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1.
Managing Complexity and COVID-19: Life, Liberty, or the Pursuit of Happiness ; : 130-144, 2022.
Article in English | Scopus | ID: covidwho-1975137
2.
Managing Complexity and COVID-19: Life, Liberty, or the Pursuit of Happiness ; : 1-220, 2022.
Article in English | Scopus | ID: covidwho-1975136

ABSTRACT

This book brings together insights and perspectives from leading medical, legal, and business professionals, as well as academics and other members of civil society, on the threats and opportunities to life during the COVID-19 pandemic. It provides a uniquely interdisciplinary perspective for policymakers, researchers, and medical professionals to assess the different practical strategies, and risk and crisis management processes available to them in addressing the very difficult choices with which they are presented and their implications. The book presents a framework for the different facets of strategic choices faced by policymakers between life and livelihood, and the challenges of protecting health versus reopening the economy. It also evaluates the intense challenges faced by frontline medical professionals and scientists during an unfolding catastrophe. Finally, the authors explore the societal and human elements of the pandemic and its impact on family dynamics, society, education, and business, including the technology, creative, entertainment, and leisure industries. This book is deliberately short and captures key insights on the COVID-19 pandemic to form an interdisciplinary overview for professionals, policymakers, and business leaders to consider the long-term implications of the pandemic and lessons for future crises. © 2023 selection and editorial matter, Aurobindo Ghosh, Amit Haldar, and Kalyan Bhaumik;individual chapters, the contributors. All rights reserved.

3.
Managing Complexity and COVID-19: Life, Liberty, or the Pursuit of Happiness ; : 201-207, 2022.
Article in English | Scopus | ID: covidwho-1970463
4.
2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2022 ; 2022.
Article in English | Scopus | ID: covidwho-1932097

ABSTRACT

Depression is an unfamous mental health disorder that has affected half the population worldwide. In December 2019, the break of the COVID-19 pandemic was first spotted in Wuhan, China, and later spread to 212 countries and territories worldwide, impacting half the population. It took a significant toll on their physical health and their mental health. Many among the population lost their loved ones, businesses, and being in quarantine for years, completely shifted to the online mode made everyone's life miserable. Many may be dealing with escalated levels of alcohol and drug use, sleeplessness, and an anxious state of mind. So, the need to address this and help the severely affected ones is significant. Self-quarantine also causes additional stress and challenges the mental health of citizens. This paper intends to identify the people who were mentally affected by the pandemic using machine learning techniques. A survey was conducted among college-going students and professionals. The paper used classification techniques such as Naive Bayes, KNN, Random Forest, Logistic Regression, k-fold cross-validation to get results. Support Vector Machine gave the maximum accuracy of 99.35%. © 2022 IEEE.

5.
Studies in Systems, Decision and Control ; 445:199-211, 2023.
Article in English | Scopus | ID: covidwho-1930289

ABSTRACT

We consider the problem of estimating diversity measures for a stratified population and discuss a general formulation for the entropy based diversity measures which includes the previously used entropies as well as a newly proposed family of logarithmic norm entropy (LNE) measures. Our main focus in this work is the consideration of statistical properties (asymptotic efficiency and finite sample robustness) of the sample estimates of such entropy-based diversity measures for their validation and appropriate recommendations. Our proposed LNE based diversity is indeed seen to provide the best trade-offs at an appropriately chosen tuning parameter. Along the way, we also show that the second best candidates are the hypoentropy based diversities justifying their consideration by Leandro Pardo and his colleagues in 1993 over the other entropy families existing at that time. We finally apply the proposed LNE based measure to examine the demographic (age and gender based) diversities among Covid-19 deaths in USA. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

6.
Indian Journal of Rheumatology ; 17(2):153-156, 2022.
Article in English | EMBASE | ID: covidwho-1928755

ABSTRACT

Background: The coronavirus disease or COVID-19 pandemic is the major global health crisis of the present time. Various rheumatological manifestations have been reported during or after COVID-19 infection, but data are scarce. In this observational study, we have tried to analyze the clinical characteristics of COVID-19 associated arthralgia/arthritis. Methods: We have collected the clinical data of 14 patients over the past 6 months who have developed arthralgia or arthritis during or after symptomatic COVID-19 infection, proven by a positive reverse transcription-polymerase chain reaction test from nasopharyngeal swab. Results: The most common symptoms during COVID-19 infection in the 14 patients were fever and myalgia, being present in 92.8% and 64.3% patients, respectively. Arthralgia/arthritis occurred at a mean interval of 20 days (range: 0-60 days). Knee was the most commonly involved joint (78.6%), followed by the wrist and metacarpophalangeal joints (each in 57.1%). Enthesitis was documented in 21.4% patients. The mean duration of COVID-19 associated arthralgia or arthritis was 53.9 days (range: 7-210 days). In 85.7% patients, joint pains improved within 2 months;in only a small proportion of patients (14.3%), joint pains persisted after 6 months. Nonsteroidal anti-inflammatory drugs (NSAIDs) (given in 64.3% patients) and corticosteroids (in 50%) were the most commonly prescribed and effective treatment options. Conclusion: COVID-19 infections mostly caused reactive arthritis, though acute and chronic arthritis is also seen. In the majority of cases, arthritis started about 3 weeks after COVID-19 infection and subsided within 2 months. NSAIDs and corticosteroids are the most effective treatment options.

