Medical Philately and the World of COVID-19 Postage Stamps: Issues of Truth, Health and Wealth

The political, social, cultural and economic worlds have been gripped by unexpected, tumultuous and ad hoc worlds since the emergence of COVID-19 on the world scene in early 2020. It is safe to acknowledge that all, or nearly all, humans on the planet have been adversely affected by its appearance, re-appearance and re-re-appearance in some and many ways. The world's states have also struggled with effective, efficient and acceptable ways to respond to the pandemic at personal, local and national scales. The visibility of COVID-19 is evident in the rise and fluctuating number of cases and deaths as they appear in daily and weekly news reports. An additional perspective of the serious nature of the virus is the appearance on postage stamp issues about diseases, a research focus of medical philately. Since early 2020 nearly 100 countries have issued stamps with a COVID-19 theme. Some countries devastated by cases and deaths and have issued a single or set of stamps with images about the negative impacts on the health and welfare of their populations, economies and environments. Others have not. Some countries have issued a low value stamp about COVID-19 while other countries have issued COVID-19 stamps that are extremely expensive and are unlikely to ever be seen or used by their population. These stamps are designed and produced by private companies for international stamp markets, which include collectors who collect stamp issues with health and disease themes. This chapter examines the COVID stamp issues through August 2021, their major themes and the costs. Stamps, as pieces of visual diplomacy, are observed to be more than reflecting a country's health or human condition, but also to generate income. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Patchwork: The Hidden, Human Labor of AI Integration within Essential Work

This paper examines the rapid introduction of AI and automation technologies within essential industries amid the COVID-19 pandemic. Drawing on participant observation and interviews within two sites of waste labor in the United States, we consider the substantial effort performed by frontline workers who smooth the relationship between robotics and their social and material environment. Over the course of the research, we found workers engaged in continuous acts of calibration, troubleshooting, and repair required to support AI technologies over time. In interrogating these sites, we develop the concept of "patchwork": human labor that occurs in the space between what AI purports to do and what it actually accomplishes. We argue that it is necessary to consider the often-undervalued frontline work that makes up for AI's shortcomings during implementation, particularly as CSCW increasingly turns to discussions of Human-AI collaboration. © 2023 Owner/Author.

Pattern of COVID 19 Infection among Front line Health Workers and Contact Tracing Done among them. Our Experience in a Tertiary Care Hospital

Background: A considerable number of front-line workers are under risk due to repeated infection and exposure. The pattern of COVID 19 infection among the front-line workers was important, so that more focus would be laid on protecting them. Contact tracing is one key strategy for interrupting chains of transmission of SARS-CoV-2. This study aimed to find the pattern of COVID 19 infection among front line health workers and describe the process of contact tracing. Methodology: The list of front-line workers with possible symptoms of COVID-19 or had come in direct contact with a "case" was shared with the department of community medicine for contract tracing activity as per the guidelines. The front-line workers who were categorized as High Risk were quarantined immediately and those who were categorized as Low-Risk were advised to be vigilant regarding the development of symptoms and were asked to continue with their routine duties with extra precautionary measures as they form a very vital part of the resource in this combat against COVID-19. Result(s): About 138 front line health workers were affected by COVID-19 among which staff nurses (51) amounted to the maximum number who were affected. Conclusion(s): COVID-19 was high among front-line workers and had a large number of high-risk contacts. Nurses were found to be most affected with COVID 19 infection.Copyright © 2023, Institute of Medico-legal Publication. All rights reserved.

Various intelligent approaches for classification using CT-scan images: A Systematic Literature Review

COVID-19 has caused a devastating effect in every aspect across the world. The pandemic brought life to a standstill. Frontline workers are working day and night to treat patients and save lives. As critical is the timely and quick detection of this communicable disease, it necessitates the need for a diagnostic system that is automatic and as accurate as possible. The number of false negatives and hysteresis must be as low as possible. CT scans of the lungs can help in quicker detection of the presence of the virus as opposed to RT-PCR test. The purpose of this article is to present a survey of current scientific work on CT scan classification techniques, outlining and structuring what is currently available. We conduct a systematic literature review in which we compile and categorize the latest papers from top conferences to present a synopsis of CT scan images data classification techniques and their issues. This review identifies the present state of CT image classification research and decides where further research is needed. A review paper discusses different classification methods for CT scan images, including a comparative study of major classification techniques. © 2022 Copyright for this paper by its authors.

