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A more focused approach is needed to understand the SARS-CoV-2 virulence, structure, and genomics to devise more effective diagnostic and treatment interventions as this virus can evade the immune attack and causes life-threatening complications such as cytokine storm. The spread of the virus is still amplifying and causing thousands of new cases worldwide. It is essential to review current diagnostics and treatment approaches to pave the way to correct or modify our current practices to make more effective interventions against COVID-19. COVID-19 vaccine development has moved at a breakneck pace since the outbreak began, utilizing practically all possible platforms or tactics to ensure the success of vaccines. A total of 42 vaccine candidates have already entered clinical trials, including promising results from numerous vaccine candidates in phase 1 or phase 2 trials. Further, many existing drugs are being explored on broad-spectrum antiviral medications for their use in clinical recovery against COVID-19. The present review attempts to re-examine the SARS-CoV-2 structure, its viral life cycle, clinical symptoms and pathogenesis, mode of transmission, diagnostics, and treatment strategies that may be useful for resorting to more effective approaches for controlling COVID-19. Various antiviral drugs and vaccination strategies with their strengths and weaknesses are also discussed in the paper to augment our understanding of COVID-19 management.Copyright © 2022 Bentham Science Publishers.
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Background: COVID-19-related lung injury may persist or require time to recover in some individuals. Objective(s): To better understand the post-acute pathophysiology of non-critical COVID-19 by evaluating respiratory system resistance and reactance in COVID-19 survivors 4-weeks and 6-months after recovery, compared to neverCOVID-19 controls. Method(s): Individuals with no history of lung disease who were hospitalized or home-isolated during the acute-phase of COVID-19, and age-matched never-COVID-19 controls, performed respiratory oscillometry (tremoFlo C-100) to measure resistance (Rrs5Hz, Rrs19Hz , Rrs5-19Hz ), reactance (Xrs5Hz ) and area of reactance (A ), Six Minute Walk Test, and completed the Borg Dyspnea Scale 4-weeks (visit 1) and 6-months (visit 2) post-recovery. Result(s): Hospitalized (n=9), home-isolated (n=20) and never-COVID-19 (n=17) cohorts were similar with respect to age (p=0.12), sex (p=0.89), and BMI (p=0.08). Figure 1 shows that Rrs5-19Hz (p=0.04) and A (p=0.009) were higher, and Xrs5Hz (p=0.007) was more negative, in the hospitalized cohort compared to control cohort at visit 1, but not visit 2. These measures were associated with greater dyspnea and decreased six-minute walk distance. Conclusion(s): Increased peripheral resistance and reactance following recovery from non-critical COVID-19 may only be prevalent among hospitalized COVID-19 survivors, within which these respiratory abnormalities may be temporary.
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Introduction: Telemedicine acted as one of the biggest medium in treating Coronavirus Disease-2019 (COVID-19) patients during the second wave of the still ongoing pandemic. Although the symptoms were taken care of and treated through teleconsultation, the loneliness and social support system of these patients went largely unrecognised. The morbidity pattern, effect of self-isolation and quarantine, uncertainties in social support were major contributors to loneliness among patients suffering from COVID-19. Aim: To estimate the proportion of loneliness and level of social support experienced by COVID-19 patients seeking advice from a telemedicine centre of Kolkata and to find out their socio-clinical profile and the associated relationship. Materials and Methods: An observational study with cross-sectional design was conducted on 403 COVID-19 patients who had taken advice from the telemedicine centre of Institute of Post Graduate Medical Education and Research (IPGME and R), Kolkata for a period of 12 weeks (May-July 2021). Loneliness was assessed by the 11-item De Jong Gierveld Loneliness scale, whereas social support was assessed using 12-item Multidimensional Scale of Perceived Social Support scale through telephonic interview. Data were tabulated in the Microsoft Office Excel 2019 (Microsoft Corp, Redmond, WA, USA) and the analysis was performed using Statistical Package for the Social Sciences (IBM, New York City, USA) version 25.0. Results: Out of 403, more than half of the study population, 194 (48.2%) belonged to 18-35 years of age. Of the total, 235 (58.3%) were males, 319 (79.2%) were currently married and 300 (74.4%) were Hindus. About 142 (35.2%) respondents had experienced severe loneliness, while 297 (73.7%) had experienced high social support. There was a significant negative correlation found between loneliness and social support (r=-0.495, p-value <0.01). It was found that being male, belonging to nuclear family, education upto higher secondary level, being addicted, loneliness due to physical distancing, and those who had socialised frequently had higher odds of loneliness, whereas unemployed, unskilled, semiskilled and skilled occupation, having one chronic disease had lower odds of social support. Conclusion: About 338 (84%) patients had experienced loneliness which was strikingly high. This shows a deeper aspect into the actual picture of how COVID-19 impacts mental health of those who are affected. Future interventions are needed to address loneliness and develop social support system along with addressing healthcare needs of COVID-19 patients.
