Smart Visual Sensing for Overcrowding in COVID-19 Infected Cities Using Modified Deep Transfer Learning

Currently, COVID-19 is circulating in crowded places as an infectious disease. COVID-19 can be prevented from spreading rapidly in crowded areas by implementing multiple strategies. The use of unmanned aerial vehicles (UAVs) as sensing devices can be useful in detecting overcrowding events. Accordingly, in this article, we introduce a real-time system for identifying overcrowding due to events such as congestion and abnormal behavior. For the first time, a monitoring approach is proposed to detect overcrowding through the UAV and social monitoring system (SMS). We have significantly improved identification by selecting the best features from the water cycle algorithm (WCA) and making decisions based on deep transfer learning. According to the analysis of the UAV videos, the average accuracy is estimated at 96.55%. Experimental results demonstrate that the proposed approach is capable of detecting overcrowding based on UAV videos' frames and SMS's communication even in challenging conditions. © 2005-2012 IEEE.

A narrative review of research on healthcare staff's burnout during the COVID-19 pandemic

Background: During the 2019 coronavirus disease (COVID-19) pandemic, burnout emerges as a critical health problem that might involve workers in many occupations, particularly healthcare personnel. Although burnout syndrome is not necessarily proved to be nosologic, it yields serious physical, mental, and social outcomes. However, it is essential to provide practical strategies and effective instruments for people so that they can adapt to such highly stressful conditions. Objective(s): The present review was conducted to explore preliminary evidence for nature, treatment, and prevention of burnout among healthcare workers during the COVID-19 pandemic. Method(s): Related English literatures published from the beginning of January 2020 to the end of September 2020 were searched in PubMed, Web of Science, Scopus, and Google scholar databases. "Burnout," "COVID-19," "healthcare workers," "medical staff," and "pandemic" constituted the search terms. A narrative technique was implemented for material synthesis and creating a compelling and cohesive story. Result(s): Final results provided the burnout history and its major effects, causes, and prevalence among healthcare workers. Also, some strategies were listed to be employed by hospital medical staff and organizations to deal with the COVID-19 pandemic. Conclusion(s): Recent evidence demonstrated that healthcare staff could gain significant benefits from interventions to modify burnout syndrome, especially from organization-directed interventions. So, health policymakers and practitioners should adopt such interventions and develop context-specific approaches promoting a healthy workplace and averting burnout during the COVID-19 crisis. Copyright © The Author(s) 2022.

An Elusive and Deadly Presentation of Tracheobronchial Aspergillosis Coinfection in Covid-19

INTRODUCTION: Tracheobronchial aspergillosis (TBA) can be a difficult diagnosis despite being seen in up to a third of critically ill patients with COVID-19 with a high mortality rate. We report a case of TBA found on bronchoscopy shortly after COVID-19 infection diagnosis. DESCRIPTION: A 49-year-old woman with diabetes presented with encephalopathy, nausea, and malaise and a 1-day history of COVID-19 infection. She was initially stable, on 2 liters of oxygen via nasal cannula. She had a WBC of 27,000, procalcitonin: 10.9 ng/ml along with diabetic ketoacidosis (DKA), hemoglobin A1C: 15.5%, and acute kidney injury. She was treated for DKA and started on Remdesivir and Dexamethasone for COVID-19 infection. DKA resolved within 24 hours, however, she became hypoxic, bradycardic and had a PEA arrest on second day. After resuscitation, her beta-glucan was 364 pg/ml and Micafungin was started. She then developed severe worsening compliance on the ventilator. CT scan of the chest was non-revealing. Bronchoscopy for concerns of airway obstruction showed diffuse black-green dense exudate in trachea and bilateral airways with acute inflammation, suggestive of aspergillosis, later confirmed on cultures. She was switched to Amphotericin B. Patient further declined and was transitioned to comfort care after 5 days. DISCUSSION: TBA mostly afflicts patients with neutropenia, leukemia, HIV, transplantation and >3 weeks steroid treatment. It is also described in ventilated patients with viral infections like influenza. COVID-19 patients are also found to be susceptible to developing TBA with reports of 19-33% of patients having aspergillus in their BAL with mortality rate up to 65%. The diagnosis is often elusive prompting redefining guidelines for diagnosis of TBA. Except for short course of steroids and uncontrolled diabetes, our case did not have any other risk factors for TBA. Her accelerated TBA course can be attributed to her COVID-19 and/or its treatment. The CT imaging and ventilator findings could have been explained by COVID-19 infection. The high peak pressures on the ventilator prompted further investigation by bronchoscopy that uncovered her aspergillus infection. Considering the high mortality rate of TBA, our case shows high level of suspicion and thorough workup is needed for its diagnosis.

