Handbook of Health and Well-Being: Challenges, Strategies and Future Trends

This evidence-based book focuses on contemporary issues related to human health and well-being. Drawing on the first-hand experiences of academics and researchers, it provides a holistic perspective on the importance of both mental and physical health for quality of life. It is divided into seven sections: changing perspectives on well-being;the mental health of students;the well-being of elderly people and marginalized populations;the role of family and teachers;psycho-social support;the right to health;and future perspectives. Covering current topics, such as the challenges posed by pandemics like COVID-19, the book discusses future strategies for addressing contemporary and emerging health issues and the overall well-being of the general public, an area not covered in any of the previous volumes. Furthermore, it explores the need for the involvement of multidisciplinary professionals in examining general health and well-being issues. Given its scope, it is an indispensable resource for a wide range of professionals and researchers from various fields, such as the social sciences, law, public health, medicine, education, and environmental studies. In addition, the book appeals to health policymakers, educational administrators, law enforcement agencies, as well as health workers, psychologists, and social workers dealing with clients in hospitals, educational institutions, and at the community level. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Taylor and Francis Pte Ltd. 2022.

COVID-19 and Mental Health of Indian Youth: Association with Background Variables and Stress

The coronavirus has become a public health concern of the decade, affecting the economic, social, and psychological stability of the whole world. Having understood the detrimental impact of the pandemic to the mental health of people of all age groups, youth is understood to be the most vulnerable population who receives its direct impact. The broad objective was to study the mental health status of Indian youth and its association with various demographic variables. Psychological stress and mental health was another relationship that was explored. A group of 317 participants between the age group of 19 to 29 voluntarily took part in the online survey. Gender was found to be associated with overall mental health status (p < 0.01) as well its correlates, namely anxiety (p < 0.05), depression (p < 0.05), and loss of behavioral control (p < 0.01). Association between age and loss of positive affect (p < 0.05), number of siblings and loss of behavioral control (p < 0.01), and family environment and overall mental health scores (p < 0.001) were found. Similarly, feeling of restlessness during lockdown (p < 0.001), availability of support (p < 0.001), and feeling the need to consult a mental health professional were associated with the overall mental health score as well as all its sub-scales. Further, there were strong negative correlations between psychological stress and overall mental health scores, as well as that of anxiety, depression, and loss of behavioral control and positive affect sub-scales. The study highlighted the need for psychological support services for the youth population of the country to cope with and adapt to the new situation. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Taylor and Francis Pte Ltd. 2022.

IPO Underpricing and Short-Term Performance: A Comparative Analysis During the COVID-19 Pandemic and Tranquil Periods in a Cross-Country Setting

This study presents a comparative analysis of under-pricing and short-term performance of IPOs issued during the COVID-19 pandemic period and the pre-pandemic tranquil decade (2009 to 2019) in a cross-country setup. We find evidence of higher underpricing of IPOs issued during COVID-19 which, however, gets corrected shortly. Factors, such as underwriter reputation, percentage of the net proceeds to the company, new shareholder participation, industry affiliation of the issuing firm and the severity of the pandemic in respective countries seem to affect these patterns. Our results are robust and remain mostly unaltered through a series of robustness tests. © 2023 Taylor & Francis Group, LLC.

Survival prediction of COVID-19 patients using multi-modal dataset

COVID-19 has proved to be a global emergency that has fractured the healthcare systems to the extent that its impact is too challenging to encompass. Though many Computer-Aided Diagnoses (CAD) systems have been developed for automatic detection of COVID-19 from Chest X-rays and chest CT images, very few works have been done on detecting COVID-19 from a clinical dataset. Resources needed for obtaining Clinical data like blood pressure, liver disease, past traveling history, etc., are inexpensive compared to collecting Chest CT images for COVID-19 infected patients. We propose a novel multi-model dataset for the survival prediction of patients infected with COVID-19. The dataset proposed is collected and created at Mahatma Gandhi Memorial Medical College, Indore. The dataset contains clinical data and chest X-ray images obtained from the same patient infected with COVID-19. For proper prognosis of the COVID19 positive patients from the clinical dataset, we have proposed a Bi-Stream Gated Attention-based CNN (BSGA-CNN) model. The BSGA-CNN model achieved an accuracy of 96.90% (± 3.05%). A CNN based on pre-trained VGG-Net is used to classify the corresponding Chest X-Ray images. It gave an accuracy of 87.76% (± 8.78%)%. © 2022 IEEE.

