shaded arcades, height variance, and spacious in-between spaces. Moreover, it is obvious that the city morphology is being transformed over time toward safer conditions; urban areas constructed recently have low vulnerability to respiratory pandemics outbreak compared to older areas.

Life cycle assessment of Tehran Municipal solid waste during the COVID-19 pandemic and environmental impacts prediction using machine learning

Life cycle assessment and machine learning were combined to find the best option for Tehran's waste management for future pandemics. The ReCipe results showed the waste's destructive effects after COVID-19 were greater than before due to waste composition changes. Plastic waste has changed from 7.5 to 11%. Environmental burdens of scenarios were Sc-1 (increase composting to 50%) > Sc-3 > Sc-4 > Sc-b2 > Sc-5 > Sc-2 (increase recycling from 9 to 20%). The artificial neural network and gradient-boosted regression tree could predict environmental impacts with high R2. Based on the results, the environmental burdens of solid waste after COVID-19 should be investigated.

The impact of COVID-19 on visitors' wayfinding within healthcare centers

The novel COVID-19 pandemic has caused substantial calamities in developing countries such as Iran, which initially suffered from inadequate infrastructure essential for the pandemic control. Due to the ongoing development of this malady, healthcare centers are recognized as one of the most significant hotspots within public settings so they are directly pertinent to the physical and mental health of visitors. The main objective for conducting the present study is to investigate the impact of the COVID-19 pandemic on the visitors' wayfinding procedure within Qa'em hospital, located in Rasht, northern Iran. The adopted methodology in the present study is based on a comparison between the collected data regarding the wayfinding behavior of visitors before and after the outbreak of the COVID-19 pandemic using mixed methods, namely Space Syntax, gate counting, people following, and semi-structured interviews. The obtained empirical results displayed that visitors were significantly confused and hesitant throughout their wayfinding process after the outbreak of the pandemic. Indeed, spatial accessibility and legibility were not found to be adequate for facilitating the wayfinding of the visitors. Moreover, the requirements for the reconfiguration of furniture layout in the waiting areas, according to the underlying notions of social distancing, became conspicuous as the pragmatic implications for the post-pandemic healthcare centers.

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.

Evaluation of indoor environmental quality, personal cumulative exposure dose, and aerosol transmission risk levels inside urban buses in Dalian, China

The transmission of pollutants in buses has an important impact on personal exposure to airborne particles and spread of the COVID-19 epidemic in enclosed spaces. We conducted the following real-time field measurements inside buses: CO2, airborne particle concentration, temperature, and relative humidity data during peak and off-peak hours in spring and autumn. Correlation analysis was adopted to evaluate the dominant factors influencing CO2 and particle mass concentrations in the vehicle. The cumulative personal exposure dose to particulate matter and reproduction number were calculated for passengers on a one-way trip. The results showed the in-cabin CO2 concentrations, with 22.11% and 21.27% of the total time exceeding 1000 ppm in spring and autumn respectively. In-cabin PM2.5 mass concentration exceeded 35 μm/m3 by 57.35% and 86.42% in spring and autumn, respectively. CO2 concentration and the cumulative number of passengers were approximately linearly correlated in both seasons, with R value up to 0.896. The cumulative number of passengers had the most impact on PM2.5 mass concentration among tested parameters. The cumulative personal exposure dose to PM2.5 during a one-way trip in autumn was up to 43.13 μg. The average reproductive number throughout the one-way trip was 0.26; it was 0.57 under the assumed extreme environment. The results of this study provide an important basic theoretical guidance for the optimization of ventilation system design and operation strategies aimed at reducing multi-pollutant integrated health exposure and airborne particle infection (such as SARS-CoV-2) risks.

Investigating impacts of COVID-19 on urban mobility and emissions

The COVID-19 pandemic has severely impacted human activities in a way never documented in modern history. The prevention policies and measures have abruptly changed well-established urban mobility patterns. In this context, we exploit different sources of urban mobility data to gain insights into the effects of restrictive policies on the daily mobility and exhaust emissions in pandemic and post-pandemic periods. Manhattan, the most densely populated borough in New York City, is chosen as the study area. We collect data generated by taxis, sharing bikes, and road detectors between 2019 and 2021, and estimate exhaust emissions using the COPERT (Computer Programme to calculate Emissions from Road Transport) model. A comparative analysis is conducted to identify important changes in urban mobility and emission patterns, with a particular focus on the lockdown period in 2020 and its counterparts in 2019 and 2021. The results of the paper fuel the discussion on urban resilience and policy-making in a post pandemic world.

