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At the end of 2019, COVID-19 outbreaks occurred one after another in countries worldwide. Managing the outbreak efficiently and stably is an essential public health issue facing countries worldwide. In this paper, based on the SEIR model, we propose a SCEIR model that incorporates close contacts (C) and self-protectors (P). Firstly, the epidemic data of China, the USA, and Italy are predicted and compared with the actual data. Secondly, sensitivity analysis of each parameter in the SCEIR model was conducted using Anylogic. The study shows that the SCEIR model established in this paper has a certain validity. The infection rate in contact with E () etc., has positive effects on the basic regeneration number (R0); the self-isolation rate (φ) etc., has a negative effect on the basic regeneration number (R0). Emergency management measures are proposed according to the influencing factors corresponding to the model parameters. These can provide theoretical guidance for developing effective epidemic prevention and control measures in areas where the epidemic has not yet been controlled. It also provides some reference for formulating prevention and control policies for similar epidemics that may occur in the future. [ABSTRACT FROM AUTHOR]

The main consequence of the arrival of COVID-19 was the application of social distancing, which mainly had implications regarding the economies of affected countries. This affected students, whose economy was weakened because of social restrictions. Therefore, private universities that depend strongly on the continuity of students had to face some difficult periods, with an eventual increase in desertions. In this way, the general managers' offices had to find new and consistent directions for correct decision making without reducing the quality of the service. This paper presents a scheme that was applied in a private university in the south of Lima city during the period of 2020–2021. It is shown that the usage of a set of variables and their combinations to make a decision only appears to be relevant if a such decision is free of either external or internal noise. The results exhibit a minimization of 70% in the potential number of desertions in 2021 due to the gained experiences of 2020. [ABSTRACT FROM AUTHOR]

As the main methods of the coronavirus disease (COVID-19) transmission are air and physical contact, actions to mitigate and suppress its spread must be developed in order to change population dynamics and provide efficient control strategies. Here, these actions are described as a simple heuristic framework to establish public policies. Two control systems were studied: the first organized in the form of an algorithm stratified into three levels and the second as a minimization problem similar to optimal control strategies, applied to both social distancing and vaccination. The possible effects of these actions are modeled and applied to an extension of the Susceptible - Infected - Removed (SIR) compartmental model. The control system is developed, which is organized in the form of an algorithm stratified into three levels. These levels intend to represent social distancing strategies implemented by sanitary authorities around the globe, representing stronger or weaker grades of isolation intensity according to the ability of the healthcare system to cope with symptomatic individuals. The algorithm control is applied in a simulation, and the results give evidence of the effectiveness of the procedures adopted against the coronavirus. The model dynamics are analyzed and validated with simulations considering parameters obtained from epidemiological data from Brazil and Uruguay and in a more detailed way for three Brazilian states: São Paulo, Minas Gerais and Rio de Janeiro. The model was validated using cumulative data on cases and deaths. For cases of death, the results were satisfactory, while for case data, the response was reasonable, considering the possibility of adding delays or variations in parameters in the model. In addition, the effective reproduction number was proposed for the cities studied in Brazil, the result being relevant because it has a qualitative behavior similar to that published by official centers. This paper also discusses the implementation and optimization of social distancing and vaccination control strategies, considering different parameters and their effects on reducing the number of cases and deaths. Model simulations present promising results for developing strategies to attack COVID-19 dissemination. [ABSTRACT FROM AUTHOR]

Mass vaccination is proving to be the most effective method of disease control, and several methods have been developed for the operation of mass vaccination clinics to administer vaccines safely and quickly. One such method is known as the hockey hub model, a relatively new method that involves isolating vaccine recipients in individual cubicles for the entire duration of the vaccination process. Healthcare staff move between the cubicles and administer vaccines. This allows for faster vaccine delivery and less recipient contact. In this paper we present a simulation tool which has been created to model the operation of a hockey hub clinic. This tool was developed using AnyLogic and simulates the process of individuals moving through a hockey hub vaccination clinic. To demonstrate this model, we simulate six scenarios comprising three different arrival rates with and without physical distancing. Findings demonstrate that the hockey hub method of vaccination clinic can function at a large capacity with minimal impact on wait times. [ABSTRACT FROM AUTHOR]

This paper presents a control system for indoor safety measures using a Faster R-CNN (Region-based Convolutional Neural Network) architecture. The proposed system aims to ensure the safety of occupants in indoor environments by detecting and recognizing potential safety hazards in real time, such as capacity control, social distancing, or mask use. Using deep learning techniques, the system detects these situations to be controlled, notifying the person in charge of the company if any of these are violated. The proposed system was tested in a real teaching environment at Rey Juan Carlos University, using Raspberry Pi 4 as a hardware platform together with an Intel Neural Stick board and a pair of PiCamera RGB (Red Green Blue) cameras to capture images of the environment and a Faster R-CNN architecture to detect and classify objects within the images. To evaluate the performance of the system, a dataset of indoor images was collected and annotated for object detection and classification. The system was trained using this dataset, and its performance was evaluated based on precision, recall, and F1 score. The results show that the proposed system achieved a high level of accuracy in detecting and classifying potential safety hazards in indoor environments. The proposed system includes an efficiently implemented software infrastructure to be launched on a low-cost hardware platform, which is affordable for any company, regardless of size or revenue, and it has the potential to be integrated into existing safety systems in indoor environments such as hospitals, warehouses, and factories, to provide real-time monitoring and alerts for safety hazards. Future work will focus on enhancing the system's robustness and scalability to larger indoor environments with more complex safety hazards. [ABSTRACT FROM AUTHOR]

Regarding the problem of epidemic outbreak prevention and control, infectious disease dynamics models cannot support urban managers in reducing urban-scale healthcare costs through community-scale control measures, as they usually have difficulty meeting the requirements for simulation at different scales. In this paper, we propose combining contact networks at different spatial scales to study the COVID-19 outbreak in Shanghai from March to July 2022, calculate the initial R t through the number of cases at the beginning of the outbreak, and evaluate the effectiveness of dynamic non-pharmaceutical interventions (NPIs) adopted at different time periods in Shanghai using our proposed approach. In particular, our proposed contact network is a three-layer multi-scale network that is used to distinguish social interactions occurring in areas of different sizes, as well as to distinguish between intensive and non-intensive population contacts. This susceptible–exposure–infection–quarantine–recovery (SEIQR) epidemic model constructed based on a multi-scale network can more effectively assess the feasibility of small-scale control measures, such as assessing community quarantine measures and mobility restrictions at different moments and phases of an epidemic. Our experimental results show that this model can meet the simulation needs at different scales, and our further discussion and analysis show that the spread of the epidemic in Shanghai from March to July 2022 can be successfully controlled by implementing a strict long-term dynamic NPI strategy. [ABSTRACT FROM AUTHOR]

