A Grey-TOPSIS Approach to Minimize COVID-19 Transmission for the Betterment of Public-Health in the Indian Context

In view of diminishing the transmission of the coronavirus pandemic (COVID-19) in the community, an essential intervention strategy has been the consideration of public health measures. However, at the present scenario, these measures can be considered as the only available tools for mitigation of this virus impact. An attempt was made in this study with the use of grey technique for order of preference by similarity to ideal solution (Grey-TOPSIS) method for prioritizing the precautionary measures for the public health in order to enable taking appropriate steps by the general public of India to protect them from virus transmission. © 2022 IGI Global. All rights reserved.

Sustainability Challenges in the MSME Sector of India Post COVID-19: Ranking by MCDM Method

MSME area adds to business age at a low capital expense contrasted with enormous businesses. It helps in the sustainable industrialization of metropolitan and country zones, decreases provincial irregularity, and guarantees evenhanded conveyance of pay and abundance. This examination paper talks about the different issues of financing for miniature, little, and medium undertakings during and post COVID-19. Through the writing survey, plainly money is a basic device for the development and improvement of SMEs. The challenges of sustainable MSMEs are money, policies, environmental regulation, supply chain, etc., but during and post COVID-19, problems and challenges are tackling financial issues of companies, restart supply chain management, new startups, etc. Although government endeavors have been made for the smooth progression of credit to MSMEs areas, MSMEs require more research. Hence, an attempt is made to prioritize challenges of MSMEs during and post COVID-19. Copyright © 2022, IGI Global.

COVId-19 Vaccine Ranking Using ANP Method

Wuhan Province in China reported the first case of novel corona virus as pneumonia outbreak during December 2019. The novel coronavirus was soon declared a pandemic by the World Health Organization. On 16th of July 2021, the number of COVID-19 confirmed cases was 188,128,952 globally, out of which 4,059,339 individuals succumbed to this deadly virus. In a short span of time, eight vaccines were approval for emergency use in different nations. The selection of vaccine depends upon many criteria. Concepts from multi-criteria decision making (MCDM) are appropriate to compare and rank them. The paper proposes analytical network processing (ANP) method to rank the eight vaccines according to seven criteria. The study proposes a decision tool to select the best vaccine among the candidate vaccines. A mathematical model based on ANP approach with three clusters having interrelationships within and among the clusters is proposed. © International Journal of Operations Research and Information Systems. All rights reserved.

Machine learning approach for corona virus disease extrapolation: A case study

Supervised/unsupervised machine learning processes are a prevalent method in the field of Data Mining and Big Data. Corona Virus disease assessment using COVID-19 health data has recently exposed the potential application area for these methods. This study classifies significant propensities in a variety of monitored unsupervised machine learning of K-Means Cluster procedures and their function and use for disease performance assessment. In this, we proposed structural risk minimization means that a number of issues affect the classification efficiency that including changing training data as the characteristics of the input space, the natural environment, and the structure of the classification and the learning process. The three problems mentioned above improve the broad perspective of the trajectory cluster data prediction experimental coronavirus to control linear classification capability and to issue clues to each individual. K-Means Clustering is an effective way to calculate the built-in of coronavirus data. It is to separate unknown variables in the database for the disease detection process using a hyperplane. This virus can reduce the proposed programming model for K-means, map data with the help of hyperplane using a distance-based nearest neighbor classification by classifying subgroups of patient records into inputs. The linear regression and logistic regression for coronavirus data can provide valuation, and tracing the disease credentials is trial.

Mental Risk Faced During Lockdown in COVID-19: A Grey-TOPSIS Approach - A Case Study of Odisha

The Indian Government has taken broad steps and declared lockdowns to reduce the community transmission of the novel coronavirus. Many people tried to utilize this period by doing online work and household work simultaneously. Many small-scale industries, shops, agencies, school colleges shut their door following government rules and regulations to avoid spreading the virus. People working or engaged in these activities or duties became unemployed. As man is a social animal and feels safe and secure in society, the increase in distance from society from office space and due to financial crises negative thoughts have impacted them. In this study, an attempt was made to prioritize the cause of mental pressure faced by common people. Precautionary measures can be taken for the public health such that appropriate steps can be taken to protect their health from the transmission of this virus by using the grey technique for order of preference by similarity to ideal solution (grey-TOPSIS) method.