The Correlation Between Mental Health and Corona Anxiety Among Pre-hospital Emergency Medicine Clinicians Abstract

Background: According to the literature, pre-hospital emergency medicine clinicians' mental health has most likely been ignored during the COVID-19 outbreak.Objectives: This study aimed to investigate the correlation between mental health and corona anxiety among pre-hospital emer-gency medicine clinicians during the COVID-19 outbreak in Isfahan, Iran.Methods: A cross-sectional study was conducted in Isfahan, Iran, from May to July 2020. All participants voluntarily participated in this study, and an informed consent was obtained. Data collection was done through completing two questionnaires, including the Corona Disease Anxiety Scale (CDAS) and the General Health Questionnaire (GHQ). Data were analyzed with SPSS version 18 using the Pearson correlation test and multivariate linear regression.Results: A preliminary data analysis of CDAS and GHQ showed a significant correlation between the total score of COVID-19 anxiety and general health (r = 0.6, P < 0.001). Also, multivariate analysis showed that field of study (B = 0.13;P-value = 0.005) and COVID-19 anxiety were significant predictors for general health (B = 0.6;P-value < 0.001).Conclusions: According to the findings, the higher the anxiety score, the poorer one's general health and the more likely they are to have mental disorders. Therefore, it is recommended to provide mental health services for pre-hospital emergency medicine clinicians during the COVID-19 pandemic.

Smart Visual Sensing for Overcrowding in COVID-19 Infected Cities Using Modified Deep Transfer Learning

Currently, COVID-19 is circulating in crowded places as an infectious disease. COVID-19 can be prevented from spreading rapidly in crowded areas by implementing multiple strategies. The use of unmanned aerial vehicles (UAVs) as a sensing devices can be useful in detecting overcrowding events. Accordingly, in this paper, we introduce a real-time system for identifying overcrowding due to events such as congestion and abnormal behavior. For the first time, a monitoring approach is proposed to detect overcrowding through the UAV and social monitoring system (SMS). We have significantly improved identification by selecting the best features from the water cycle algorithm (WCA) and making decisions based on Deep Transfer Learning (DTL). According to the analysis of the UAV videos, the average accuracy is estimated at 96.55%. Experimental results demonstrate that the proposed approach is capable of detecting overcrowding based on UAV videos' frames and SMS's communication even in challenging conditions. IEEE

IoT Solutions for Smart Management of Hospital Buildings: A General Review towards COVID-19, Future Pandemics, and Infectious Diseases

Background: The paper aims to review existing solutions for smart building to increase hospitality levels in locations like hospitals. The answers could support features such as online appointments, smart navigation, and queue management in the building through mobile phone and navigation to the desired location by highlighting the point of interest and facilities and checking the spaces’ occupancy. Methods: Such a solution addresses all mentioned issues regarding the smart building by integrating and utilizing various data sources collected by the internet of things (IoT) sensors. This solution mainly deals with location determining, also known as positioning, using Bluetooth Low Energy radio. The goal is to implement a low-power, low-cost indoor positioning system that utilizes existing hardware. Results: Positions determining is considered a significant section of these kinds of solutions. Previous attempts with indoor positioning systems intensify statistical fingerprinting methods, mainly using IEEE 802.11 (WLAN) as the platform. Conclusions: Some efforts have been made with purely signal strength-based positioning, but indoor environments have been shown to work inauspiciously for these kinds of methods.