Designing efficient vaccination programs that consider the needs of the population is very relevant to prevent reoccurrence of the COVID-19 pandemic. The government needs to provide vaccination points to give out vaccine doses to the population. In this paper, the authors analyze the location of vaccination points whilst addressing the inhabitants' preferences. Two objectives that prevent crowding of inhabitants are considered. The government aims for the minimum distance between located vaccination points is maximized, and for the number of inhabitants that attend the different vaccination points to be equitable. One of the key aspects of this problem is the assumption that inhabitants freely choose the located vaccination point to go. That decision affects the objectives of the government, since crowding at vaccination points may appear due to the inhabitants' decisions. This problem is modeled as a bi-objective, bi-level program, in which the upper level is associated to the government and the lower level to the inhabitants. To approximate the Pareto front of this problem, a cross-entropy metaheuristic is proposed. The algorithm incorporates criteria to handle two objective functions in a simultaneous manner, and optimally solve the lower-level problem for each government decision. The proposed algorithm is tested over an adapted set of benchmark instances and pertinent analysis of the results is included. An important managerial insight is that locating far vaccination points does not lead us to a more equitable allocation of inhabitants. [ABSTRACT FROM AUTHOR]

Ongoing social restrictions, including social distancing and lockdown, adopted by many countries to inhibit spread of the the COVID-19 epidemic, must attempt to find a trade-off between induced economic damage, healthcare system collapse, and the costs in terms of human lives. Applying and removing restrictions on a system with a given latency as represented by an epidemic outbreak (and formally comparable with mechanical inertia), may create critical instabilities, overshoots, and strong oscillations in the number of infected people around the desirable set-point, defined in a practical way as the maximum number of hospitalizations acceptable by a given healthcare system. A good understanding of the system reaction to any change of the input control variable can be reasonably achieved using a proportional-integral-derivative controller (PID), which is a widely used technique in various physics and technological applications. In this paper, this control theory to is proposed to be applied epidemiology, to understand the reaction of COVID-19 propagation to social restrictions and to reduce epidemic damages through the correct tuning of the containment policy. Regarding the synthesis of this interdisciplinary approach, the extended to the susceptible-infectious-recovered (SIR) model name "SIR-PID" is suggested. [ABSTRACT FROM AUTHOR]

Social distancing measures are proposed as the primary strategy to curb the spread of the COVID-19 pandemic. Therefore, identifying situations where these protocols are violated has implications for curtailing the spread of the disease and promoting a sustainable lifestyle. This paper proposes a novel computer vision-based system to analyze CCTV footage to provide a threat level assessment of COVID-19 spread. The system strives to holistically interpret the information in CCTV footage spanning multiple frames to recognize instances of various violations of social distancing protocols, across time and space, as well as identification of group behaviors. This functionality is achieved primarily by utilizing a temporal graph-based structure to represent the information of the CCTV footage and a strategy to holistically interpret the graph and quantify the threat level of the given scene. The individual components are evaluated in a range of scenarios, and the complete system is tested against human expert opinion. The results reflect the dependence of the threat level on people, their physical proximity, interactions, protective clothing, and group dynamics, with a system performance of 76% accuracy. [ABSTRACT FROM AUTHOR]

The COVID-19 spread abruptly changed the fruition of indoor environments, where necessary adaptive measures have since been implemented. Buildings open to the public were suddenly equipped with physical devices aiming to encourage users' appropriate behaviors, such as hand sanitizing, social distancing, and temperature monitoring. Through a twofold architectural-psychological perspective, the paper presents a research aiming to understand how users perceive these devices in the Italian context and to identify the design features that could improve their effectiveness in enhancing individuals' awareness. With an interdisciplinary approach, four methods were adopted: observational field surveys, background and normative framework analysis, survey research through an online questionnaire, and case studies survey research. The results confirm the overall effectiveness of the implemented anti-COVID strategies, their suitability in encouraging individuals' appropriate behaviors, and the importance of regulating the users' flow indoors. The research allowed defining the devices (hand-sanitizing devices and temperature-measurement instruments) and wayfinding systems more suited to be included in the prevention strategy and identified their more appropriate design features in relation to the users' feedback. Operational suggestions are presented as well. The adopted experimental approach can be useful in supporting decision making in managing of the built environment in both the current and future contexts. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic of 2020–2022 has revealed the vulnerability of modern society to a highly contagious airborne virus. Many spaces in the urban and built environment designed during the late twentieth and early twenty-first century are ill-suited to maintain the level of social distancing required to reduce the probability of virus transmission. Enclosed spaces—in particular, communal circulation spaces such as corridors, elevators and lobbies—have proven loci of transmission, together with circulating reticulated air and lack of proper ventilation. While urban planning needs to incorporate the lessons learnt during COVID-19 in order to future-proof our communities through the provision of well-designed greenspaces, the main burden will fall on architects, who will play an instrumental role in designing buildings that are fit-for purpose. This conceptual paper reviews the status quo and discusses a number of strategies to future-proof human habitation for the inevitable next pandemic. [ABSTRACT FROM AUTHOR]

Occupancy density is a dynamic measurement that reveals the relationship between the floor area and occupant count, usually in a room or building. The research presented in this paper probes further into the relationship between the physical properties of space and occupants' activity, to expand the understanding of occupancy density. The presented outcome is an evidence-based technique for determining room and activity-specific occupancy density limits that can support the design and be integrated into the design process. In this study, occupant information, namely, positioning, is simulated in the spatial context, including room dimensions and furniture layout. Controllable distancing variables, such as those globally introduced in response to the COVID-19 pandemic to prevent the spread of infectious diseases in indoor environments, are used to assess occupancy density thresholds. [ABSTRACT FROM AUTHOR]

The transmission rate of COVID-19 varies over time. There are many reasons underlying this mechanism, such as seasonal changes, lockdowns, social distancing, and wearing face masks. Hence, it is very difficult to directly measure the transmission rate. The main task of the present paper was to identify the variable transmission rate ( β 1 ) for a SIR-like model. For this, we first propose a new compartmental forced SEYNHRV-S differential model. We then drive the nonlinear differential equation and present the finite difference technique to obtain the time-dependent transmission rate directly from COVID-19 data. Following this, we show that the transmission rate can be represented as a linear combination of radial kernels, where several forms of radial kernels are explored. The proposed model is flexible and general, so it can be adapted to monitor various epidemic scenarios in various countries. Hence, the model may be of interest for policymakers as a tool to evaluate different possible future scenarios. Numerical simulations are presented to validate the prediction of our SEYNHRV and forced SEYNHRV-S models, where the data from confirmed COVID-19 cases reported by the Ministry of Health in Saudi Arabia were used. These confirmed cases show the second wave of the infected population in Saudi Arabia. By using the COVID-19 data, we show that our model (forced SEYNHRV-S) is able to predict the second wave of infection in the population in Saudi Arabia. It is well known that COVID-19 epidemic data cannot be accurately represented by any compartmental approach with constant parameters, and this is also true for our SEYNHRV model. [ABSTRACT FROM AUTHOR]