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Purpose: The COVID-19 pandemic compelled the education system to switch over to emergency learning-teaching that is organised remotely. The present study investigated the experience of emergency remote learning (ERL) provided to higher-education learners. The study explores learners' perceived experience regarding the quality of learning resources, the effectiveness of teaching in a virtual climate and the scope of interaction in ERL. Design/methodology/approach: Utilising a snowball sampling method, data were obtained from 470 Indian students of higher education through a cross-sectional online survey using a questionnaire through social media platforms. Data were analysed with relevant statistics. Findings: The majority of students agreed that they had benefited from ERL. The overall impression of the ERL is positive;nevertheless, the students are perplexed and lack confidence in many aspects of the ERL. The Quality of ERL Resources, Teaching Effectiveness, Peer Interaction and Workloads were found to be significant factors in determining the quality of ERL. Originality/value: Learning from the crisis of a pandemic is paramount for the education system. The education system could not go back to what was considered normal before the pandemic;rather it is time to assess and finalise strategies from the experience during this pandemic that could be taken by the higher-education institutions to make the ecosystem better equipped to create 21st-century learning climate. Accommodating the components of remote learning-teaching and engaging technology towards hybridisation are the needs of the time. Hence, assessing the quality of ERL from the learner's perspective might contribute to redesigning future remote learning. © 2022, Mrinal Mukherjee and Chanchal Maity.
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How do we transition service-learning from high touch to high-tech? While Waldner et al. (2012) were trying to answer this question in the Web 2.0 era, humankind is facing the unprecedented crisis caused by COVID-19. As it is evident that we cannot return to the world as it was before, hence our common humanity necessitates solidarity. (UNESCO 2020). The crisis has hinted at the need for reconnection between ‘humanity and planet’ hence human-centric learning teaching is the need of the hour. Conventional higher education (HE) institutions undergo forced transition to technology engaging Remote Teaching-Learning (RTL) through a virtual platform on an emergency basis. Most of the teachers are facing challenges on how to make remote learning relatable to the everyday life of the learners and how to inculcate the sense of humanity in RTL. Hence the service-learning is facing two-fold challenges—first, how to design engagement of the student with the community to ensure experiential learning at a time when people are mostly remaining separated from conventional social interaction, and second, how teachers can effectively explore constructionist pedagogy through the virtual communication mode in such RTL. Along with technology, pedagogy has emerged as the most instrumental factor in self-direction, collaboration, resilience, and learning with confidence (Panda 2020). A new pedagogical discourse is evolving, highlighting the symbiotic journey between teachers and learners (Young 2020) which is pertinent in service-learning too. In this exploratory research, semiformal telephonic interviews, online written responses through Google form, and online Focused Group Discussions (FGDs) were adopted to collect relevant information, and subsequently data analysis and interpretation were done by triangulation. The work explored and documented the experience of the faculties of higher education regarding emerging trends of techno-pedagogical innovations which could be instrumental for service learning in RTL format. © 2022 by Nova Science Publishers, Inc.
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Since the outbreak of the COVID-19 pandemic, indoor air quality has become increasingly important. The interdisciplinary grouping of academic majors focused on the pursuit of solutions that identify or prevent the airborne transmission and inhalation, initially of Coronavirus and secondarily of viruses such as influenza. Throughout the research work, we aim to contribute by elaborating the teaching-learning technique to select and identify the optimal attributes of viruses' variants of the indoor atmosphere. The novelty is based on the objective to enable real-time identification of the density of the airborne molecules to prevent virus propagation. Several sensors and systems came into the spotlight by conducting a systematic literature review that, in conjunction with our innovative idea, could construct a revolutionary new solution that could eliminate the risk of exposure to viable viruses. The proposed teaching-learning based attribute selection optimisation is among the most popular bio-inspired meta-heuristic methods. Therefore, evolutionary logic and provocative performance can be widely utilised to solve the aforementioned humanitarian problem. The proposed frame constitutes three pivotal steps: the new update mechanism, the novel method of selecting the principal teacher in the teacher's phase, and the support vector machine method to compute the fitness function of optimisation. © 2022 IEEE.