7.
6th International Conference on Trends in Electronics and Informatics, ICOEI 2022 ; : 846-850, 2022.
Article in English | Scopus | ID: covidwho-1901464

ABSTRACT

Health-care costs are rising on a daily basis after the advent of Covid. Most importantly, health issues are becoming more prevalent and critical. As a result, predicting medical insurance cost has become unavoidable as many people choose insurance. However, for a secure system, the entire prediction model for each customer should be encrypted end-to-end. To create a better prediction model, Machine learning regression algorithms are used. The prediction model will be encrypted end-to-end. This paper will give the steps of developing a reliable medical insurance cost prediction model. © 2022 IEEE.

8.
134th Annual Meeting of the American-Economic-Association (AEA) ; 112:313-318, 2022.
Article in English | Web of Science | ID: covidwho-1896417
9.
Advances in Dual Diagnosis ; 2022.
Article in English | Scopus | ID: covidwho-1878860

ABSTRACT

Purpose: Individuals with dual diagnoses might experience significant clinical and social vulnerabilities during the pandemic and lockdown. This study aims to compare medication adherence, substance use, clinical stability and overall functioning before and during lockdown periods. Design/methodology/approach: This was a cross-sectional survey among patients registered in dual diagnosis clinic of an addiction psychiatry center in Northern India between March 2019 and February 2020. This study approached 250 patients for telephonic interviews. This study assessed adherence to medications with the brief adherence rating scale (BARS). Global functioning was measured by global assessment of functioning. Clinical interviews assessed substance use and the clinical status of psychiatric disorders. Findings: One hundred fifty patients were recruited. The mean age of the sample was 35.8 years. The sample had a slight preponderance of alcohol dependence. Depressive disorder was the largest category of psychiatric diagnosis. Compared to prelockdown period, during the lockdown, there were an increased number of days of nonadherence (X2 17.61, p < 0.05), proportion of patients underdosing (X2 8.96, p = 0.003) and lower BARS scores (t = 10.52, df = 144, p < 0.0001). More patients were abstinent from substances during the lockdown (X2 49.02, p < 0.0001). Clinical stability of psychiatric disorders did not differ during the two-time points, but overall functioning decreased during the lockdown (t = 2.118, p = 0.036). This study observed a small positive correlation (r = 0.2, p = 0.02) between functioning and adherence levels. Originality/value: Lockdown was associated with poor medication adherence, change in substance use patterns and functional impairment. In the future, treatment programs and policies must take preemptive steps to minimize the effects of restrictions. © 2022, Emerald Publishing Limited.

10.
PubMed; 2021.
Preprint in English | PubMed | ID: ppcovidwho-333818

ABSTRACT

INTRODUCTION: The role of overcrowded and multigenerational households as a risk factor for COVID-19 remains unmeasured. The objective of this study is to examine and quantify the association between overcrowded and multigenerational households, and COVID-19 in New York City (NYC). METHODS: We conducted a Bayesian ecological time series analysis at the ZIP Code Tabulation Area (ZCTA) level in NYC to assess whether ZCTAs with higher proportions of overcrowded (defined as proportion of estimated number of housing units with more than one occupant per room) and multigenerational households (defined as the estimated percentage of residences occupied by a grandparent and a grandchild less than 18 years of age) were independently associated with higher suspected COVID-19 case rates (from NYC Department of Health Syndromic Surveillance data for March 1 to 30, 2020). Our main measure was adjusted incidence rate ratio (IRR) of suspected COVID-19 cases per 10,000 population. Our final model controlled for ZCTA-level sociodemographic factors (median income, poverty status, White race, essential workers), prevalence of clinical conditions related to COVID-19 severity (obesity, hypertension, coronary heart disease, diabetes, asthma, smoking status, and chronic obstructive pulmonary disease), and spatial clustering. RESULTS: 39,923 suspected COVID-19 cases presented to emergency departments across 173 ZCTAs in NYC. Adjusted COVID-19 case rates increased by 67% (IRR 1.67, 95% CI = 1.12, 2.52) in ZCTAs in quartile four (versus one) for percent overcrowdedness and increased by 77% (IRR 1.77, 95% CI = 1.11, 2.79) in quartile four (versus one) for percent living in multigenerational housing. Interaction between both exposures was not significant (beta interaction = 0.99, 95% CI: 0.99-1.00). CONCLUSIONS: Over-crowdedness and multigenerational housing are independent risk factors for suspected COVID-19. In the early phase of surge in COVID cases, social distancing measures that increase house-bound populations may inadvertently but temporarily increase SARS-CoV-2 transmission risk and COVID-19 disease in these populations.