Clinico-demographic Profile and Outcome of Scrub Typhus in North Eastern State of India

Scrub typhus is a form of bacterial Zoonosis caused by Orintia tsutsugamushi usually presents as Acute febrile illness with multiorgan involvement as a complication and is associated with significant mortality. This study aims to document the clinico-demographic profile, laboratory parameters and complication of Scrub Typhus in North Eastern Hilly State of Tripura with background of tropical climate. This retrospective study was conducted at Tripura Medical College, including 42 patients admitted with acute febrile illness between June, 2020 to December, 2021 during the era of COVID-19 Pandemic. The diagnosis was established by Rapid card test, Lateral Flow Metry Assay (LFA) followed by confirmation through IgM, ELISA test and pathognomic Eschar where feasible. The clinlical, demographic and laboratory profile were documented and analysed. Post rainy season and people from rural area with farming background were mostly affected population. Apart from Fever and Flu like symptom, respiratory and Gastrointestinal (GI) symptoms were more prominent feature. Pathognomic skin lesion eschar was found in maximum cases followed by shortness of breath, GI involvement and Renal failure. Acute Respiratory Distress Syndrome (ARDS), Acute Kidney Injury (AKI), Hepatic encephalopathy and meningitis were the serious complications. While evaluating cases of acute febrile illness with multiorgan involvement clinician should have high index of suspicion for Scrub typhus specially resource poor areas of North Eastern (NE) state of India so that early detection and time bound intervention may help to reduce the mortality.Copyright © 2022 Indian Medical Association. All rights reserved.

CoVSeverity-Net: an efficient deep learning model for COVID-19 severity estimation from Chest X-Ray images

Purpose: COVID-19 is not going anywhere and is slowly becoming a part of our life. The World Health Organization declared it a pandemic in 2020, and it has affected all of us in many ways. Several deep learning techniques have been developed to detect COVID-19 from Chest X-Ray images. COVID-19 infection severity scoring can aid in establishing the optimum course of treatment and care for a positive patient, as all COVID-19 positive patients do not require special medical attention. Still, very few works are reported to estimate the severity of the disease from the Chest X-Ray images. The unavailability of the large-scale dataset might be a reason. Methods: We aim to propose CoVSeverity-Net, a deep learning-based architecture for predicting the severity of COVID-19 from Chest X-ray images. CoVSeverity-Net is trained on a public COVID-19 dataset, curated by experienced radiologists for severity estimation. For that, a large publicly available dataset is collected and divided into three levels of severity, namely Mild, Moderate, and Severe. Results: An accuracy of 85.71% is reported. Conducting 5-fold cross-validation, we have obtained an accuracy of 87.82 ± 6.25%. Similarly, conducting 10-fold cross-validation we obtained accuracy of 91.26 ± 3.42. The results were better when compared with other state-of-the-art architectures. Conclusion: We strongly believe that this study has a high chance of reducing the workload of overworked front-line radiologists, speeding up patient diagnosis and treatment, and easing pandemic control. Future work would be to train a novel deep learning-based architecture on a larger dataset for severity estimation. © 2023, The Author(s), under exclusive licence to The Brazilian Society of Biomedical Engineering.

Machine learning-based S-CNN model for automated post-covid X-RAY identification

COVID-19 has transmuted the globe and spread throughout the world. The COVID has streamlined and expedited regional procedures. Because the disease spreads via people, the CO VID test and data are pretty prevalent in humans. It is therefore vital to identify those who are affected. It's time to get on with your life. Chest X-ray and CT-SCAN are the most commonly used CO VID testing procedures. A chest X-ray is the quickest and least expensive treatment. There are no cyclopean amplitude test packets for COVID employing chest X-ray and model. FCNN is a standard image processing algorithm. The model should be able to recognize CO VID from a photo quickly. We proposed an S-CNN model as the foundation for the whole CNN in the study. The model we developed is very adaptable to any gear system and has low temporal complexity. The method can detect COVID in an unknown image with 92 percent accuracy. The model provides a reasonable and adequate response for estimating COVID from private data. © 2022 IEEE.

MANAGING POST COVID-19 CRISIS IN THE TOURISM AND HOSPITALITY SECTOR THROUGH SUSTAINABLE RECOVERY STRATEGIES

This study aims to formulate effective and sustainable strategies to manage post COVID-19 crisis in the tourism and hospitality industry. Data were collected from 290 respondents using the purposive sampling procedure in which the response rate is 70.7%. The structural equation modeling software, SmartPLS 3.0, is used to analyze the constructs of this study and hypothesis testing. The result shows that among the 6 hypothesized paths, 4 were supported, and out of 32 relationship paths, 25 paths are significant. This study reveals that travel risk management, service delivery system, hygiene and safety are the most influencial factors of travel behavior. © 2022 Editura Universitatii din Oradea. All rights reserved.

