Infectivity, Morbidity, Emerged Variants of SARS-CoV-2 and Vaccination in Indian Perspectives from First Wave to Second Wave of COVID-19

More than one year since coronavirus disease 2019 was declared a deadly disease by World Health Organization, the deadly severe acute respiratory syndrome Coronavirus 2 continues to disrupt public life worldwide. Several nations have seen a two-wave design in revealed instances of COVID 19 pandemic, with a first wave of infection during early 2K20 followed by the ongoing second wave. An investigation of the predominance of COVID-19 in India from the first wave to the second wave was utilized to characterize critical contrasts, infection patterns, lethality, and therapeutic measures through extensive vaccination pan-India free of cost. The quantitative transmission brought about COVID-19, including meteorological boundaries and topographical locale information, was gathered using web search tools and available literature and substantiated by factual advanced numerical tools. Experimental information showed that the qualities of the infection impacts do change between the two-time frames. The examination between the first wave and second wave, where COVID-19 flare-ups were now turning out to be scourged, was contemplated. Contrasts in age reach and seriousness of the infection have been accounted for, albeit the relative qualities of the two waves remain to a great extent obscure. The present investigation recommends that variability in environmental temperature and moisture contents may not be the significant components influencing the COVID-19 mortality during different waves of infection. The second wave of COVID-19 is influencing a large portion of the world, and especially the middle age group person relies on the principal top. © 2022 by the authors.

Bridging the gap with Pando

The Pando app is UK based and part of the NHS Clinical Communication Procurement Framework, which is designed to provide continuity of care with virtual patient management (https://www.bad.org.uk/healthcare-professionals/covid-19/re mote-dermatology-guidance), and drive tech-enabled connectivity across the National Health Service (NHS). This has also been used in the British Army to help defence medical staff connect with and seek advice from their colleagues in the UK while in the field (www.hellopando.com). Lack of on-site medical illustration, the COVID-19 pandemic and plastic surgeons operating in a NHS-funded private setting with no access to Picture Archiving and Communication System (PACS) in our Trust prompted use of the Pando app to capture prebiopsy pictures, avoid wrong-site surgery and improve interdepartmental communication. We present our multidisciplinary quality improvement project, involving dermatology and plastic surgery, evaluating the use of the Pando app from September to December 2020, mostly from 2-week-wait skin cancer clinics. All dermatology and plastic surgery colleagues downloaded the Pando app to their mobile phones and created a group entitled 'Dermatology/Plastics' to share their patient photos with identity labels. Patient photos could also be emailed to the clinicians' NHS email addresses - all done with patient consent. We evaluated our project with pre-and post-Pando feedback questionnaires. In the pre-Pando questionnaires, the majority of 14 colleagues involved were concerned with the varying quality of photos emailed by patients, the time lag in photos being uploaded to PACS and any likelihood of compromising patient safety. With post-Pando questionnaires, the majority found the app to be user-friendly, that the photographs taken were of superior quality, that there were no reported concerns with patient consent and they preferred using the app to the previous pathway. Comments suggested the Pando app to be invaluable for site recognition in patients with cognitive impairment, multiple lesions, difficult-to-see areas, medicolegal, educational and audit purposes, and local cancer multidisciplinary discussions. The drawbacks were the lack of seamless connection between the app and PACS, the inability to search for pictures in the app with patient identification and lack of access to previously shared pictures for new users. Despite some limitations, the Pando app has immensely improved patient safety and proved to be invaluable for our joint dermatology and plastic surgery interactions. However, there is an unmet need for a system with the ability to instantly transfer pictures to PACS and patient electronic records, to improve things further.

2019 Novel Coronavirus: A Review on Epidemiology, Pathophysiology, Diagnosis and Current Clinical Trials in Vaccine Development

Coronavirus disease-2019, a viral disease caused by the novel severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2), was identified by the Centre for Disease Control (CDC), China, on January 7, 2020. This mysterious respiratory epidemic occurred in Wuhan, China, in late December 2019. A month after its outbreak in China, the World Health Organization (WHO) de-clared it as a public health emergency of international concern (PHEIC) due to its severity and high transmission potential throughout the world, leading many nations to implement multiple lock-down sessions and strict social distancing measures. As of September 21, 2020, 30,675,675 active cases and 954,417 deaths had been reported worldwide. Intensive research is being carried out across the globe to identify precise diagnostic techniques and develop novel, effective vaccines against the virus. Herein, we elaborate on details of epidemiology, genetics, pathophysiology, diag-nosis, prevention, and vaccine trials related to this pandemic.Copyright © 2021 Bentham Science Publishers.

