Testing Strategy of Covid-19: A Mechanistic Approach

The deadliness associated with the COVID-19 disease caused by the SARS-CoV-2 virus plunged the entire global community into the worst times of this century. It was globally realized that a timely diagnosis, effective treatment, and prevention were the key factors in its management. Responding to the emergent scenario, sequencing of the genome of this virus was performed and shared with the scientific community in the nick of time. Thereafter, diverse sets of test kits to detect the SARS-CoV-2 and to detect the antibodies in the patients of the COVID-19 were developed at the war scale. It was indeed a war but with a microscopic spiky package of 50–200 nanometres in diameter having a genome of about 29.9 kb encoding deadly tools in its arsenal. For the reason, patients of the COVID-19 exhibit diverse symptoms from mild influenza-like to potentially fatal ones that overlap with other respiratory diseases, only efficient testing was essential during the early stages of infection to identify COVID-19 patients among others. The diverse test kits designed exclusively for rapid and accurate outcomes proved instrumental in identifying individuals among asymptomatics, presymptomatics, and symptomatics. The test kits have also been playing an appreciable role in identifying communities with hot spots to facilitate proper management. To meet the demand of higher throughput and simplification of the testing process, novel ways were devised that did not otherwise allow the testing spree to get hit with pandemic supply bottlenecks. Mechanistic models have played an essential role in shaping public health policy. The regulatory agencies, both at the world health and the regional public health levels, shared the knowledge and experience on the test kits that helped in the development and improvement in the testing capability and efficiency of the testing infrastructure. The information about the emergence of variants of the SARS-CoV-2 happening due to intrinsic behavior of the viral genomes drew attention of the test kit developers, regulatory agencies, and end-users to be vigilant over the test outcomes. Offering a mechanistic approach, in this chapter, testing strategies for the detection of SARS-CoV-2 virus and COVID-19 disease are delineated. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2021.

Impact of Covid-19 Pandemic on a British Columbia Children's Hospital Youth Advisory Committee

Purpose of Study: Between September 2020 and November 2021, a survey was developed in partnership with children, youth, and community members experiencing vulnerabilities in a Vancouver Inner City Neighbourhood (ICN) to explore challenges encountered during the COVID-19 pandemic. In the survey, participants were asked questions about their financial status, housing security, food accessibility, and other social determinants of health. Other equity-seeking groups in Vancouver, including youth experiencing developmental and/or other medical diversity, wished to adapt the ICN COVID-19 survey to explore the impact of the pandemic in their community. These youth are active members of the province's pediatric tertiary care teaching hospital's Youth Advisory Committee (YAC), and in sharing their lived experience as patients, they strive to improve the quality of healthcare for children and youth throughout British Columbia. The objectives of this study were to: 1) adapt the COVID-19 survey to capture the views and needs of youth experiencing developmental and/or other medical diversity;and 2) identify how the COVID-19 pandemic impacted this group's social determinants of health. Methods Used: Ethics board approval was obtained for this observational, cross-sectional study (H20-00987). The research team and YAC co-constructed an adapted COVID-19 survey via Zoom dialogues. YAC members completed the survey online via Qualtrics from May 2022-August 2022. Demographic information and survey results were analyzed using descriptive statistics. Summary of Results: In total, 12 participants completed the survey, including 11 youth and 1 staff member. The median age of the youth participants was 23 years (n=11, min=14, max=29). During the COVID-19 pandemic, 82% (9/11) of youth reported changes in their ability to attend work or school, 36% (4/11) reported concern around reliable and affordable access to medications/medical treatment, and 46% (5/11) reported difficulty in caring for themselves. 46% (5/11) of youth also reported difficulty in caring for older adults or people in their families with disabilities. Many youth (6/11;55%) reported they had less than five people to turn to for support in times of stress, and 46% (5/11) of youth reported the pandemic changed their ability to connect with these people. Furthermore, 82% (9/11) of youth reported experiencing some level of distress related to the pandemic. 73% (8/11) of youth reported heightened anxiety, 82% (9/11) reported worsened mood, 55% (6/11) reported difficulty sleeping, and 64% (7/11) reported difficulty exercising. Conclusion(s): Youth with developmental and/or other complex medical diversity experienced difficulties accessing work and education, reliable and affordable medical care, and social support due to the COVID-19 pandemic. The pandemic negatively impacted the social, emotional, and physical wellbeing of these youth, indicating a need for future dialogue and advocacy to ensure the views and voices on rights of children and youth are honoured.