Socio-ecological value chain resilience and cleaning workers

Examining the ways that industries survived the COVID-19 pandemic can teach us a great deal about the resilience of value chains, the ways value chain dynamics shape worker resilience, and the measures states can adopt to support both. In this paper we critically examine the thriving body of theory known broadly as supply chain resilience and explore a branch that embraces socio-ecological perspectives. We first develop a theoretical model that takes what we perceive to be the most fruitful elements of these literatures for industrial relations scholarship and bring it together with approaches tangential to industrial relations concerned with value chain actor and worker agency and resilience. We then apply this model in an analysis of the Australian commercial cleaning sector during the pandemic. Finally, we assess federal and state measures to assist and "buffer” employment and the economy in Australia, including JobKeeper and JobSeeker. We find that these government measures, combined with earlier restructuring of the labour market and restrictive immigration policies, provided the institutional scaffolding for the failure of the cleaning industry during the pandemic, exactly when cleaning became an essential service for the resilience of the whole of society.

Design and Simulation of an Automated Domestic Helper Robot for Home Isolated COVID-19 Patients

COVID-19 is a disease that is spreading exponentially worldwide. This pandemic situation proved that doctors, nurses, and front-line workers are real-life heroes. The patients and their family affected due to COVID-19 are increasing drastically. Family members who treat the patients wear all their personal protective accessories and clothes, yet, they get influenced as well. Family members can't be in direct contact with the patients frequently since the probability of spread is high. The patients need to be served with food, medicines, and other required things on a regular time basis. To reduce the spread, human force, and to maintain a strategic distance fromCOVID-19 affected patients, the best way is to utilize an COVID-19 Assist Robot (COBOT) to carry the essentials and transport it to the patient's room as per the line map given. A home environment that depicts the isolated ward with the COVID-19 affected patients is created using webots. The robots are simulated with the environment as such two robots take care of the left half and the other two robots take care of the right half of the isolated ward. © 2022 IEEE.

Socio-ecological value chain resilience and cleaning workers

Examining the ways that industries survived the COVID-19 pandemic can teach us a great deal about the resilience of value chains, the ways value chain dynamics shape worker resilience, and the measures states can adopt to support both. In this paper we critically examine the thriving body of theory known broadly as supply chain resilience and explore a branch that embraces socio-ecological perspectives. We first develop a theoretical model that takes what we perceive to be the most fruitful elements of these literatures for industrial relations scholarship and bring it together with approaches tangential to industrial relations concerned with value chain actor and worker agency and resilience. We then apply this model in an analysis of the Australian commercial cleaning sector during the pandemic. Finally, we assess federal and state measures to assist and "buffer" employment and the economy in Australia, including JobKeeper and JobSeeker. We find that these government measures, combined with earlier restructuring of the labour market and restrictive immigration policies, provided the institutional scaffolding for the failure of the cleaning industry during the pandemic, exactly when cleaning became an essential service for the resilience of the whole of society.

Exploring Biomarker Identification and Mortality Prediction of COVID-19 Patients Using ML Algorithms