Novel coronavirus disease (COVID-19): Emergence, early infection rate, and deployment strategies for preventive solutions

Novel coronavirus commonly known as coronavirus disease 19 (COVID-19) has rapidly spread worldwide and triggered the current global health crisis. It mostly affects humans through the zoonotic transmission of coronavirus 2 (SARS-CoV-2). This chapter focuses on the various epidemics and pandemics (plague, cholera, Spanish flu, etc.) in the history of human civilization, principal component analysis (PCA) for the interpretation of COVID-19 spreading kinetics during the first wave (in the year 2020), the potential use of herbal medicines, dietary remedies, and allopathic therapy to fight COVID-19, and various preventive measures undertaken to combat the pandemic during the first wave. The numbers of confirmed, recovered, active, and deceased cases is considered for the mapping of PCA within different countries. This study can be used as an informative approach for anticipating and strategy-making against COVID-19 or some other pandemics in the ensuing times. © 2022 Elsevier Inc. All rights reserved.

Vitamin - D level and severity of COVID-19 among the Admitted Patients in a COVID Dedicated Tertiary Care Hospital - An Prospective Study

Background The impact of vitamin D on COVID-19 infection has been much discussed recently. Our aim was to evaluate the association between baseline serum 25(OH)D with severity of COVID-19. Method This was an prospective study, where 174 COVID- 19 in-patients of an academic hospital in an urban setting were enrolled from Aug, 2020 to Oct, 2020. After detailed clinical history taking and examination, patients were divided into mild, moderate, severe and critical categories. Vitamin D level was measured. Relation between level of vitamin D and disease severity was determined. Result Mean age of the study population was 52.01 years. 13% mild, 22.99% moderate, 26.44% severe and 3.45% were critical cases. 76% of the patients had vitamin D deficiency. No statistical significance between the level of vitamin D and disease severity was seen (p= 0.430). Conclusion Our study revealed there is no correlation between vitamin D deficiency and severity of COVID-19. [ FROM AUTHOR] Copyright of Journal of Bangladesh College of Physicians & Surgeons is the property of Bangladesh College of Physicians & Surgeons and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

YOLOv3 based Real Time Social Distance Violation Detection in Public Places

The prevalent COVID 19 pandemic is incessantly taking toll on the lives of people throughout the world. Moreover, the dearth of effectual remedies has caused an expeditious rise in the total COVID 19 cases. Though vaccines have been developed, the enormous task of vaccinating a large population is still challenging. Also, as new variants emanate, the resilience from infections conceivably decreases. Hence, it's most unlikely that we'll achieve herd immunity globally so soon. Thus, since the transmission of COVID causing coronavirus roots mainly to social proximity between people, it is necessary to stringently comply to the non pharmaceutical preventive measures of wearing masks and maintaining physical distancing. Howbeit, it has evidently been found that people are being lethargically ignorant to the social distancing norms with passing time. Hence, an autonomous mechanism intended at social distancing violation detection through monitoring of people is needed to be introduced at an authority level. In this paper, the implementation of YOLO Object detection transfer learning process has been used for accomplishing this aim of real time detection of social distancing violation. Our social distance prediction approach uses a pre-trained YOLOv3 object tracking algorithm for identifying people in an input video stream. A Distance estimation algorithm is further used, that works by computing euclidean distance between the centroids of each pair of detected people. This approach highlights the people violating the social distancing criteria as well as calculates the number of times social distancing gets violated as any two people get closer than a set threshold value of minimum permissible distance. A number of experiments on various pre-recorded video streams has been conducted in order to estimate the viability of this method. Through experimental outcomes, it has been found that this YOLO based object detection method with the proposed social distance prediction algorithm produces favourable results for tracking social distancing in public spaces. © 2021 IEEE.