The coronavirus disease (COVID-19) has exposed the unpreparedness of governments in their capacities to prevent, detect, and respond to emerging infectious diseases. Many healthcare systems have been overburdened and the coordinated efforts in different countries have focused on containment and mitigation, with varying degrees of success. A delay in the detection of and response to infectious diseases can lead to the overburdening of already challenged health systems. Containment strategies, such as social distancing, contact tracing, quarantining of exposed individuals and lockdowns, can help control the spread of the infection in communities. Still, long-term solutions should be sought to counter future outbreaks. In this paper, we focus on Zimbabwe to identify and discuss public health strategies that can result in an effective response to future infectious disease outbreaks. We consider potential solutions to facilitate early detection, control, and mitigation of any similar emerging infectious disease. We argue that sustained financial support in public health infrastructure, both locally and nationally, integrated surveillance response systems, and improved communication and research within and across public and private sectors can be instrumental in limiting the damage caused by future outbreaks. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has negatively affected aspects of human life and various sectors, especially the health sector. These conditions led to the creation of new patterns of life that people have had to deal with to reduce the spread of the epidemic by committing to social distancing, among others. Therefore, governments and technological organizations had to take advantage of technological developments in the current era to overcome these challenges that were created by these conditions. In this paper, we will discuss the role of the blockchain in combating the COVID-19 crisis. Then we will review the recently recorded blockchain-based research proposals to control the COVID-19 pandemic. Finally, we will highlight the challenges of using blockchain to combat the COVID-19 pandemic and find solutions to mitigate these challenges. [ABSTRACT FROM AUTHOR]

Random outbreaks of infectious diseases in the past have left a persistent impact on societies. Currently, COVID-19 is spreading worldwide and consequently risking human lives. In this regard, maintaining physical distance has turned into an essential precautionary measure to curb the spread of the virus. In this paper, we propose an autonomous monitoring system that is able to enforce physical distancing rules in large areas round the clock without human intervention. We present a novel system to automatically detect groups of individuals who do not comply with physical distancing constraints, i.e., maintaining a distance of 1 m, by tracking them within large areas to re-identify them in case of repetitive non-compliance and enforcing physical distancing. We used a distributed network of multiple CCTV cameras mounted to the walls of buildings for the detection, tracking and re-identification of non-compliant groups. Furthermore, we used multiple self-docking autonomous robots with collision-free navigation to enforce physical distancing constraints by sending alert messages to those persons who are not adhering to physical distancing constraints. We conducted 28 experiments that included 15 participants in different scenarios to evaluate and highlight the performance and significance of the present system. The presented system is capable of re-identifying repetitive violations of physical distancing constraints by a non-compliant group, with high accuracy in terms of detection, tracking and localization through a set of coordinated CCTV cameras. Autonomous robots in the present system are capable of attending to non-compliant groups in multiple regions of a large area and encouraging them to comply with the constraints. [ABSTRACT FROM AUTHOR]

All face-to-face studies were affected by the COVID-19 pandemic, as they could not be run in person due to rules and guidance linked to social distancing which were in force during the outbreak. Finding and testing an available COVID-secure approach for both participants and researchers was important as was the need to continue conducting such studies during this critical time. At present, the extant literature indicates a clear gap in research that elucidates how to carry out a Q methodology study online, step by step. This paper describes an option for online Q methodology using an approach that simulates all of the steps performed in a face-to-face setting using an open-source software known as Easy-HtmlQ. Using a case study in telemedicine adoption as illustration, this paper also considers the perspective of both research participants and Q methodology researchers via semi-structured interviews. Using Easy-HtmlQ V1.1 in online Q methodology studies appears to be an affordable, practical and user-friendly solution. Some of the benefits associated with running Q methodology studies online were the decreased costs, enabling the recruitment of wider number of participants, providing a COVID-19-secure environment and offering convenience to both participants and researchers during the research process. The findings of this study may contribute to increasing the number of online Q methodology studies in the future, as it has succeeded in offering a feasible approach for Q methodology researchers. [ABSTRACT FROM AUTHOR]

While there are increasing concerns on COVID-19 situation in Democratic People's Republic of Korea (DPRK, or North Korea), little is known about North Korea's health system function for Non-Communicable Diseases. Given the scarcity of available evidence, a scoping review was conducted in peer review articles from MEDLINE, SCOPUS, and Web of Science, and policy literatures from Rodongshinmun, state-run media in North Korea to analyze the North Korea health system and COVID-19 pandemic. The transition to a market economy is expected to deepen the gap between the rich and the poor over access to health care, causing a new type of health inequality in North Korea. COVID-19 lockdown intensified the DPRK's economic predicament exacerbating shortage of health financing on non-communicable diseases. The case study of mixed evidence from scoping review indicates that NCDs prevention and management are not functional in the transitioning health system under chronic economic crisis and isolation. This study indicates that NCDs prevention and management are not functional in the transitioning health system under chronic economic crisis and isolation. The destabilized markets under COVID-19 lockdown intensified the DPRK's economic predicament and exacerbated the chronic shortage of health financing especially to NCDs. [ABSTRACT FROM AUTHOR]

Background: The mental health of Black youth during the COVID-19 pandemic is potentially influenced by various systemic factors, including racism, socioeconomic disparities, and access to culturally sensitive mental health support. Understanding these influences is essential for developing effective interventions to mitigate mental health disparities. Methods: Our project used a community-based participatory (CBP) research design with an intersectional theoretical perspective. An advisory committee consisting of fourteen Black youth supported all aspects of our project. The research team consisted of experienced Black researchers who also trained six Black youths as research assistants and co-researchers. The co-researchers conducted individual interviews, contributed to data analysis, and mobilized knowledge. Participants were recruited through the advisory committee members and networks of Black youth co-researchers and sent an email invitation to Black community organizations. Forty-eight Black identified were interviewed between the ages of 16 and 30 in Canada. The data was analyzed thematically. We kept a reflexive note throughout all aspects of the project. Results: Participants reported significant challenges with online schooling, including a lack of support and access to resources. Lockdowns exacerbated stress, particularly for those living in toxic living/home environments. Financial burdens, such as food insecurity and precarious employment, were prevalent and exacerbated mental health challenges. Additionally, experiences of anti-Black racism and police brutality during the pandemic heightened stress and anxiety among participants. Conclusions: The findings underscore the complex interplay of systemic factors in shaping the mental health of Black youth during the COVID-19 pandemic. Addressing these disparities requires targeted interventions that address structural inequities and provide culturally competent support to mitigate the impact on mental well-being. [ABSTRACT FROM AUTHOR]

This study examines stock market response (SMR) to the Japanese tourism industry (TI) after the government's announcement of travel subsidies (TRSs) during the COVID-19 pandemic in 2020, using a sample comprising 80 listed Japanese firms in the TI and an event study method (ESM) to determine the impact of government policy responses (GPRs) to the pandemic. This study found that investors in the TI reacted positively to the announcement of subsidies; this positive effect persisted for 50 trading days after the announcement but was weaker for transportation firms. The results suggest that TRSs are important for the TI, with a stronger link to travel-related firms, such as airlines and travel agencies, hotels, and amusement services. However, investors in the TI reacted negatively to policies that directly addressed the pandemic, such as social distance policies (SDPs). These results are robustly confirmed when we measure abnormal returns by using a three-factor model. The results offer useful insights for policymakers and practitioners aiming to mitigate economic loss from disasters such as the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has made the world face a whole new reality, and the measures taken for slowing the spread of the disease included social distancing, as well as national, regional, or local lockdowns and restrictions regarding the movement of people. For such an important sector of the Romanian economy as the construction industry, the restrictive measures imposed by the authorities led to postponing, delaying, or stopping the current projects. This paper aims at investigating the main effects of the COVID-19 pandemic on the Romanian construction sector, as well as the role of the digitalization process in mitigating the negative impact on the companies activating in this domain, in the context of the current levels of use of information technologies. The research involved the use of qualitative (exploratory in-depth interviews) and quantitative (online surveys applied to members of the local building sector, such as engineers, architects, contractors, and managers) data collection methods, respectively descriptive statistics for data categorization and interpretation. In general, the Romanian construction sector was only slightly affected, with the most significant negative effects being the delays and cost overrun due to safety and health issues. This paper is one of the first studies to explore and assess the effects of COVID-19 on the Romanian construction industry and the role of digitalization in reducing the pandemic impact. The main findings reveal that the companies in this industry that have a high digitalization degree overpassed in a smoother manner the current pandemic situation. [ABSTRACT FROM AUTHOR]

