Impact of COVID-19 pandemic and national lockdown on hospital attendance of various infectious disease patients at an apex infectious disease hospital of Eastern India

Aim: To find out variation in hospital attendance and admission for various infectious diseases (IDs) during the national lockdown as compared to prelockdown era. Materials and Methods: This observational descriptive cross-sectional study was conducted at a state-level ID hospital in West Bengal. Data related to the turnout of ID patients at the hospital outpatient department and indoor admission during the lockdown and unlock phases of 2020 were collected by review of hospital records and compared with the pre-COVID period of 2019. Collected data were entered into an MS Excel sheet, and analysis was performed by SPSS 20.0. Results: Since April 2020, inpatient and outpatient turnout has gone far below the similar months of 2019. Outpatient consultation, indoor admission, anti-rabies clinic attendance, and childhood immunization against vaccine-preventable diseases had decreased significantly by 66.9%, 84.3%, 87%, and 85.2%, respectively, during lockdown (April-June 2020) compared to January-March 2020. Dramatic reduction noticed in hospital admission of diarrhea (93%), measles (96.5%), chicken pox (99.2%), acute respiratory illness (93.9%), diphtheria (66.7%), rabies (66.6%), and typhoid (98.2%) patients;while no cases of tetanus, swine flu, meningococcal meningitis, and mumps were admitted during lockdown period. Conclusion: It is evidenced that measures put in place by the government to curb COVID-19 spread disrupted other ID patient attendance at hospitals. Stigma and fear of contracting COVID-19 during hospital visits and unavailability of transport due to lockdown could be the main reason for reduced attendance.

The Genomes of SARS-CoV-2 and Related Coronaviruses: Are Accessory Proteins Key for Pathogenesis?

Objective: In January 2020, scientists deciphered the first genome of SARS-CoV-2 that has created a ravage in the world by infecting over 30 million people worldwide with above 0.95 million deaths as of mid-September 2020. With no potent therapeutics against COVID-19, research-ers around the world are relentlessly working for the development of a vaccine that can ease the pain the world is suffering today, both in terms of economic and psychological instability. Understanding the genome of SARS-CoV-2 is essential to decipher the keys that would help scientists to develop drugs or vaccines to prevent the disease. Method(s): Coronaviruses are not unknown to the human as other than SARS-CoV-2, at least six ad-ditional coronaviruses (SARS-CoV, MERS-CoV, HCoV-229E, HCoV-NL63, HCoV-OC43, and HCoV-HKU1) are known that causes mild to severe diseases in human. We have compared the se-quences of these seven coronaviruses to identify the key regions which are responsible for pathoge-nesis. Result(s): The genomes of the seven coronaviruses that are known to infect humans differ signifi-cantly, especially in the regions of accessory genes. Conclusion(s): The analysis of these virus genomes is the key to find out targets for the development of a potent drug or vaccine against COVID-19.Copyright © 2021 Bentham Science Publishers.

UV-C Disinfection Smart Device

SARS-CoV-2 started a global epidemic that resulted in COVID-19, a real infectious disease that disrupted regular living all over the world. Sterilizing our hands is crucial since the virus and other diseases are spread by touching contaminated surfaces. In this manuscript, a prototype for low-cost sterilisation is created that uses an IR thermal sensor to measure temperature and UV C light rays to disinfect our hands. Numerous bacteria are affected throughout the sanitization process, which has a number of advantages over chemical-based sanitization techniques. In contrast to relevant, it is also easy to customise. There are proprietary devices that can be purchased commercially. This gadget is an excellent illustration of open-source technology. automatic, quick, and safe hand sanitising device. © 2022 IEEE.

Secure 6G Communication in Smart City Using Blockchain

The next-generation cellular network will aim to overcome the existing Fifth Generation (5G) networks' shortcomings. At the moment, academics and business are concentrating their efforts on the Sixth Generation (6G) network. This 6G technology is expected to be the next great game-changer in the telecommunications sector. Due to the outbreak of COVID-19, the entire globe has turned to virtual meetings and live video interactions in various fields as healthcare, business, and education. We explore the most recent viewpoints and future technology trends that are most likely to drive 6G in this paper. The incorporation of blockchain in 6G, will allow the network to efficiently monitor and manage resource consumption and sharing. We explore the potential of blockchain for sharing in 6G utilizing a variety of application scenarios in the smart city. To strengthen security and privacy in 6G networks, we introduce potential difficulties and solutions with various 6G technologies. In addition, we examine the security and privacy issues that may arise as a result of the current 6G standards and prospective 6G uses. Overall, our study aims to give insightful direction for future 6G security and privacy research. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