The Role of Big Data and Smart Technologies in Autonomous Vehicles

This paper presents a definition of what Autonomous Vehicle and their functions are, how they operate, gather data and what threats they have to encounter-with throughout their operations. As explained later in the article, we can observe that there are a handful of ways by which a self-driving vehicle can gather information about it's surrounding environment beside the sensors it is equipped with. These methods of gathering data are thoroughly explained in the article alongside their advantages and disadvantages. This article also tries to have a look at the methods an AV can overcome the computing challenges it is facing while guiding and controlling the vehicle, as well as staying in communication with other vehicles or any object that can provide useful information to the vehicle. © 2021 ACM.

Extracting Major Topics of COVID-19 Related Tweets

With the outbreak of the Covid-19 virus, the activity of users on Twitter has significantly increased. Some studies have investigated the hot topics of tweets in this period;however, little attention has been paid to presenting and analyzing the spatial and temporal trends of Covid-19 topics. In this study, we use the topic modeling method to extract global topics during the nationwide quarantine periods (March 23 to June 23, 2020) on Covid-19 tweets. We implement the Latent Dirichlet Allocation (LDA) algorithm to extract the topics and then name them with the "reopening", "death cases", "telecommuting", "protests", "anger expression", "masking", "medication", "social distance", "second wave", and "peak of the disease"titles. We additionally analyze temporal trends of the topics for the whole world and four countries. By analyzing the graphs, fascinating results are obtained from altering users' focus on topics over time. © 2021 IEEE.

Prevalence and Associated Factors of Personal Protective Measures Among the Southeastern Iranian Population During the COVID-19 Pandemic

Introduction: The main focus of our study was on prevalence and potential explanatory factors related to personal protective measures (e.g., face mask wearing and physical distancing) in the southeastern Iranian population during the coronavirus disease 2019 (COVID-19) pandemic. Methods: A total of 351 participants in southeastern Iran were examined in this cross-sectional study using self-report scales of personality traits, COVID-19 risk perception, face mask wearing, physical distancing, and public trust. The data were analyzed by SPSS v25 software at the significance level of p<0.05. Results: Descriptive statistics showed that the prevalence rates of COVID-19 risk perception and face mask wearing among participants were 76.4% and 77.8%, respectively. In addition, 59.6% of participants reported that they maintained an adequate distance when communicating with others;41.9% acknowledged that they did not allow relatives and friends to visit them at home;46.8% said they preferred to stay at home and not go outside unless necessary. Furthermore, multiple regression analysis revealed that older age, being female, lower levels of neuroticism and higher levels of extraversion, agreeableness, conscientiousness, COVID-19 risk perception, and public trust were associated with higher adherence to personal protective measures. Conclusions: These results emphasized the importance of individual differences in reacting to the COVID-19 pandemic and provided essential information about related factors of personal protective measures. However, further investigations should be carried out due to inconsistent findings concerning the roles of age, gender, education, income, and neuroticism in adopting personal protective measures.

Bidirectional GRU networks-based next POI category prediction for healthcare

The Corona Virus Disease 2019 has a great impact on public health and public psychology. People stay at home for a long time and rarely go out. With the improvement of the epidemic situation, people began to go to different places to check in. To maintain public mental health, it is necessary to propose a point-of-interest (POI) prediction model which can mine users' interests. However, the current techniques suffer from lower precision during prediction and the practical value is poor, which is due to the sparse data of users' check-in. Faced with this challenge, we propose an attention-based bidirectional gated recurrent unit (GRU) model for POI category prediction (ABG_poic). We regard the user's POI category as the user's interest preference because the fuzzy POI category is easier to reflect the user's interest than the POI. This method can alleviate the data sparsity, and protect users' location privacy. Since users' preferences are variable, we utilize a bidirectional GRU to capture the dynamic dependence of users' check-ins. Furthermore, since the neural network is similar to a “black box” in feature learning, the decision-making stage is opaque. Thus, we combine the attention mechanism with bidirectional GRU to selectively focus on historical check-in records, which can improve the interpretability of the model. Considering the time impact on users' check-in, we utilize the time sliding window in the ABG_poic model. Experiments on two data sets demonstrate that our ABG_poic outperforms the comparison models for POI category prediction on sparse check-in data. © 2021 Wiley Periodicals LLC

Letter to the editor: Burnout management among health care workers in the age of coronavirus disease-19