The COVID-19 Pandemic has punched a devastating blow on the majority of the world's population. Millions of people have been infected while hundreds of thousands have died of the disease throwing many families into mourning and other psychological torments. It has also crippled the economy of many countries of the world leading to job losses, high inflation, and dwindling Gross Domestic Product (GDP). The duo of social distancing and contact tracing are the major technological-based non-pharmaceutical public health intervention strategies adopted for combating the dreaded disease. These technologies have been deployed by different countries around the world to achieve effective and efficient means of maintaining appropriate distance and tracking the transmission pattern of the diseases or identifying those at high risk of infecting others. This paper aims to synthesize the research efforts on contact tracing and social distancing to minimize the spread of COVID-19. The paper critically and comprehensively reviews contact tracing technologies, protocols, and mobile applications (apps) that were recently developed and deployed against the coronavirus disease. Furthermore, the paper discusses social distancing technologies, appropriate methods to maintain distances, regulations, isolation/quarantine, and interaction strategies. In addition, the paper highlights different security/privacy vulnerabilities identified in contact tracing and social distancing technologies and solutions against these vulnerabilities. We also x-rayed the strengths and weaknesses of the various technologies concerning their application in contact tracing and social distancing. Finally, the paper proposed insightful recommendations and open research directions in contact tracing and social distancing that could assist researchers, developers, and governments in implementing new technological methods to combat the menace of COVID-19. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic resulted in stay-at-home policies and other social distancing behaviors in the United States in spring of 2020. This paper examines the impact that these actions had on emissions and expected health effects through reduced personal vehicle travel and electricity consumption. Using daily cell phone mobility data for each U.S. county, we find that vehicle travel dropped about 40% by mid-April across the nation. States that imposed stay-at-home policies before March 28 decreased travel slightly more than other states, but travel in all states decreased significantly. Using data on hourly electricity consumption by electricity region (e.g., balancing authority), we find that electricity consumption fell about 6% on average by mid-April with substantial heterogeneity. Given these decreases in travel and electricity use, we estimate the county-level expected improvements in air quality, and, therefore, expected declines in mortality. Overall, we estimate that, for a month of social distancing, the expected premature deaths due to air pollution from personal vehicle travel and electricity consumption declined by approximately 360 deaths, or about 25% of the baseline 1500 deaths. In addition, we estimate that CO2 emissions from these sources fell by 46 million metric tons (a reduction of approximately 19%) over the same time frame. [ABSTRACT FROM AUTHOR]

This work is a response to the appeal of various international health organizations and the Automatic Control Community for collaboration in addressing Coronavirus/COVID-19 challenges during the initial stages of the pandemic. Specifically, this study presents scientific evidence supporting the efficacy of three primary non-pharmacological strategies for pandemic mitigation. We propose a control system to aid in formulating a public decision policy aimed at managing the spread of COVID-19 caused by the SARS-CoV-2 virus, commonly known as coronavirus. The primary objective is to prevent overwhelming healthcare systems by averting the saturation of intensive care units (ICUs). In the context of COVID-19, understanding the peak infection rate and its time delay is crucial for preparing healthcare infrastructure and ensuring an adequate supply of intensive care units equipped with automatic ventilators. While it is widely recognized that public policies encompassing confinement and social distancing can flatten the epidemiological curve and provide time to bolster healthcare resources, there is a dearth of studies examining this pivotal issue from the perspective of control system theory. In this study, we introduce a control system founded on three prevailing non-pharmacological tools for epidemic and pandemic mitigation: social distancing, confinement, and population-wide testing and isolation in regions experiencing community transmission. Our analysis and control system design rely on the susceptible-exposed–infected–recovered–deceased (SEIRD) mathematical model, which describes the temporal dynamics of a pandemic, tailored in this research to account for the temporal and spatial characteristics of SARS-CoV-2 behavior. This model incorporates the influence of conducting tests with subsequent population isolation. An On–off control strategy is analyzed, and a proportional–integral–derivative (PID) controller is proposed to generate a sequence of public policy decisions. The proposed control system employs the required number of critical beds and ICUs as feedback signals and compares these with the available bed capacity to generate an error signal, which is utilized as input for the PID controller. The control actions outlined involve five phases of "Social Distancing and Confinement" (SD&C) to be implemented by governmental authorities. Consequently, the control system generates a policy sequence for SD&C, with applications occurring on a weekly or biweekly basis. The simulation results underscore the favorable impact of these three mitigation strategies against the coronavirus, illustrating their efficacy in controlling the outbreak and thereby mitigating the risk of healthcare system collapse. [ABSTRACT FROM AUTHOR]

As an uninterrupted water supply is crucial for water, sanitation and hygiene (WASH) practices, a water shortage exacerbates the propagation of communicable and often life-threatening diseases. Melaka, a water-stressed state in Malaysia, had to impose a two-month water rationing exercise amid the COVID-19 pandemic in early 2020. Taking advantage of these concurrent occurrences, this study thus examines the impact of water rationing on the state's residents' WASH practices during that time. In particular, it seeks to examine whether there has been any shift in their WASH performance during the periods of pandemic and rationing. It also analyzes the effect of external water collection activity during rationing on the residents' social-distancing performance. This study collects its data from 120 respondents; the data are tested using non-parametric tests and frequency analyses. The results demonstrate that most of the respondents had a significant negative perception of how the rationing affected their WASH practices during the pandemic. Yet even with the ongoing rationing, their WASH levels of performance had recorded significant growth. They also viewed external water collection activities as detrimental to their social distancing performance. [ABSTRACT FROM AUTHOR]

Coronavirus 2019 (COVID-19) has posed a serious threat to the lives and health of the majority of people worldwide. Since the early days of the outbreak, South Korea's government and citizens have made persistent efforts to provide effective prevention against further spread of the disease. In particular, the participation of individual citizens in complying with the necessary code of conduct to prevent spread of the infection, through measures such as social distancing and mask wearing, is as instrumental as the geographical tracking of the trajectory of the infected. In this paper, we propose an activity recognition method based on a wristband equipped with an IR array and inertial measurement unit (IMU) to detect individual compliance with codes of personal hygiene management, such as mask wearing, which are recommended to prevent the spread of infectious diseases. The results of activity recognition were comparatively analyzed by applying conventional machine learning algorithms and convolutional neural networks (CNNs) to the IMU time series and IR array thermal images collected from 25 subjects. When CNN and 24 × 32 thermal images were used, 97.8% accuracy was achieved (best performance), and when 6 × 8 low-resolution thermal images were used, similar performance with 97.1% accuracy was obtained. In the case of using IMU, the performance of activity recognition was lower than that obtained with the IR array, but an accuracy of 93% was achieved even in the case of applying machine learning algorithms, indicating that it is more suitable for wearable devices with low computational capability. [ABSTRACT FROM AUTHOR]