The coronavirus pandemic led to the collapse of the healthcare systems of several countries worldwide, including the highly developed ones. The sudden rise in hospitalization requirements for the patients suffering from the disease, caused a tremendous pressure not only on the healthcare system but also on the frontline workers. So, for early diagnosis and prognosis of the patients, identification of the biomarkers pertaining to the coronavirus disease became an essential requirement. Thus, a machine learning (ML) based mortality prediction model was developed that was able to predict the mortality of the patients using a combination of only six features. The six selected features included, four identified biomarkers, namely, lactate dehydrogenase (LDH), neutrophils percentage (NP), fibrin degradation products (FDP), and erythrocyte sedimentation rate (ESR);and, other two features as age and the coronavirus detection test. The developed model with a novel semiautomated method of medical data handling technique, achieved an accuracy of over 98%, and was able to predict the final outcome of the patients on an average of 8 days in advance. The corresponding work was carried out with the intent to ease the burden on the healthcare system, by providing a faster and accurate clinical assessment of the patients suffering from the coronavirus disease. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Deeper look into the process of Developing-Safe Box, An Automated Sterilization Chamber

Covid-19 has shaken the entire globe. In the fight against this pandemic, the doctors and frontline workers are the real heroes who are facing an unseen enemy. The Masks, PPE Kits, and other protective wearables are used by patients, doctors, and other front-line workers for only one time. This leads to increased costs and supply issues, and also leads to huge environmental pollution. That is the problem that the product 'Safe Box' Addresses. The proposed system sterilizes Masks, PPE Kits, and other wearables making them reusable. 'Safe Box' plays a vital role in aiding hospitals, laboratories, clinics, and other healthcare facilities where non-reusable kits like masks, PPE, and other wearables are widely used. © 2022 IEEE.

Designing of Six Wheel Robotic Vehicle for Instant Disinfection and Sanitization

In today's global pandemic situation, as countries implement restrictions in an effort to reduce the number of persons infected with COVID-19, many people are attempting to make significant modifications to their everyday routines. The Covid-19 pandemic has resulted in a severe loss of human life, especially among frontline workers, who are mostly those who operate in the sanitization and disinfection profession. The main objective of this paper is to develop a unmanned vehicle for instant disinfection and sanitize an area using a 6 WD Robot with achieving the proper and most important aim of unmanned operation using the vehicle with the help of 2.4 Ghz Radio link which provides the system or vehicle the flexibility of going anywhere covering a large distance without humans directly getting affected. It uses the functions of UV rays for the complete disinfection with the mixture, Using UV Lights and ethanol IP (95% v/v) - 62.00% w/w equivalent to 84.24% v/v∗ absolute alcohol denatured with isopropyl alcohol 3.1% Disinfect the area by simultaneously spraying and flashing the light used for complete eradication of Virus and sanitization of the places. This unmanned vehicle is developed for a 6WD vehicle based on the terrain and atmosphere. Need to design the appropriate body/frame, spray mechanism, payload tank, and atomizer for the 6WD vehicle. In terms of hygiene, it will have a significant influence since spots that are too tiny for humans to get will be accessible with the help of this machine. © 2022 IEEE.

Understanding the condition of disease prevention and control workforce by disciplines, duties, and work stress during the COVID-19 pandemic: A case from Beijing disease prevention and control system.