METFORMIN USE IS ASSOCIATED WITH LESS FREQUENT SEVERE LIVER INJURY UPON HOSPITAL ADMISSIN WITH COVID-19

Introduction In a study involving > 10,000 patients hospitalized with COVID-19, we found that liver injury, which was present in ~70% of patients upon hospital admission, correlates with in-hospital mortality (Satapathy et al., Eur J Gastroenterol Hepatol 2021). Curiously, severe liver chemistry abnormalities (LCA) were seen less often in patients with diabetes or hypertension, although these diseases confer increased risk of severe disease. This raises the question whether home medications protect from COVID-19 associated liver injury. We now analyzed associations between LCA and twenty-six groups of antidiabetic, antihypertensive, and other common mediations. Results 9898 patients hospitalized with COVID-19 in 13 hospitals in New York between March 1 to August 31, 2020, who had an complete records on admission were retrospectively analyzed. LCA measured were alanine and aspartate aminotransferases, alkaline phosphatase, and bilirubin, and were defined as absent, mildmoderate (up to four times elevated), or severe. Diseases and socioeconomic factors were similar to the initial study. 67.2% had hypertension, and 40.8% had diabetes. The most common medications included insulin (12.2%), metformin (18.3%), sulfonylureas (6.8%), DDP4 inhibitors (6.3%), ACE inhibitors (14.8%), ARBs (18.6%), beta-blockers (33.2%), calcium-channel blockers (26.5%), diuretics (21.6%), statins (41.5%), PPIs (22.1%), H2- blockers (6.8%), antiplatelets (31.0%), anticoagulants (20.5%). Comparisons between groups were analyzed using Kruskal-Wallis test, chi-squared test, and Fisher's exact test. Univariate and multivariate regression analysis were performed. Univariate analysis showed a higher risk for severe LCA in men, Asian and Black race, non-Hispanic ethnicity. As in our prior analysis, hypertension and diabetes were associated with less frequent severe LCA. In addition, hyperlipidemia, CAD, CHF, atrial fibrillation, CKD, ESRD, GERD, asthma, COPD, cancer, and liver disease were inversely associated with severe LCA. Medications that were associated with less frequent severe LCA included statins, ACE, ARBs, calcium-channel blockers, betablockers, diuretics, antiplatelet medications, insulin, biguanide, sulfonylureas, PPIs, H2- blockers, and anticoagulants, but not oral steroids. In multivariate analysis, male gender, Asian and Black race were associated with increased risk of severe LCA. Hypertension, ESRD and asthma were associated with less frequent severe LCA, but not diabetes. Among medications, only metformin showed a statistically significant correlation with severe LCA on admission, with a hazard ratio 0.57 (p 0.0002). Conclusions Metformin use was inversely associated with severe liver chemistry abnormalities upon hospital admission with COVID- 19 in a large cohort of patients during the initial pandemic in New York.

THE POSITIVE EFFECTS OF NURSE EDUCATORS ON AGE COHORT SCREENING IN THE OUTPATIENT PRIMARY CARE SETTING FOR HEPATITIS C IN A LARGE HEALTH SYSTEM PERSIST DURING THE COVID PANDEMIC