Aggregation operators of quadripartitioned single-valued neutrosophic Z-numbers with applications to diverse COVID-19 scenarios

Z-number, proposed by Zadeh is an ordered pair of fuzzy numbers which have the capability to depict both the reliability and certainty of any available information. Likewise, based upon Belnap's four-valued logic, Quadripartitioned Single-Valued Neutrosophic Sets (QSVNSs) are characterized by four independent components of truth, contradiction, ignorance, and falsity degrees to represent uncertain information or data at hand. In fact, QSVNSs are extensions of Single-Valued Neutrosophic Sets (SVNSs) where the indeterminacy component is further partitioned into two parts - contradiction and ignorance. However, QSVNSs alone cannot reflect the reliability measure of decision maker's preferences or allocations. Thus, for better modeling of uncertainty and to combine the information of truth, contradiction, ignorance, and falsity degrees with their respective reliability attributes, we put forward a hybrid framework for the first time in this study. Hence, we propose a notion of Quadripartitioned Single-Valued Neutrosophic Z-number (QSVNZN), which is constructed as a generalization of the Z-number and the QSVNSs. Some basic operations and a score function are also defined for ranking QSVNZNs. Moreover, we also aggregate QSVN information with the help of three weighted aggregation operators viz., QSVNZN weighted arithmetic averaging (QSVNZNWAA) operator, QSVNZN weighted geometric averaging (QSVNZNWGA) operator, and QSVNZN weighted hybrid averaging (QSVNZNWHA) operator. Several suitable properties and relations between these operators are also presented. The applicability of our newly proposed operators and the score function is demonstrated in three multi-criteria decision making (MCDM) occasions specifically in the COVID-19 context. Meticulous comparative analysis, the validity of our proposed approaches, sensitivity analysis, and runtime analysis is also carried out to depict the legitimacy, veracity, and feasibility of our theoretical construct. The obtained results shall greatly benefit the decision-makers to deal with indeterminate and inconsistent information efficiently. Or in other words, the new framework shall exhibit sufficient descriptive ability from the human-cognition-based perspective.

A Computational Approach for Predicting the Termination of COVID-19

In 2019, there was an epidemic to the human society, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus causes coronavirus disease 2019 (COVID-19). It is an uncertain disease encountered in society for which the technology and human society had not prepared before. COVID-19 first spread over the Wuhan city of China. Since, the past two years of time-span, it has affected the citizen's life culture and expectancy. Now, most of the population are concern about when will be COVID-19 terminate. Basically, this paper aims to analyze the COVID-19 data with features as total confirmed cases, death rate, and vaccination rate around the world-wide region. On analyzing the data, with the help of Machine Learning (ML) algorithms, we estimate the termination of COVID-19. The rapid expansion of the COVID-19 epidemic has compelled the need for technology in this field. © 2022 IEEE.

Teaching Fluid Mechanics and Heat Transfer in Hands-on and Virtual Settings with Low Cost Desktop Learning Modules

Although there is extensive literature documenting hands-on learning experiences in engineering classrooms, there is a lack of consensus regarding how student learning during these activities compares to learning during online video demonstrations. Further, little work has been done to directly compare student learning for similarly-designed hands-on learning experiences focused on different engineering subjects. As the use of hands-on activities in engineering continues to grow, understanding how to optimize student learning during these activities is critical. To address this, we collected conceptual assessment data from 763 students at 15 four-year institutions. Students completed activities with one of two highly visual low-cost desktop learning modules (LCDLMs), one focused on fluid mechanics and the other on heat transfer principles, using two different implementation formats: either hands-on or video demonstration. Conceptual assessment results showed that assessment scores significantly increased after all LCDLM activities and that gains were statistically similar for hands-on and video demonstrations, suggesting both implementation formats support an impactful student learning experience. However, a significant difference was observed in effectiveness based on the type of LCDLM used. Score increases of 31.2% and 24% were recorded on our post-activity assessment for hands-on and virtual implementations of the fluid mechanics LCDLM compared to pre-activity assessment scores, respectively, while significantly smaller 8.2% and 9.2% increases were observed for hands-on and virtual implementations of the heat transfer LCDLM. In this paper, we consider existing literature to ascertain the reasons for similar effectiveness of hands-on and video demonstrations and for the differing effectiveness of the fluid mechanics and heat transfer LCDLMs. We discuss the practical implications of our findings with respect to designing hands-on or video demonstration activities.