This study employs surface and remote sensing data jointly with deep learning techniques to examine the influence of vehicular traffic in the seasonal patterns of CO, NO2, PM2.5, and PM10 concentrations in the São Paulo municipality, as the period of physical distancing (March 2020 to December 2021), due to SARS-CoV-2 pandemic and the resumption of activities, made it possible to observe significant variations in the flow of vehicles in the city of São Paulo. Firstly, an analysis of the planetary boundary layer height and ventilation coefficient was performed to identify the seasons' patterns of pollution dispersion. Then, the variations (from 2018 to 2021) of the seasonal average values of air temperature, relative humidity, precipitation, and thermal inversion occurrence/position were compared to identify possible variations in the patterns of such variables that would justify (or deny) the occurrence of more favorable conditions for pollutants dispersion. However, no significant variations were found. Finally, the seasonal average concentrations of the previously mentioned pollutants were compared from 2018 to 2021, and the daily concentrations observed during the pandemic period were compared with a model based on an artificial neural network. Regarding the concentration of pollutants, the primarily sourced from vehicular traffic (CO and NO2) exhibited substantial variations, demonstrating an inverse relationship with the rate of social distancing. In addition, the measured concentrations deviated from the predictive model during periods of significant social isolation. Conversely, pollutants that were not primarily linked to vehicular sources (PM2.5 and PM10) exhibited minimal variation from 2018 to 2021; thus, their measured concentration remained consistent with the prediction model. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has led to a rise in confirmed cases, making epidemiological studies crucial for identifying the source of transmission and developing effective treatment methods. We conducted a study on the clinical characteristics of patients with asymptomatic and mild symptoms of COVID-19 at a rescue hospital in Indonesia. Methods: This is an epidemiological study involving 6102 patients who were admitted to the Indrapura forefront hospital in Surabaya from May 2020 to February 2021. We described demographic data, clinical signs and symptoms, laboratory data, therapy, and clinical outcomes. Results: A total of 6102 patients were involved in this study, with 3664 (60.04%) being male and 2438 (39.95%) being female. The age range of 21–30 years was the most prevalent, accounting for 31.1% (1898 patients). The population had 1476 patients (24.2%) with comorbid conditions. The most prevalent comorbidity observed among these patients was hypertension, affecting 1015 individuals (16.6%). Out of the total 6006 patients observed, 40.7% (n = 2486) were asymptomatic, 54.6% (n = 3329) had mild symptoms, and 3.1% (n = 191) had moderate symptoms. All patients were administered supportive therapy without the use of antiviral medication. Out of the 6102 patients included in the study, 5923 patients (97.1%) achieved a cure, 36 patients (0.6%) are currently undergoing treatment, 142 patients (2.3%) were referred for desaturation indications (SpO2 < 94%), and one patient died due to a suspected cardiovascular event. Out of the total number of patients, 74.5% (4529 patients) had an average length of stay (LOS) of less than 10 days, while 25.6% (1563 patients) had an average length of stay of more than 10 days. Conclusion: The clinical presentation of asymptomatic and mild COVID-19 patients at a rescue hospital varies significantly based on the age and sex of patients. Cough and hyposmia are commonly observed symptoms. Supportive therapy is effective, and strict implementation of social distancing is crucial in preventing the spread of this disease from individuals who are asymptomatic or have mild symptoms. [ABSTRACT FROM AUTHOR]

Social distancing is crucial to restrain the spread of diseases such as COVID-19, but complete adherence to safety guidelines is not guaranteed. Monitoring social distancing through mass surveillance is paramount to develop appropriate mitigation plans and exit strategies. Nevertheless, it is a labor-intensive task that is prone to human error and tainted with plausible breaches of privacy. This paper presents a privacy-preserving adaptive social distance estimation and crowd monitoring solution for camera surveillance systems. We develop a novel person localization strategy through pose estimation, build a privacy-preserving adaptive smoothing and tracking model to mitigate occlusions and noisy/missing measurements, compute inter-personal distances in the real-world coordinates, detect social distance infractions, and identify overcrowded regions in a scene. Performance evaluation is carried out by testing the system's ability in person detection, localization, density estimation, anomaly recognition, and high-risk areas identification. We compare the proposed system to the latest techniques and examine the performance gain delivered by the localization and smoothing/tracking algorithms. Experimental results indicate a considerable improvement, across different metrics, when utilizing the developed system. In addition, they show its potential and functionality for applications other than social distancing. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of the effects of the COVID-19 pandemic on the air quality of the Metropolitan Region of São Paulo (MRSP). The effects of social distancing are still recent in the society; however, it was possible to observe patterns of environmental changes in places that had adhered transportation measures to combat the spread of the coronavirus. Thus, from the analysis of the traffic volumes made on some of the main access highways to the MRSP, as well as the monitoring of the levels of fine particulate matter (PM2.5), carbon monoxide (CO) and nitrogen dioxide (NO2), directly linked to atmospheric emissions from motor vehicles–which make up about 95% of air polluting agents in the region in different locations–we showed relationships between the improvement in air quality and the decrease in vehicles that access the MRSP. To improve the data analysis, therefore, the isolation index parameter was evaluated to provide daily information on the percentage of citizens in each municipality of the state that was effectively practicing social distancing. The intersection of these groups of data determined that the COVID-19 pandemic reduced the volume of vehicles on the highways by up to 50% of what it was in 2019, with the subsequent recovery of the traffic volume, even surpassing the values from the baseline year. Thus, the isolation index showed a decline of up to 20% between its implementation in March 2020 and December 2020. These data and the way they varied during 2020 allowed to observe an improvement of up to 50% in analyzed periods of the pollutants PM2.5, CO and NO2 in the MRSP. The main contribution of this study, alongside the synergistic use of data from different sources, was to perform traffic flow analysis separately for light and heavy duty vehicles (LDVs and HDVs). The relationships between traffic volume patterns and COVID-19 pollution were analyzed based on time series. [ABSTRACT FROM AUTHOR]

