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Uniform practices and quality control methods are needed to detect and quantify airborne viruses across sampling and analysis platforms. We compared detection of airborne SARSCoV-2 RNA in residences of individuals with COVID-19 using two commonly used criteria: environmental (at least one SARS-CoV-2-specific gene and internal control amplified by PCR with Ct ≤ 40) and clinical (at least two SARS-CoV-2-specific genes and internal control amplified with Ct ≤ 37). 24-hr total aerosol samples were collected in a self-isolation room and an additional room without manipulating subjects' behavior/activities. Under the environmental criterion, 7/16 samples in primary rooms and 7/15 samples in secondary rooms were positive. Comparable but lower positive sample proportions were observed using the more rigorous clinical criterion: 6/16 primary rooms and 5/15 secondary rooms. A consensus SARS-CoV-2 environmental sampling and analysis framework is needed for comparisons between studies. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.
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center dot Context.& mdash;Physicians face a high rate of burnout, especially during the residency training period when trainees often experience a rapid increase in professional responsibilities and expectations. Effective burnout prevention programs for resident physicians are needed to address this significant issue.Objective.& mdash;To examine the content, format, and effectiveness of resident burnout interventions published in the last 10 years.Design.& mdash;The literature search was conducted on the MEDLINE database with the following keywords: internship, residency, health promotion, wellness, occupational stress, burnout, program evaluation, and program. Only studies published in English between 2010 and 2020 were included. Exclusion criteria were studies on interventions related to the COVID-19 pandemic, studies on duty hour restrictions, and studies without assessment of resident well-being postintervention.Results.& mdash;Thirty studies were included, with 2 randomized controlled trials, 3 case-control studies, 20 pretest and posttest studies, and 5 case reports. Of the 23 studies that used a validated well-being assessment tool, 10 reported improvements postintervention. These effective burnout interventions were longitudinal and included wellness training (7 of 10), physical activities (4 of 10), healthy dietary habits (2 of 10), social activities (1 of 10), formal mentorship programs (1 of 10), and health checkups (1 of 10). Combinations of burnout interventions, low numbers of program participants with high dropout rates, lack of a control group, and lack of standardized well-being assessment are the limitations identified.Conclusions.& mdash;Longitudinal wellness training and other interventions appear effective in reducing resident burnout. However, the validity and generalizability of the results are limited by the study designs.
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Introduction: The respiratory tract infections (RTIs), including pneumonia, influenza and Coronavirus Disease 2019 (COVID-19), are the leading cause of hospitalization and mortality worldwide, contributing to elevated healthcare and societal costs. There is conflicting evidences about the effects of angiotensin converting enzyme inhibitor (ACEIs) or angiotensin II receptor blockers (ARBs) on the susceptibility of RTIs. Method(s): Systematic review of interventional and observational studies that reported use of ACEI or/and ARB on incidence of pneumonia or influenza or COVID-19. Searching was conducted in the databases of PubMed, Excerpta Medica Database (Embase), Web of Science, Cochrane Central Register of Controlled Trials (CENTRAL), including the Cochrane Library until April 2022, and references of retrieved relevant articles. We assessed the quality of included studies by using Cochrane Collaboration Risk of Bias tool for Randomized Controlled Trials and Newcastle-Ottawa Scale for observational studies. DerSimonian Laird random-effects meta-analysis was conducted to pool effects for the incidence of pneumonia, influenza and COVID-19. Subgroup analyses were carried according to baseline morbidities (hypertension or cardiovascular diseases, cerebrovascular diseases, chronic kidney disease (CKD) and other non-communicable diseases). Pooled estimates of odds ratios (OR) and corresponding 95% confidence intervals (95% CI) were computed, and heterogeneity among studies was assessed using Cochran's Q test and the I2 metrics, with two tailed P values. Result(s): 73 studies met the inclusion criteria, of which 38 studies assessed the odds of pneumonia, 32 studies assessed Covid-19 and 3 studies assessed influenza. The quality of included studies was moderate. Use of ACEIs was associated with a significantly reduced odds of pneumonia (23 studies: OR 0.74, 95% CI 0.64 to 0.85;I2=76.8%), of COVID-19 (24 studies: OR 0.87, 95% CI 0.82 to 0.92;I2=81.9%) and influenza (3 studies: OR 0.75, 95% CI 0.57 to 0.98, I2=97.7%), compared with control treatment. Use of ARBs was also associated with reduced odds of COVID-19 (25 studies: OR 0.90, 95% CI 0.83 to 0.97;I2=91.9%), but not with odds of pneumonia or influenza. These findings remain consistent in the community population, patients with history of cerebrovascular diseases or cardiovascular diseases, but not in those with CKD, diabetes and chronic obstructive pulmonary diseases. Conclusion(s): The current evidence favours a putative protective role of ACEIs, not ARB in odds of pneumonia, COVID-19 and influenza. Patient populations that may benefit most are those within the community, history of cerebrovascular diseases and cardiovascular diseases. No conflict of interestCopyright © 2023