Dynamical behavior of SIRS model incorporating government action and public response in presence of deterministic and fluctuating environments

In the present article, global characteristics of a generalized SIRS (susceptible-infected-recovered-susceptible) epidemic model have been investigated incorporating government policy, public response and social behavioral reaction. The effects of environmental fluctuations and time-dependent control strategies on the disease dynamics have also been analyzed. In the case of deterministic model, it is shown that the disease invades in this system when the basic reproduction number (R-0) is greater than 1, whereas the dynamics of the stochastic model can be controlled by its associated basic reproduction number R-s. Specifically, this work emphasizes the importance of nonlinear dynamic analysis of epidemic modeling, as well as the significant impact of social and government actions on disease dynamics. Numerical figure depicts that the governmental action plays a crucial role to control an epidemic situation, and the system turns out to be disease-free sooner if the government takes action at an early stage during a disease outbreak. Furthermore, one of the most key developments is that random fluctuations can prevent disease outbreaks, which can lead to the development of useful control techniques to restrict disease dynamics. The governmental actions and the clinical treatment are considered to be the effective control pair in this model, and it can be observed that the simultaneous implementation of the control strategies significantly reduces the disease burden.

DETRIMENTS TO PULMONARY REHABILITATION REFERRAL IN A RURAL COMMUNITY HOSPITAL: THE QI PROJECT

SESSION TITLE: Respiratory Care: Oxygen, Rehabilitation, and Inhalers SESSION TYPE: Original Investigation Posters PRESENTED ON: 10/18/2022 01:30 pm - 02:30 pm PURPOSE: 1.To educate patients about the severity of their disease process while inpatient. 2.To educate about and offer Pulmonary rehabilitation before they are discharged from the hospital. METHODS: 1.Education of care team regarding importance of pulmonary rehabilitation. 2.Forming a report in EPIC (EMR) enlisting current COPD exacerbation inpatients. 3.Approaching patients in a multidisciplinary fashion regarding pulmonary rehabilitation. RESULTS: There was an average of 3-5 inpatient COPD exacerbation patients per week who were approached from the begining of our QI. We saw less than 5% of patients actually enrolling and coming through pulmonary rehabilitation. CONCLUSIONS: In a rural setting there are a lot of factors that play a role in healthcare disparities. Taking an example of COPD patients in our community, despite having a functional pulmonary rehabilitation program and innovative approach to education and enrollment regarding pulmonary rehabilitation, we still faced a lot of obstacles in having the COPD patients to be able to follow and get help through outpatient pulmonary rehabilitation. The main reasons stated are as follows - lack of transportation, high fall risk, no insurance/inability to afford pulmonary rehabilitation, baseline poor functional status. CLINICAL IMPLICATIONS: Despite numerous benefits, traditional Pulmonary Rehabilitation (PR) as a resource remains underutilized in chronic lung disease.Less than 3% of eligible candidates for PR attend one or more sessions after hospitalization due to many barriers, including the ongoing COVID-19 pandemic. Our QI project is an effort to educate COPD patients on the importance of pulmonary rehabilitation while they are admitted inpatient for an exacerbation. Going through the project, we identified several valid reasons on why Pulmonary rehabilitation still remains an underutilized tool in this disease. We hope to establish reasons and work on approaching different hurdles that are faced. Accessibility in terms of higher number of rehabilitation programs and functional transport to and from, better insurance coverage on the part of insurance companies, better understanding of the disease process and patient education are all the different factors that can be worked on. DISCLOSURES: No relevant relationships by Parul Dutta No relevant relationships by Gaurav Dutta

Teacher Impact on Student Learning Using LC-DLM Implementations in the Classroom

Our team has developed Low-Cost Desktop Learning Modules (LCDLMS) as tools to study transport phenomena aimed at providing hands-on learning experiences. With an implementation design embedded in the community of inquiry framework, we disseminate units to professors across the country and train them on how to facilitate teacher presence in the classroom with the LC-DLMs. Professors are briefed on how create a homogenous learning environment for students based on best-practices using the LC-DLMs. By collecting student cognitive gain data using pre/posttests before and after students encounter the LC-DLMs, we aim to isolate the variable of the professor on the implementation with LC-DLMs. Because of the onset of COVID-19, we have modalities for both hands-on and virtual implementation data. An ANOVA whereby modality was grouped and professor effect was the independent variable had significance on the score difference in pre/posttest scores (p<0.0001) and on posttest score only (p=0.0004). When we divide out modality between hands-on and virtual, an ANOVA with an F-test using modality as the independent variable and professor effect as the nesting variable also show significance on the score difference between pre and posttests (p-value=0.0236 for hands-on, and p-value=0.0004 for virtual) and on the posttest score only (p-value=0.0314 for hands-on, and p-value<0.0001 for virtual). These results indicate that in all modalities professor had an effect on student cognitive gains with respect to differences in pre/posttest score and posttest score only. Future will focus on qualitative analysis of features of classrooms yield high cognitive gains in undergraduate engineering students. © American Society for Engineering Education, 2022.