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

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

Multi-camera BEV video-surveillance system for efficient monitoring of social distancing

The current sanitary emergency situation caused by COVID-19 has increased the interest in controlling the flow of people in indoor infrastructures, to ensure compliance with the established security measures. Top view camera-based solutions have proven to be an effective and non-invasive approach to accomplish this task. Nevertheless, current solutions suffer from scalability problems: they cover limited range areas to avoid dealing with occlusions and only work with single camera scenarios. To overcome these problems, we present an efficient and scalable people flow monitoring system that relies on three main pillars: an optimized top view human detection neural network based on YOLO-V4, capable of working with data from cameras at different heights; a multi-camera 3D detection projection and fusion procedure, which uses the camera calibration parameters for an accurate real-world positioning; and a tracking algorithm which jointly processes the 3D detections coming from all the cameras, allowing the traceability of individuals across the entire infrastructure. The conducted experiments show that the proposed system generates robust performance indicators and that it is suitable for real-time applications to control sanitary measures in large infrastructures. Furthermore, the proposed projection approach achieves an average positioning error below 0.2 meters, with an improvement of more than 4 times compared to other methods.

Effects of COVID-19 pandemic on spatial preferences and usage habits of users in shopping malls and its relation with circulation layout

The COVID-19 pandemic, with its global impact on every scale of life, has caused a change in all the habits of people regarding daily life, and this change has also been directly reflected in the human-space interaction. The bidirectional relationship between human and space has evolved by being influenced by each other during the pandemic process. Within the scope of this study, which aims to determine the predicted change in the spatial preferences and usage habits of the users in the shopping malls due to the COVID-19 pandemic, which causes changes in the space and the usage habits of the users from micro scale to macro scale, analyzes were made with the research methodology consisting of three stages (i. space syntax, ii. survey, iii. comparison) and the results were evaluated. According to the results obtained from the case study, it was determined that the COVID-19 pandemic deeply changed people's spatial perception, preferences and usage habits in shopping malls and circulation layout also plays a role on it. To summarize, while personal preferences were at the forefront in places to be preferred for shopping before the pandemic, the characteristics of the place gained importance after the pandemic.

An evaluation of the impact of COVID-19 safety measures in public transit spaces on riders' Worry of virus contraction

The coronavirus pandemic has brought about global change in travel behavior. Transit ridership volumes have dropped to record lows. Concerning environmental, health, and social consequences lie in store if transit networks are not able to regain a substantial portion of pre-pandemic users. Transit providers have implemented several interventions aimed at both slowing the spread of the virus and retaining riders as travel restrictions lift. While the effectiveness of these measures has been evaluated with respect to spread rate reduction, little consideration has been given to their impact on riders' feelings of worry regarding virus contraction. By deploying a photo-simulation approach in a randomized control trial, this study finds that level of compliance with safety measures and the conditions of transit spaces themselves significantly impact riders' levels of worry. Given these findings, a series of recommendations are made regarding compliance practices that are expected to lessen rider worry regarding the risks of COVID-19 infection.

Computational decision-support tools for urban design to improve resilience against COVID-19 and other infectious diseases: A systematic review

The COVID-19 pandemic highlighted the need for decision-support tools to help cities become more resilient to infectious diseases. Through urban design and planning, non-pharmaceutical interventions can be enabled, impelling behaviour change and facilitating the construction of lower risk buildings and public spaces. Computational tools, including computer simulation, statistical models, and artificial intelligence, have been used to support responses to the current pandemic as well as to the spread of previous infectious diseases. Our multidisciplinary research group systematically reviewed state-of-the-art literature to propose a toolkit that employs computational modelling for various interventions and urban design processes. We selected 109 out of 8,737 studies retrieved from databases and analysed them based on the pathogen type, transmission mode and phase, design intervention and process, as well as modelling methodology (method, goal, motivation, focus, and indication to urban design). We also explored the relationship between infectious disease and urban design, as well as computational modelling support, including specific models and parameters. The proposed toolkit will help designers, planners, and computer modellers to select relevant approaches for evaluating design decisions depending on the target disease, geographic context, design stages, and spatial and temporal scales. The findings herein can be regarded as stand-alone tools, particularly for fighting against COVID-19, or be incorporated into broader frameworks to help cities become more resilient to future disasters.