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Coronavirus disease 2019 (COVID-19) has been spreading since late 2019, leading the world into a serious health crisis. To control the spread rate of infection, identifying patients accurately and quickly is the most crucial step. Computed tomography (CT) images of the chest are an important basis for diagnosing COVID-19. They also allow doctors to understand the details of the lung infection. However, manual segmentation of infected areas in CT images is time-consuming and laborious. With its excellent feature extraction capabilities, deep learning-based method has been widely used for automatic lesion segmentation of COVID-19 CT images. But, the segmentation accuracy of these methods is still limited. To effectively quantify the severity of lung infections, we propose a Sobel operator combined with Multi-Attention networks for COVID-19 lesion segmentation (SMA-Net). In our SMA-Net, an edge feature fusion module uses Sobel operator to add edge detail information to the input image. To guide the network to focus on key regions, the SMA-Net introduces a self-attentive channel attention mechanism and a spatial linear attention mechanism. In addition, Tversky loss function is adopted for the segmentation network for small size of lesions. Comparative experiments on COVID-19 public datasets show that the average Dice similarity coefficient (DSC) and joint intersection over Union (IOU) of proposed SMA-Net are 86.1% and 77.8%, respectively, which are better than most existing neural networks used for COVID-19 lesion segmentation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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The Coronavirus Disease 2019 (COVID-19) outbreak in late 2019 threatens global health security. Computed tomography (CT) can provide richer information for the diagnosis and treatment of COVID-19. Unfortunately, labeling of COVID-19 lesion chest CT images is an expensive affair. We solved the challenge of chest CT labeling by simply marking point annotations to the lesion areas, i.e., by marking individual pixels for each lesion area in the chest CT scan. It takes only a few seconds to complete the labeling using this labeling strategy. We also designed a lightweight segmentation model with approximately 10% of the number of model parameters of the conventional model. So, the proposed model segmented the lesions of a single image in only 0.05 s. In order to obtain the shape and size of lesions from point labels, the convex-hull based segmentation (CHS) loss function is proposed in this paper, which enables the model to obtain an approximate fully supervised performance on point labels. The experiments were compared with the current state-of-the-art (SOTA) point label segmentation methods on the COVID-19-CT-Seg dataset, and our model showed a large improvement: IoU improved by 28.85%, DSC improved by 28.91%, Sens improved by 13.75%, Spes improved by 1.18%, and MAE decreased by 1.10%. Experiments on the dataset show that the proposed model combines the advantages of lightweight and weak supervision, resulting in more accurate COVID-19 lesion segmentation results while having only a 10% performance difference with the fully supervised approach. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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The COVID-2019 pandemic has a huge impact on tourism industry, and mastering the spatial and temporal characteristics of tourists' travel behavior during the period is very crucial for the recovery and the development of the tourism industry. This study adopts time series statistics and complex network analysis to compare and examine the network evolution features of Hong Kong before and during the COVID-19 pandemic based on the comments data generated from TripAdvisor website in 2019 and 2020. The results show that: (1) Tourists' travel behavior patterns have changed to a certain degree, and they prefer to visit a small number of destinations during an itinerary. (2) Key destinations still play important roles in connecting other destinations, but the tourism community formed around the key destinations has varied from extremely dense to relatively sparse gathering. (3) The number of tourists in extremely hot destinations has been greatly declined, those destinations with fewer tourists and relatively far away from the downtown have attracted more attention. Moreover, industrial destinations have been always the most popular type of tourism destination. © 2022, Science Press. All rights reserved.