Psychological Assessment of Health-Care Workers Working during COVID-19 Pandemic Condition in Bangladesh

The study was aimed to assess the psychological aspects and relevant factors of the health-care workers (HCWs) working in COVID 19 pandemic condition in Bangladesh. This online cross-sectional survey was conducted from different tertiary, secondary and primary hospitals in Bangladesh. Eligible 638 HCWs who were directly involved in the caring of confirmed or suspected COVID-19 patients were recruited in this study. The mental health was assessed by the Patient Health Questionnare-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7) and Athens Insomnia Scale (AIS). High frequency of depression 536(84.0%), anxiety 386(60.5%) and insomnia 302(47.3%) was found among the HCWs, which were significantly higher in physicians (p<0.001) than nurses. Moderate to severe depression was significantly higher in female, whereas minimal to mild depression was significant in male HCWs (p=0.014). Symptoms of depression (p<0.001), anxiety (p<0.001) and insomnia (p=0.004) were significantly higher among the HCWs of primary and secondary compared to the tertiary level. The HCWs developed psychological trauma due to family health (45.3%) and contagious disease property (66.6%). After adjusting confounders, multivariable logistic regression analysis showed that physicians and HCWs of secondary hospital had significant symptoms of severe depression (OR=2.95, 95% CI=0.50-17.24;p<0.001), anxiety (OR=2.64, 95% CI=0.80-8.72;p<0.001) and insomnia (OR=2.67, 95% CI=1.23-5.84;p=0.018);whereas female HCWs had more risk of developing symptoms of severe insomnia (OR= 1.84;95% CI=1.23-2.75;p=0.003). High rate of depression, anxiety and insomnia was found among HCWs working in the COVID-19 pandemic condition in this survey.

Smart Camera for Enforcing Social Distancing

The COVID-19 pandemic presents an unprecedented challenge to public health, food systems and the demand and supply chains. 'Coronavirus' spreads when an infected person coughs, sneezes or talks, and droplets from their mouth are launched into the air and inhaled by people in the vicinity. Mid-2021 witnessed the production and supply of effective vaccines against Coronavirus, and around 4.5 billion vaccine doses have been utilised globally, reducing fatalities significantly. Given the Government's plans to ease quarantine restrictions for schools, offices, and public places, Social Distancing has become even more critical than ever before. This project incorporates Computer Vision techniques using the high-performance YOLOv4 library, DSFD Face detector, Deep Learning Darknet and Pre-trained ResNet models, and RaspberryPi to create a plug-and-play extension for CCTV cameras established in public places. The system uses the frame by frame information of CCTVs to detect people and classify violations of Social Distancing norms. The device also performs real-time Face Mask Detection, and this technique is robust to varying geometries of face masks and degrees of natural illumination. In case of a detected violation of Social Distancing norms, a buzzer blares in the background. The timestamp of violation with the snapshot of the frame highlighting the associated people is sent to a database and emailed to a centralised server for further investigation. © 2021 IEEE.All rights reserved.

Eye-Gaze Based Hands Free Access Control System for Smart City Public Interfaces

The working of Blink-Con, a framework for intelligent vision directed mobilization of onscreen mouse-cursor, without engaging any auxiliary assistance, is illustrated in this chapter. Blink-Con is framed keeping in mind the adverse effect of Covid-19 in our society, especially the smart cities of India. During this global pandemic, it is not at all advised to touch electronic gadgets at public places as infections spread through touch. Here, Blink-Con is believed to play a significant role in minimizing peoples’ physical contact with public computers present in cyber cafes, malls, banks, etc. which are an inevitable part of a smart city. In this work the system explored are technically dominated in color spaces, precise ROI detection, closed loop sequential interaction and eventually a system gets constructed and attained where the GUI onscreen pointer (mouse cursor) gets directed by interpreting significant eye gaze and blink. The algorithm for eye blink detection from 2-D image feed been classified in two major steps for accomplishing a heuristic based region of interest (ROI) identification and tracking the ROI for rapid detection of volunteered blinks. The sequence of volunteered blinks is taken as trigger of robust mouse mobilization function input that results in highly optimized smooth mouse-travel, divulging an adaptable mouse control performance with only eye blink. The experimental results are favorable to the system viability for multi facet use of the algorithms and can be deployed effectively in various public places in a smart city. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A retroprospective study of an antimicrobial stewardship program on antimicrobial utilization at a tertiary care medical center in kolkata

The WHO has set Defined Daily Dose which represent the average daily dose of an antibiotic in a standard patient. The DDD mai nly focuses on population-based parameters & assumes that patients as well as hospitals are homogenous entities. DOTs are very useful in order to classify antibiotic days based on patient-level exposure. DOTs merely mean the number of days that a patient is on an antibiotic, irrespective of dose. DOTs signifies that the underlying assumptions about antibiotic dosing was appropriate. Additionally, when patients receive more than one antibiotic, supplementary DOT may be counted. The 300-bed tertiary care medical center serves adults and paediatrics. An all-time Microbiology Consultant and a Clinical Pharmacology trainee used to go for round daily and used to collect data for ASP for the period of 3 months that is April to June,2021. In this study we have compared DOT of some important antibiotics for a specific period of time for both COVID and NON COVID patient. ASP-focused antibiotics were antibiotics routinely evaluated by the ASP team for appropriateness during therapy and the potential to optimize their appropriate use through policies, protocols, formulary restrictions, or clinician education. ASP-focused antibiotics included meropenem, linezolid, pip-taz, poly b, colistin, teicoplanin. In this study we have compared the DDD for 2 specific period of time for better understanding the consumption of those antibiotics. In conclusion, following the initiation of an ASP, significant decreases in utilization, increases in cost savings occurred. In our study we have reduced the consumption and DDD of linezolid which is clinically significant. When it comes to DOTs;We have reduced the DOTs of piptaz and teicoplanin for covid patient And Reduced the DOTs of meropenem and teicoplanin for non-covid patient which is clinically and statistically significant.