The Impact of Preventive Strategies Adopted during Large Events on the COVID-19 Pandemic: A Case Study of the Tokyo Olympics to Provide Guidance for Future Large Events

This study aimed to analyze the impact of hosting large events on the spread of pandemics, taking Tokyo Olympics 2020 as a case study. A risk assessment method for the whole organization process was established, which could be used to evaluate the effectiveness of various risk mitigation measures. Different scenarios for Games participants and Japanese residents during the Tokyo Olympics were designed based on the infection control protocols proposed by the Olympic Committee and local governments. A modified Wells-Riley model considering the influence of social distance, masking and vaccination, and an SIQRV model that introduced the effect of quarantine and vaccination strategies on the pandemic spread were developed in this study. Based on the two models, our predicted results of daily confirmed cases and cumulative cases were obtained and compared with reported data, where good agreement was achieved. The results show that the two core infection control strategies of the bubble scheme and frequent testing scheme curbed the spread of the COVID-19 pandemic during the Tokyo Olympics. Among Games participants, Japanese local staff accounted for more than 60% of the total in positive cases due to their large population and most relaxed travel restrictions. The surge in positive cases was mainly attributed to the high transmission rate of the Delta variant and the low level of immunization in Japan. Based on our simulation results, the risk management flaws for the Tokyo Olympics were identified and improvement measures were investigated. Moreover, a further analysis was carried out on the impact of different preventive measures with respect to minimizing the transmission of new variants with higher transmissibility. Overall, the findings in this study can help policymakers to design scientifically based and practical countermeasures to cope with pandemics during the hosting of large events.

Understanding the Impacts of the COVID-19 Pandemic on Small Businesses and Workers Using Quantitative and Qualitative Methods

The COVID-19 pandemic has simultaneously exacerbated and elucidated inequities in resource distribution for small businesses across the United States in terms of worker health and the financial stability of both owners and employees. This disparity was further intensified by the constantly changing and sometimes opposing health and safety guidelines and recommendations to businesses from the local, state, and federal government agencies. To better understand how the pandemic has impacted small businesses, a cross-sectional survey was administered to owners, managers, and workers (n = 45) in the beauty and auto shop sectors from Southern Arizona. The survey identified barriers to safe operation that these businesses faced during the pandemic, illuminated worker concerns about COVID-19, and elicited perceptions of how workplaces have changed since the novel coronavirus outbreak of 2019. A combination of open-ended and close-ended questions explored how businesses adapted to the moving target of pandemic safety recommendations, as well as how the pandemic affected businesses and workers more generally. Almost all the beauty salons surveyed had to close their doors (22/25), either temporarily or permanently, due to COVID-19, while most of the auto repair shops were able to stay open (13/20). Beauty salons were more likely to implement exposure controls meant to limit transmission with customers and coworkers, such as wearing face masks and disallowing walk-ins, and were also more likely to be affected by pandemic-related issues, such as reduced client load and sourcing difficulties. Auto shops, designated by the state of Arizona to be 'essential' businesses, were less likely to have experienced financial precarity due to the pandemic. Content analysis of open-ended questions using the social-ecological model documented current and future worker concerns, namely financial hardships from lockdowns and the long-term viability of their business, unwillingness of employees to return to work, uncertainty regarding the progression of the pandemic, conflict over suitable health and safety protocols, and personal or family health and well-being (including anxiety and/or stress). Findings from the survey indicate that small businesses did not have clear guidance from policymakers during the pandemic and that the enacted regulations and guidelines focused on either health and safety or finances, but rarely both. Businesses often improvised and made potentially life-changing decisions with little to no support. This analysis can be used to inform future pandemic preparedness plans for small businesses that are cost-efficient, effective at reducing environmental exposures, and ultimately more likely to be implemented by the workers.