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Objective: To explore the epidemiological characteristic of a COVID-19 outbreak caused by 2019-nCoV Omicron variant BF.7 and other provinces imported in Shenzhen and analyze transmission chains and characteristics. Methods: Field epidemiological survey was conducted to identify the transmission chain, analyze the generation relationship among the cases. The 2019-nCoV nucleic acid positive samples were used for gene sequencing. Results: From 8 to 23 October, 2022, a total of 196 cases of COVID-19 were reported in Shenzhen, all the cases had epidemiological links. In the cases, 100 were men and 96 were women, with a median of age, M (Q1, Q3) was 33(25, 46) years. The outbreak was caused by traverlers initial cases infected with 2019-nCoV who returned to Shenzhen after traveling outside of Guangdong Province.There were four transmission chains, including the transmission in place of residence and neighbourhood, affecting 8 persons, transmission in social activity in the evening on 7 October, affecting 65 persons, transmission in work place on 8 October, affecting 48 persons, and transmission in a building near the work place, affecting 74 persons. The median of the incubation period of the infection, M (Q1, Q3) was 1.44 (1.11, 2.17) days. The incubation period of indoor exposure less than that of the outdoor exposure, M (Q1, Q3) was 1.38 (1.06, 1.84) and 1.95 (1.22, 2.99) days, respcetively (Wald χ2=10.27, P=0.001). With the increase of case generation, the number and probability of gene mutation increased. In the same transmission chain, the proportion of having 1-3 mutation sites was high in the cases in the first generation. Conclusions: The transmission chains were clear in this epidemic. The incubation period of Omicron variant BF.7 infection was shorter, the transmission speed was faster, and the gene mutation rate was higher. It is necessary to conduct prompt response and strict disease control when epidemic occurs.
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COVID-19 , Epidemics , Male , Humans , Female , SARS-CoV-2 , COVID-19/epidemiology , Disease Outbreaks , China/epidemiologyABSTRACT
Using the non-parametric thermal optimal path method, we investigate the dynamic lead–lag relationship between carbon emission trading and stock markets in China, and further consider the impact of different types of exogenous shocks on the lead–lag relationship. The empirical results show that the stock market leads the carbon market on most trading days, and the relationship reverses when the mean values of carbon market return are significantly smaller than zero. In addition, the lead–lag relationships when the carbon market leads the high energy-consuming stock market sectors are more obvious. We also find that there exist significant heterogeneous effects of different types of exogenous shocks on the lead–lag relationship between the two markets, including government policy, the Sino-US trade war and the Covid-19 outbreak. These findings have the potential to help regulators understand the interrelationship between components of the financial market, and be of great value for investors to optimize portfolio allocation by incorporating carbon assets into the portfolio. © 2023 Elsevier Inc.
