The (mis)uses of community: a critical analysis of public health communication for COVID-19 vaccination in the United States

Community engagement is heralded as a panacea for the inherent political challenges of public health governance. For COVID-19 vaccination planning in the United States, appeals for community engagement emerged in response to the disproportionate mortality and morbidity burdens on marginalized groups and as a bulwark against a political climate of vaccine hesitancy, scientific disinformation, and mistrust of public health. In this article, we use a culture-centered analytical framework to critique the discursive construct of "community" within public health documents that discuss community engagement strategies for COVID-19 vaccination. Through a critical-abductive analysis of more than 400 state public health department documents, we recognized the diverse axes on which appeals to the community are framed. Our findings show that the construct of "community" refers to both a material/tangible space marked by discursive struggle and one containing a moral economy of responsibility. We discuss the challenges and opportunities of conceptualizing community in these ways.

Do Preprocessing and Class Imbalance Matter to the Deep Image Classifiers for COVID-19 Detection? An Explainable Analysis

In a world withstanding the waves of a raging pandemic, respiratory disease detection from chest radiological images using machine-learning approaches has never been more important for a widely accessible and prompt initial diagnosis. A standard machine-learning disease detection workflow that takes an image as input and provides a diagnosis in return usually consists of four key components, namely input preprocessor, data irregularities (like class imbalance, missing and absent features, etc.) handler, classifier, and a decision explainer for better clarity. In this study, we investigate the impact of the three primary components of the disease-detection workflow leaving only the deep image classifier. We specifically aim to validate if the deep classifiers may significantly benefit from additional preprocessing and efficient handling of data irregularities in a disease-diagnosis workflow. To elaborate, we explore the applicability of seven traditional and deep preprocessing techniques along with four class imbalance handling approaches for a deep classifier, such as ResNet-50, in the task of respiratory disease detection from chest radiological images. While deep classifiers are more capable than their traditional counterparts, explaining their decision process is a significant challenge. Therefore, we also employ three gradient visualization algorithms to explain the decision of a deep classifier to understand how well each of them can highlight the key visual features of the different respiratory diseases. © 2020 IEEE.

Social Protection and SocialDeterminants of Health in COVID-19 and Related Disasters: What CanWe Do About Them?

Health of the populations and individual health statuses are influenced by their social position and social situations determine the health of people. In this sense, health is not only a ‘state' but also a resource that needs to be nurtured over people's lifetimes. Poverty, or lack of material resources impact health adversely and in particular, result in infectious diseases as people are forced to live in damp, crowded conditions. This is particularly relevant in the face of outbreaks of diseases that can shut down economies and force people into poverty where such conditions exacerbate. The SARS-COV-2 viral outbreak is a currently ongoing pandemic worldwide is a case in point. This has resulted in widespread lockdowns in different countries. While lockdown is used as containment measures to control the spread of the virus and limit viral infection, it also has the downstream effect of shutting down part or whole economies, thus leading to further worsening of poverty and social distress. The World Bank has estimated that in the wake of COVID-19, 60–70 million more poor people will be added to the pool of already existent poor population around the world, due to emergence of noveau poor. So what needs to be done? One possible way to address the problem of poverty and ill-health of societies in the wake of COVID-19 is to strengthen social protection measures. Social protection refers to the funds catering to the mix of programmes and policies governments establish to protect the vulnerable members of the society from poverty-related adverse life events and circumstances. These programmes prevent and mitigate adverse consequences of poverty by providing a scaffold so that people can lead a sustainable healthy life and maintain their mental, physical and social well-being. Beyond such protection, such programmes ensure inclusive and sustainable growth. Hence, the goal of this chapter is to examine social protection policies and develop models to address what may happen and what needs to be done to strengthen social protection systems for securing the health and well-being of vulnerable populations in the face of unforeseen phenomena such as natural disasters and outbreaks that threaten the structure of existent social protection and threaten the sustainability of the target populations. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

MODELING AND SIMULATION FOR THE SPREAD OF COVID-19 IN AN INDIAN CITY: A CASE STUDY

We present a case study on modeling and predicting the course of Covid-19 in the Indian city of Pune. The results presented in this paper are concerned primarily with the wave of infections triggered by the Delta variant during the period between February and June 2021. Our work demonstrates the necessity for bringing together compartmental stock-and-flow and agent-based models and the limitations of each approach when used individually. Some of the work presented here was carried out in the process of advising the local city administration and reflects the challenges associated with employing these models in a real-world environment with its uncertainties and time pressures. Our experience, described in the paper, also highlights the risks associated with forecasting the course of an epidemic with evolving variants. © 2022 IEEE.