Restarting MSMEs and start-ups post COVID-19: a grounded theory approach to identify success factors to tackle changed business landscape

Purpose: Small businesses and start-ups have started to reopen post coronavirus disease 2019 (COVID-19) lockdowns but are facing numerous challenges mainly due to changed customer preferences and the need to fine-tune the business models. This research aims to identify the important aspects that start-ups need to focus on, as they weather the COVID-19 pandemic storm. Design/methodology/approach: Research uses constructivist grounded theory methodology to analyse data collected through in-depth semi-structured interviews with entrepreneurs and senior employees at start-ups. A conceptual model based on nine categories impacting a start-up’s performance is investigated. Interview memos are thematically analysed to identify repeated ideas, concepts or elements that become apparent. Findings: Study reveals that employees’ and customers’ safety, prudent cost management and online presence/doorstep services are key for start-ups to succeed today's changed business landscape due to COVID-19. Practical implications: Findings act as a practical guide for start-ups in setting mechanisms, optimizing operations and fine-tuning strategy to address COVID-19 challenges. Start-ups are advised to evaluate the implications of the three findings on their respective businesses to successfully tackle the challenges posed by COVID-19. Originality/value: This research, being cognizant of a start-up’s unique characteristics and nuances, takes a fresh approach to identify key aspects that start-ups need to focus on and fine-tune in the wake of COVID-19. The paper enriches scientific research of understanding impact of COVID-19 on organizations by specifically surfacing how start-ups can learn and adapt by knowing how other start-ups are surviving today. © 2022, Emerald Publishing Limited.

Diagnostic accuracy of serum ferritin, C-reactive protein, lactate dehydrogenase and D-dimer in COVID-19

Background: The Utility ofserum biomarker in the Diagnosis of COVID-19 cases has not been studied thoroughly. Aims and Objective: Tofind outthe diagnostic accuracy of serum ferritin, c-reactive protein, lactate dehydrogenase and d-dimer with RT-PCR positive moderate to severe covid-19 patients. Methods: This diagnostic accuracy study was conducted on suspected COVID-19 SARI patients and were divided into two groups based on the results of RT-PCR for COVID-19 and to minimise false-negative RT-PCR in the control group patientshaving HRCT-thorax CORADS-5 were excluded, and the accuracy of different serum biomarker in predicting cases withpositive RT-PCR was evaluated using the area under the ROC curve (AUC),In thisstudy, AUC 0.9 to 1 was defined as excellent accuracy, 0.8 to0.9 as very good, 0.7 to 0.8 as good, 0.6 to 0.7 as sufficient, 0.5to 0.6 as bad, and < 0.5 as poor(useless test). Results: 126 cases with the mean age of 47.2(range: 19- 84) years were studied (73% male). The result of RT-PCR for COVID-19 waspositive in 62(49.2%) cases. Patients with positive RT-PCR had significantly higher C-reactive protein (CRP) (p = <0.001),D-dimer (p=0.001), lactate dehydrogenase (LDH) (p = <0.001) and non-significantly Serum ferritin (p=0.188).AUC of CRP(AUC=0.832), LDH (AUC=0.809), D-Dimer (AUC=0.667) and serum ferritin(AUC=0.568). Conclusion: Our findings suggest that levelsof Serum Ferritin as bad,D-DIMER as sufficient,CRPand LDH as very good biomarker to predict the results of the COVID-19 test. CRP and LDH can help in the detection of COVID-19 patients.