Background: New York State mandates the offering of hepatitis C (HCV) testing for persons born between 1945-1965 in the outpatient primary care setting. Despite this, < 50% of HCV screening candidates are offered screening and among those found to be HCV Ab positive, not all undergo HCVRNA testing and even fewer are linked to care. We evaluated the use of nurse educators to visit 60 outpatient primary care offices to educate PCPs and their staffs about HCV screening and linkage to care. During the COVID pandemic, most primary care offices were closed to in-person visits, in-person PCP patient visits decreased significantly and tele-medicine visits increased. We evaluated the long-term effects of nurse educators on HCV screening and the effects of the COVID pandemic on HCV screening and linkage to care. Results: From April 1, 2018- August 31, 2021, 50,047 previously unscreened patients born between 1945-1965 were screened with an HCV AB with reflex testing to HCVRNA in all positive HCV ABs. The use of nurse educators increased the rates of HCV AB screening and linkage to care when compared to same office historical records. Overall, 47863 tests were negative. 1093 had a positive HCV AB (2.2%) and 245 (0.5%) were HCVRNA positive. 23% of HCVAB positive patients were HCVRNA positive. The number of patients screened in the year 1, 2 and 3 were 18969, 15309 and 15,769, respectively. The number of HCV AB positives in year 1, 2 and 3 were 334, 345 and 412, respectively. The number of HCVRNA positives in years 1,2,3 were 94, 56, and 94, respectively. In year 1, 28% of the HCV AB positives were HCVRNA positive compared with 17% and 23% in years 2 and 3. All 245 HCV RNA positive patients were referred to an HCV provider. 101 patients completed therapy, 93 are currently in treatment or follow up and 22 declined therapy. 5 are awaiting initiation of DAA therapy and 8 were transplanted prior to initiation of therapy. 10 subjects expired prior to initiating treatment and 6 were lost to follow up. In year 3, nurse educators visits were suspended due to the COVID pandemic. Despite this, the rate of screening for HCV and linkage to care was not affected by the COVID pandemic. Conclusions: Over the three-year study period, screening for HCV infection by primary care providers in patients born between 1945-1965 remained stable despite the COVID pandemic and an increase in the use of telemedicine visits. The legacy of staff education by nurse educators led to continued HCV screening despite lack of in-person staff re-education. Linkage to care for newly diagnosed HCV patients remained high throughout the study period, despite the COVID pandemic in year 3 with a high percentage of patients initiating DAA therapy. Nurse educators are a vital component in increasing HCV awareness in the community setting

Student Performance Prediction Using Classification Models

The education industry has gone through major changes amidst the recent COVID-19 pandemic. Facing unforeseen circumstances, educational institutions were forced to shift to an online learning model rather than an offline, classroom-based learning model. The sudden change in the learning model impacted not only students but also the teaching faculty. Even though many resources are available online, simulating a classroom-like study environment is not an easy task. Hence mapping student performance in the new learning model is an essential task. The main goal of our work is to predict the student performance in the online learning model implemented by many colleges and universities amidst the COVID-19 pandemic. Unlike the previous work in this domain, we are purely focusing on an online study system. An online survey was conducted to collect the data from the students who had undergone the aforementioned learning model for at least one semester. The data set for the research includes features that would have an impact on a student’s performance having various attributes. The model strives to predict a student’s performance with good accuracy and help infer where the online learning model can be improved. Several classifiers such as KNN, Gradient boost, Adaboost, Decision tree, SVM, Gaussian NB were used to classify the student data. To validate the performance of these classifiers we have compared them with the latest state-of-the-art works. The Gradient Boost, Xgboost Classifier, and SVM classifiers returned the highest accuracies, in essence, 97.46, 97.45, and 97.45%, respectively. This indicates that the performance of the students is predictable with the given features. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Designing a multi-criteria decision-making framework to reduce the Post-COVID-19 impact on SMEs

After the agricultural sector, micro, small, and medium enterprises (MSME) play a vital role in the development of India. Micro, small, and medium enterprises (MSME) are contributing about 25% of the country's GDP (gross domestic product) from service activities and 33% to the manufacturing amount produced for India. Micro, small, and medium (MSME) entrepreneurs have been highly impacted due to the COVID-19 pandemic lockdown. Due to lockdown, MSME sector who could not export, nor get their ancillary parts, had problems with transportation leading to the inability to do marketing. Most important migrant laborers rushed to their villages or natives. Hence, without labor or workforce, the assembly lines stopped. In this chapter, an attempt is made to identify the challenges of the MSME sector and deal with the efforts often taken to restart them. © 2021, IGI Global.