The most effective methods of preventing COVID-19 infection include maintaining physical distancing and wearing a face mask while in close contact with people in public places. However, densely populated areas have a greater incidence of COVID-19 dissemination, which is caused by people who do not comply with standard operating procedures (SOPs). This paper presents a prototype called PADDIE-C19 (Physical Distancing Device with Edge Computing for COVID-19) to implement the physical distancing monitoring based on a low-cost edge computing device. The PADDIE-C19 provides real-time results and responses, as well as notifications and warnings to anyone who violates the 1-m physical distance rule. In addition, PADDIE-C19 includes temperature screening using an MLX90614 thermometer and ultrasonic sensors to restrict the number of people on specified premises. The Neural Network Processor (KPU) in Grove Artificial Intelligence Hardware Attached on Top (AI HAT), an edge computing unit, is used to accelerate the neural network model on person detection and achieve up to 18 frames per second (FPS). The results show that the accuracy of person detection with Grove AI HAT could achieve 74.65% and the average absolute error between measured and actual physical distance is 8.95 cm. Furthermore, the accuracy of the MLX90614 thermometer is guaranteed to have less than 0.5 °C value difference from the more common Fluke 59 thermometer. Experimental results also proved that when cloud computing is compared to edge computing, the Grove AI HAT achieves the average performance of 18 FPS for a person detector (kmodel) with an average 56 ms execution time in different networks, regardless of the network connection type or speed. [ABSTRACT FROM AUTHOR]

For public safety and physical security, currently more than a billion closed-circuit television (CCTV) cameras are in use around the world. Proliferation of artificial intelligence (AI) and machine/deep learning (M/DL) technologies have gained significant applications including crowd surveillance. The state-of-the-art distance and area estimation algorithms either need multiple cameras or a reference object as a ground truth. It is an open question to obtain an estimation using a single camera without a scale reference. In this paper, we propose a novel solution called E-SEC, which estimates interpersonal distance between a pair of dynamic human objects, area occupied by a dynamic crowd, and density using a single edge camera. The E-SEC framework comprises edge CCTV cameras responsible for capturing a crowd on video frames leveraging a customized YOLOv3 model for human detection. E-SEC contributes an interpersonal distance estimation algorithm vital for monitoring the social distancing of a crowd, and an area estimation algorithm for dynamically determining an area occupied by a crowd with changing size and position. A unified output module generates the crowd size, interpersonal distances, social distancing violations, area, and density per every frame. Experimental results validate the accuracy and efficiency of E-SEC with a range of different video datasets. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic is spreading across the globe, and it could take years for society to fully recover. Personal protective equipment (PPE), various hygiene measures, and social distancing have been implemented to reduce "human to human" contact, which is an essential part of outbreak prevention. The pressure of the pandemic combined with decreased communication and social contact have taken a toll on the mental health of many individuals, especially with respect to anxiety and depression. Effective use of robots and technology as a substitute for—or in coordination with—traditional medicine could play a valuable role in reducing psychological distress now more than ever. This paper summarizes the results of a comprehensive review of clinical research on PARO, a therapeutic seal robot, which has been used extensively as a biofeedback medical device and socially assistive robot in the field of mental health. PARO has proven to be an effective and economical non-pharmacological intervention method for both mental and physical well-being during the COVID-19 pandemic. Utilization of PARO during these times has provided more data for consideration and has helped in mitigating the negative stigma surrounding using robots in therapeutic settings. [ABSTRACT FROM AUTHOR]

Coronavirus has affected millions of people worldwide, with the rate of infected people still increasing. The virus is transmitted between people through direct, indirect, or close contact with infected people. To help prevent the social transmission of COVID-19, this paper presents a new smart social distance system that allows individuals to keep social distances between others in indoor and outdoor environments, avoiding exposure to COVID-19 and slowing its spread locally and across the country. The proposed smart monitoring system consists of a new smart wearable prototype of a compact and low-cost electronic device, based on human detection and proximity distance functions, to estimate the social distance between people and issue a notification when the social distance is less than a predefined threshold value. The developed social system has been validated through several experiments, and achieved a high acceptance rate (96.1%) and low localization error (<6 m). [ABSTRACT FROM AUTHOR]

Public health guidelines implemented during the COVID-19 pandemic have changed the way many people practice religion. In the realm of Islam, practices from the margins—attending online mosques and prayer groups, or praying alone—suddenly became commonplace. This paper addresses the question: What religious processes have become more evident among pluralist Muslim women during the pandemic? Based on 34 open-ended online surveys completed by pluralist Muslim women living chiefly in the USA and the UK, our analysis evidences the existence of four narratives that reflect fluctuations in the intensity and type of religious practice. The first and most prominent narrative in our dataset conveys enthusiastic embrace of social-distanced practices; the second describes a profound sense of aberration impossible to overcome in spiritual ways. The third highlights that for some Muslims, the pandemic brought no changes, as they continued to be isolated from their communities. The fourth is focused on an affirmation of a "remote" sociality experienced online. While some respondents acknowledge the increased individuation in their religious practice, they also find fulfilment in collective, if transformed, sociality. The changes in social interaction have led to a re-evaluation of salient aspects of their religious identity or, alternatively, highlighted longstanding modalities of exclusion. [ABSTRACT FROM AUTHOR]

The unprecedented COVID-19 pandemic is showing dramatic impact across the world. Public health authorities attempt to fight against the virus while maintaining economic activity. In the face of the uncertainty derived from the virus, all the countries have adopted non-pharmaceutical interventions for limiting the mobility and maintaining social distancing. In order to support these interventions, some health authorities and governments have opted for sharing very fine-grained data related with the impact of the virus in their territories. Geographical science is playing a major role in terms of understanding how the virus spreads across regions. Location of cases allows identifying the spatial patterns traced by the virus. Understanding these patterns makes controlling the virus spread feasible, minimizes its impact in vulnerable regions, anticipates potential outbreaks, or elaborates predictive risk maps. The application of geospatial analysis to fine-grained data must be urgently adopted for optimal decision making in real and near-real time. However, some aspects related to process and map sensitive health data in emergency cases have not yet been sufficiently explored. Among them include concerns about how these datasets with sensitive information must be shown depending on aspects related to data aggregation, scaling, privacy issues, or the need to know in advance the particularities of the study area. In this paper, we introduce our experience in mapping fine-grained data related to the incidence of the COVID-19 during the first wave in the region of Galicia (NW Spain), and after that we discuss the mentioned aspects. [ABSTRACT FROM AUTHOR]

In response to insufficient understanding of the determinants of change in e-shopping behaviors during the COVID-19 pandemic in developing countries, this paper used the data from 355 respondents, collected in Hanoi during the social distancing period (April 2020), to explore the factors associated with shopping online more frequently (i.e., representing the growth of e-shopping) for five product types (food, medical products, clothing, electronics, and books) in Hanoi, Vietnam. The results showed that nearly 80% of the respondents engaged in e-shopping more frequently than they did before the outbreak of COVID-19. As regards shopping online more frequently in general (i.e., for at least one product type), females were more likely to do so. In-store shopping enjoyment and a decrease in income were a facilitator and a deterrent, respectively. Regarding specific product types, completely working from home had a positive association with more frequent e-purchasing for electronics. Fear of disease encouraged higher frequencies of e-shopping for food and medical products. Notably, the shortage of physical supply was not a determinant of buying any product type online more frequently. As for the implications of our findings, supporting and encouraging low-income shoppers, older persons, and females to engage in e-shopping is necessary to limit the detrimental effects of the pandemic on their lives. The growth of internet purchasing expresses a need to manage the development of urban delivery services, to limit the uncontrolled proliferation of motorcycles. E-shopping requires delivery to complete the online-to-offline process; therefore, protecting the health of delivery drivers to ensure the safety of the whole online shopping process would be necessary. [ABSTRACT FROM AUTHOR]