A DIGITAL TWIN BASED APPROACH FOR ENSURING BUSINESS CONTINUITY PLAN AND SAFE RETURN TO WORKPLACE

Organizations are struggling to ensure business continuity without compromising on delivery excellence in the face of Covid19 pandemic related uncertainties. The uncertainty exists along multiple dimensions such as virus mutations, infectivity and severity of new mutants, efficacy of vaccines against new mutants, waning of vaccine induced immunity over time, and lockdown/opening-up policies effected by city authorities. Moreover, this uncertainty plays out in a non-uniform manner across nations, states, cities, and even within the cities thus leading to highly heterogeneous evolution of pandemic. While Work From Home (WFH) strategy has served well to meet ever-increasing business demands without compromising on individual health safety, there has been an undeniable reduction in social capital. With Covid19 pandemic showing definite waning trends, organizations are considering the possibility of safe transition from WFH to Work From Office (WFO) or a hybrid mode of operation. An effective strategy needs to score equally well on possibly interfering dimensions such as risk of infection, project delivery, and employee wellness. As large organizations will typically have a large number of offices spread across a geography, the problem of arriving at office-specific strategies becomes non-trivial. Moreover, the strategies need to adapt over time to changes that cannot be deduced upfront. This calls for an approach that is amenable to quick and easy adaptation. Our contribution in this regard is constructing a Digital Twin by leveraging various modelling techniques to realistically represent the above mentioned aspects of interest that can be subjected to what-if scenario analysis. We further demonstrate its efficacy using a case study from a large organization. © 2022 Society for Modeling & Simulation International (SCS)

Strategies for Vaccine Design for Coronavirus All Variants of Concern Using Immunoinformatics Techniques

Aims: A short sequence of viral protein/ peptide could be used as a potential vaccine to treat coronavirus. Considering all variants of concern (VOC), designing a peptide vaccine for severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) is a challenging task for scientists. Materials & Methods: In this study, an epitope-containing vaccine peptide in nonstructural protein 4 (nsp4) of SARS-CoV-2 was predicted. Using a modified method for both B and T cell epitope prediction (verified by molecular docking studies), linear B and T cell epitopes of nsp4 protein were predicted. Predicted epitopes were analyzed with population coverage calculation and epitope conservancy analysis. Findings: The short peptide sequence74QRGGSYTNDKA84 was selected as B-cell epitope by considering the scores of surface accessibility, hydrophilicity, and beta turn for each amino acid residue. Similarly, the peptide sequences 359 FLAHIQWMV367 and359FLAHIQWVMFTPLV373 were predicted as T cell epitopes for MHC-I and MHC-II molecules. These two potential epitopes could favor HLA-A*02:01 and HLA-DRB*01:01 as MHC allelic proteins with the lowest IC50 values, respectively. No amino acid mutations were observed in GISAID (global initiative on sharing all influenza data) database for alpha, beta, gamma, and delta variants of concerns. Among seven amino acid point mutations in nsp4 protein of omicron variant, none were present in the peptide sequences of the predicted epitopes. Conclusion: Short peptide sequences could be predicted as vaccines to prevent infections caused by coronavirus variants of concerns. © 2022, TMU Press.

Shaping OTT Movie Consumption through Immersive Cinema: A Qualitative Investigation of Consumer Perspectives

Due to the COVID-19 pandemic, the entertainment sector saw a worldwide disruption with restrictions on outdoor activities. Consequently, consumers turned towards video and music streaming services for their entertainment consumption. Several film studios have taken the digital release route on over-the-top (OTT) sites to avoid revenue losses and indefinite delays. However, these non-theatrical OTT film releases need to experiment with different strategies to bring the experiences to par with theatrical ones. This exploratory study aims to provide insights on whether Immersive Cinema can be used to imitate the physical world through digital simulation on OTT platforms to gain credibility in a competitive entertainment market. We conducted semi-structured, qualitative interviews with 21 consumers and Focus Group Discussions with 14 MBA students to understand perspectives about Immersive Cinema consumption on OTT platforms and its potential when compared to traditional theatrical releases. Using the findings of this study, OTT platforms can curate their new films as a direct alternative to theatrical releases. © 2022 MDI.