From Chalk Boards to Smart Boards: An Integration of IoT into Educational Environment During Covid-19 Pandemic

Internet of Things (IoT) is a unique paradigm shift in the domain of Information Technology. It converts the real-life things into intelligent virtual devices to ensure a machine-to-machine transmission of information. With its increasingly technological magnitude, it ascertains an imperative role in almost all spheres of life, and the global education markets have incredibly reaped benefits. The present paper gives an insight into the radical evolution and integration of IoT trends in education sector—the transition from conventional chalk boards to modern smart boards, especially at the outset of Covid-19 pandemic when the exigencies of global education demanded it the most. The paper also lists out opportunities, obstructions, scalability of tools and technology, and services allied to IoT expertise, while at the same time bridges the gap between educational and technical applications amid Covid-19 pandemic. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Doctor's Dilemma: Evaluating an Explainable Subtractive Spatial Lightweight Convolutional Neural Network for Brain Tumor Diagnosis

In Medicine Deep Learning has become an essential tool to achieve outstanding diagnosis on image data. However, one critical problem is that Deep Learning comes with complicated, black-box models so it is not possible to analyze their trust level directly. So, Explainable Artificial Intelligence (XAI) methods are used to build additional interfaces for explaining how the model has reached the outputs by moving from the input data. Of course, that's again another competitive problem to analyze if such methods are successful according to the human view. So, this paper comes with two important research efforts: (1) to build an explainable deep learning model targeting medical image analysis, and (2) to evaluate the trust level of this model via several evaluation works including human contribution. The target problem was selected as the brain tumor classification, which is a remarkable, competitive medical image-based problem for Deep Learning. In the study, MR-based pre-processed brain images were received by the Subtractive Spatial Lightweight Convolutional Neural Network (SSLW-CNN) model, which includes additional operators to reduce the complexity of classification. In order to ensure the explainable background, the model also included Class Activation Mapping (CAM). It is important to evaluate the trust level of a successful model. So, numerical success rates of the SSLW-CNN were evaluated based on the peak signal-to-noise ratio (PSNR), computational time, computational overhead, and brain tumor classification accuracy. The objective of the proposed SSLW-CNN model is to obtain faster and good tumor classification with lesser time. The results illustrate that the SSLW-CNN model provides better performance of PSNR which is enhanced by 8%, classification accuracy is improved by 33%, computation time is reduced by 19%, computation overhead is decreased by 23%, and classification time is minimized by 13%, as compared to state-of-the-art works. Because the model provided good numerical results, it was then evaluated in terms of XAI perspective by including doctor-model based evaluations such as feedback CAM visualizations, usability, expert surveys, comparisons of CAM with other XAI methods, and manual diagnosis comparison. The results show that the SSLW-CNN provides good performance on brain tumor diagnosis and ensures a trustworthy solution for the doctors.

Possible opportunities in face mask manufacturing during the COVID-19 Pandemic: A study from India

COVID-19 has been primarily regarded as a respiratory disease, and until a safer and effective treatment or vaccine becomes available, the prevention of COVID-19 may continue through interventions based on non-pharmaceutical measures such as maintaining of physical distances and use of personal protective equipment like facemasks, etc. Therefore, an attempt was made in this study to explore the drawbacks with the presently available facemasks for protection from COVID-19 viruses in the state of Odisha in India, and also to explore the possible opportunities for further development of these facemasks. The associated discomforts;strength, weaknesses, opportunities, and threats (SWOT) analysis of existing facemasks in Odisha;possible opportunities for "Make in India"of these facemasks;along with safer use have been analyzed with the help of interpretive structural modelling (ISM) approach followed by MICMAC analysis. Copyright © 2022, IGI Global.

3D printing in managing supply disruptions related to COVID-19 pandemic: Food and Drug Administration's current thinking on regulation

Recent developments and collaborations of pharmaceutical manufacturers, hospitals, and government funded research bodies using 3D printing technology have been highlighted for the management of the healthcare crisis. 3D printing is a process of converting virtual 3D models developed by computer aided design into physical forms upon addition of material layer-by-layer (also known as additive manufacturing). This 3D printing is supposed to revolutionize significantly the healthcare system in the coming years. This process involves a tailored deposition of biomaterials layer by layer such as polylactic acid (PLA), polyvinyl alcohol (PVA), or other suitable pharma-grade polymers, copolymers, and their combinations to formulate three-dimensional custom designs with controlled architecture and composition. Food and Drug Administration (FDA) is currently thinking on regulation to ease the import restrictions for products intended for the detection and diagnosis of COVID-19 to ensure the timely availability of test kits.