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Coronavirus Disease 2019 (COVID-19) is a highly transmissible and pathogenic coronavirus that emerged in late 2019 and has caused a pandemic of acute respiratory disease. Before vaccines are widely used or the invention of specific medication, many measures have been taken by human beings to prevent the spread of the epidemic. Quarantining infected groups and locking down high-risk regions are common means used by the latest medical experience. As such measures are generally carried out under administrative divisions, issues of imprecise epidemic control and unquantifiable risk warning are exposed gradually. In order to better achieve the purpose of precise epidemic prevention and control, we propose a kind of dynamic block division technology based on GeoHash which can be used to monitor, mark out and control the epidemic regions. By using GeoHash, we divide the earth map into connected dynamic blocks. Dynamic blocks are easily visualized in geographic information systems (GIS) equipped in electronic devices. GeoHash blocks are dynamically overlaid on the map as grids. Each block contains essential epidemic-related data and important features which are concerned by professional medical work. Quantitative analysis of epidemic data is carried out on each block. Our research shows the analysis results can support decision-making, measures formulation, and effectiveness assessment of COVID-19 prevention and control. Such research can be applied not only to COVID-19 but also to other infectious diseases. © 2022 IEEE.
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Since April 2022, severe acute hepatitis of unknown origin in children has spread to 35 countries and regions around the world, and more than 1 010 cases have been reported. Since the severe acute hepatitis of unknown origin involves a wide range of areas and has a high rate, it is critical to identify the etiology and establish effective preventive, diagnostic and therapeutic measures as soon as possible. This study discusses the possible mechanisms and countermeasures of the severe acute hepatitis of unknown origin in children. It speculates that the occurrence of the recent severe acute hepatitis might be related to adenovirus, adeno-associated virus infection, and the COVID-19 epidemic, while the difference in HLA polymorphism among different races might be related to the fact that reported cases were more common in Europe and the United States. Based on the currently available evidence, it can be preliminarily judged that the risk of large-scale outbreak of severe acute hepatitis of unknown origin in children would be low in China, but the persistent awareness and vigilance of the etiology is still needed.
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COVID-19 , Hepatitis , Child , Humans , United States , Disease Outbreaks , Hepatitis/epidemiology , China/epidemiologyABSTRACT
Disasters such as earthquakes, typhoons, floods and COVID-19 continue to threaten the lives of people in all countries. In order to cover the basic needs of the victims, emergency logistics should be implemented in time. Location-routing problem (LRP) tackles facility location problem and vehicle routing problem simultaneously to obtain the overall optimization. In response to the shortage of relief materials in the early post-disaster stage, a multi-objective model for the LRP considering fairness is constructed by eval-uating the urgency coefficients of all demand points. The objectives are the lowest cost, delivery time and degree of dissatisfaction. Since LRP is a NP-hard problem, a hybrid metaheuristic algorithm of Discrete Particle Swarm Opti-mization (DPSO) and Harris Hawks Optimization (HHO) is designed to solve the model. In addition, three improvement strategies, namely elite-opposition learning, nonlinear escaping energy, multi-probability random walk, are intro-duced to enhance its execution efficiency. Finally, the effectiveness and perfor-mance of the LRP model and the hybrid metaheuristic algorithm are verified by a case study of COVID-19 in Wuhan. It demonstrates that the hybrid meta-heuristic algorithm is more competitive with higher accuracy and the ability to jump out of the local optimum than other metaheuristic algorithms.
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This paper presents a diode-free double metal oxide varistor-resistor-capacitor (MOV2-RC) snubber to provide overvoltage protection in dc solid-state circuit breakers (dc SSCBs). MOV2-RC snubber is introduced and investigated for SSCBs application. The proposed MOV2-RC snubber prevents voltage overshoot and ensures smooth turn-off voltage slew rate of SSCBs. Meanwhile, compared with conventional MOV-resistor-capacitor-diode (MOV-RCD) snubber, utilization of a low-voltage MOV (LMOV) to replace the diode avoids the influence of availability issue of state-of-the-art SiC Schottky diode raised by global power semiconductor shortage due to coronavirus disease 2019 (COVID-19). It also reduces the cost of snubber circuit for SSCBs. Working principles and design procedures of the MOV2-RC snubber are presented. The effectiveness of the proposed snubber circuit is verified by experiments with a 400V/140A dc SSCB prototype. The experimental results show the clamping voltage of 1.1 kV and turn-off dv/dt of 7.9 V/ns. The key component of MOV2-RC snubber (LMOV) has 30 times lower cost and 3 times better availability compared to MOV-RCD-based counterpart. © 2022 IEEE.