GPnome-wide mutation/SNP analysis. biological characteristics, and Pan-Inaia prevalence of SARS-CoV-2 variants or concern

The origin of COVID-19 pandemic, caused by SARS-CoV-2, was traced to Wuhan, China. Thereafter, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) evolved into various variants owing to genome-wide mutations, causing emergence of multiple variants, including Variant of Interest and Variant of Concern. Here, we discuss genomic architecture of SARS-CoV-2, as well as its multiple variantsalpha, beta, gamma, and delta, along with their biological properties, such as transmissibility, reduction in antibody-mediated neutralization, virulence, disease severity, vaccine effectiveness, and the prevalence across the India vis-a-vis world. Our data on VOC, pooled from the Global Initiative on Sharing All Influenza Data up to 31 October 2021, shows around 89% prevalence of delta VOC across various Indian States. Whereas alpha, beta, and gamma variants show 10.44%, 0.57%, and 0.11% prevalence, respectively. Compared with global scale, the reported Indian prevalence of alpha, beta, gamma, and delta are 0.40%, 0.63%, 0.04%, and 1.7%, respectively. Furthermore, prevalent vaccines of various natures show significantly reduced effectiveness against these VOCs, necessitating urgent need for development of effective prophylactic vaccines and potential therapy to contain the pandemic.

Secure 6G Communication in Smart City Using Blockchain

The next-generation cellular network will aim to overcome the existing Fifth Generation (5G) networks’ shortcomings. At the moment, academics and business are concentrating their efforts on the Sixth Generation (6G) network. This 6G technology is expected to be the next great game-changer in the telecommunications sector. Due to the outbreak of COVID-19, the entire globe has turned to virtual meetings and live video interactions in various fields as healthcare, business, and education. We explore the most recent viewpoints and future technology trends that are most likely to drive 6G in this paper. The incorporation of blockchain in 6G, will allow the network to efficiently monitor and manage resource consumption and sharing. We explore the potential of blockchain for sharing in 6G utilizing a variety of application scenarios in the smart city. To strengthen security and privacy in 6G networks, we introduce potential difficulties and solutions with various 6G technologies. In addition, we examine the security and privacy issues that may arise as a result of the current 6G standards and prospective 6G uses. Overall, our study aims to give insightful direction for future 6G security and privacy research. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Work Life Balance of Health Care Workers during COVID-19 in India

The term work-life balance is a blend of two words, work and life. In the context of the extant research, the focus is on the health care workers, who are the front-line warriors during the Covid-19 pandemic. They include amongst others doctors, nurses and health care administrators. The contributions of these front-line workers during this pandemic are invaluable. However, they have to face the brunt of the pandemic which affected work life balance enormously. In this background, the present paper is an initiative to understand the various dynamics related to the work life balance of health care workers, particularly during the pandemic, and for this purpose four leading hospitals in the study area were selected for collection of data. The entire data were analyzed under four parameters, work pressure related issues, infrastructure and family issues, organizational policy, and work related cash and non-cash benefits. Overall, twenty two hypotheses were developed and out of these seven null hypotheses and fifteen alternative hypotheses were accepted. As regards to the work pressure related issues three null and three alternative hypotheses were developed, whereas basing on infrastructure and family issues one null and three alternate hypotheses were accepted. On the basis of organization policies, seven hypotheses were developed and all of them were accepted as all alternative hypotheses and similarly, out of five hypotheses related to cash and non-cash benefits, three null and two alternative hypotheses were accepted. Concerns and difficulties in the workplace and personal problems in life affect each other. The equilibrium of work-life is conceived as a dual system that gives fair weightage to the demands of both the employees and employers. There cannot be an equal balance between work and life, rather, it is a matter of prioritizing and managing the burden of each field in order to align time, energy, and resources so that work and life are satisfied. The present paper is an endeavor to decode the various issues related to work life balance of health care workers in the new normal. The paper is hoped to be an additional contribution to the existing literature. © 2022 Sami Publishing Company. All rights reserved.