RNA-extraction-free diagnostic method to detect SARS-CoV-2: An assessment from two States, India

With increasing demand for large numbers of testing during COVID-19 pandemic, came alternative protocols with shortened turn-around time. We evaluated the performance of such a protocol wherein 1138 consecutive clinic attendees were enrolled;584 and 554 respectively from two independent study sites in the cities of Pune and Kolkata. Paired nasopharyngeal and oropharyngeal swabs were tested by using both reference and index methods in blinded fashion. Prior to conducting RT-PCR, swabs collected in viral transport medium (VTM) were processed for RNA extraction (reference method) and swabs collected in dry tube without VTM were incubated in Tris-EDTA-Proteinase K buffer for 30 minutes and heat inactivated at 98oC for 6 minutes (index method). Overall sensitivity and specificity of the index method were 78.9% (95% CI 71% to 86%) and 99 % (95% CI 98% to 99.6%) respectively. Agreement between the index and reference method was 96.8 % (k = 0.83, SE=0.030). The reference method exhibited enhanced detection of viral genes (E, N and RdRP) with lower Ct values compared to the index method. The index method can be used for detecting SARS-CoV-2 infection with appropriately chosen primer-probe set and heat treatment approach in pressing time;low sensitivity constrains its potential wider use. © 2021 Cambridge University Press. All rights reserved.

Living Conditions and Social Distancing Barriers in India

The tenth part of the Social Change Indicators series gives an account on the living conditions that work as barriers against social distancing in different states of India. This segment especially focusses on aspects such as the percentage of households (rural and urban) that own a house, the percentage share of nuclear households (rural and urban), the mean number of persons sleeping per room in households, the percentage of households (rural and urban) in which cooking is done in a separate room, the percentage of households (rural and urban) in which water is not treated prior to drinking, the percentage of households (rural and urban) with an improved non-sharable sanitation facility and the proportion of households living in slums. © 2021 Council for Social Development.

Applicability of State Responsibility for Health Emergencies and the Unique Situation of China amid SARS-COV-2

This article aims to understand the limitations in enforcing state responsibility on a state of Origin for Trans boundary harm caused by a health emergency by analyzing the factors of force majeure, multiparty involvement, and inability to provide reparations, and the unclear source of the virus as the chief hindrances in prosecuting such states. The authors further explore the various conditions that triggered other PHEICs in history and the consequences and actions taken due to the trans-boundary harm caused. Furthermore, the article also studies the conditions that gave rise to the wet markets and traces the shortcomings in law that hurt the Origin of the virus. After that, the article studies how countries have failed to invoke the state responsibility of trans-boundary harm caused by a PHEIC. Lastly, the article further delves into how Chinese authorities blame both pre and post-outbreak and examine how the global community can negotiate with China for adequate compensation.

A survey of ai imaging techniques for covid-19 diagnosis and prognosis

The Coronavirus Disease 2019 (COVID-19) has caused massive infections and death toll. Radiological imaging in chest such as computed tomography (CT) has been instrumental in the diagnosis and evaluation of the lung infection which is the common indication in COVID-19 infected patients. The technological advances in artificial intelligence (AI) furthermore increase the performance of imaging tools and support health professionals. CT, Positron Emission Tomography – CT (PET/CT), X-ray, Magnetic Resonance Imaging (MRI), and Lung Ultrasound (LUS) are used for diagnosis, treatment of COVID-19. Applying AI on image acquisition will help automate the process of scanning and providing protection to lab technicians. AI empowered models help radiologists and health experts in making better clinical decisions. We review AI-empowered medical imaging characteristics, image acquisition, computer-aided models that help in the COVID-19 diagnosis, management, and follow-up. Much emphasis is on CT and X-ray with integrated AI, as they are first choice in many hospitals. © 2021, Polish Association for Knowledge Promotion. All rights reserved.