Multi-paradigm Methodology for Enterprise Modelling using Agent-based Modelling and System Dynamics

The Covid-19 pandemic has significantly altered business operating models. Enterprise decision makers responsible for devising actionable business operational strategies are confronted with making informed decisions in the state of continuously evolving pandemic landscape. As pandemic concerns subside, their objective is to formulate a workplace opening strategy that mitigates employee infections and the subsequent impact on project delivery. It is therefore critical to appropriately model the underlying aspects of the enterprise system and enable strategy evaluation. Enterprise in this context represents a complex, dynamic system composed of multiple sub-systems with varying characteristics, levels of uncertainty, granularity, data availability and scale. Owing to these distinctions, different modelling paradigms are better suited to individually model these sub-systems, and their integration results in a comprehensive model that is a close approximation of the real system. This paper presents a hybrid/multiparadigm approach for modelling the enterprise ecosystem, by building on the established concepts of Agent Based Modelling (ABM) and System Dynamics (SD) that enables evaluating the impact of operational strategies on employee infections. The model is formulated as integration of multiple subsystems and their interactions - infection module, employee and dependent, office infrastructure and society modules. These four dimensions, comprising the enterprise ecosystem, significantly influence the employee infection dynamics. While the SD model quantifies the aggregated infection dynamics of society at the population scale, ABM models fine-grained specifics of employees, dependents, infrastructure, and the resulting infection dynamics. © 2022 Copyright for this paper by its authors.

Holistic strategies to mitigate the economic, societal, and health burden of the Coronavirus disease-2019 pandemic

Pandemics are not the unique features of modern civilization;epidemics/pandemics can be traced back to ancient civilization. History is replete with such pandemics. Coronavirus first originated in Hubei province, China, in November 2019 and then manifested in Wuhan but within a very short span of time it has spread like wildfire all over the world and its impact has been multifaceted. It is indeed an indication of the fact that we live in a truly globalized world. Due to the outbreak of Coronavirus disease-2019 (COVID-19), people lost their lives but due to the consequent lockdown, people lost their livelihood, and the economy is shattered. Global GDP and trade experienced a huge contraction during the period of pandemic and the improvements to date are not worth mentioning. Actually, pandemic acts like a serial killer and its aftermath is devastating on human lives and the global economy. © 2022 Elsevier Inc. All rights reserved.

Themes of recovery from a child and adolescent eating disorder unit: Staff experiences

Background: We looked at how our staff from a tier 4 child and adolescent eating disorder unit have been impacted by the Covid-19 pandemic. We hope to encourage a period of self-reflection with our staff to think of ways their work has changed and how far they have come. Method(s): We created a questionnaire with three questions: (1) How much has your work of looking after young people with eating disorders been impacted by the Covid-19 pandemic? (2) How much do you think the experience of being treated in a child and adolescent eating disorder unit has changed since the Covid-19 pandemic? (3) How much do you feel like you have adapted the way you work as a result of the Covid-19 pandemic? We used a Likert scale with free-text boxes for expansion of answers. Result(s): We are in the process of collecting data but the following themes are emerging: Question 1: emotional distress and social isolation, lockdown triggered illness and worsening of symptoms, and short staffing. Question 2: new protocols for patients, feelings of claustrophobia from reduced visits and outings. Question 3: personal protective equipment, staff testing and 'the new normal.' Discussions/Conclusions: Further data are needed to draw more solid conclusions however it seems staff have seen first-hand signs of lockdown induced mental distress and the triggering of eating disorders. Staff struggled to express ways they have adapted their working as they have accepted that Covid- 19 is a permanent part of their job role.

A Digital Twin Based Approach For Ensuring Business Continuity Plan And Safe Return To Workplace

Organizations are struggling to ensure business continuity without compromising on delivery excellence in the face of Covid19 pandemic related uncertainties. The uncertainty exists along multiple dimensions such as virus mutations, infectivity and severity of new mutants, efficacy of vaccines against new mutants, waning of vaccine induced immunity over time, and lockdown / opening-up policies effected by city authorities. Moreover, this uncertainty plays out in a non-uniform manner across nations, states, cities, and even within the cities thus leading to highly heterogeneous evolution of pandemic. While Work From Home (WFH) strategy has served well to meet ever-increasing business demands without compromising on individual health safety, there has been an undeniable reduction in social capital. With Covid19 pandemic showing definite waning trends, organizations are considering the possibility of safe transition from WFH to Work From Office (WFO) or a hybrid mode of operation. An effective strategy needs to score equally well on possibly interfering dimensions such as risk of infection, project delivery, and employee wellness. As large organizations will typically have a large number of offices spread across a geography, the problem of arriving at office-specific strategies becomes non-trivial. Moreover, the strategies need to adapt over time to changes that cannot be deduced upfront. This calls for an approach that is amenable to quick and easy adaptation. Our contribution in this regard is constructing a Digital Twin by leveraging various modelling techniques to realistically represent the above mentioned aspects of interest that can be subjected to what-if scenario analysis. We further demonstrate its efficacy using a case study from a large organization. © 2022 SCS.