AIM: We aimed to understand the early warning signs and symptoms of occupational burnout as red flags among health care workers during the COVID-19 pandemic. METHODS: Based on the suggestions of the International Federation of Red Cross and Red Crescent Societies [8], health-care providers need to be trained to increase three components of resilience across the three levels of individual, team, and organization so that they can optimally manage their psychological responses to catastrophes. RESULTS: It seems that both targeted individual and organizational strategies are critical for the overall wellness of health care workers during the COVID-19 pandemic. CONCLUSION: Health care workers experience high levels of burnout during the COVID-19, which warrants attention and support from health policy-makers and practitioners. Current evidence demonstrated that health-care staff could gain significant benefits from interventions to modify burnout syndrome, especially from organization-directed interventions.

Burnout in hospital medical staff during the COVID-19 pandemic: Diagnosis, treatment, and prevention

During the 2019 coronavirus disease (COVID-19) pandemic, burnout emerges as a critical health problem that might involve workers in many occupations, particularly healthcare personnel. Although burnout syndrome is not necessarily proved to be nosologic, it yields serious physical, mental, and social outcomes. However, it is essential to provide practical strategies and effective tools for people so that they can adapt to such highly stressful situations. This mini literature review aims to present the burnout history briefly and provides a summary of its major effects, causes,and prevalence among healthcare workers. Also, some strategies are listed to be employed by hospital medical staffand organizations to deal with the COVID-19 pandemic.

Worden’s task-based model for treating persistent complex bereavement disorder during the coronavirus disease-19 pandemic: A narrative review

BACKGROUND: A wide range of studies has shown that the coronavirus disease (COVID)-2019 pandemic could cause many deaths on the global scale by the end of 2020 because of the high speed of transmission and predicted case-fatality rates. AIM: This paper is a narrative review aiming to address the treatment of persistent complex bereavement disorder (PCBD) during the COVID-19 crisis using Worden’s task-based model. MATERIALS AND METHODS: Related papers published from 2000 to 2020 were searched in the EMBASE, PubMed, Web of Science, Scopus, Cochrane Library, and Google Scholar databases. Bereavement, COVID-19, pandemics, and Worden’s task-based model constituted the search terms. A narrative technique was implemented (including reading, writing, thinking, interpreting, arguing, and justifying) for material synthesis and creating a compelling and cohesive story. RESULTS: A few studies have specifically addressed the grief experiences within the COVID-19 crisis. They managed to identify some potential obstacles to grieving during the pandemic, namely, “anticipatory grief” and “multiple losses.” This study tried to use Worden’s task-based model to address the treatment of PCBD during the pandemic. CONCLUSIONS: Despite the paucity of information, Worden’s task-based model seems to have a considerable impact on the reduction of the PCBD symptoms. Nonetheless, further research is needed to perceive the effect of this approach on PCBD during the COVID-19 pandemic.

Contribution value of coagulation abnormalities in COVID-19 prognosis: a bright perspective on the laboratory pattern of patients with coronavirus disease 2019.

OBJECTIVE: From the beginning of the novel coronavirus infection (COVID-19) pandemic in the world, much efforts have been accomplished to explain a precise clinical feature for the disease and to find the best therapeutic approach for the patients. Although coagulation abnormalities have found in novel coronavirus infection (COVID-19) patients, still little is known about the association between the disease and changes in coagulation parameters. Our purpose is to evaluate the differences between the coagulation parameters between COVID-19 patients and healthy counterparts. PATIENTS AND METHODS: 63 patients with confirmed COVID-19 infection were admitted to the present study. We evaluated coagulation value in these patients and in 40 healthy individuals. RESULTS: We found that although there was no significant difference between PT and PTT values in patients and healthy counterparts, the fibrinogen values in patients were higher than the control group (p < 0.05). Moreover, the values of fibrin/fibrinogen degradation products (FDP) and D-dimer in all COVID-19 cases were considerably higher than those in control people (p < 0.05). Of note, FDP and D-dimer in patients with regular COVID-19 infection were lower than patients with severe forms. CONCLUSIONS: It seems that the conduction of routine blood coagulation test could be a beneficial supplementary approach for early diagnosis of COVID-19. In addition, our study shed more light on the therapeutic value of anti-coagulant-based treatment for COVID-19 patients, especially for those with severe type of the disease.