Objective: The duties, discipline cross-complementation, and work stress of professional staff during the COVID-19 pandemic are analyzed and summarized to provide a scientific basis for workforce allocation and reserve in respect of infectious disease prevention and control in the disease prevention and control (DPC) system. Method: The cross-sectional survey was made in April-May 2021 on professional staff in the Beijing DPC system by way of typical + cluster sampling. A total of 1,086 staff were surveyed via electronic questionnaire, which was independently designed by the Study Group and involves three dimensions, i.e., General Information, Working Intensity & Satisfaction, and Need for Key Capacity Building. This paper focuses on the former two dimensions: General Information, Working Intensity, and Satisfaction. The information collected is stored in a database built with Microsoft Excel 2010 and analyzed statistically with SPSS 22.0. The results are expressed in absolute quantities and proportions. Assuming that the overload of work stress is brought by incremental duties and cross-discipline tasks, a binary logistic regression model is constructed. Results: Among the 1086 staff surveyed, 1032 staff were engaged in COVID-19 prevention and control works, and they can be roughly divided into two groups by their disciplines: Public Health and Preventive Medicine (hereinafter referred to P, 637 staff, as 61.72%) and Non-Public Health and Preventive Medicine (hereinafter referred to N-P, 395 staff, as 38.28%). During the COVID-19 pandemic, the 1,032 staff assumed a total of 2239 duties, that is, 2.17 per person (PP), or 2.45 PP for the P group and 1.72 PP for the N-P group. As to four categories of duties, i.e., Spot Epidemiological Investigation and Sampling, Information Management and Analysis, On-site Disposal, Prevention, Control Guidance, and Publicity, the P group accounts for 76.14, 78.50, 74.74, and 57.66%, respectively, while the N-P group accounts for 23.86, 21.50, 25.26, and 42.34%, respectively. Obviously, the former proportions are higher than the latter proportions. The situation is the opposite of the Sample Detection and Other Works, where the P group accounts for 25.00 and 31.33%, respectively, while the N-P group accounts for 75.00 and 68.67%, respectively. The analysis of work stress reveals that the P group and N-P group have similar proportions in view of full load work stress, being 48.67 and 50.13%, respectively, and the P group shows a proportion of 34.38% in view of overload work stress, apparently higher than the N-P group (24.05%). Moreover, both groups indicate their work stresses are higher than the pre-COVID-19 period levels. According to the analysis of work stress factors, the duty quantity and cross-discipline tasks are statistically positively correlated with the probability of overload work stress. Conclusion: The front-line staff in the DPC system involved in the COVID-19 prevention and control primarily fall in the category of Public Health and Preventive Medicine discipline. The P group assumes the most duties, and the N-P group serves as an important cross-complement. The study results indicate that the prevention and control of same-scale epidemic require the duty post setting at least twice than usual. As to workforce recruitment, allocation, and reserve in respect of the DPC system, two solutions are optional: less addition of P staff, or more addition of N-P staff. A balance between P and N-P staff that enables the personnel composition to accommodate both routine DPC and unexpected epidemic needs to be further discussed.

IRHMP: IoT-Based Remote Health Monitoring and Prescriber System

The Novel Corona Virus Disease-2019 (COVID-19), which created this pandemic, makes us realize the importance of universal social and health care systems. The frontline workers worked restlessly during the pandemic and few of them also lost their lives. There is a need for a remote IoT health monitoring system that takes care of the health of infected patients, conducts regular health checks, and reduces contact between an infected person and health workers. This especially helps the patients with mild symptoms who are home quarantined. The IoT system monitors a person 24/7 and a report can be generated and sent to the doctor at the same time. However, such a procedure will produce a large amount of data. A major research challenge addressed in this paper is to effectively transfer health care data with the help of existing network infrastructure and transfer it to the cloud. In this paper, we have identified the key network and infrastructure requirements for a standard health monitoring system based on real-time event updates, bandwidth requirements, data collection, and data analysis. After that, we propose IRHMP- IoT-based remote healthcare device that delivers health care data efficiently to the cloud and the web portal. Finally, we have proposed a machine-learning algorithm to provide and predict future health risks with the help of recorded data. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Robots are a Promising Investment to Fight Pandemics

Coronavirus caused pandemics as many viruses did through human history. The current pandemic causes overwhelmed healthcare system, locked down cities, and massive fatality among humans. Thus, different robots have been used since the COVID outbreak worldwide to reduce spreading infectious diseases and support frontline healthcare workers. This paper sets out the different robots implemented for hospital, non-hospital use, and possible use that can be deployed amidst the pandemic. A literature survey of versatile robots during COVID-19 is introduced. Roboticists contributed with wheeled and drone robots with various applications to assist medical care systems and society during the ongoing crisis. Pandemics are common throughout human history and difficult to avoid or prevent;thus, we intend to encourage societies, academia, engineers and innovators to invest more in robots that cannot catch the virus and consequently introduce beneficial solutions to fight such pandemic in the future. © 2021 IEEE