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Background: Medical students are already under extreme academic pressure which causes disruption in their sleep patterns. Due to the COVID-19 pandemic, though they have been relieved of their hectic schedules this has also led to complete lack of hands-on training and bedside clinical teaching which might have given rise to increased anxiety in this population. Aims: To assess the sleeping pattern and determinants of poor sleep quality among medical students during the COVID-19 pandemic. Materials and Methods: A cross-sectional study was conducted among 343 undergraduate medical students of a tertiary care teaching hospital from August 17, 2020, to September 17, 2020, via an online questionnaire containing questions on sociodemographic parameters, lifestyle factors, Pittsburgh Sleep Quality Index, COVID-19-related stress and academic delay-related stress. Statistical Analysis: Analysis was done with the Statistical Package for the Social Sciences (SPSS) Version 20.0. Results: About 52.47% had a poor sleep quality;92.4% had a high level of academic uncertainty-related stress and 64.7% experienced a high level of COVID-19-related stress. Significant association was found between Poor Sleep Quality (PSQI score) and urban residence, nuclear family, smoking, excessive caffeine consumption, and high levels of COVID-19 stress. Conclusion: Although poor sleepers had decreased in number from before the COVID-19 pandemic, they were still much higher than the general population. This might be due to high levels of academic delay-related stress present virtually in the entire population. This can be circumvented by proper counseling of the students and sensitive planning of the academic activities once the pandemic will over. © Medical Journal of Dr. D.Y. Patil Vidyapeeth 2022.
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The use of Artificial Intelligence (AI) in combination with Internet of Things (IoT) drastically reduces the need to test the COVID samples manually, saving not only time but money and ultimately lives. In this paper, the authors have proposed a novel methodology to identify the COVID-19 patients with an annotated stage to enable the medical staff to manually activate a geo-fence around the subject thus ensuring early detection and isolation. The use of radiography images with pathology data used for COVID-19 identification forms the first-ever contribution by any research group globally. The novelty lies in the correct stage classification of COVID-19 subjects as well. The present analysis would bring this AI Model on the edge to make the facility an IoT-enabled unit. The developed system has been compared and extensively verified thoroughly with those of clinical observations. The significance of radiography imaging for detecting and identification of COVID-19 subjects with severity score tag for stage classification is mathematically established. In a Nutshell, this entire algorithmic workflow can be used not only for predictive analytics but also for prescriptive analytics to complete the entire pipeline from the diagnostic viewpoint of a doctor. As a matter of fact, the authors have used a supervised based learning approach aided by a multiple hypothesis based decision fusion based technique to increase the overall system’s accuracy and prediction. The end to end value chain has been put under an IoT based ecosystem to leverage the combined power of AI and IoT to not only detect but also to isolate the coronavirus affected individuals. To emphasize further, the developed AI model predicts the respective categories of a coronavirus affected patients and the IoT system helps the point of care facilities to isolate and prescribe the need of hospitalization for the COVID patients © 2021. the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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This study aims to analyze the dynamics of the published articles and preprints of Covid-19 related literature from different scientific databases and sharing platforms. The PubMed, ScienceDirect, and ResearchGate (RG) databases were under consideration in this study over a specific time. Analyses were carried out on the number of publications as (a) function of time (day), (b) journals and (c) authors. Doubling time of the number of publications was analyzed for PubMed "all articles" and ScienceDirect published articles. Analyzed databases were (1A) PubMed (01/12/2019-12/06/2020) "all_articles" (16) PubMed Review articles) and (1C) PubMed Clinical Trials (2) ScienceDirect all publications (01/12/2019- 25/05/2020) (3) RG (Article, Pre Print, Technical Report) (15/04/2020 - 30/4/2020). Total publications in the observation period for PubMed, ScienceDirect, and RG were 23000, 5898 and 5393 respectively. The average number of publications/day for PubMed, ScienceDirect and RG were 70.0 +/- 128.6, 77.6 +/- 125.3 and 255.6 +/- 205.8 respectively. PubMed shows an avalanche in the number of publications around May 10, the number of publications jumped from 6.0 +/- 8.4/day to 282.5 +/- 110.3/ day. The average doubling time for PubMed, ScienceDirect, and RG was 10.3 +/- 4 days, 20.6 days, and 2.3 +/- 2.0 days respectively. The average number of publications per author for PubMed, ScienceDirect, and RG was 1.2 +/- 1.4, 1.3 +/- 0.9, and 1.1 +/- 0.4 respectively. Subgroup analysis, PubMed review articles mean review <0 vertical bar 17 +/- 17 vertical bar 77> days: and reducing at a rate of -0.21 days (count)/day. The number of publications related to the COVID-19 until now is huge and growing very fast with time. It is essential to rationalize and limit the publications.