Investigating Theatre Cancellation Rates in a London Hospital

Aim: Theatre cancellations are a major source of system inefficiency, placing an unnecessary financial burden on the NHS. The aim of this audit was to investigate the reasons for cancellations in elective surgery at a London hospital and to assess the general preoperative process. Method: We undertook a retrospective audit of all elective theatre cancellations from the day surgery unit over a three-month period in 2021. 78 cancellations were identified, and their cause was investigated. Furthermore, we reviewed theatre lists over one month to calculate the proportion of cancelled procedures per specialty. Also, to gain a further understanding of the present protocol, we reviewed the current preoperative process. Results: The main reason for cancellations was patients being physiologically unfit for surgery, accounting for 28.2% of all cases. The next biggest reason for cancellation, at 16.7%, was failure to comply with COVID protocol. The specialty with the largest proportion of cancellations was General Surgery, at 17.7%. Conclusions: We believe many reasons for cancellations were avoidable. Therefore, we propose a change to the preoperative process to target multiple reasons for cancellation. We suggest a more comprehensive pre-admission phone call five days prior to surgery which includes discussions around medication compliance and changes in condition. We also suggest a set of observations and routine bloods are taken when patients come in for their COVID swab. This allows any potential cause of cancellation to be identified and rectified, or the patient could be rescheduled. This should increase efficiency of the day surgery unit.

On Entropy Based Diversity Measures: Statistical Efficiency and Robustness Considerations

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.

Hospital based social prescribing: Investigating parental views on the proposed Introduction of link workers for children with disabilities

Introduction: Children and young people (CYP) with a neurodisability often have complex needs which can significantly impact their quality of life. Unmet non-medical needs negatively correlate with well-being. COVID-19 has amplified the pressure on this population, catalysing our proposal for a new support service, based on the social prescribing link worker (LW) model supported by NHS England. Our innovative scheme will identify eligible CYP and their families within a hospital setting: with their agreement, a LW will help find and embed solutions to their unmet non-medical needs. The aim of this study was to gather the views of parents of CYP with neurodisability on the proposed service. Patients and methods: Forty UK-based parents of CYP with neurodisability completed an online survey (Qualtrix), distributed through social media. 11/40 were parents of young adults with disability. 28/40 of the CYP had cerebral palsy and/or autism. Results: COVID-19 had exacerbated circumstances for 90% of respondents. All except one were in favour of LW support;though only 7.5% previously knew what a LW was. Supporting their child towards independence;mental health;and social networks were priority areas. Reservations included not wanting input if their child was seriously ill;distinguishing between LW and social worker;worrying about whether the LW would have adequate specialist knowledge;and funding (postcode lottery). Conclusion: Respondents were strongly in favour of the proposed service. Based on their feedback, we will provide detailed information about the LW role, and recruit and support LW staff to provide a high quality, sustainable service.

Burnout in diabetes and endocrinology specialist registrars across England, Scotland and Wales in the pre-COVID era.

BACKGROUND: Presence of either emotional exhaustion, depersonalization or lack of personal accomplishment define Burnout Syndrome which may lead to decreased workforce productivity, increased absenteeism, depression and medical errors as well as decreased patient satisfaction. OBJECTIVE: The aim of this study was to assess the frequency of burnout syndrome among Diabetes Specialist Registrars across England, Scotland and Wales and to identify any self-reported factors which may be contributory to burnout. METHODS: Over 430 Diabetes Specialist Registrars were invited to anonymously participate in an electronic survey which used Maslach Burnout Inventory and selfreporting questionnaire to identify burnout and contributory factors. RESULTS: In this pre-pandemic times study, Burnout was identified in 61 (57.5%; n = 106) respondents using Maslach burnout cut-off scores. 45.2% (48/106) participants had scored high in Emotional Exhaustion, while lack of personal accomplishment and depersonalization was seen in 24.5% (26/106) and 21.6% (23/106) of the respondents respectively. The commonest self-reported stressors by participants were "General Internal Medicine workload" 60.4% (64/106) followed by "Lack of specialty training" 36.8% (39/106) and "Lack of audit/research/Continuing Professional Development time" 10.8% (11/106) CONCLUSION: Burnout syndrome is frequent among the participating Diabetes Specialist Registrars and urgent steps may be required address this problem nationally to ensure that these physicians remain physically and mentally healthy, especially after the pandemic.