Twitter Sentiment Analysis on COVID-19

The COVID-19 pandemic has been a trending topic on social media since it first started in December 2019. This pandemic originated in the city of Wuhan in China. India was vastly affected by this pandemic due to its large population. However, due to its vast population, India has a large number of social media users, which can provide crucial insight into people's perspectives on topics related to the pandemic. This paper uses natural language processing and sentiment analysis on the posts created by users on the social media platform of Twitter. The study uses APIs and keywords to get the data to analyze and understand the emotions of the tweets linked to topics like oxygen, vaccine, beds, and lockdown in the times of COVID-19. The results and observations are presented using various graphs, charts, and word clouds. This paper aims to help the government, researchers, and frontline workers to get an insight into the sentiment on social media about various topics concerning the covid-19 pandemic. © 2021 IEEE.

Autonomous Bot Assistant in Covid-19 Patient Isolation Wards

Objective: In the current coronavirus situation around the globe, there are many challenges in front of the doctors health workers carrying out their duty in hospitals/isolation wards. The direct contact with affected patients despite ensuring safety measures has led to the loss of many lives of the frontline workers. Thus, it is valuable to design an autonomous robot system that can fulfill daily chores in the isolation ward. Method: In this paper, we design a robotic system to fulfill the daily requirements of a patient such as delivering meals medicines, also it can collect health stats such as SPO2 body temperature. The robot traces a line that leads it to the beds in the isolation ward, an RFID tag reader is installed on the robot that detects the RFID cards placed on the bed. Then the robot conveys the patients through the display to place a finger on the SPO2 sensor temperature sensor, this recorded data is instantly stored through ESP8266 on a ThingSpeak server in the patient's database. Results: The robot can achieve its objective, with exact blood oxygen saturation readings approximate body temperature reading. The daily meals medicines are delivered in a package having a label of bed number on it. Conclusions: This study contributes the first cost-effective robot with a combination of unique features in a total project cost of INR 7000(approx.95USD). The routine work of health workers is replaced alternatively by an autonomous robot, thus preventing direct contact with patients. © 2021 IEEE.

Arduino Based Smart Dustbin for Waste Management during Covid-19

India has perceived an enormous upsurge in garbage during 21st century due to adopting urbanization rapidly. In addition, as the population increasing drastically, which inherently produce huge amount of garbage. The overflow of the garbage and lack of maintenance are the main reasons for many diseases and also for spoiling earth environment. However, the entire world has faced the problems due to spreading of Covid-19 through the air due to the hazard waste is being generated in specific Covid hospitals, quarantine zones are directly dumped into the dustbins. The frontline warriors (doctor, nurse, police) working in these unorganized dustbin environments are getting infected with Covid-19 and are became a primary contact for spreading of virus. Thus, it is very much essential to implement a systematized and well-designed mechanism to overcome this problem. In this work, a hardware prototype for smart dustbin based on Arduino has been proposed that can be installed in hospitals, medical centres, municipalities, households for the sake of environment as well as front-line workers. The proposed model of smart dustbin has PIR sensor and ultrasonic sensor for human motion detection and sense the level of garbage present in the bin respectively. An alert system based on GSM has been interfaced with Arduino which sends the alert messages based on level of the garbage at regular intervals. The proposed model work with very low power and increase the security for front-line workers from the Covid-19. © 2021 IEEE.

A Pathfinding Algorithm for Lowering Infection Exposure of Healthcare Personnel Working in Makeshift Hospitals.

Due to the recent COVID-19 outbreak, makeshift (MS) hospitals have become an important feature in healthcare systems worldwide. Healthcare personnel (HCP) need to be able to navigate quickly, effectively, and safely to help patients, while still maintaining their own well-being. In this study, a pathfinding algorithm to help HCP navigate through a hospital safely and effectively is developed and verified. Tests are run using a discretized 2D grid as a representation of an MS hospital plan, and total distance traveled and total exposure to disease are measured. The influence of the size of the 2D grid units, the shape of these units, and degrees of freedom in the potential movement of the HCP are investigated. The algorithms developed are designed to be used in MS hospitals where airborne illness is prevalent and could greatly reduce the risk of illness in HCP. In this study, it was found that the quantum-based algorithm would generate paths that accrued 50-66% less total disease quantum than the shortest path algorithm with also about a 33-50% increase in total distance traveled. It was also found that the mixed path algorithm-generated paths accrued 33-50% less quantum, but only increased total distance traveled by 10-20%.