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The purpose of studying the mechanism of epidemic situation-related network emergency in the context of COVID-19 is to provide scientific guidance for preventing epidemic situation-related network emergency, resolving the social risks caused by epidemic situation-related conflicts and maintaining social stability. Using the grounded theory method, the authors make Open Coding, Axial Coding, Selective Coding and Saturation test, and then get five main categories and four core categories. On this basis, we establish a conceptual model of the epidemic situation-related network emergency happening mechanism. The research finds that measures of prevention is the stimulus factor of epidemic situation-related network emergency. Network media is the media factor of epidemic situation-related network emergency. Social cognition is the intermediary variable of epidemic situation-related network emergency. Group psychology and group interest are the direct driving force of epidemic situation-related network emergency. Accordingly, we bring up some beneficial countermeasures and suggestions for the government emergency management to prevent, resolve and control the epidemic situation-related network emergency. © 2021 IEEE.
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Over the past two years, the COVID-19 pandemic had a major worldwide health, economic and daily life impact. Amongst many dramatic consequences, such as major human mobility disruptions at all scales, the tourism sector has been largely affected. This raises the need for the development of quantitative and qualitative research to favor a better understanding of the impact of the pandemic on human travel behaviors. This study introduces a computational approach that combines inference mechanisms and statistics to quantify tourists’ travel behaviors before and during the pandemic by exploring the evolution of the patterns extracted from a local tourism social network from 2019 to 2020 in the city of Hong Kong. The results show that the COVID-19 pandemic: 1) has a major influence on travel intentions that mainly swift from journeys with generally long sequences of attractions to rather single attractions;2) lead to a decline when considering connections between popular attractions, while the strength of connections within other attractions increase;3) generates novel patterns such as tourists preferring relaxing visits and even minor attractions. © 2022, Springer Nature Switzerland AG.
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Science storytelling is an effective way to turn abstract scientific concepts into easy-to-understand narratives. Science storytelling in immersive virtual reality (VR) can further optimize learning by leveraging rich interactivity in a virtual environment and creating an engaging learning-by-doing experience. In the current context of the COVID-19 pandemic, we propose a solution to use interactive storytelling in immersive VR to promote science education for the general public on the topic of COVID-19 vaccination. The educational VR storytelling experience we have developed uses sci-fi storytelling, adventure and VR gameplay to illustrate how COVID-19 vaccines work. After playing the experience, users will understand how the immune system in the human body reacts to a COVID-19 vaccine so that it is prepared for a future infection from the real virus.
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Due to the COVID-19, the Ministry of Education of China announced that the 2020 spring semester will be postponed, on-site classes should be cancelled and schools should give online classes instead. It's the first time that online classes have been given priority to higher education, which is a new challenge to both schools and online learning platforms. It's necessary to carry out stable and efficient IT solutions to secure the smooth process of online classes. This paper takes South China University of Technology as an example to put forward some IT solutions and ways of improving teaching quality of online classes for higher education during COVID-19 pandemic.
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The prevention and control of the novel coronavirus (COVID-19) is the priority work to maintain the public health security of the world nowadays. The COVID-19 prevention and control model using multi-agent modeling and simulation technology is proposed. The model can simulate the different dynamic development trend of the epidemic under different prevention and control measures. Taking Taiyuan as an example, according to the researched COVID-19 transmission rules, the prevention and control simulation of COVID-19 has been achieved under the designing rule of the interactive infection process and status transition process between various resident agents. Multi-scenario simulation experiments are realized under different policy measures of hospital and government. The experimental results show that the multi-agent modeling method is effective in analyzing the spread of COVID-19 and can provide decision support for city epidemic prevention and control. © 2020, The Editorial Board of Journal of System Simulation. All right reserved.