Corporate governance and risk management: a systematic review and synthesis for future research

Purpose: The world economy has experienced several economic downturns, and each phase emphasised that no industry is immune to inappropriate risk-management practices. Against the backdrop of the recent COVID-19 pandemic, which had far more effects than a financial crisis, the existing paper reviewed the state of current research in the realm of corporate governance and risk-management practices. Design/methodology/approach: This study rigorously followed a systematic approach in identifying, selecting and critically synthesising the existing literature on corporate governance and risk management. The review was carried out on the Web of Science and Scopus database until December 31, 2022. In total, 72 research works were examined and reviewed. Findings: This systematic literature review showed that companies with strong governance mechanisms are less exposed to corporate risks. Several attributes, such as higher institutional ownership stakes, concentrated family ownership structures, lower CEO compensation and duality, higher presence of females in the management, better board dynamics in terms of independent boards and gender diversity are all strong mechanisms for mitigating risk. Additionally, socially responsible companies are better positioned to mitigate corporate risks. Furthermore, several themes emphasising the governance risk link have been identified to understand this domain further. Originality/value: By analysing and synthesising existing corporate governance and risk-management themes, this study ascertained various research gaps that can be addressed in future studies. Furthermore, drawing on this paper's essential cues, researchers can significantly differentiate their work from existing ones in the field. © 2023, Emerald Publishing Limited.

Modified Extraoral Retentive Technique for Retaining Obturator in Bilateral Maxillectomy Defects Secondary to Mucormycosis in Healing Phase

Background: Our country struggled with a plethora of mucormycosis cases during the second wave of coronavirus disease 2019 (COVID-19). The dental community was burdened with different maxillectomy defects in which bilateral maxillectomy cases posed a significant challenge for rehabilitation. Rehabilitating a patient after maxillectomy with conventional obturator prosthesis to close oronasal communication can be an effective way of restoring speech, deglutition, and mastication, and preventing nasal regurgitation. But the main problem is the retention of an obturator in large defects, and there is sparse literature pertaining to the management of bilateral maxillectomy cases in the surgical obturation phase. Purpose: The purpose of this case was to rehabilitate patients with a bilateral maxillectomy defect in the healing phase with an obturator prosthesis retained using extraoral aid where intraoral retention is not possible. Technique: Two different modification techniques in the extraoral retentive method were tried here to overcome difficulties encountered during the rehabilitation of such cases, with special emphasis on augmenting patient comfort. The customized headgear facebow assembly was used for extraoral retention. In the first case, an orthodontic was used to retain the prosthesis to the customized headgear or extraoral elastic straps through orthodontic elastics. The orthodontic facebow has two parts inner and outer bow. The inner bow was attached to the obturator at the level of the occlusion plane by fabricating bilateral posterior acrylic pillars so that the outer bow passes along the commissures of the mouth, but there was the problem of lip trap and feeding difficulties due to the horizontal connecting bar. To overcome these problems, in the second case, the facebow was customized using a 19 gauge orthodontic wire to eliminate horizontal component. Conclusion: The obturator with extraoral retention in the healing phase is a viable retentive aid in patients with extensive maxillary defects, and it was found that the patient was more comfortable with a customized facebow-retained obturator. © TheAuthor(s). 2022.