11.
Case Rep Med ; 2022: 5603919, 2022.
Article in English | MEDLINE | ID: covidwho-1789045

ABSTRACT

Immune thrombocytopenic purpura (ITP) has been reported following vaccinations such as MMR as well as after viral infections such as hepatitis C and HIV. Few case reports have been reported of ITP after COVID-19 infections and COVID-19 vaccines. Herein, we present a patient who presented with severe ITP with a platelet count of 0 after receiving the second dose of the BNT162b2 mRNA COVID-19 vaccine (also known as the Pfizer BioNTech). She subsequently recovered with a prolonged treatment course.

12.
20th International Conference on Ubiquitous Computing and Communications, 20th International Conference on Computer and Information Technology, 4th International Conference on Data Science and Computational Intelligence and 11th International Conference on Smart Computing, Networking, and Services, IUCC/CIT/DSCI/SmartCNS 2021 ; : 557-564, 2021.
Article in English | Scopus | ID: covidwho-1788750

ABSTRACT

One of our greatest present challenges are designing vaccines against SARS COV2 and its variants. Rational vaccine design uses computational methods prior to development of a vaccine for testing in animals and humans the latest methods in rational vaccine design use machine learning techniques to predict binding affinity and antigenicity but offer the researchers only isolated stand-Alone tools. A difficulty that software engineers and data scientist face in development of tools for doctors and researchers is their lack of knowledge of the medical domain. This paper presents a set of domain model developed in collaboration between software engineers and a medical researcher in the process of building a tool scientists could use to predict binding affinity and antigenicity of potential designs of SARS COV2 vaccines. A domain model visualizes the real-world entities and their interrelationships, that together define the domain space. This domain model will be useful to other software engineers trying to predict other characteristics of vaccines, such as potential autoimmunity response. © 2021 IEEE.

13.
Acta Crystallographica a-Foundation and Advances ; 77:C197-C197, 2021.
Article in English | Web of Science | ID: covidwho-1762485
15.
12th International Conference on Computing Communication and Networking Technologies, ICCCNT 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1752349

ABSTRACT

In this study, few convolutional neural networks (CNN) have been trained with a transfer learning method to facilitate either binary classification of radiography images into COVID-19 infected and normal or ternary classification into normal, pneumonia, and COVID-19 infected. As the number of COVID-19 cases grow exponentially, the proposed solution can provide an early home based computer-aided diagnosis to ease the pressure on healthcare. The decision made by the model can advise a patient on whether it is critical to visit a doctor or not. In this paper, a CNN based transfer learning model was used to provide a superior precision in image classification. The neural network model was trained and tested using 1,183 radiography images to report the precision that can be attained in authentic conditions using three different CNNs. The accuracy of the model in classifying radiography images is 97.46% for ternary classification and 99.36% accuracy for binary classification using VGG-16 CNN architecture. In addition, the tested algorithm is also developed as a web application for detecting COVID-19 with Chest X-ray images and deployed in the cloud for public use. © 2021 IEEE.

16.
Journal of Food Processing and Preservation ; 2022.
Article in English | Scopus | ID: covidwho-1745883

ABSTRACT

Post COVID-19 pandemic realization and expanding consumer demand for functional nutrition have compelled the food industry to focus on one, clean-label technologies to improve energy expenditure, microbial inactivation, shelf stability, and retention of functional nutrients, and second on the systematic evaluation of food matrices for bioactive potential (functionality) and designing novel food matrices and products healthier than the existing formats. The food industry is rapidly heading toward a “technological convergence” with the goal of establishing highly efficient processing technologies for safe, shelf-stable functional products. Novelty impact statement: In this review, we evaluated the utility and efficiency of various non-thermal processing technologies (cold plasma, ultra-sonication, high pressure, pulsed electric field, pulsed light processing) with respect to their capabilities to retain phytonutrient functionality and antioxidant potential in processed foods. The review also discusses existing gaps in current non thermal processing techniques and explores potential improvements necessary to foster reliable next-generation processing technologies. © 2022 Wiley Periodicals LLC.

