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1.
Cureus ; 14(8):e27814, 2022.
Article in English | MEDLINE | ID: covidwho-2030310

ABSTRACT

Introduction The COVID-19 pandemic has been a major public health threat for the past three years. The RNA virus has been constantly evolving, changing the manifestations and progression of the disease. Some factors which impact the progression to severe COVID-19 or mortality include comorbidities such as diabetes mellitus, hypertension, and obesity. In this study, we followed a cohort of patients to evaluate the risk factors leading to severe manifestations and mortality from COVID-19. Methodology We conducted a prospective observational study of 589 COVID-19 patients to assess the risk factors associated with the severity and mortality of the disease. Results In our cohort, 83.5% were male, with a median age (p25, p75) of 39.71 (30-48) years. The most common comorbidities included diabetes mellitus (7.8%) and hypertension (7.9%). About 41.7% had an asymptomatic disease, and of the symptomatic, 45% were mild, 6% moderate, and 7% severe. The mortality rate was 4.1%. Risk factors for severity included breathlessness (p=0.02), leukocytosis (p=0.02), and deranged renal function (p=0.04). Risk factors for mortality included older age (p=0.04), anemia (p=0.02), and leukocytosis (p=0.02). Conclusions COVID-19 commonly leads to asymptomatic or mild illness. The major factors we found that were associated with severity include breathlessness at presentation, leukocytosis, and deranged renal functions. The factors associated with mortality include older age, anemia, and leukocytosis.

2.
13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, BCB 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2029550

ABSTRACT

Supervised machine learning models are, by definition, data-sighted, requiring to view all or most parts of the training dataset which are labeled. This paradigm presents two bottlenecks which are intertwined: risk of exposing sensitive data samples to the third-party site with machine learning engineers, and time-consuming, laborious, bias-prone nature of data annotations by the personnel at the data source site. In this paper we studied learning impact of data adequacy as bias source in a data-blinded semi-supervised learning model for covid chest X-ray classification. Data-blindedness was put in action on a semi-supervised generative adversarial network to generate synthetic data based only on a few labeled data samples and concurrently learn to classify targets. We designed and developed a data-blind COVID-19 patient classifier that classifies whether an individual is suffering from COVID-19 or other type of illness with the ultimate goal of producing a system to assist in labeling large datasets. However, the availability of the labels in the training data had an impact in the model performance, and when a new disease spreads, as it was COVID9-19 in 2019, access to labeled data may be limited. Here, we studied how bias in the labeled sample distribution per class impacted in classification performance for three models: A Convolution Neural Network based classifier (CNN), a semi-supervised GAN using the source data (SGAN), and finally our proposed data-blinded semi-supervised GAN (BSGAN). Data-blind prevents machine learning engineers from directly accessing the source data during training, thereby ensuring data confidentiality. This was achieved by using synthetic data samples, generated by a separate generative model which were then used to train the proposed model. Our model achieved comparable performance, with the trade-off between a privacy-Aware model and a traditionally-learnt model of 0.05 AUC-score, and it maintained stable, following the same learning performance as the data distribution was changed. © 2022 Owner/Author.

3.
Journal of Indian Academy of Forensic Medicine ; 44(1):54-56, 2022.
Article in English | Scopus | ID: covidwho-2025244

ABSTRACT

The recent Covid-19 pandemic has raised a lot of questions regarding the mode of transmission of the virus. The rapid spread across the globe has compelled researchers to focus on this issue. Theories claiming droplet transmission, fbmites as well as airborne transmission have cropped up. The primary concern for the autopsy surgeons is whether the dead bodies harbor the virus and if so for how long. The present study was undertaken to find out the possibility of the virus being isolated from the human cadavers by testing at specified intervals after death. Out of the 74 cases examined, 59.5% of cases tested positive 1 day after death and 20.5% were still positive 5 days after death. The diflerence between males and females was not significant. The age of the subjects in our study ranged from 20 days to 90 years. The results of the study clearly indicate that the virus persists in the human cadavers for a sufficient period of time to act as a potential source of infection. Adequate precautionary measures while packing the body and autopsy examination are of utmost essential to prevent the spread of the disease among the dead body handlers and the family members while performing the last rites © 2022. Journal of Indian Academy of Forensic Medicine.All Rights Reserved.