GRAPES: semi-automatic approach for forecasting models to predict GameStop prices using cloud computing and machine learning

Since the Covid-19 pandemic, we have seen a surge of retail investors that now can easily trade anywhere in the world with just a Smartphone. Social media groups like Reddit’s WallStreetBets have almost put a few hedge funds close to bankruptcy by driving GameStop share prices to the sky. In this work, we propose a framework called GRAPES which uses Cloud Computing and Machine Learning to explore various forecasting techniques in predicting GameStop prices. In addition to this, this work also provides light insight into semi-automating forecasting models using tools such as Google Cloud Platform (GCP), Airflow and Streamlit. Moreover, we monitored the investment funds from Ark Invest to provide additional insight into the market in general. Overall, the paper shows the Autoregressive Moving Average (ARMA) model gives the best accuracy based on the Mean Absolute Percentage Error (MAPE) of 1.12%. This means the predictive model is out with an average of 1.12% from the actual price. Copyright © 2022 Inderscience Enterprises Ltd.

GRAPES: semi-automatic approach for forecasting models to predict GameStop prices using cloud computing and machine learning

Since the Covid-19 pandemic, we have seen a surge of retail investors that now can easily trade anywhere in the world with just a Smartphone. Social media groups like Reddit's WallStreetBets have almost put a few hedge funds close to bankruptcy by driving GameStop share prices to the sky. In this work, we propose a framework called GRAPES which uses Cloud Computing and Machine Learning to explore various forecasting techniques in predicting GameStop prices. In addition to this, this work also provides light insight into semi-automating forecasting models using tools such as Google Cloud Platform (GCP), Airflow and Streamlit. Moreover, we monitored the investment funds from Ark Invest to provide additional insight into the market in general. Overall, the paper shows the Autoregressive Moving Average (ARMA) model gives the best accuracy based on the Mean Absolute Percentage Error (MAPE) of 1.12%. This means the predictive model is out with an average of 1.12% from the actual price.

Health Insurance Cost Prediction using Machine Learning

This paper represents a machine learning-based health insurance prediction system. Recently, many attempts have been made to solve this problem, as after Covid-19 pandemic, health insurance has become one of the most prominent areas of research. We have used the USA's medical cost personal dataset from kaggle, having 1338 entries. Features in the dataset that are used for the prediction of insurance cost include: Age, Gender, BMI, Smoking Habit, number of children etc. We used linear regression and also determined the relation between price and these features. We trained the system using a 70-30 split and achieved an accuracy of 81.3%. © 2022 IEEE.

Effect of COVID-19 pandemic on women entrepreneurial sustainability: the role of Islamic microfinance institutions

Purpose The breakout of the COVID-19 pandemic has forced governments all over the globe to bring radical changes to all walks of life. Strict lockdowns are not only adversely affecting the social, economic, and psychological wellbeing of individuals but also questioning the sustainability of most businesses. In wake of the current scenario, this study is aimed at exploring how the COVID-19 pandemic is influencing the sustainability of entrepreneurship particularly from a female perspective and further providing insights into the role of Islamic financial institutions in the sustainability of businesses during COVID-19. Design/methodology/approach This is a qualitative study that takes social constructivism approach to study the underlying phenomenon. Semi-structured interviews are conducted to collect primary data. Secondary data are also utilized in this study to theoretically define various concepts relating to entrepreneurial sustainability. The application of thematic analysis revealed various risks associated with sustainability. The interviews reveal the ground realities and tell us about the hardships being faced by the entrepreneurs due to ongoing crises. The participants of the study also shed light on the role of Islamic financial institutions during the pandemic. Findings The study results revealed that it may look impossible for women entrepreneurs to halt or avoid the adverse consequences of the pandemic;however, a few female entrepreneurs strived to guard their existing portfolios with the help of Islamic microfinance institutions. Whereas, several women, especially those running home-based businesses, lost their income streams. Despite these rapid challenges, most female entrepreneurs are working on inventive online systems to sustain their business activities during the crisis. Finally, guidelines are suggested which can help achieve sustainability of the entrepreneurial startups. Research limitations/implications The outcomes of this study are expedient for funding agencies, government authorities and Islamic financial institutions as well as for non-government institutions to establish sustainable and broader policies for women to become successful entrepreneurs during severe disasters like COVID-19. Moreover, the study is a helpful tool for women entrepreneurs to avert the worst impact of the pandemic with the help of Islamic microfinance institutions. The themes of this study help generate realistic information to appraise the strategies to create facilitating business environments that drive the women to carry out the entrepreneurial activity during any crisis like the COVID-19. Practical implications The results of this study provide evidence that crisis can be anticipated up to some extent if entrepreneurs become able to take proactive decisions in case of expected or identifiable threats. The study may also help the women entrepreneurs to comprehend the serious consequences of the pandemic by shifting their mode of financing to Islamic finance. Although this pandemic is a cause of physical discomfort instead this research may encourage the female entrepreneurs not to lose heart, just find the potential opportunities for their home-based and small businesses and manage funding from the Islamic microfinance institutions. Originality/value The study adds to the existing literature on entrepreneurial sustainability with a particular focus on the role of Islamic microfinance institutions for women entrepreneurs' sustainability in Pakistan. Secondly, the study employs the entrepreneurial sustainability model (ESM) that, according to the best of our knowledge, has not been used by the researchers earlier to study the given research phenomenon. Thirdly, the study findings are expedient for funding agencies, government authorities and financial institutions as well as for non-government institutions to establish sustainable and broader policies for women to become successful entrepreneurs during disasters like COVID-19.