Secure Smart City Infrastructure Using Digital Twin and Blockchain

A digital replica of any physical system or process is referred to as a digital twin (DT). In general, a DT is a software program that accepts real-world data of a physical system at ground level as inputs and creates useful outputs in the form of insights. At the moment, the manufacturing industry and business are concentrating their efforts on this technology. Due to the outbreak of COVID-19, the entire globe has turned to virtual meetings and live video interactions in various fields as health care, business, and education. We explore the most recent viewpoints and future technology trends that are most likely to drive DT in this study. The incorporation of blockchain in DT will allow the network to efficiently monitor and manage resource consumption and sharing. The idea of a digital twin of a smart city is presented in this article. Smart city development aims to enhance not just the city's overall performance, but also its basic infrastructure, procedures, and facilities, as well as its socioeconomic wellbeing. In order to strengthen security and privacy in smart cities, we examine how security may influence the DT. We offer a complete analysis of blockchain-based DT in this article. Overall, our study aims to give insightful direction for digital twin security and privacy research. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Genome-wide mutation/SNP analysis, biological characteristics, and Pan-India prevalence of SARS-CoV-2 Variants of Concern

The origin of COVID-19 pandemic, caused by SARS-CoV-2, was traced to Wuhan, China. Thereafter, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) evolved into various variants owing to genome-wide mutations, causing emergence of multiple variants, including Variant of Interest and Variant of Concern. Here, we discuss genomic architecture of SARS-CoV-2, as well as its multiple variants-alpha, beta, gamma, and delta, along with their biological properties, such as transmissibility, reduction in antibody-mediated neutralization, virulence, disease severity, vaccine effectiveness, and the prevalence across the India vis-à-vis world. Our data on VOC, pooled from the Global Initiative on Sharing All Influenza Data up to 31 October 2021, shows around 89% prevalence of delta VOC across various Indian States. Whereas alpha, beta, and gamma variants show 10.44%, 0.57%, and 0.11% prevalence, respectively. Compared with global scale, the reported Indian prevalence of alpha, beta, gamma, and delta are 0.40%, 0.63%, 0.04%, and 1.7%, respectively. Furthermore, prevalent vaccines of various natures show significantly reduced effectiveness against these VOCs, necessitating urgent need for development of effective prophylactic vaccines and potential therapy to contain the pandemic. © ScienceIn Publishing.

MLTF: Model less time-series forecasting

Multi-step ahead long term forecasting remains a pertinent challenge in time series literature due to non-stationary behaviour of real-world data. Predominantly most traditional time series models are parametric in nature and they use the predicted values to generate forecast for future time steps. This leads to error accumulation in each step of the forecasting horizon which causes increasingly poorer forecast in long-term. Other than the problem of error accumulation, most parametric algorithms also require significant pre-processing, hyper-parameter tuning, training and post-processing which can often put high computational burden on the system. Therefore, this paper proposes, Model Less Time-series Forecasting (MLTF), a non-parametric approach for forecasting which does not require any pre-processing or traditional training (i.e. Backpropagation). MLTF is a non-parametric method which uses statistical representations such as trend, linearity, entropy etc. to cluster series from a pre-defined repository and the series from same cluster are tagged as similar series. The trajectory of the target series is extracted from these similar series after applying an adaptive re-sampling technique. There is minimal training involved in MLTF, therefore this framework is computationally very efficient. The model-less nature also enables it to not suffer from error accumulation in long-horizon forecast. MLTF is validated empirically with a rich set of experiments involving M1, M3 competition dataset, Electricity, Volatility and COVID-19 data (over 4500 independent uni-variate series of different frequencies i.e. Hourly, Daily, Monthly, Quarterly and Yearly). The experiments demonstrate that, MLTF is significantly faster while being similar (or better) in terms of forecasting accuracy than the state-of-the-art DL methods and other non-parametric time series model. © 2022 Elsevier Inc.

Covid-19 Data Analysis of India and Future Forecasting Using FbProphet

COVID-19, the pandemic has created a fearsome sensation the entire world. Although in India, many articles of research had examined the spread of this deadly disease, but the population of our country makes it more important to have a closer look at the present scenario of the individual states. The whole world is facing this pandemic for the past 15 months since December 2019, the month when the very first case of covid-19was discovered in China from where it got spread throughout the globe. In January 2020, the first case of covid-19 in India was witnessed. For this very scenario in this paper, we have analyzed and crafted different sections for treatment methodologies and safety measures undertaken, detailed analysis of various States and Union Territories during the outbreak of Covis-19, count on the number of people infected, cured, and died along with the prediction of the upcoming situation of covid-19 in future months. Moreover several time series models had been studied among which FbProphet model fits best to our purpose of study. © 2021 IEEE.