17.
Pharma Times ; 52(9):14-16, 2020.
Article in English | Scopus | ID: covidwho-1743829

ABSTRACT

The COVID-19 pandemic has taken the world into a dark time. The number of infected people has crossed 12 crores and the death counts to more than 5 lakhs until July, 2020 worldwide[1]. Moreover, this lockdown is breaking the pillar of the economy of the countries and the condition is getting worse day by day. So, keeping these things in mind, a vaccine or medicine or some other method of cure is urgently needed. To make this possible, the study of the structure of the virus needs to be done very carefully. That is why we focused upon the structural proteins of the SARS-CoV-2 virus. We all know that in this short period of time it is nearly impossible to do XRD and to know the exact structures of all the proteins present in the discussed virus. So in this article, we have tried to predict the most accurate 3D structure of a yet-unmodelled protein of the SARS-CoV-2, so that in the future, our finding may appear helpful for researchers in case of performing XRD of this protein and further research. © 2020, Indian Pharmaceutical Association. All rights reserved.

18.
5th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1741143

ABSTRACT

The present situation of the COVID-19 pandemic called for very strict safety norms to contain the virus. One of these mainly included wearing a mask at all places outside one's home. However, many do not take that seriously. This paper deals with an approach to detect a face mask on an individual's face, which if violated can be used in an alert system, and the like. The algorithm used to train the machine is convolution neural networks (CNN) for face(image) recognition in real-time. The results and inferences obtained are with respect to the model structure, algorithm, and variation in the dataset. The final model had an accuracy of 96.18% and was stable in real-time with no false-negative results. The final application alerts the concerned if the mask is not worn properly and captures an image to be sent to the concerned. © 2021 IEEE.

19.
Indian Journal of Medical Microbiology ; 39:S57, 2021.
Article in English | EMBASE | ID: covidwho-1734462

ABSTRACT

Background:The present world is experiencing COVID-19 pandemic caused by severe acute respiratory syndrome coro- navirus (SARS-CoV-2), which has been confirmed in nearly 5.6 crores cases and 13.4 lakhs deaths worldwide. The majori- ty of COVID-19 infections are asymptomatic or mild symptomatic, however a considerable part of infected persons re- quires hospitalization. As there is no definitive treatment, prevention and control of infection is the key to fight against this infection. In India a significant proportion of healthcare workers (HCWs) are getting affected and on March2020, ICMR recommended the use of Hydroxychloroquine (HCQ) as prophylaxis for SARS-CoV-2 infection. Some observational studies support the anti-viral activity of HCQ but still there is no extensive work on HCWs that evaluates the prophylac- tic potential of HCQ against SARS- CoV-2 infection. This study evaluated the potential role of HCQ as prophylactic drug among COVID-19 affected and non-affected individuals. Methods:We have conducted a cross-sectional study in a tertiary care hospital, Kolkata from July 2020 to Novem- ber2020 where 199 HCWs were interviewed using structured questionnaire related to HCQ intake, dose and duration, side effects, signs and symptoms, COVID 19 test report, duration of illness etc. Results:Among 199 HCW, 90(45.22%) HCW took HCQ prophylaxis and among them 62% were the COVID19 negative which also reflects its prophylactic efficacy. We have also evaluated the role of HCQ for various clinical symptoms and found around 34% HCQ non exposed COVID positive individuals encountered shortness of breath whereas only 14% HCQ exposed COVID positive patients encountered the same. In case of chest HRCT scan data, the study found signifi- cant lung involvement for HCQ non exposed individuals whereas the other group rarely developed any involvements. Conclusions:There is statistically significant protective role of HCQ for COVID-19 (OR=0.51724, 95% CI=0.292-0.914, p- value=0.023). Further study with more clinical and preclinical data will help to explore the potential prophylactic role of HCQ.

20.
2021 IEEE International Conference on Big Data, Big Data 2021 ; : 4356-4364, 2021.
Article in English | Scopus | ID: covidwho-1730882

ABSTRACT

Societal functions have stalled during COVID-19 to reduce its spread in the population. It has been shown that visits to different venues have a large effect on spreading the virus. Hence, population-level mobility interventions like reopening selective category of venues have been proposed, for example, opening schools and offices but preventing people from visiting restaurants. These measures, although help to mitigate infection, still fail to satisfy people's needs and hope of going back to normality. In this context, here we propose an individual level POI recommendation system that can recommend venues to users according to their preference and at the same time, can lead to as few infections as possible. The key idea behind the system is that the risk of getting infected grows with the number of unique customers that had visited the venue previously, and it is safer to visit a less crowded place during a specific time slot. We evaluate the proposed system using both theory and real check-in datasets from three cities. Based on simulation on real-world data, we present a surprising result: it is possible to recommend POIs in such a way that the total infected population reduces by up to 50% compared to that following original check-ins. This result is comparable to that when 50% of the visits are blocked, yet our method allows all check-in needs. © 2021 IEEE.

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