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5.
International Journal of Productivity and Performance Management ; 2022.
Article in English | Web of Science | ID: covidwho-1997108

ABSTRACT

Purpose The purpose of this study is to augment the perceived service quality (PSQ) dimensions as well as evaluate the effects of pandemic susceptibility and severity by appending crucial enablers of customer satisfaction (CS) in the restaurant industry (RI). Design/methodology/approach The top 10 restaurants from Mumbai and Kolkata were selected based on the Conde Nast Traveller Magazine List, 2020. The study used a cross-sectional design to collect responses from 840 respondents across the two major metropolitans of India after the second wave of COVID-19 by employing a structured questionnaire. The proffered hypotheses in this study were validated using factor analysis and structural equation modelling (SEM) techniques. Findings This research espies pivotal facilitators of CS and customers' perceived value (CPV). The results divulge that food quality (FQ) and tangibility dimensions markedly enhance CS while the FQ and digital technologies (DT) dimensions augment CPV in Indian restaurants. The study asserts that CPV acts as a partial mediator between FQ and DT on the one hand and CS on the other. In addition, perceived pandemic susceptibility (PPSU) and perceived pandemic severity (PPSE) moderate the association between CPV and CS in restaurants. Research limitations/implications This study exemplifies the critical enablers of CS and CPV that may invigorate restaurant owners, managers and policymakers to prioritize the identified dimensions to aggrandize CS and CPV quotients. Originality/value The study enriches the literature by assimilating DT and CPV dimensions in a comprehensive theoretical framework. The research is unique in attempting to unfurl the moderating effects of PPSU and PPSE in the RI.

6.
World of Media ; 2022(2):78-105, 2022.
Article in English | Scopus | ID: covidwho-1994906

ABSTRACT

This article represents an example of a non-Western study into the public perception of the mass media’s role during the coronavirus pandemic in Bangladesh, which is of particular importance given the global environment of a high level of informational uncertainty and health risk that is equally applicable to countries around the world. Quantitative research methodology was used to gather perceptions of citizens across the country on the role and performance of the mass media’s coverage of the coronavirus pandemic. The responses gathered demonstrated that the pandemic generated an increased demand for news and information on the virus, which was used as a means of attempting to reduce personal risk and harm. In this time of an increased demand for information, respondents tended to perceive the information that they received from mainstream media news sources as being credible and rated media performance positively. This final observation is seemingly bucking the general global trend of decreased public trust in news media sources. © 2022, Lomonosov Moscow State University, Faculty of Journalism. All rights reserved.

8.
PubMed; 2020.
Preprint in English | PubMed | ID: ppcovidwho-333622

ABSTRACT

IMPORTANCE: False negative SARS-CoV-2 tests can lead to spread of infection in the inpatient setting to other patients and healthcare workers. However, the population of patients with COVID who are admitted with false negative testing is unstudied. OBJECTIVE: To characterize and develop a model to predict true SARS-CoV-2 infection among patients who initially test negative for COVID by PCR. DESIGN: Retrospective cohort study. SETTING: Five hospitals within the Yale New Haven Health System between 3/10/2020 and 9/1/2020. Participants : Adult patients who received diagnostic testing for SARS-CoV-2 virus within the first 96 hours of hospitalization. EXPOSURE: We developed a logistic regression model from readily available electronic health record data to predict SARS-CoV-2 positivity in patients who were positive for COVID and those who were negative and never retested. MAIN OUTCOMES AND MEASURES: This model was applied to patients testing negative for SARS-CoV-2 who were retested within the first 96 hours of hospitalization. We evaluated the ability of the model to discriminate between patients who would subsequently retest negative and those who would subsequently retest positive. RESULTS: We included 31,459 hospitalized adult patients;2,666 of these patients tested positive for COVID and 3,511 initially tested negative for COVID and were retested. Of the patients who were retested, 61 (1.7%) had a subsequent positive COVID test. The model showed that higher age, vital sign abnormalities, and lower white blood cell count served as strong predictors for COVID positivity in these patients. The model had moderate performance to predict which patients would retest positive with a test set area under the receiver-operator characteristic (ROC) of 0.76 (95% CI 0.70 - 0.83). Using a cutpoint for our risk prediction model at the 90 th percentile for probability, we were able to capture 35/61 (57%) of the patients who would retest positive. This cutpoint amounts to a number-needed-to-retest range between 15 and 77 patients. CONCLUSION AND RELEVANCE: We show that a pragmatic model can predict which patients should be retested for COVID. Further research is required to determine if this risk model can be applied prospectively in hospitalized patients to prevent the spread of SARS-CoV-2 infections.