Reflections of Health Care Workers on Their In-Hospital Experiences During the Onset of COVID-19

The early stages of the COVID-19 pandemic intensified the role of healthcare workers in hospitals. This study examines how healthcare workers reflected on their in-hospital experiences in the early stages of the pandemic in North America. Audio diary entries from The Nocturnist podcast recorded from March – June 2020 were analyzed using epistemic network analysis (ENA) and heat map models. Overall, there was a shift from responding to immediate needs in March 2020 (such as Anger with Policies and Fear with Resource Availability) to deeper reflections in May-June 2020, more focused on Psychosocial Support and Purpose and more complex emotions involving Sadness and Compassion. Uncertainty was a prominent emotion throughout the May – June 2020 period. These results help document the complexity of reflections early in the pandemic, while informing ways to better support health care workers in future crisis. © 2022, Springer Nature Switzerland AG.

Examining the cost-effectiveness of personal protective equipment for formal healthcare workers in Kenya during the COVID-19 pandemic.

BACKGROUND: Healthcare workers are at a higher risk of COVID-19 infection during care encounters compared to the general population. Personal Protective Equipment (PPE) have been shown to protect COVID-19 among healthcare workers, however, Kenya has faced PPE shortages that can adequately protect all healthcare workers. We, therefore, examined the health and economic consequences of investing in PPE for healthcare workers in Kenya. METHODS: We conducted a cost-effectiveness and return on investment (ROI) analysis using a decision-analytic model following the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) guidelines. We examined two outcomes: 1) the incremental cost per healthcare worker death averted, and 2) the incremental cost per healthcare worker COVID-19 case averted. We performed a multivariate sensitivity analysis using 10,000 Monte Carlo simulations. RESULTS: Kenya would need to invest $3.12 million (95% CI: 2.65-3.59) to adequately protect healthcare workers against COVID-19. This investment would avert 416 (IQR: 330-517) and 30,041 (IQR: 7243 - 102,480) healthcare worker deaths and COVID-19 cases respectively. Additionally, such an investment would result in a healthcare system ROI of $170.64 million (IQR: 138-209) - equivalent to an 11.04 times return. CONCLUSION: Despite other nationwide COVID-19 prevention measures such as social distancing, over 70% of healthcare workers will still be infected if the availability of PPE remains scarce. As part of the COVID-19 response strategy, the government should consider adequate investment in PPE for all healthcare workers in the country as it provides a large return on investment and it is value for money.

Experiences of mental health professionals supporting front-line health and social care workers during COVID-19: qualitative study.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic is having a well-documented impact on the mental health of front-line health and social care workers (HSCWs). However, little attention has been paid to the experiences of, and impact on, the mental health professionals who were rapidly tasked with supporting them. AIMS: We set out to redress this gap by qualitatively exploring UK mental health professionals' experiences, views and needs while working to support the well-being of front-line HSCWs during the COVID-19 pandemic. METHOD: Mental health professionals working in roles supporting front-line HSCWs were recruited purposively and interviewed remotely. Transcripts of the interviews were analysed by the research team following the principles of reflexive thematic analysis. RESULTS: We completed interviews with 28 mental health professionals from varied professional backgrounds, career stages and settings across the UK. Mental health professionals were motivated and driven to develop new clinical pathways to support HSCWs they perceived as colleagues and many experienced professional growth. However, this also came at some costs, as they took on additional responsibilities and increased workloads, were anxious and uncertain about how best to support this workforce and tended to neglect their own health and well-being. Many were professionally isolated and were affected vicariously by the traumas and moral injuries that healthcare workers talked about in sessions. CONCLUSIONS: This research highlights the urgent need to consider the mental well-being, training and support of mental health professionals who are supporting front-line workers.