E-Adoption of Emerging Technology in the Health Sector During COVID-19

In the 21st century, COVID-19 made a profound impact on the world. This pandemic had a detrimental impact worldwide, causing massive economic damage and enormous mortality. Emerging technologies play an essential role in every sector, and the health sector is not exceptional in this line. This paper examines the health sector before, during, and after the COVID-19 era by taking a view of emerging technologies. Artificial intelligence, cloud computing, IoT, learning paradigms, blockchain, and others are emerging technologies. E-adoption of these technologies becomes important to face critical situations during COVID-19. Using these technologies, it is possible to care for and monitor remote patients by keeping medical record management. This study includes a brief examination of similar work. In addition, the impact of e-adoption on health sector is discussed in this research. Furthermore, this study suggested a paradigm for comprehending the application of developing technologies to manage and overcome the health sector???s burden. Finally, research is concluded with remarks on the future.

Perspectives Moving Forward

Growing differentiation among developing countries, declining capital flows and remittances, uncertain external aid, weakening global architecture, and rising China are reviewed. In 2021, developed countries, led by the United States, had begun a recovery. Considerable progress was achieved in developing countries prior to the COVID-19 pandemic in reducing poverty;infant and child mortality, stunting, wasting, anemia;increasing food security and nutrition;and improving gender empowerment. Impacts of the pandemic on the poverty-food security-nutrition-health nexus and implications for action are described. Agricultural total factor productivity growth across regions and countries shows huge differences in aggregate productivity growth performance. Countries with low growth also lagged in structural transformation. Premature deindustrialization in developing countries peaks at earlier levels of per capita GDP than for industrialized countries. All farm sizes can achieve productivity growth and success, but smallholders require the functioning of factor and product markets, with strong public policy. Productivity growth measures have not included changes in the quality or quantity of natural resources, but that is changing. Overall, the issue of low financial flows to developing countries needs to be addressed, and available resources need to be used strategically to leverage greater public and private investments to food and agriculture. Substantial investments are needed in human and institutional capital and physical infrastructure for new technologies. The G20's contribution to the global architecture for food and agriculture has not met its potential relative to a promising early start. For 54 industrial and emerging countries monitored by the Organisation for Economic Co-operation and Development, changes in their agricultural policies offer scope for improvement in the overall policy environment and investment climate at the global level, including release of valuable resources for building better. © Uma Lele, Manmohan Agarwal, Brian C. Baldwin, and Sambuddha Goswami 2021.

Food for All: International Organizations and the Transformation of Agriculture

This book is a historical review of international food and agriculture since the founding of the international organizations following the Second World War, including the World Bank and the Food and Agriculture Organization of the United Nations (FAO), the World Food Programme (WFP) and into the 1970s, when CGIAR was established and the International Fund for Agricultural Development (IFAD) was created to recycle petrodollars. The book concurrently focuses on the structural transformation of developing countries in Asia and Africa, with some making great strides in small farmer development and in achieving structural transformation of their economies. Some have also achieved Sustainable Development Goals (SDGs), particularly SDG2, but most have not. Not only are some countries, particularly in South Asia and sub-Saharan Africa, lagging behind, but they face new challenges of climate change, competition from emerging countries, population pressure, urbanization, environmental decay, dietary transition, and now pandemics. Lagging developing countries need huge investments in human capital, and physical and institutional infrastructure, to take advantage of rapid change in technologies, but the role of international assistance in financial transfers has diminished. The COVID-19 pandemic has not only set many poorer countries back but starkly revealed the weaknesses of past strategies. Transformative changes are needed in developing countries with international cooperation to achieve better outcomes. Will the change in US leadership bring new opportunities for multilateral cooperation?. © Uma Lele, Manmohan Agarwal, Brian C. Baldwin, and Sambuddha Goswami 2021.

A Gamified Social Media-Based Head and Neck Radiology Education Initiative of the American Society of Head and Neck Radiology: Viewership and Engagement Trends at 3 Years.