Dining comfort in elderly care facility dining rooms and influencing factors before and after the outbreak of the COVID-19 pandemic

Introduction

The COVID-19 pandemic has changed dining modes in elderly care facilities. This study explores the relationship between the dining environment of four elderly care facilities and the sensitivity of the elderly residents to it before and after the outbreak of the COVID-19 pandemic.

Methods

The study examined differences in subjective comfort levels by measuring the facilities' physical environment, analysing dining behavior, and surveying the elderly residents. And the study examined how the interaction between the physical environment factors, demographic factors, and dining mode affected the residents' evaluation of their dining comfort.

Results

(1) The physical environmental parameters of the four dining rooms differed between the pre- and post-epidemic periods, as shown by increased Sound Pressure Level (SPL), humidity, and temperature levels. (2) The residents' evaluations of physical environment comfort also changed after the outbreak of the COVID-19 pandemic. The subjective comfort levels of the 'dining with baffle' and 'dining across a seat' modes decreased, though the level of the former was slightly higher than that of the latter. The elderly had stronger SPL tolerance in the dining with baffle mode and dining across a seat mode, and their subjective comfort levels for thermal environment and air quality were higher in the dining across a seat mode. (3) When dining time, crowd density, and communication frequency were kept equal, the subjective comfort level of the elderly in the dining with baffle mode and dining across a seat mode was lower than that in the 'normal' dining mode, when the level in the dining with baffle mode was lower than that in the dining across a seat mode. (4) Differences were observed in subjective comfort levels according to age, education level, and residence duration across the dining modes.

Discussion

The need for changes in dining modes during the COVID-19 isolation period require dining rooms in elderly care facilities to design their physical environments in a way that improves dining comfort for the elderly.

Changes on local travel behaviors under travel reduction-related interventions during COVID-19 pandemic: a case study in Hong Kong

The emerging Omicron variant poses a serious threat to human health. Public transports play a critical role in infection spread. Based on the data of nearly 4 billion smartcard uses, between January 1, 2019 and January 31, 2021 from the Mass Transit Railway Corporation of Hong Kong, we analyzed the subway travel behavior of different population groups (adults, children, students and senior citizens) due to the COVID-19 pandemic and human travel behavior under different interventions (e.g. work suspension, school closure). Due to the pandemic, the number of MTR passengers (the daily number of passengers in close proximity in subway carriages) decreased by 37.4% (40.8%) for adults, 80.3% (78.5%) for children, 71.6% (71.6%) for students, and 33.5% (36.1%) for senior citizens. Due to work from home (school suspension), the number of contacted adults (students/children) in the same carriage during the rush hours decreased by 39.6% (38.6%/43.2%). If all workers, students, and children were encouraged to commute avoiding rush hours, the possible repeated contacts during rush hour of adults, children and students decreased by 73.3%, 77.9% and 79.5%, respectively. Since adults accounted for 87.3% of the total number of subway passengers during the pandemic, work from home and staggered shift pattern of workers can reduce the infection risk effectively. Our objective is to find the changes of local travel behavior due to the pandemic. From the perspective of public transports, the results provide a scientific support for COVID-19 prevention and control in cities.

Analysis on the thermal performance of low-temperature radiant floor coupled with intermittent stratum ventilation (LTR-ISV) for space heating