9.
Journal of Asian Finance Economics and Business ; 9(4):29-38, 2022.
Article in English | Web of Science | ID: covidwho-1798671

ABSTRACT

The enormous sway of COVID-19 on the international financial market has been felt across the globe. The financial markets of Bangladesh have also been similarly affected by the global epidemic and experienced a significant increase in volatility. To scrutinise the connection between COVID-19 and the Dhaka Stock Exchange (DSE) indices' return and instability, this study uses data of the DSE from February 2014 to September 2021. A comparative examination of the return and instability of the stock indices of the DSE has also been done considering the outbreak of the current COVID-19 situation. After using the GJR-GARCH (1,1) model, this review uncovers that the outbreak of COVID-19 has a statistically positive noteworthy association with the DSE stock indices' instability, which increases the market's volatility. Traders' fear and the rising frequency of COVID-19 reported patients could cause this. Besides, according to this study, COVID-19 shows a substantial positive linkage with stock market returns that increases the market's return. An appealing valuation, lower interest rates in the banking channel, economic rebound following the closure to prevent coronavirus transmission, improved remittance inflows, and a return of export revenues could all have contributed to this outcome. In addition, the findings also reveal that all market indices are in a mean-reverting phase.

10.
Asian Pacific Journal of Tropical Medicine ; 15(2):90-92, 2022.
Article in English | Scopus | ID: covidwho-1760917
11.
12th International Conference on Computing Communication and Networking Technologies, ICCCNT 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1752383

ABSTRACT

Vaccination of the global population against COVID-19 is one of the challenging tasks in supply chain management that humanity has ever faced. The rapid roll-out of the COVID-19 vaccine is a must for making the worldwide immunization campaign successful, but its effectiveness depends on the availability of an operational and transparent distribution chain that can be audited by all related stakeholders. In this paper, the necessity of Blockchain and Machine Learning in supply-chain management with demand forecasting of the COVID-19 vaccine has been presented. The aim is to understand how the convergence of Blockchain technology and ML monitor the prerequisite of vaccine distribution with demand forecasting. Here, we have proposed an approach consists of Blockchain and Machine Learning which will be used to ensure the seamless COVID-19 vaccine distribution with transparency, data integrity, and end-to-end traceability for reducing risk, assuring the safety, and also immutability. Besides this, we have performed demand forecasting for appropriate COVID-19 vaccines according to the geographical area and the storage facilities. Lastly, we have discussed research challenges and also mentioning the limitations with future directions. © 2021 IEEE.

12.
12th International Conference on Computing Communication and Networking Technologies, ICCCNT 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1752361

ABSTRACT

Online Education has become a buzzword since the COVID-19 hit the World. Most of the educational institutions went online to continue educational activities while developing countries like Bangladesh took a significant period of time to ensure online education at every education level. Students of several levels also faced many difficulties when they got introduced to online education. It is important for the decision-makers of educational institutions to be informed about the effectiveness of online education so that they can take further steps to make it more beneficial for the students. Our main motivation is to contribute to this matter by analyzing the relevant factors associated with online education. In this work, we have collected students' information of all three different levels(School, College, and University) by conducting both online and physical surveys. The surveys form consists of an individual's socio-demographic factors. To get an idea about the effectiveness of online education we have applied several machine learning algorithms named Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and also Artificial Neural Network(ANN) on our dataset to predict the adaptability level of the students to online education. Among used algorithms, the Random Forest classifier achieved the best accuracy of 89.63% and outperformed other algorithms. © 2021 IEEE.