BACKGROUND AND PURPOSE: Social media has made inroads in medical education. We report the creation and 3-year (2018-2021) longitudinal assessment of the American Society of Head and Neck Radiology Case of the Week (#ASHNRCOTW), assessing viewership, engagement, and impact of the coronavirus disease 2019 (COVID-19) pandemic on this Twitter-based education initiative. MATERIALS AND METHODS: Unknown cases were tweeted from the American Society of Head and Neck Radiology account weekly. Tweet impressions (number of times seen), engagements (number of interactions), and new followers were tabulated. A social media marketing platform identified worldwide distribution of Twitter followers. Summary and t test statistics were performed. RESULTS: #ASHNRCOTW was highly visible with 2,082,280 impressions and 203,137 engagements. There were significantly greater mean case impressions (9917 versus 6346), mean case engagements (1305 versus 474), case engagement rates (13.06% versus 7.76%), mean answer impressions (8760 versus 5556), mean answer engagements (908 versus 436), answer engagement rates (10.38% versus 7.87%), mean total (case + answer) impressions (18,677 versus 11,912), mean total engagements (2214 versus 910), and total engagement rates (11.79% versus 7.69%) for cases published after the pandemic started (all P values < .001). There was a significant increase in monthly new followers after starting #ASHNRCOTW (mean, 134 versus 6; P < .001) and significantly increased monthly new followers after the pandemic started compared with prepandemic (mean, 178 versus 101; P = .003). The American Society of Head and Neck Radiology has 7564 Twitter followers throughout 130 countries (66% outside the United States). CONCLUSIONS: Social media affords substantial visibility, engagement, and global outreach for radiology education. #ASHNRCOTW viewership and engagement increased significantly during the COVID-19 pandemic.

Depression, anxiety, and stress of first-year MBBS students of Kanpur during coronavirus disease 2019 pandemic

Background: Globally undergraduate medical students have a high prevalence of depression, anxiety, and stress. Few studies from different parts of India have reported the increased prevalence of depression, anxiety, and stress in MBBS students due to the coronavirus disease 2019 (COVID-19) pandemic. However, the causal relationship between COVID-19 and the deranged mental health of the students have not been established. Further, data from the Kanpur city of Uttar Pradesh are lacking regarding the impact of COVID-19 on the mental health of 1st-year MBBS students. Aims and Objectives: The aim of the study was to explore the impact of COVID-19 on depression, anxiety, and stress of MBBS students in their first professional year. Material(s) and Method(s): The present cross-sectional study was conducted on undergraduate medical students of a Government Medical College in Kanpur, Uttar Pradesh, India. Seventy-two male and forty-six female students were involved in the study. Most of the students were 17-25 years old. An online Google form was used to know the score of depression, anxiety, and stress scale (DASS). The demographic profile of the participants was also assessed through an online survey using Google form. The Chi-square test was used for testing relationships between categorical variables wherever required and P <= 0.05 was considered significant. Result(s): The prevalence of depression, anxiety, and stress was 39%, 52%, and 37%. Gender, age, place of residence, mediation practice, and type of family had no relation with the DASS-21 score. Conclusion(s): First-year MBBS students of Kanpur have an almost similar prevalence of depression, anxiety, and stress as reported by the previous Indian studies during the pre-COVID-19 pandemic. Copyright © 2022 Pravesh Kumar, et al.

Modelling the impact of lock-downs in top-4 COVID-19 spreading states of India

A pandemic like COVID-19 being a highly infectious disease has severely affected mankind and business activities. Seeing the critical situation, the honourable Prime Minister of India has called for a lockdown in the entire country in order to suppress the spread of this pandemic. While there are many debates about the spread of disease and lockdown in the entire country, we wish to mathematically understand the diffusion of this pandemic in the context of four highly infected states of India. Moreover, through this paper, we wish to examine the impact of these lockdown periods in order to understand the spread of COVID-19. Copyright © 2022 Inderscience Enterprises Ltd.

Monitoring Social Distancing for Industries and in Public Areas Using Machine Learning

The deadly coronavirus disease 2019 (COVID-19) has been spreading vigorously and has led to a global crisis, with its spread to more than 220 countries and territories. About 153, 504, 608 confirmed cases of the coronavirus COVID-19 that originated from Wuhan, China, and a death toll of 3, 216, 577 deaths as on 3 May 2021. At the time of writing, India is the worst affected country by COVID-19 and the death ratio is increasing day by day. To date we have more than four vaccines available, but social distancing has been identified as the best way to overcome and fight against this disease. In order to ensure social distancing protocols in overcrowded places and workplace, this tool can monitor whether or not people are ensuring a safe distancing protocol from each other by analyzing real-time video streams with the help of a constant camera feed. To keep track of people in various workplaces, factories, and shops we can use this tool to their security camera systems and can monitor whether or not people are keeping a secured distance from one another. This chapter proposes a Machine Learning and Python-based framework for monitoring social distancing using surveillance video with the help of a camera. In this proposed framework, we are utilizing the YOLO v3, an object detection model for separating the foreground details from the background details and OpenCV for tracking the humans by using the bounded boxes and assigning IDs to them. © 2022 selection and editorial matter, Abhinav Goel, Anand Chauhan and A.K. Malik.

Modelling the impact of lock-downs in top-4 COVID-19 spreading states of India

A pandemic like COVID-19 being a highly infectious disease has severely affected mankind and business activities. Seeing the critical situation, the honourable Prime Minister of India has called for a lockdown in the entire country in order to suppress the spread of this pandemic. While there are many debates about the spread of disease and lockdown in the entire country, we wish to mathematically understand the diffusion of this pandemic in the context of four highly infected states of India. Moreover, through this paper, we wish to examine the impact of these lockdown periods in order to understand the spread of COVID-19.