COVID-19 Face Mask Recognition with Advanced Face Cut Algorithm for Human Safety Measures

In the last year, the outbreak of COVID-19 has deployed computer vision and machine learning algorithms in various fields to enhance human life interactions. COVID-19 is a highly contaminated disease that affects mainly the respiratory organs of the human body. We must wear a mask in this situation as the virus can be contaminated through the air and a non-masked person can be affected. Our proposal deploys a computer vision and deep learning framework to recognize face masks from images or videos. We have implemented a Boundary dependent face cut recognition algorithm that can cut the face from the image using 27 landmarks and then the preprocessed image can further be sent to the deep learning ResNet50 model. The experimental result shows a significant advancement of 3.4 percent compared to the YOLOV3 mask recognition architecture in just 10 epochs. © 2021 IEEE.

Water-sanitation-health nexus in the Indus-Ganga-Brahmaputra River Basin: need for wastewater surveillance of SARS-CoV-2 for preparedness during the future waves of pandemic

The Indus-Ganga-Brahmaputra River Basin (IGBRB) is a trans-boundary river basin flowing through four major countries in South Asia viz., India, Pakistan, Bangladesh, and Nepal. Contamination of surface water by untreated or inadequately treated wastewater has been a huge problem for pathogenic microorganisms in economies in transition. Recent studies have reported that sewage surveillance can provide prior information of the outbreak data, because faeces can contain the novel coronavirus (SARS-CoV-2) shed by infected humans. Hence, in this study we geo-spatially mapped the COVID-19 hotspots during the peak time in the first and second wave of pandemic to demonstrate the need and usefulness of wastewater surveillance strategy in IGBRB during ongoing pandemic. Further we discussed the status of sanitation, health and hand-hygiene in the IGBRB along with characterization of the challenges posed by the pandemic in achieving the United Nations Sustainable Development Goals (UN-SDGs). Monthly Geographical Information System (GIS) mapping of COVID-19 hotspots in the IGBRB showed an increase in the spread along the direct sewage discharge points. The social inequalities expose the vulnerabilities of the urban poor in terms of the burden, risks and access to Water, Sanitation, and Hygiene (WASH) needs. Such an evidence-based image of the actual SARS-CoV-2 viral load in the community along the IGBRB can provide valuable insights and recommendations to deal with the future waves of COVID-19 pandemic in this region that can go a long way in achieving the UN-SDGs. © 2021

Impact of COVID-19 on Online Education in Developing Countries - An Overview

The COVID-19 lockdown since March 2020 necessitates higher education institutions to deliver education online. Although education institutions in high and higher middle-income countries could relatively easily transition face to face education to online delivery, most higher education institutions in low-income and lower middle-income countries were unable to do it. World-wide, more than half of the world's 1.5 billion students is out of online education activities especially in developing and emerging nations. Hence, the primary objective is to examine the difficulties and challenges experienced by some of those countries in their higher education institutions' transition to online education. The study focuses on internet infrastructure, accessibility, affordability, digital learning management system, academics and students' perspectives and digital knowledge gap related to online education. The study finds that poor or no internet infrastructures/connections, streaming devices, learning management system, inexperience in online education, and socioeconomic conditions are the main impedances for slow or no transition to online education in most emerging and developing countries. Some action plans (recommendations) to overcome these challenges are also compiled.

Impacts of COVID-19: A Comprehensive Study Using Linear Regression Analysis in a Predictive Approach

Human history is observing a very strange time fighting an invisible enemy;the novel COVID-19 is the greatest challenge to humankind since the Second World War. The current outbreak of COVID-19 coronavirus infection among humans in Wuhan (China) and its spreading around the globe is heavily impacting global health and mental health. Novel coronavirus (n-CoV) is a generic name given to severe acute respiratory syndrome coronavirus 2(SARs-CoV-2). It has rapidly spread around the world posing enormous mental, social, economic, and environmental challenges to the entire human population. This paper evolved from an overview of the coronavirus and its effect on public health and economics. The main focus of this paper is to survey the various species and types of COVs. The overall statistics of the count around the world and an inclusive survey of its impact on society is being discussed in this paper. In this paper, the linear regression analysis of different vaccines commissioned around the world in COVID-19 and manifold updated information across India has been analyzed in a statistical approach. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.