13.
3rd International Conference on Communication, Devices and Computing, ICCDC 2021 ; 851:607-617, 2022.
Article in English | Scopus | ID: covidwho-1750658

ABSTRACT

COVID-19 was first discovered in the city of Wuhan. From then onward the virus has spread rapidly infecting thousands of people. The virus is still spreading and attempts are being made to predict and control the growth and spread of this virus. The trend of spread of this virus is highly unpredictable and normal statistical methods of predictions have not provided promising results, thus another approach of predicting the growth of this virus is required. This approach must be able to predict the nonlinear growth of the virus. Thus, an attempt is made to predict the growth of this virus and to show that the normal statistical methods are not able to predict the growth of the virus with high accuracy. The linear predicting algorithms used are Linear Regression, Support Vector Machine, Polynomial Regression and Auto Regressive Integrated Moving Average. The nonlinear predicting algorithm used is Prophet Algorithm for the prediction of exponential growth of spread of the virus. A comprehensive study is done to show how the spread of the virus takes place in different countries. A comparative study is also done to show the differences in performance parameters based on Absolute Mean Error, Mean Squared Error and R-squared (R2) score among different types of predictors. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

14.
Indian Journal of Hematology and Blood Transfusion ; 37(SUPPL 1):S150-S151, 2021.
Article in English | EMBASE | ID: covidwho-1638408

ABSTRACT

Introduction: About 20% of the Indian population has been vaccinated till September, since its first roll out on 16 January 2021.Majority of Indian population was vaccinated through ChAdOx1nCoV-19 Corona Virus Vaccine (Recombinant), commonly known asCovishield. Although the vaccine is proven safe but occasional reports of Vaccine-induced thrombocytopenia and thrombosis raisedfew eyebrows regarding the safety. Apheresis-derived platelet concentrates are frequently required in all sorts of clinical illnesses andpost-vaccination decrement of platelet counts might lead to increaseddeferral of the plateletpheresis donors.Aims &Objectives: The study aims to find out the effect of Covishield vaccination on deferral rates of plateletpheresis donors. Theprimary objective is to compare deferral rates of vaccinated plateletdonors (cases) from non-vaccinated ones (control) and the secondaryobjective is to correlate vaccination with pre-donation platelet countsof the donors.Materials &Methods: A blood sample was collected from thepotential platelet donors after the standard questionnaire for a complete blood count. Data collected were tabulated in an MS Excelspreadsheet and was analyzed with SPSS v23, p-value ≥ 0.05 wastaken as significant. We compared this data with an equal number ofage and sex-matched platelet donors (controls) from the year 2019.Result: The mean age of cases and controls was 29.69 ± 8.57 and30.15 ± 7.11 respectively. There is a marked difference betweenplatelet counts of cases (188,496.35 ± 72,065.66/mm3) and controls(269,524.50 ± 53,981.60/mm3) with p-value of <0 > 3 than thosewho received 2 doses 179,970.83 ± 66,773.73/mm3. The differencein deferral rates was remarkable between the two groups 34.7% vs0.9% with p-value <0.001. Weak positive correlation is establishedbetween days after 2nd dose and platelet count (rho = 0.2, p = 0.002).Conclusions: Vaccination undoubtedly increased the deferral rates ofplateletpheresis donors due to low platelet counts. Average plateletcounts were low in fully vaccinated individuals, however, plateletreturned to normal counts as the days progress post-vaccination.

15.
Studies in Computational Intelligence ; 1001:401-420, 2022.
Article in English | Scopus | ID: covidwho-1592202

ABSTRACT

In this time of COVID-19 crisis, the threat posed by the propagation of misinformation leading to mistrust needs to be kept in check. Misinformation related to the vaccines, remedies, false symptoms, etc. are spiraling out of control. We might not be able to directly put a stop to the flow or spread of fake news to a large extent at the moment, but it may be able to identify it as such with the help of Natural Language Processing (NLP) tools and Deep Learning (DL) algorithms. Steps involved in achieving this goal can be narrowed down into collection and analysis of data from various sources, sorting out the articles as covid-relevant and categorizing them as real or fake using DL models. However, DL models use different optimizers in the learning process, which plays an important role in identifying the fake news. This chapter also compares the efficiency of different optimizers in the context of COVID-19 fake news detection using DL models. The newly developed Continuous Coin Betting (CoCoB) Optimizer for DL studied extensively for fake news detection and performed compared with four other widely used optimizers. The comparative analysis shows the CoCoB as well as popularly used Adam optimizers are quite effective in finding optimal classification results for detection of fake news related to COVID-19. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

16.
International Journal of Quality & Reliability Management ; ahead-of-print(ahead-of-print):24, 2021.
Article in English | Web of Science | ID: covidwho-1583872