AIBLOCK: Blockchain based Lightweight Framework for Serverless Computing using AI

Artificial intelligence (AI)-based studies have been carried out recently for the early detection of COVID-19. The goal is to prevent the spread of the disease and the number of fatal cases. In AI-based COVID-19 diagnostic studies, the integrity of the data is critical to obtain reliable results. In this paper, we propose a Blockchain-based framework called AIBLOCK, to offer the data integrity required for applications such as Industry 4.0, healthcare, and online banking. In addition, the proposed framework is integrated with Google Cloud Platform (GCP)-Cloud Functions, a serverless computing platform that automatically manages resources by offering dynamic scalability. The performance of five different machine learning models is evaluated and compared in terms of Accuracy, Precision, Recall, F-Score and Area under the curve (AUC). The experimental results show that decision trees gives the best results in terms of accuracy (98.4 %). Further, it has been identified that utilization of Blockchain technology can increase the load on memory. © 2022 IEEE.

ELECTROCONVULSIVE THERAPY (ECT): A CLINICAL AND TECHNICAL UPDATE

Background: Electroconvulsive therapy (ECT) remains most effective treatment for mood and other psychiatric disorders. New techniques are emerging, while others are being refined based on evidence to maximise benefits and reduce side effects. Research, real-life data and lived experience are integral elements of such progress. The Royal Australian and New Zealand College of Psychiatrists, Section of ECT and Neurostimulation (RANZCP SEN) have developed guidelines to nurture culture of academic rigour and clinical utility. Major challenges face neurostimulation such as utilisation in adolescents and children, which remains somewhat controversial and regulated in legislations in most jurisdictions, and the new COVID-19 pandemic challenge to clinical practice over past 2 years. Objectives: To present clinical and technical aspects of ECT, such as factors affecting efficacy and side effects, and to improve knowledge of the latest evidence from research and real-life data. The symposium will provide an opportunity for clinicians to understand the latest evidence while allowing a personalised treatment approach. Methods: Relevant literature and naturalistic data will be presented to illustrate the potential role of neuroimaging as a neuronavigational tool in ECT electrode placement, the impact on efficacy and side effects and proposing guidance for more accurate dosing, Case reports pertinent to ECT in adolescents and children, in addition to results from an international survey on the impact of COVID-19 on ECT practice, will be presented. Findings: Neurostimulation therapies are evolving fields. Ongoing research, naturalistic data and lived experience can guide new techniques and refinements. Conclusion: Psychiatrists and clinicians are encouraged to develop evidence-based personalised treatment plans for patients.