With increasing energy use and outbreaks of respiratory infectious diseases (such as COVID-19) in buildings, there is a growing interest in creating healthy and energy-efficient indoor environments. A novel heating system named low-temperature radiant floor coupled with intermittent stratum ventilation (LTR-ISV) is proposed in this study. Thermal performance, indoor air quality, energy and exergy performance were investigated and compared with conventional radiant floor heating (CRFH) and conventional radiant floor heating with mixing ventilation (CRFH + MV). The results indicated that LTR-ISV had a more uniform operative temperature distribution and overall thermal sensation, and air mixing was enhanced without generating additional draft sensation. Compared with CRFH and CRFH + MV, the indoor CO₂ concentration in LTR-ISV can be reduced by 1355 ppm and 400 ppm, respectively. Airborne transmission risk can also be reduced by 5.35 times. The coefficient of performance for CRFH, CRFH + MV, and LTR-ISV during working hours was 4.2, 2.5, and 3.4, respectively. The lower value of LTR-ISV was due to the high energy usage of the primary air handing unit. In the non-working hours, LTR-ISV was 0.6 and 1.3 higher compared to CRFH and CRFH + MV, respectively. The exergy efficiency of LTR-ISV, CRFH, and CRFH + MV was 81.77 %, 76.43 %, and 64.71 %, respectively. Therefore, the LTR-ISV system can meet the requirements of high indoor air quality and thermal comfort and provides a reference for the energy-saving use of low-grade energy in space heating.

Automatic social distance estimation for photographic studies: Performance evaluation, test benchmark, and algorithm

The social distancing regulations introduced to slow down the spread of COVID-19 virus directly affect a basic form of non-verbal communication, and there may be longer term impacts on human behavior and culture that remain to be analyzed in proxemics studies. To obtain quantitative results for such studies, large media and/or personal photo collections must be analyzed. Several social distance monitoring methods have been proposed for safety purposes, but they are not directly applicable to general photo collections with large variations in the imaging setup. In such studies, the interest shifts from safety to analyzing subtle differences in social distances. Currently, there is no suitable benchmark for developing such algorithms. Collecting images with measured ground-truth pair-wise distances using different camera settings is cumbersome. Moreover, performance evaluation for these algorithms is not straightforward, and there is no widely accepted evaluation protocol. In this paper, we provide an image dataset with measured pair-wise social distances under different camera positions and settings. We suggest a performance evaluation protocol and provide a benchmark to easily evaluate such algorithms. We also propose an automatic social distance estimation method that can be applied on general photo collections. Our method is a hybrid method that combines deep learning-based object detection and human pose estimation with projective geometry. The method can be applied on uncalibrated single images with known focal length and sensor size. The results on our benchmark are encouraging with 91% human detection rate and only 38.24% average relative distance estimation error among the detected people.

Virus transmission risk of college students in railway station during Post-COVID-19 era: Combining the social force model and the virus transmission model

In the post-epidemic era, people's lives are gradually returning to normal, and travel is gradually resuming. The safe evacuation of cross-regional travelers in railway station has also attracted more and more attention, especially the evacuation behavior of college students in railway station. In this paper, considering the pedestrian dynamics mechanism in the emergency evacuation process during the COVID-19 normalized epidemic prevention and control, an Agent-based social force model was established to simulate the activities of college students in railway station. Combined with the virus infection transmission model, Monte Carlo simulation was used to calculate the total exposure time and the number of high-risk exposed people in the railway station evacuation process. In addition, sensitivity analysis was conducted on the total exposure time and the number of high-risk exposed people under 180 combinations of the number of initial infections, social distance, and the proportion of people wearing masks incorrectly. The results show that with the increase of social distances, the total exposure time and the number of high-risk exposures do not always decrease, but increase in some cases. The presence or absence of obstacles in the evacuation scene has no significant difference in the effects on total exposure time and the number of high-risk exposures. During the evacuation behavior of college students in railway station, choosing the appropriate number of lines can effectively reduce the total exposure time and the number of high-risk exposures. Finally, some policy suggestions are proposed to reduce the risk of virus transmission in the railway station evacuation process, such as choosing dynamic and reasonable social distance and the number of queues, and reducing obstacles.

A frontal air intake may improve the natural ventilation in urban buses

In this report we analyze the air flow across the open windows (natural ventilation) of an urban bus model and the consequent dispersion of aerosols emitted in the passengers area. The methods include computational fluid dynamics simulations and three ways to characterize the dispersion of passive tracers: a continuous concentration-based model, a discrete random model and a parametric scalar based on the so-called mean age of air. We also conducted experiments using a 1:10 scale bus model and [Formula: see text] as a passive tracer to assess the ventilation characteristics. We found that dispersion and expulsion of aerosols is driven by a negative pressure in the standard bus design equipped with lateral windows. Also, the average age of air is 6 minutes while the air flow promotes aerosol accumulation to the front (driver's area). To speed up the expulsion of aerosols and reduce their in-cabin accumulation, we propose a bus bodywork prototype having a frontal air intake. All the numerical models and experiments conducted in this work agreed that the expulsion of aerosols in this novel configuration is significantly increased while the average age of air is reduced to 50 seconds. The average air flow also changes with the presence of frontal air intakes and, as a consequence, the expulsion of aerosols is now driven by a frontal velocity field.