ABSTRACT

Purpose The meddling of foreign players into the Indian hotel industry has triggered fervent competitiveness, and therefore, consumers' attitude, intention and behavior have been the epicenter of all activities. This study endeavors to explicate enablers of online hotel booking intention (OHBI) in the Indian hospitality industry. Design/methodology/approach The study examined OHBI of 560 travelers during the first wave of COVID-19 pandemic in India using structural equation modeling and an extended technology acceptance model. Direct and indirect associations were explored using mediation and moderation. Findings The results manifest that hotel website credibility, perceived website interactivity and perceived ease of use (PEU) aggrandize perceived usefulness (PU), which, in turn, considerably magnifies travelers' OHBI. PEU and PU partially mediate the relationship in the model. Into the bargain, service affordability reinforces the relationship, while perceived pandemic risk enfeebles the relationship between PU and OHBI. Research limitations/implications The study unfurls pressing determinants of PEU, PU and OHBI that may facilitate hoteliers to lure travelers and enhance profitability. Originality/value There is a paucity of literature on "hotel website credibility" and "perceived pandemic risk" in the hospitality industry. Hence, the study enriches literature by assimilating underlying constructs through an epigrammatic conceptual model. The study is distinctive because it unearths the possibilities of mediation and moderation amongst the aforementioned constructs and posits the calamitous effects of the COVID-19 pandemic on the tourism and hospitality sector.

18.
Silicon ; : 14, 2021.
Article in English | Web of Science | ID: covidwho-1491433

ABSTRACT

TFET based label-free biosensors are fast, sensitive and more power efficient as compared to CMOS biosensors, which are prone to short channel effects (SCEs). However, literature is flooded with various TFET biosensors that have become the reason of dilemma for researchers during pandemic situations like COVID-19. Therefore, in this work, a physically doped (PD), charge plasma (CP) and electrically doped (ED) dielectric modulated (DM) TFET based label-free biosensors are compared, which cover almost the entire range of doping and junctionless devices. Also, we found that the ED based TFET biosensors provide better current sensitivities of 5.10 x 10(7), 4.77 x 10(8) and 7.11 x 10(8) for biomolecules with K=12, positive charge= 1 x 10(13) C/cm(2) and negative charge= -1 x 10(13) C/cm(2) respectively. Hence, ED-DM-TFET based biosensors can act as promising candidates to provide better detection and identification quality.

19.
Indian Journal of Forensic Medicine and Toxicology ; 15(4):2696-2704, 2021.
Article in English | EMBASE | ID: covidwho-1449617

ABSTRACT

Background: Suicide is a global issue, with an estimated 75.5% of the cases occurring in developing countries, and India alone accounting for 26.6% of all global suicidal deaths. With an advent of COVID-19 in the early months of 2020, India observed a rapid rise in suicidal deaths. Though, various media reports predicted loneliness, mental illness and economical instabilities as the major triggering factors, there is a lack of analytical or descriptive studies confirming this hypothesis. In this context, the present cross-sectional study was planned to determine the socio-demographic profiles of the victims and the triggering factors of the suicidal deaths during the COVID-19 phase, in context to the victims of suicide from 2017 to the Pre-COVID phase. Methods: The present cross-sectional study was conducted by analyzing the suicidal deaths from 2017 to 30th June, 2020, interviewing the deceased family members during the COVID-19 phase and studying the Inquest reports, with the documents from the Institutional Medical Record Section. Conclusion: The authors feel that suicide is an act of moment in mind, so any decision made under excitement or incitement is the real culprit. To curb the menace of suicide, state and society should ensure education, employment and socioeconomic well-being, along with strict law enforcement.

20.
4th International Conference on Communication, Device and Networking, ICCDN 2020 ; 776:143-149, 2022.
Article in English | Scopus | ID: covidwho-1437227

ABSTRACT

Ever since Covid-19 has affected our lives, the use of masks in public places has taken extreme importance. Be it restaurants, shopping malls, workplaces, airports or any other public place, wearing a mask is essential for the safety of oneself as well as the others. However, there are always some defaulters who will not wear a mask putting all at risk. In this paper, a mask/no mask detector has been implemented to identify such defaulters. Also, a face recognition system has been incorporated to identify those who are not wearing a mask. This setup could be used in offices or any other place of public gathering to ensure the safety of one and all. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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