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In this study using publicly available panel data on bio-medical waste from all the Apollo-Hospitals in Chennai we try to explore the effect of COVID-19 pandemic on four categories (clinical, infectious, sharp and bottle waste) waste generation. The entire sample study is categorized into pre-lockdown (no-suspension on activities) and lockdown period (all activities were suspended) as was declared in India by the government in 2020. Using a fixed effect model technique and COVID-19 active treatment cases in the district where the hospital is located, we find significant effect of severity of COVID19 on hospital's wastes generation. Severity of COVID-19 in the districts increases the proportion of infectious waste generation but reduces the proportion of sharp and bottle waste generation to the total waste. The results indicate the huge increase in the bio-medical waste generation during the post COVID-19 era which pauses a threat to both sanitation and sustainable development goals, is a result of infectious waste and is generated from some few hospitals with specialty treatment like childcare and cancer, ignoring these features can lead to upward bias in the estimation. © The Electrochemical Society
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The unprecedented rise and spread of the pandemic in form of nCOVID-19 has really raised high concerns in the socioeconomic front. The usual diagnosis is made by an RT-PCR test, which is highly specific can incorrectly identify some nCOVID-19 individuals to cause a serious compromise in overall accuracy. Since the drug application in its full swing is still some months away, hence, the need of the hour is to find a more accurate technique which can be used by health care centers having basic point of care facilities. The increase in the number of cases in India and lack of test kits in some of the less known diagnostic centers has added more concerns to the increasing problems. Additionally, the test kits incur a significant cost making it less affordable to some of the diagnostic centers. Hence, this research group in this article has proposed an algorithm centered around the concept of Internet of Things, a dual deep learning based algorithm, and collating the decision by a strong decision fusion technique. The objective of the algorithm is to detect and isolate the nCOVID-19 subjects in a cost-effective way to keep a check on the spread. This pandemic detection and isolation technique (PANDIT) is based on two different radiography image technology and uses a state-of-the-art deep learning algorithm for the purpose. The radiography technique has long been the most acceptable technique for cases related to pneumonia. The group has developed the algorithm based on X-ray and CT scan as its training data. The novelty of this paper is best described by a multi-fold methodology. Firstly, the significance of radiography imaging for detecting and identification of COVID-19 subjects. A simple connected value chain driven by Internet of Things (IoT) would enable the isolation process in an efficient and accelerated manner. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Purpose: COVID-19, a global pandemic, first appeared in the city of Wuhan, China, and has since spread differently across geographical borders, classes, and genders from various age groups, sometimes mutating its DNA strands in the process. The sheer magnitude of the pandemic's spread is putting a strain on hospitals and medical facilities. The need of the hour is to deploy IoT devices and robots to monitor patients' body vitals as well as their other pathological data to further control the spread. There has not been a more compelling need to use digital advances to remotely provide quality healthcare via computing devices and AI-powered medical aids. Method: This research developed a deployable Internet of Things (IoT) based infrastructure for the early and simple detection and isolation of suspected coronavirus patients, which was accomplished via the use of ensemble deep transfer learning. The proposed Internet of Things framework combines 4 different deep learning models: DenseNet201, VGG16, InceptionResNetV2, and ResNet152V2. Utilizing the deep ensemble model, the medical modalities are used to obtain chest high-resolution computed tomography (HRCT) images and diagnose the infection. Results: Over the HRCT image dataset, the developed deep ensemble model is collated to different state-of-the-art transfer learning (TL) models. The comparative investigation demonstrated that the suggested approach can aid radiologists inefficiently and swiftly diagnosing probable coronavirus patients. Conclusion: For the first time, our group has developed an AI-enabled Decision Support System to automate the entire process flow from estimation to detection of COVID-19 subjects as part of an Intelligent Value Chain algorithm. The screening is expected to eliminate the false negatives and asymptomatic ones out of the equation and hence the affected individuals could be identified in a total process time of 15 minutes to 1 hour. A Complete Deployable System with AI Influenced Prediction is described here for the first time. Not only did the authors suggest a Multiple Hypothesis based Decision Fusion Algorithm for forecasting the outcome, but they also did the predictive analytics. For simple confined isolation or hospitalization, this complete Predictive System was encased within an IoT ecosystem. © 2021 Lifescience Global. All Rights Reserved.