Post COVID-19: cautious or courageous travel behaviour?

This paper focuses on post-pandemic travel behaviour and examines the relationship between destination-risk image and pre-travel behaviour using health-protective behaviour and media engagement as mediators. It empirically tests the model proposed by Bhati et al. The researchers adopt a pragmatist paradigm and utilise mixed methods to develop and test the adapted PMT framework. The findings confirm that, in the COVID-19 pandemic context, destination health-risk image has an effect on pre-travel behaviour via media engagement and health protective behaviour. Respondents preferred destinations that handled the pandemic crisis effectively, implemented hygiene and safety protocols, and had robust healthcare systems. © 2022 Asia Pacific Tourism Association.

Intelligent Solutions for Manipulating Purchasing Decisions of Customers Using Internet of Things during Covid-19 Pandemic

It is a well-known fact that consumers may gain significant benefits from the effective use of IoT in pandemic and post-pandemic settings. Security vulnerabilities can be seen in the ever-increasing Internet of Things (IoT) ecosystem from cloud to edge, which is crucial to note in this particular circumstance. Most merchants, even luxury stores, have failed to implement robust IoT cyber security procedures. Therefore, the researchers sought to put forth secondary research methodologies to bring forward efficient scrutiny regarding this particular issue to properly comprehend the influence of IoT in various devices, including a smartwatch, power displaying metre, brilliant weight showing gadgets and many more. The secondary research approach allowed the researchers to collect a large quantity of data quickly, acquiring a wide range of possible solutions for security and privacy issues in Consumer IoT (CIoT) devices. Secondary research also will enable scholars to compare and contrast several papers' philosophies and research findings to get a quick conclusion. To gather information, the researchers used publications and the internet efficiently. In this situation, it helped to save a significant amount of time. Findings suggested that vulnerabilities occur in smart IoT gadgets, including the intelligent power consumption metre and brilliant weight displaying widget, due to their low-standard and conventional security system. Thus, this paper has suggested possible solutions to protect IoT devices against phishing and theft attacks. © 2022 by Dr Avinash Rajkumar, Pankhuri Agarwal, Dr Mohit Rastogi, Dr Vipin Jain, Dr Chanchal Chawla and Dr Manoj Agarwal.

Impact of Public Debt on the Economic Growth of Subnational Economies in India: A Case of Uttar Pradesh

This study examines both the short- and long-run impact of public debt on the economic growth of Uttar Pradesh during the post-reform period of 30 years by employing the vector error correction model. The empirical analysis revealed that the increase in public debt-to-gross state domestic product ratio and interest payments burden would have an adverse impact on the long-run economic growth of UP, while having no significant impact on the short-run growth. It is also notable that the effective interest rate has negatively correlated with the gross capital formation in UP, and the latter has shown significant positive long-run association with the economic growth. In order to attract investments and economic growth, the state Government of UP should continue a countercyclical fiscal stance that would help in adhering to fiscal sustainability rules by smoothing out the repercussions of the COVID-19 pandemic. © 2022 Economic and Political Weekly. All rights reserved.

COV-ELM classifier: An extreme learning machine based identification of COVID-19 using chest X-ray images

Coronaviruses constitute a family of viruses that gives rise to respiratory diseases. COVID-19 is an infectious disease caused by a newly discovered coronavirus also termed Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). As COVID-19 is highly contagious, early diagnosis of COVID-19 is crucial for an effective treatment strategy. However, the reverse transcription-polymerase chain reaction (RT-PCR) test which is considered to be a gold standard in the diagnosis of COVID-19 suffers from a high false-negative rate. Therefore, the research community is exploring alternative diagnostic mechanisms. Chest X-ray (CXR) image analysis has emerged as a feasible and effective diagnostic technique towards this objective. In this work, we propose the COVID-19 classification problem as a three-class classification problem to distinguish between COVID-19, normal, and pneumonia classes. We propose a three-stage framework, named COV-ELM based on extreme learning machine (ELM). Our dataset comprises CXR images in a frontal view, namely Posteroanterior (PA) and Erect anteroposterior (AP). Stage one deals with preprocessing and transformation while stage two deals with feature extraction. These extracted features are passed as an input to the ELM at the third stage, resulting in the identification of COVID-19. The choice of ELM in this work has been motivated by its faster convergence, better generalization capability, and shorter training time in comparison to the conventional gradient-based learning algorithms. As bigger and diverse datasets become available, ELM can be quickly retrained as compared to its gradient-based competitor models. We use 10-fold cross-validation to evaluate the results of COV-ELM. The proposed model achieved a macro average F1-score of 0.95 and the overall sensitivity of 0.94 +/- 0.02 at a 95% confidence interval. When compared to state-of-the-art machine learning algorithms, the COV-ELM is found to outperform its competitors in this three-class classification scenario. Further, LIME has been integrated with the proposed COV-ELM model to generate annotated CXR images. The annotations are based on the superpixels that have contributed to distinguish between the different classes. It was observed that the superpixels correspond to the regions of the human lungs that are clinically observed in COVID-19 and Pneumonia cases.