Work Life Balance of Health Care Workers in the New Normal: A Review of Literature

Life of a health care worker is very different compared with any other professional. This is distinct not from the perspective of an overwhelming level of personal and professional accomplishment, but from the huge amount of psychological stress and anxiety involved in it. Earlier studies show that health workers, particularly medical practitioners, are vulnerable to mental health developments. Furthermore, workplace stress has been related to emotional exhaustion, which can result in a lack of enthusiasm for work, feelings of powerlessness, depression, and defeat. Emotional factors inherent to the job, responsibilities related to patient needs, feeling of being overburdened, organizational responsibilities, and issues related to working relationships and career growth are commonly identified as occupational stressors among medical professionals. Emotional fatigue is commonly referred to as burnout among professionals. The present paper is an initiative to understand the various dynamics of work life balance during pandemic and to undertake the empirical study on the topic. In this regard, the authors undertook the secondary sources for preparing the paper. The present initiative will be a value addition to the existing literature. © 2022 by SPC (Sami Publishing Company).

No Rumours Please! A Multi-Indic-Lingual Approach for COVID Fake-Tweet Detection

The sudden widespread menace created by the present global pandemic COVID-19 has had an unprecedented effect on our lives. Man-kind is going through humongous fear and dependence on social media like never before. Fear inevitably leads to panic, speculations, and spread of misinformation. Many governments have taken measures to curb the spread of such misinformation for public well being. Besides global measures, to have effective outreach, systems for demographically local languages have an important role to play in this effort. Towards this, we propose an approach to detect fake news about COVID-19 early on from social media, such as tweets, for multiple Indic-Languages besides English. In addition, we also create an annotated dataset of Hindi and Bengali tweet for fake news detection. We propose a BERT based model augmented with additional relevant features extracted from Twitter to identify fake tweets. To expand our approach to multiple Indic languages, we resort to mBERT based model which is fine tuned over created dataset in Hindi and Bengali. We also propose a zero shot learning approach to alleviate the data scarcity issue for such low resource languages. Through rigorous experiments, we show that our approach reaches around 89% F-Score in fake tweet detection which supercedes the state-of-the-art (SOTA) results. Moreover, we establish the first benchmark for two Indic-Languages, Hindi and Bengali. Using our annotated data, our model achieves about 79% F-Score in Hindi and 81% F-Score for Bengali Tweets. Our zero shot model achieves about 81% F-Score in Hindi and 78% F-Score for Bengali Tweets without any annotated data, which clearly indicates the efficacy of our approach. © 2021 IEEE.

Recent developments and future scopes of electrical vehicles in power market on COVID-19 pandemic situation

The largest source of climate pollution in the world is transportation. To solve the climate crisis, we need to make the vehicles on our roads as clean as possible. We have only a decade left to change the way we use energy to avoid the worst impacts of climate change. Emissions from cars and trucks are not only bad for our planet;they’re bad for our health. Air pollutants from gasoline- and diesel-powered vehicles cause asthma, bronchitis, cancer, and premature death. The long-term health impacts of localized air pollution last a lifetime, with the effects borne out in asthma attacks, lung damage, and heart conditions. As the COVID-19 pandemic — a respiratory disease — continues to spread, a study by Harvard University found “a striking association between long-term exposure to harmful fine particulate matter and COVID-19 mortality in the United States” One of the primary causes of fine particulate matter pollution (PM2.5) is combustion from gasoline and diesel car engines. So in this paper we are mainly focused on development of Electrical Vehicles and what are problems to implementation in India. We have discussed different types of Government policies and future scope policies which have been taken by government. From this paper researchers will get clear idea of future of Non Pollutant Vehicles. So this paper is very important in Covid-19 pandemic situations because we will safe and secure from these types of pandemic disease only if our environment will be free from air pollution which creates by conventional vehicles. © 2021 Institute of Physics Publishing. All rights reserved.

Fuzzy mixed graphs and its application to identification of COVID19 affected central regions in India

In the recent phenomenon of social networks, both online and offline, two nodes may be connected, but they may not follow each other. Thus there are two separate links to be given to capture the notion. Directed links are given if the nodes follow each other, and undirected links represent the regular connections (without following). Thus, this network may have both types of relationships/ links simultaneously. This type of network can be represented by mixed graphs. But, uncertainties in following and connectedness exist in complex systems. To capture the uncertainties, fuzzy mixed graphs are introduced in this article. Some operations, completeness, and regularity and few other properties of fuzzy mixed graphs are explained. Representation of fuzzy mixed graphs as matrix and isomorphism theorems on fuzzy mixed graphs are developed. A network of COVID19 affected areas in India are assumed, and central regions are identified as per the proposed theory.