Developmental biology of Corona vaccine: Lipid nanoparticle mRNA-encapsulation, a safe approach

The global pandemic of COVID-19 is progressing rapidly across the world and declared as health emergency. The novel Coronavirus can cause severe lower respiratory tract infections primarily in geriatric, immunocompromised persons, infants and patients with comorbidities, or genetic disorders. Few Emergency Use Authorizations (EAUs) have been granted by FDA and other regulatory bodies with an aim to repurpose the existing approved medicines to fight the disease, and till date no specific treatment methodologies or preventive measures are available. At present, numerous medications which are already approved for other therapeutic indications, as well as the new medications, are undergoing clinical trials for the evaluation of safety and efficacy against COVID-19 infection. These therapeutic ranges include antimalarial, antiviral, steroids, convalescent plasma containing antibodies, and immune modulators, etc. Nevertheless, the primary focus is on preventive care and currently more than hundred potential vaccine candidates are under development by leading biotech companies across the globe which are at different phases of clinical development. Lipid based mRNA delivery, DNA delivery and mAbs are the most advanced technologies being embraced besides whole-virion inactivated vaccine, attenuated live vaccine, non-replicating viral vector-based vaccine, protein single unit vaccine and multiunit vaccine. This review focuses on the current progress in drug delivery systems of COVID-19 vaccine across industries, academics, and government funded research institutes with a special focus on the synthetic mRNA-based lipid nanoparticle (LNP).

Corona virus-A review concerns about the present scenario

Corona virus first reported in the early December, 2019 from Wuhan, a city in Hubei Province in the Republic of China. A novel corona virus is belongs to the Severe acute respiratory syndrome corona virus-2 the same family as SARS-CoV and MERS corona virus. The SARS-Corona virus-2 had similarity with acute respiratory distress syndrome (ARDS), which had also the high mortality during 2002-2003. The corona virus has rapidly spread all over the world and emerged as a deadly disease. The World Health Organization declared this as a public health emergency and a pandemic disease named as corona virus disease-19 (COVID-19). The transmission of virus from human to human causes high rise in death rates around the globe. Acute lungs injury at all stages of life or in some individuals with high-risk was reported earlier shows that, such as old age people or those persons affected with multi-morbidities, this novel virus can cause serious pneumonia like condition, ARDS, followed by multi-organ failure, these factors are the main cause of acute respiratory failure with higher death rates. Affected persons typically show different types of dyspnoea as well as radiological signs. The personal protective equipment (PPE) is highly recommended for wearing at some specific areas. The sign and symptoms of this novel COVID-19 are mainly high fever, mild dry cough, sore throat, headache, fatigue, mild dyspnoea and gastrointestinal issues. To test the presence of novel corona virus, swabs are collected from the nasal, tracheal aspirate and Broncho-alveolar lavage and for the samples testing, Real-time PCR is being used. Computed tomography (CT) results are crucial for the diagnosis and follow-up process. According to Epidemiological studies the people with old age and patients having diseases like hypertension, high blood sugar previously were more susceptible this disease, while children tends to have mild symptoms. In this review, we highlighted the Structural, epidemiological, statistical data, signs and symptoms as well as the treatments and vaccine available for the treatment of this novel corona virus. © 2021, Universitatea de Vest Vasile Goldis din Arad. All rights reserved.

Analysis of COVID-19 pandemic - Origin, global impact and Indian therapeutic solutions for infectious diseases

The first case of COVID-19 was reported in China in December 2019(ref. 1) and almost 213 countries have reported around 5,350,000 COVID-19 cases all over the world, with the mortality rate up to 3.4% as of May 23,2020. On March 11, 2020, the WHO (World Health Organization) declared COVID-19 as a global pandemic. Moving towards from epidemic to global pandemic situation just in two months, COVID-19 has caused tremendous adverse effects on people's well being and the economy all over the world. Scientists and researchers around the globe have a vested interest in researching and mitigating to handle the dire situation. This paper covers the COVID-19's origin, characteristics of the virus and reasons behind the outbreak, and precautionary measures that have to be followed to handle the critical situation. Several therapeutic solutions in the Indian healing tradition have been discussed to improve the immune system in order to equip ourselves to deal with the outbreak of COVID-19.