Populist leaders have consistently rejected evidence-based policies in responding to the ongoing COVID-19 pandemic. They acted later and with less intensity than non-populists in implementing public health measures such as physical distancing, lockdowns, and developing public health data sets. We describe the responses of ten large countries with populist leadership at the onset of the pandemic (Brazil, Hungary, India, Mexico, the Philippines, Poland, Russia, Turkey, the United Kingdom, and the United States). Together, these countries account for a disproportionately large number of cases and deaths associated with COVID-19 relative to their population. We categorize the policy responses into two types: (1) slow and ineffective, and (2) strict and illiberal. We conclude that while not all countries that responded poorly to the pandemic were led by populists, no countries with populist leadership performed well in either applying public health measures or achieving desirable health outcomes. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has affected not only public health but also people's daily lives. Among various strategies to prevent infection, mask wearing and vaccination are considered to be the most effective methods; however, they may affect the comfortable interpersonal distance (IPD) for social interactions. In 2023, although the COVID-19 epidemic is considered to be similar to influenza, the public health sector of Taiwan still plans to give each person at least one dose per year, and even two does for special cases such as the elderly; and more than 90% of Taiwanese are still accustomed to wearing masks in public areas. Compared with mask wearing, studies examining the effects of vaccination on IPD are lacking. Therefore, an online survey was conducted in this study to collect the IPD data of 50 male and 50 female participants to elucidate the effects of mask wearing, vaccination, and target sex variables on IPD. The results showed that all variables significantly affected IPD (all p < 0.001). The effect of masks on IPD (49.1 cm) was slightly greater than that of vaccination (43.5 cm). The IPDs reported for wearing and not wearing masks were 145.7 and 194.8 cm, respectively, and those for vaccinated and unvaccinated were 148.5 and 192.0 cm, respectively. Regardless of participant sex, the IPDs for the female targets were significantly shorter than those for the male targets, which was consistent with the results of previous studies. Although mask wearing and vaccination are functionally different in nature, the findings indicate that the effects of both on IPD are nearly identical, jointly shortening IPD to approximately 93 cm. This implies that not only masks but also vaccination could lead to the shortening of IPD and may cause challenges in the prevention and control of COVID-19 transmission. [ABSTRACT FROM AUTHOR]

Meeting the mental health needs of perinatal women during the COVID-19 pandemic is a serious concern. This scoping review looks at how to prevent, mitigate or treat the mental health problems faced by women during a pandemic, and lays out suggestions for further research. Interventions for women with pre-existing mental health problems or health problems that develop during the perinatal period are included. The literature in English published in 2020–2021 is explored. Hand searches were conducted in PubMed and PsychINFO using the terms COVID-19, perinatal mental health and review. A total of 13 systematic and scoping reviews and meta-analyses were included. This scoping review shows that every woman should be assessed for mental health issues at every stage of her pregnancy and postpartum, with particular attention to women with a history of mental health problems. In the COVID-19 era, efforts should be focused on reducing the magnitude of stress and a perceived sense of lack of control experienced by perinatal women. Helpful instructions for women with perinatal mental health problems include mindfulness, distress tolerance skills, relaxation exercises, and interpersonal relationship building skills. Further longitudinal multicenter cohort studies could help improve the current knowledge. Promoting perinatal resilience and fostering positive coping skills, mitigating perinatal mental health problems, screening all prenatal and postpartum women for affective disorders, and using telehealth services appear to be indispensable resources. In future, governments and research agencies will need to pay greater attention to the trade-offs of reducing the spread of the virus through lockdowns, physical distancing, and quarantine measures and developing policies to mitigate the mental health impact on perinatal women. [ABSTRACT FROM AUTHOR]

The risk of COVID-19 infection from virulent aerosols is particularly high indoors. This is especially true for classrooms, which often do not have pre-installed ventilation and are occupied by a large number of students at the same time. It has been found that precautionary measures, such as the use of air purifiers (AP), physical distancing, and the wearing of masks, can reduce the risk of infection. To quantify the actual effect of precautions, it is not possible in experimental studies to expose subjects to virulent aerosols. Therefore, in this study, we develop a computational fluid dynamics (CFD) model to evaluate the impact of applying the aforementioned precautions in classrooms on reducing aerosol concentration and potential exposure in the presence of index or infected patients. A CFD-coupled Wells–Riley model is used to quantify the infection probability (IP) in the presence of index patients. Different cases are simulated by varying the occupancy of the room (half/full), the volumetric flow rate of the AP, two different locations of the AP, and the effect of wearing masks. The results suggest that using an AP reduces the spread of virulent aerosols and thereby reduces the risk of infection. However, the risk of the person sitting adjacent to the index patient is only marginally reduced and can be avoided with the half capacity of the class (physical distancing method) or by wearing face masks of high efficiencies. [ABSTRACT FROM AUTHOR]

A decade-long neglect of rhinovirus as an important agent of disease in humans was primarily due to the fact that they were seen as less virulent and capable of causing only mild respiratory infections such as common cold. However, with an advent of molecular diagnostic methods, an increasing number of reports placed them among the pathogens found in the lower respiratory tract and recognized them as important risk factors for asthma-related pathology in childhood. As the spread of rhinovirus was not severely affected by the implementation of social distancing and other measures during the coronavirus disease 2019 (COVID-19) pandemic, its putative pathogenic role has become even more evident in recent years. By concentrating on children as the most vulnerable group, in this narrative review we first present classification and main traits of rhinovirus, followed by epidemiology and clinical presentation, risk factors for severe forms of the disease, long-term complications and the pathogenesis of asthma, as well as a snapshot of treatment trials and studies. Recent evidence suggests that the rhinovirus is a significant contributor to respiratory illness in both high-risk and low-risk populations of children. [ABSTRACT FROM AUTHOR]

During the coronavirus disease 19 (COVID-19) pandemic it has become very important to comply with preventive measures. We aimed to assess compliance with applicable restrictions and to explore the links between the level of compliance and the risk of COVID-19. This cross-sectional study included Polish adults who were asked to complete a validated questionnaire. The study period was from 1 November 2020 to 31 January 2021 and a computer-assisted web interview method was chosen to perform the survey. The study involved 562 women and 539 men. COVID-19 was reported in 11.26% of participants. A good level of compliance with the sanitary restrictions was reported for 38.87% of participants, an average level of compliance for 47.96%, and a low level of compliance for 13.17%. A reduced risk of COVID-19 was associated with the following preventive measures: regular use of protective masks, social and physical distancing in public places, regular use of hand sanitizers with high ethanol content, and the use of disposable gloves in public places. Our survey revealed satisfactory public compliance with the pandemic restrictions. Sanitary and epidemiologic measures to prevent the pandemic were shown to be adequate and effective. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic caused by the coronavirus has dramatically changed the lives of students all around the world, with the virus's effects profoundly impacting students' physical and emotional well-being. Due to a series of shutdowns and lockdowns, social distancing, and further closure of schools, colleges, and institutions to ameliorate the pandemic crisis, the teaching and learning process shifted to an online form. As a result, students all over the world have been forced to deal with the problem as a last resort to accepting online education. This study looked at the efficiency of online education in the current situation and the student's reactions. To enhance the online method of education for students, we examined the success characteristics of online education in the Indian state of Odisha. The study's samples were collected from the faculty members of various graduate and post-graduate educational institutions in Odisha, who were recruited by questionnaire to get an expert opinion. [ABSTRACT FROM AUTHOR]