Impact of ionizers on prevention of airborne infection in classroom

Infectious diseases (e.g., coronavirus disease 2019) dramatically impact human life, economy and social development. Exploring the low-cost and energy-saving approaches is essential in removing infectious virus particles from indoors, such as in classrooms. The application of air purification devices, such as negative ion generators (ionizers), gains popularity because of the favorable removal capacity for particles and the low operation cost. However, small and portable ionizers have potential disadvantages in the removal efficiency owing to the limited horizontal diffusion of negative ions. This study aims to investigate the layout strategy (number and location) of ionizers based on the energy-efficient natural ventilation in the classroom to improve removal efficiency (negative ions to particles) and decrease infection risk. Three infected students were considered in the classroom. The simulations of negative ion and particle concentrations were performed and validated by the experiment. Results showed that as the number of ionizers was 4 and 5, the removal performance was largely improved by combining ionizer with natural ventilation. Compared with the scenario without an ionizer, the scenario with 5 ionizers largely increased the average removal efficiency from around 20% to 85% and decreased the average infection risk by 23%. The setup with 5 ionizers placed upstream of the classroom was determined as the optimal layout strategy, particularly when the location and number of the infected students were unknown. This work can provide a guideline for applying ionizers to public buildings when natural ventilation is used.

ELECTRONIC SUPPLEMENTARY MATERIAL ESM

the Appendix is available in the online version of this article at 10.1007/s12273-022-0959-z.

Do the determinants of COVID-19 transmission differ by epidemic wave? Evidence from U.S. counties

This paper uses data from the United States to examine determinants of the spread of COVID-19 during three different epidemic waves. We address how sociodemographic and economic attributes, industry composition, density, crowding in housing, and COVID-19-related variables are associated with the transmission of COVID-19. After controlling for spatial autocorrelation, our findings indicate that the percentage of people in poverty, number of restaurants, and percentage of workers teleworking were associated with the COVID-19 incidence rate during all three waves. Our results also show that dense areas were more vulnerable to the transmission of COVID-19 after the first epidemic wave. Regarding the density of supermarkets, our study elaborates the negative aspects of wholesale retail stores, which likely provide a vulnerable place for virus transmission. Our results suggest that sociodemographic and economic attributes were the determinants of the early phase of the pandemic, while density showed positive association with the transmission during subsequent waves. We provide implications for regions serving as gateway cities with high density and number of population. To add, we further provide evidence that non-pharmaceutical interventions in the early stage may mitigate the virus transmission.

Development of a non-contact mobile screening center for infectious diseases: Effects of ventilation improvement on aerosol transmission prevention

Under the global landscape of the prolonged COVID-19 pandemic, the number of individuals who need to be tested for COVID-19 through screening centers is increasing. However, the risk of viral infection during the screening process remains significant. To limit cross-infection in screening centers, a non-contact mobile screening center (NCMSC) that uses negative pressure booths to improve ventilation and enable safe, fast, and convenient COVID-19 testing is developed. This study investigates aerosol transmission and ventilation control for eliminating cross-infection and for rapid virus removal from the indoor space using numerical analysis and experimental measurements. Computational fluid dynamics (CFD) simulations were used to evaluate the ventilation rate, pressure differential between spaces, and virus particle removal efficiency in NCMSC. We also characterized the airflow dynamics of NCMSC that is currently being piloted using particle image velocimetry (PIV). Moreover, design optimization was performed based on the air change rates and the ratio of supply air (SA) to exhaust air (EA). Three ventilation strategies for preventing viral transmission were tested. Based on the results of this study, standards for the installation and operation of a screening center for infectious diseases are proposed.