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As the technology advances, the modern trend of lifestyle also advances. The doorbell has an important responsibility in home safety;it is one of the competent and steady systems needs to be developed for better safety which could be access at a low cost. In this era, there are many doorbells systems doing different operation. This paper focuses on touchless type automatic doorbell systems which will ring the bell automatically when a visitor approaches near the door. This system is intended to people, and due to the spread of COVID-19 pandemic situation, it would be one of the safety steps that can be taken against corona. People are now more careful about their everyday work and their family. In the year 2020, the whole world is trapped in unprecedented COVID-19 pandemic. The situation takes away all our normal lifestyle, and all the researches are going on in controlling the situation and finding a new way of life. In this work, the author is trying to establish a contactless door alarm for the household application. Motivation behind the work is that due to the Corona virus spread around the world, we have to take utmost care in every step of our life. If we use the normal door alarm, then there will be the issue of contact for every people who will arrive in. But if there will be a replacement of the conventional door alarm with the help of antenna technology, then it can solve the issue with a contactless alarm. In this paper, the author have used the HFSS software for the proposed antenna. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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The entire world faced locked down scenario due to the outbreak of nCOVID-19 corona virus outbreak. The fast and relentless spread nCOVID-19 has basically segmented the populace only into three subclasses, namely susceptible, infected, and recovered compartments. Adapting the classical SIR-type epidemic modeling framework, the direct person-to-person contact transmission is taken as the direct route of transmission of nCOVID-19 pandemic. In this research, the authors have developed two models of the nation-wide trends of the outburst of the nCOVID-19 infection using an SIR model and also an ARIMA model. They have studied the quantile plots, regression residual plots and R pair plots of the dataset by simple supervised machine learning algorithms. This study compares both models and higher correlation of the developed models with reality which suggests the extent of accuracy of these models. The study also suggested some possible way-out to get rid of this situation by providing a trade-off between ‘flattening of the curve’ as well as less economic turbulence. The projections are intended to provide an action plan for the socioeconomic counter measures to alleviate COVID-19 in India. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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In recent years it has been observed that there is a significant relationship between Geospatial Platform and Tourism Planning especially after the pandemic. Whenever any country's economy has had any significant impact, tourism has always been the first to get affected as people start cutting their travel budgets first. Tourism has a fairly direct role to play when it comes to COVID-19, which has spread extensively across most parts of our country with a large number of tourists. The pandemic COVID-19 has heavily hit the tourism economy of our country. Proper scientific planning is required for tourism that can bring the industry into right position. Recent Development in Geospatial Platform and its significance in Tourism Planning signifies that Geo-portal has a huge potential in the field of Tourism management. The present study is conducted into two parts by using primary and secondary data. In the first part, researchers have conducted a survey among 96 respondents of the Delhi and NCR region, to understand their opinion related to the information-seeking process during pretrip planning stage in context to geoportal use. The analysis of this is done with the help of SPSS indicated that if both COVID and destination related information can be gathered together through a single website will be a great help to plan the future trip. After which the second part of the study has been conducted to analyze the potential of web-based geo-portal in tourism taking Sikkim as sample. The study discusses the basic architecture and techniques of providing accurate travel-related information in a single window that helps visitors in their decision-making process. The final section of this chapter suggests the strategic implementation of a geoportal for effective tourism planning and management by creating a geoportal with ArcGIS 10.0 for Sikkim, India. © 2021 IEEE.