A Novel Compressed and Accelerated Convolution Neural Network for COVID-19 Disease Classification: A Genetic Algorithm Based Approach

Covid 19 is an infectious disease caused by SARS-Cov-2 virus. It generally affects respiratory system of human and can be fatal if not treated early. It can be caused by coming in contact with an infected person through his/her mouth or nose due to transmission of small liquid particles by way or sneezing or coughing. Since the doctors generally depend on CT scan of suspected patients to confirm if he or she is infected. Proposed research focuses on using CT scan images for Covid-19 diagnosis. In proposed Convolution Neural Network (CNN), there are three convolution layer with 32, 16 and 8 filters in respective layers. The training accuracy of proposed model is 96.71% and testing accuracy is 84.21%. The model was also trained and tested using transfer learning and best test accuracy of 94.73% was obtained using VGG19 pre-trained network. Similarly machine learning methods were also used to classify the images and Random Forest classifier gave best accuracy of 93.33%. Since storage size of pre-trained models was very large hence they were compressed using Genetic Algorithm (GA) without much loss in performance. The VGG16 model could be compressed by 81%, AlexNet by 77.8% and VGG19 by 65.74% without drop in the F1-score. The inference time was also reduced considerably by around 79% for VGG16, 78% for VGG19 and 38% for AlexNet. © 2022, Springer Nature Switzerland AG.

Data Mining Tool to Help the Scientific Community Develop Answers to Covid-19 Queries

Science has time and again proven to be one of the most powerful tools in finding solutions to the problems faced by the world. Let it be natural or man-made challenges, hard work put into finding efficient answers to tackle them has proven to safeguard the ecosystem. Sometimes the research community is put under pressure when humanity faces the challenge of survival like the Covid-19 pandemic. A great extent of published works needs to be studied to find an optimal solution to existing or new queries related to the virus. In this research work, we build an efficient data mining tool using the CORD-19 Dataset to help the community come up with answers to Covid-19 related questions. We use a combination of semantic and keyword search to reduce the solution space of our model. Our model makes use of parallelism, paraphrasing, and state-of-the-art natural language processing techniques which will serve as a time and energy-saving tool for the information need of all doctors and researchers who are trying to put an end to the pandemic and avoid future possible outbreaks. © 2021 IEEE.

Knowledge assessment and outcomes of otolaryngology practice amidst the COVID-19 pandemic in a busy ENT department.

INTRODUCTION: Otolaryngology health personnel are at high risk of acquiring COVID-19 disease and, hence, are likely to have high stress levels. This study was designed to evaluate the feedback of otolaryngology healthcare workers in ENT departments who are managing patients in the coronavirus pandemic. METHODS: A questionnaire focused on all aspects of healthcare delivery was completed by otolaryngology healthcare workers. RESULTS: The findings, based on statistical analyses, included high stress levels and inadequate disease-related information in these workers. CONCLUSIONS: Healthcare authorities need to take care of issues related to mental health in healthcare professionals in addition to spreading awareness about safe practices. Further studies are needed to continuously monitor feedback from personnel as the coronavirus pandemic unravels in the future.

Covid-19 diagnosis in asymptomatic: Rt-qpcr or any other cost-effective method?

On January 30th, 2020, the World Health Organization announced the COVID-19 outbreak as a Public Health Emergency of International Concern. In the view of this pandemic, early diagnosis is the mainstay for halting the disease progression. Quantitative real-time Reverse Transcriptase-Polymerase Chain Reaction (RT-qPCR) has been established as the cornerstone for the diagnosis of COVID-19. However, the significance of RT-qPCR positivity in asymptomatic cases with travel history, mass screening purposes, or close contact tracing remains debatable as their period of infectivity is unknown. We present a case series of 42 asymptomatic patients, who tested positive for COVID-19 and were subjected to hospitalization until they tested negative as per Government guidelines. Through our case series, we have tried to establish that RT-qPCR testing as a diagnostic criterion for asymptomatic patients with no known contact history can lead to increased psychological and economic burden on the Government, the patient as well as his family. It also overburdens the health care resources and therefore, raises the question about its necessity among this cohort of asymptomatic cases and thus the possible role of other methods in the diagnosis and isolation of such cases.