EVALUATING THE EFFICACY OF FREE STYLE LIBRE FLASH GLUCOSE MONITORING IN THE OLDER POPULATION

Background and Aims: Background: Frailty associated with older age increases the risk of complications for diabetes and its treatment, in particular hypoglycaemia. Free Style Libre is a form of flash glucose monitoring that has been commissioned for use in people living with type 1 diabetes who meet NHS criteria and can reduce hypoglycaemia. Aims: Evaluate whether patients ≥65-years-old across Birmingham Heartlands Hospital (BHH) and Solihull Hospital (SOL) are meeting AATD time in range CGM targets. Methods: BHH and SOL patients ≥65-years-old using Free-Style Libre until June 2021 were included in the study population. Patient data such as average scans per day, TIR, TAR, TBR and time <3.0mmol/L were transferred from Libreview. Demographic and HbA1c data were retrieved from electronic patient records. Results: 65 patients were identified, 44 were eligible for inclusion. 68.2% (30/44) met the TIR target of >50%, 45.5% (20/ 44) met the TAR target of <10% and 18.2% (8/44) met the TBR target of <1%. Further analysis of TBR, comparing patients to the AATD recommendation for younger people, found that 75% (33/ 44) spent <4% of time below range. 18.2% (8/44) spent <1% in hypoglycaemia (<3mmol/L) and 81.8% (36/44) spent ≥1% in hypoglycaemia. Conclusions: Despite using Free Style Libre, older patients remain at significant risk of hypoglycaemia. This risk should be managed in outpatient clinics using hypo-awareness and frailty scores. Free Style Libre data can be used as per the ABCD risk stratification criteria for triaging these patients with high risk hypoglycaemia during the COVID-19 recovery phase.

Sustainable supply chain management performance in post COVID-19 era in an emerging economy: a big data perspective

Purpose: Big data analytics capabilities are the driving force and deemed as an operational excellence approach to improving the green supply chain performance in the post COVID-19 situation. Motivated by the COVID-19 epidemic and the problems it poses to the supply chain's long-term viability, this study used dynamic capabilities theory as a foundation to assess the imperative role of big data analytics capabilities (management, talent and technological) toward green supply chain performance. Design/methodology/approach: This study was quantitative and cross-sectional. Data were collected from 374 executives through a survey questionnaire method by applying an appropriate random sampling technique. The authors employed PLS-SEM to analyze the data. Findings: The findings revealed that big data analytics capabilities play a significant role in boosting up sustainable supply chain performance. It was found that big data analytics capabilities significantly contributed to supply chain risk management and innovative green product development that ultimately enhanced innovation and learning performance. Moreover, innovation and green learning performance has a significant and positive relationship with sustainable supply chain performance. In the post COVID-19 situation, organizations can enhance their sustainable supply chain performance by giving extra attention to big data analytics capabilities and supply chain risk and innovativeness. Originality/value: The paper specifically emphasizes on the factors that result in the sustainability in supply chain integrated with the big data analytics. Additionally, it offers the boundary condition for gaining the sustainable supply chain management. © 2022, Emerald Publishing Limited.

An Inquisitive Prospect on the Shift Toward Online Media, Before, During, and After the COVID-19 Pandemic: A Technological Analysis

Online media plays a vital role in defining the future of tomorrow. Almost every field in the present-day is dependent on technology and online media either for procuring better outputs or for the satisfaction of end clients. In this paper, the authors have tried to bring out the various factors that led to the shift of the majority of individuals to online media, briefly discussing its impact on the economy and social factors. It leverages the facts of how the offline media got impacted not only during the days of the novel coronavirus but before as well. It has been plausibly displayed that fake word gets out quicker and more considerably than authentic news utilizing online media. A boost toward the use of E-platforms has predominantly been taken a closer look at in the middle sections of the paper. Further, the exploratory analysis fore- casts the number of online media users by 2031 and presents inquisitive visualizations on the study of various websites during, before, and after the pandemic in the later sections of the paper. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Tackling COVID-19 Infodemic Using Deep Learning