COVID-19 associated mixed mold infection: a rare case report of aspergillosis and mucormycosis

Background: Fungal epidemic was announced amid COVID pandemic with several cases of COVID-associated Mucormycosis and Aspergillosis being reported however there is not sufficient data regarding mixed fungal infection. Case Study: A 54-year-old male patient diagnosed with severe COVID 19 pneumonia and diabetes 1-month back presented to OPD with C/O of chest pain and breathlessness for two days associated with haemoptysis, heaviness and congestion of right nostril but no fever. O/E patient was tachypnoeic, hypoxic and in shock, Neutrophil count 87%, RBS-530 mg/dl, urine ketone body was absent. Chest x-ray showed opacity over the left upper and mid-zone, HRCTthorax showed a bird-nest-sign noted in the left upper lobe S/O invasive fungal infection. MRI PNS showed mucosal thickening S/O sinusitis, Fungal infection. Sino-nasal mucosa KHO-mount and fungal culture showed mixed infection of Rhizopus species and aspergillus flavus. Right nasal HP study showed mixed invasive moulds infection. Initially, the patient was treated conservatively later on inj. amphotericin-B was started. The patient's condition worsened on day-18 and succumbed a day later. Discussion: Uncontrolled diabetes-mellitus, and corticosteroids leading to hyperglycaemia, extensive use of broad-spectrum antibiotics increases the risk of invasive Moulds. In our case study, patients suffered from COVIDpneumonitis and had uncontrolled diabetes leading to damage of airway epithelium inviting an invasion of tissues by moulds. Conclusion: Mixed fungal infections as COVID-19 sequelae may be an emerging issue and seen particularly in post- COVID patients with uncontrolled diabetes, and on steroids. The focus should be on prompt management: hit hard approach with both medical and surgical treatment.

Impact of environmental and socio-economic stressors leading to unequal distribution of COVID-19 incidences in the state of Louisiana

Louisiana (LA) ranks fifth in the United States in cancer mortality rate. LA's infamous “cancer alley” is a well evidenced region near the southeast part of the Mississippi river surrounding the petrochemical hub of the state. LA has also experienced a high COVID-19 death rate and incidences compared to other states during the recent pandemic. In this study we analyzed publicly available datasets related to health and socio-economic parameters in LA to determine the factors triggering high incidences and deaths caused by COVID-19. Correlation analysis was performed to find the impact of different parameters on the outcome of COVID-19. Our analysis showed higher COVID-19 incidences in the parishes which are in and around the “cancer alley” with a correlation of r = 0.9. Interestingly, results also indicated a strong correlation (r = 0.9) between the death rates caused by asbestos toxicity to COVID-19 caused death rate. Furthermore, we found that office-administration related employment has a positive correlation to COVID-19 incidences in the “cancer alley.” However, we also found both white and black races are equally affected by the COVID-19 pandemic in the “cancer alley” region. In conclusion, our analysis strongly suggests that inhabiting “cancer alley” could significantly enhance the chances of getting affected by SARS-CoV-2 virus compared to other regions in LA. © 2022 Wiley Periodicals LLC.

Multi-Modal Sarcasm Detection in Social Networks: A Comparative Review

Sentiment Analysis (SA) has become an extremely sought after area of research especially post COVID-19 when people used to spent a lot of time on the social media to interact with each other. This interaction was done through posts having both textual and visual cues and also by participating in online discussions forums. Some of the inherent challenges encountered in the process of SA include discernment of sarcasm, irony, humor, negation, multi-polarity or Aspect-Level Sentiment Analysis (ASA) etc. Researchers are now gradually shifting their focus to the identification and detection of sarcasm and how it can empower SA. Sarcasm expresses a person's downside feelings by using positive words in an implicit way. It also has an overall impact on increasing the efficiency of the SA models. Eliciting sarcastic statements is tough for humans as well as for machines without the knowledge of the context or background in which it is expressed, body language and/or facial expression of the speaker and his voice modulation. This review paper studies some of the approaches used for sarcasm detection and also guides researchers in exploring the different modalities of data for developing applications like a virtual chat-bot or assistant, depression analysis, stress management system at workplace etc. © 2022 IEEE.

Comparative analysis of various supervised machine learning techniques for diagnosis of COVID-19

Coronavirus is a large family of a viruses that causes illness ranging from a normal cold to severe disease. COVID-19 is another strain that has not been distinguished in humans before. As this virus is rapidly spreading all over the globe, we need to implement a mathematical model to estimate the prediction of new cases as well as how to classify that a person is COVID-19 positive or not by considering the practical scenario in India. In this research, we proposed three different supervised machine learning techniques for diagnosis of COVID-19. We have compared classification results of different techniques, i.e., bagging algorithm, k-nearest neighbor, and random forest for classifying the datasets of COVID-19. For the classification purpose, we took symptoms from a Covid-19 tracker in India, whereas India has entered into the second stage. The performance of each technique is evaluated using various performance measures. The classification results show that the random forest gives better results, employing accuracy of 85.71% and F1 score of 0.833. © 2021 Elsevier Inc. All rights reserved.