The popularisation of complex biomedical concepts brought about by COVID-19 has led to the rapid proliferation and diffusion of scientific misinformation, particularly among individuals with inadequate levels of scientific and digital literacy. A cross-sectional online survey of a UK population sample was conducted to address three key aims: to verify whether there is a correlation between participants' belief in false information around COVID-19 and adherence to preventive measures; to investigate whether participants' scientific misinformation and preventive behaviour are associated with their demographic characteristics; and to evaluate whether participants' scientific misinformation and preventive behaviour can predict their likelihood of having contracted COVID-19. Non-parametric data analysis highlighted a strong negative correlation between participants' belief in misinformation and their trust in preventive measures. Both variables were significantly associated with participants' education levels, but not with their religious beliefs. Remarkably, neither science misinformation levels nor the trust in preventive measures were statistically associated with the likelihood of having contracted COVID-19. Taken together, these findings reinforce the urgency of ensuring that the population is equipped with adequate scientific literacy to enable them to evaluate the reliability of scientific information and recognise the importance of individual preventive behaviours to minimise community spread of infectious diseases. [ABSTRACT FROM AUTHOR]

Due to COVID-19 and prolonged lockdown, banks in the UK have reported impact on their operational activities and financial aspects. To deal with the impact of the COVID-19 pandemic, banks have adopted different strategies that reshape consumer banking behaviour during the COVID-19 pandemic. This study evaluates the impact of COVID-19 on three banks in the UK, including HSBC, Royal Bank of Scotland, and Barclays Bank. A mixed research approach was adopted where a closed-ended survey with 30 employees (10 employees from each bank) were chosen to participate in the survey. Interviews are conducted with three managers of the banks. The study findings show that banks adopted social distancing to ensure that their brand operations were not affected. The banks did not consider shutting down the branches for cutting the cost. However, there has been a shift from a manual to a digital process for client queries to improve efficiency. The banks also created the brand image of operating during the pandemic. [ABSTRACT FROM AUTHOR]

This study aimed to examine the role and impact of social media on the knowledge of the COVID-19 pandemic in Bangladesh through disseminating actual changes in health safety, trust and belief of social media's coverage statistics, isolation, and psychological numbness among students. This study used a cross-sectional design in which a quantitative approach was adopted. Data from an online survey were collected in a short period of time during the early stages of COVID-19 to determine the relationship between social media activity and knowledge of the COVID-19 pandemic with accuracy. A total of 189 respondents were interviewed using structured questionnaires during the onset of the COVID-19 outbreak in Bangladeshi university students. Exploratory factor analysis (EFA) and path analysis were performed. Out of 189 respondents, about 80% were aged between 16 and 25 years, of which nearly 60.33% were students. This study explored four factors—knowledge and health safety, trust in social media news, social distancing or quarantine, and psychological effect—using factor analysis. These four factors are also found to be positively associated in path analysis. Validation of the model was assessed, revealing that the path diagram with four latent exogenous variables fit well. Each factor coefficient was treated as a factor loading (β = 0.564 to 0.973). The results suggested that the measurement models using four elements were appropriate. The coefficient of determination was 0.98, indicating that the model provided an adequate explanation. Social media is transforming the dynamics of health issues, providing information and warnings about the adverse effects of COVID-19, having a positive impact on lockdown or quarantine, and promoting psychological wellness. This comprehensive study suggested that social media plays a positive role in enhancing knowledge about COVID-19 and other pandemic circumstances. [ABSTRACT FROM AUTHOR]

Simple Summary: Dogs are considered man's best friend, with many owners sharing a close relationship with their dogs. This affection increased during the COVID-19 pandemic, as owners spent more time with their canines during the lockdowns and quarantines. During this time, owners noted how their dogs helped reduce their stress and anxiety. Yet, of concern is the known natural infection/transmission of SARS-CoV-2 reported in numerous animals (e.g., cats, minks, deer), necessitating the need to determine if dogs can contract or spread SARS-CoV-2. In this review, we focused on the human–canine interface of COVID-19 and the positives and negatives of this interface. We found that dogs can get COVID-19 from their owners but are asymptomatic when infected—it is unclear if it affects the health of dogs. Research is inconclusive on if dogs can transmit SARS-CoV-2 to their owners. Although dogs could be hosts, they are beneficial beyond just stress relief. Sniffer dogs can rapidly detect COVID-19-positive individuals in crowds, with almost 80% success in distinguishing between positive and negative people. Overall, we emphasize that more research is needed to understand the role of dogs in the interspecies spread and even contraction of SARS-CoV-2. COVID-19 is one of the deadliest epidemics. This pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but the role of dogs in spreading the disease in human society is poorly understood. This review sheds light on the limited susceptibility of dogs to COVID-19 infections which is likely attributed to the relatively low levels of angiotensin-converting enzyme 2 (ACE2) in the respiratory tract and the phylogenetic distance of ACE2 in dogs from the human ACE2 receptor. The low levels of ACE2 affect the binding affinity between spike and ACE2 proteins resulting in it being uncommon for dogs to spread the disease. To demonstrate the role of dogs in spreading COVID-19, we reviewed the epidemiological studies and prevalence of SARS-CoV-2 in dogs. Additionally, we discussed the use of detection dogs as a rapid and reliable method for effectively discriminating between SARS-CoV-2 infected and non-infected individuals using different types of samples (secretions, saliva, and sweat). We considered the available information on COVID-19 in the human–dog interfaces involving the possibility of transmission of COVID-19 to dogs by infected individuals and vice versa, the human–dog behavior changes, and the importance of preventive measures because the risk of transmission by domestic dogs remains a concern. [ABSTRACT FROM AUTHOR]

The spread of coronavirus has caused the shutdown of businesses and classroom participation to enable social distancing. It has led to the promotion of digitalization in societies and online activities. This manuscript presents an overview of the measures education systems could take to present appropriate courses in accordance with the present movement toward digitalization, and other requirements of societies in the (post) crisis period. [ABSTRACT FROM AUTHOR]

Social distancing is one of the most important ways to prevent many diseases, especially the respiratory system, where the latest internationally spread is coronavirus disease, and it will not be the last. The spreading of this pandemic has become a major threat to human life, especially to the elderly and people suffering from chronic diseases. During the Corona pandemic, medical authorities were keen to control the spread through social distancing and monitoring it in markets, universities, and schools. This monitoring was mostly used to estimate the distance with the naked eye and interfere with estimating the distance on the observer only. In this study, a computer application was designed to monitor social distancing in closed areas, especially in schools and kindergartens, using a fast, effective and unobtrusive technique for children. In addition to this system, we use augmented reality to help to determine the location of violation of social distancing. This system was tested, and the results were accurate exceeding 98.5%. [ABSTRACT FROM AUTHOR]