Humanity is battling one of the most deleterious virus in modern history, the COVID-19 pandemic, but along with the pandemic there’s an infodemic permeating the pupil and society with misinformation which exacerbates the current malady. We try to detect and classify fake news on online media to detect fake information relating to COVID-19 and coronavirus. The dataset contained fake posts, articles and news gathered from fact checking websites like politifact whereas real tweets were taken from verified twitter handles. We incorporated multiple conventional classification techniques like Naive Bayes, KNN, Gradient Boost and Random Forest along with Deep learning approaches, specifically CNN, RNN, DNN and the ensemble model RMDL. We analyzed these approaches with two feature extraction techniques, TF-IDF and GloVe Word Embeddings which would provide deeper insights into the dataset containing COVID-19 info on online media. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

iFaaSBus: A Security- and Privacy-Based Lightweight Framework for Serverless Computing Using IoT and Machine Learning

As data of COVID-19 patients is increasing, the new framework is required to secure the data collected from various Internet of Things (IoT) devices and predict the trend of disease to reduce its spreading. This article proposes security- and privacy-based lightweight framework called iFaaSBus, which uses the concept of IoT, machine learning (ML), and function as a service (FaaS) or serverless computing to diagnose the COVID-19 disease and manages resources automatically to enable dynamic scalability. iFaaSBus offers OAuth-2.0 Authorization protocol-based privacy and JSON Web Token & Transport Layer Socket protocol-based security to secure the patient's health data. iFaaSBus outperforms response time compared to nonserverless computing while responding to up to 1100 concurrent requests. Further, the performance of various ML models is evaluated based on accuracy, precision, recall, F-score, and area under the curve (AUC) values, and the K-nearest neighbor model gives the highest accuracy rate of 97.51%.

Student perceptions of rapid transition to emergency remote learning in a hands-on agricultural college

The COVID-19 pandemic in spring 2020 led to university closures and little time to convert all face-to-face courses online. We investigated how students in the College of Agriculture, Food, and Environmental Sciences at Cal Poly, San Luis Obispo, CA perceived emergency remote teaching during the early stages of the pandemic. The college maintains a hands-on pedagogy and “Learn by Doing” approach that is challenging to replicate in a remote setting. We conducted a survey of student experiences (n = 304) during the spring of 2020. We found that most students had a negative experience with aspects of emergency remote teaching during the study period. Approximately two-thirds perceived courses to be less effective at increasing knowledge and career-related skills;approximately three-quarters stated group problem solving was less effective;and approximately two-thirds were dissatisfied with the quantity and quality of course content. Around 10% of students felt courses were more effective in these areas. Familiar instructional modes (synchronous and pre-recorded lectures) were the most common and preferred by students (with 70 to 85% finding them useful vs 7 to 15% finding them useless), even though other instructional modes can be more effective strategies for online teaching. Our results highlight the need for concrete experiences in agriculture and natural resources courses. We suggest strategies for faculty and students to improve remote teaching outcomes in agriculture and natural resources disciplines. © 2021 The Authors. Natural Sciences Education © 2021 American Society of Agronomy

Negotiating mental health during the COVID-19 pandemic: performing migrant domestic work in contentious conditions. (Special Issue: The COVID-19 pandemic: precarious migrants and outbreak inequality.)

Migrant domestic work is performed in precariously (im)mobile working conditions that mark the subaltern body in a state of constant lived experience with and in strife. In Singapore, the structural context of hire amplifies conditions of servitude, indebtedness, and subalternity that have implications for mental health. This study documents mental health narratives by migrant domestic workers during the COVID-19 pandemic, registering how mental health is negotiated amid dissension in the performance of precarious labor. While functional employment structures enabled and empowered well-being, dysfunctional structures disrupted mental health meanings, creating layers of constant contention for domestic workers to broker, limiting opportunities for mental health and well-being. Narratives gathered indicate systemic mental health precarities tied to workplace dysfunctions.