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Coronavirus disease 2019 (COVID-19) first emerged in China in November 2019. Most governments have responded to the COVID-19 pandemic by imposing a lockdown. Some evidence suggests that a period of isolation might have led to a spike in alcohol misuse, and in the case of patients with alcohol use disorder (AUD), social isolation can favour lapse and relapse. The aim of our position paper is to provide specialists in the alcohol addiction field, in psychopharmacology, gastroenterology and in internal medicine, with appropriate tools to better manage patients with AUD and COVID-19,considering some important topics: (a) the susceptibility of AUD patients to infection; (b) the pharmacological interaction between medications used to treat AUD and to treat COVID-19; (c) the reorganization of the Centre for Alcohol Addiction Treatment for the management of AUD patients in the COVID-19 era (group activities, telemedicine, outpatients treatment, alcohol-related liver disease and liver transplantation, collecting samples); (d) AUD and SARS-CoV-2 vaccination. Telemedicine/telehealth will undoubtedly be useful/practical tools even though it remains at an elementary level; the contribution of the family and of caregivers in the management of AUD patients will play a significant role; the multidisciplinary intervention involving experts in the treatment of AUD with specialists in the treatment of COVID-19 disease will need implementation. Thus, the COVID-19 pandemic is rapidly leading addiction specialists towards a new governance scenario of AUD, which necessarily needs an in-depth reconsideration, focusing attention on a safe approach in combination with the efficacy of treatment. [ABSTRACT FROM AUTHOR]

Cardiovascular (CV) engagement in coronavirus disease 2019 (COVID-19) is a huge determinant of prognosis during the acute phase of the disease. However, little is known about the potential chronic implications of the late phase of COVID-19 and about the appropriate approach to these patients. Heart failure, type 1 and type 2 myocardial infarction, arrhythmias, myocarditis, pulmonary fibrosis, and thrombosis have been shown to be related to severe acute respiratory syndrome coronavirus 2 infection, and a ‘long COVID-19’ illness has been recognized with fatigue, chest pain, and dyspnoea among the most frequent symptoms reported after discharge from hospital. This paper focuses on some open questions that cardiologists are going to face during the next months in a general cardiology outpatient clinic, in particular how to evaluate a ‘post-COVID’ patient during follow-up of CV complications of the acute phase and how to manage new CV symptoms that could be the consequence, at least in part, of heart/vessels and/or lung involvement of the previous virus infection. Present symptoms and signs, history of previous CV disease (both preceding COVID-19 and occurring during viral infection), and specific laboratory and imaging measurements during the acute phase may be of interest in focusing on how to approach the clinical evaluation of a post-COVID patient and how to integrate in our standard of care the new information on COVID-19, possibly in a multidisciplinary view. Dealing with the increased COVID-associated CV risk burden and becoming acquainted with potential new e-cardiology approaches aimed at integrating the cardiology practice are relevant new challenges brought by severe acute respiratory syndrome coronavirus 2 infection and its sequelae. [ABSTRACT FROM AUTHOR]

In the article, the author calls for papers on the topics of organizational risk and the COVID-19 pandemic, with submission deadline scheduled on February 1, 2022.

Aim: To identify the research on childhood disability service adaptations and their impact on children and young people with long‐term disability during the COVID‐19 pandemic. Method: A mapping review was undertaken. We searched the World Health Organization Global COVID‐19 database using the search terms 'children', 'chronic/disabling conditions', and 'services/therapies'. Eligible papers reported service changes for children (0–19 years) with long‐term disability in any geographical or clinical setting between 1st January 2020 and 26th January 2022. Papers were charted across the effective practice and organization of care taxonomy of health system interventions and were narratively synthesized; an interactive map was produced. Results: Reduction of face‐to‐face care and usual provision had a huge impact on children and families. Adoption of telehealth provided continuity for the care and management of some conditions. There was limited evidence of changes to mental health services, transitions of care, social care, or child‐reported satisfaction or acceptability of service changes. Interpretation: The long‐term impacts of service change during the pandemic need full evaluation. However, widespread disruption seems to have had a profound impact on child and carer health and well‐being. Service recovery needs to be specific to the individual needs of children with a disability and their families. This should be done through coproduction to ensure that service changes meet needs and are accessible and equitable. [ABSTRACT FROM AUTHOR]

Recent research has documented the immediate negative impact of the COVID‐19 pandemic on household and business consumption, but there is still limited investigation into the medium‐term effects in specific consumption categories. This paper addresses this gap using a vector autoregression analysis of a system of aggregated consumer final demand across Australia. We highlight the importance of studying a demand system, as opposed to investigating independent consumption categories, due to the interactive evolution of consumption during the pandemic. Modelling the paths of various consumption categories in response to shocks from one another, we find that, despite the large and abrupt shocks to consumption during the first two quarters of 2020, most categories reverted to pre‐COVID levels when restrictions were lifted. Importantly, transportation had the largest and most persistent decline. Overall, shocks to sectors other than food, alcohol and education were outside the counterfactual forecast confidence intervals estimated based on pre‐COVID information. [ABSTRACT FROM AUTHOR]

The COVID‐19 pandemic demanded an immediate response from the research community, including adjustment to how researchers undertake research. As in many fields, information systems scholars attempted 'rapid response' research that addresses the immediate crisis. This paper reviews the state of the immediate research and scholarly discussion in the information systems field. Seventy‐one journal papers are reviewed. The review categorises nine thematic areas and one broad area of discussion. The methods by which information systems scholars address immediate research questions are identified. The contributions are discussed, future research directions are outlined and a model is proposed that traces the pathway from initiation of research/generation of new knowledge to practice impact. A further model is proposed to help information systems scholars clearly establish the link between the study of information systems phenomena and COVID‐19. [ABSTRACT FROM AUTHOR]

With the impact of the COVID‐19 epidemic, the demand for masked face recognition technology has increased. In the process of masked face recognition, some problems such as less feature information and poor robustness to the environment are obvious. The current masked face recognition model is not quantified enough for feature extraction, there are large errors for faces with high similarity, and the categories cannot be clustered during the detection process, resulting in poor classification of masks, which cannot be well adapted to changes in multiple environments. To solve current problems, this paper designs a new masked face recognition model, taking improved Single Shot Multibox Detector (SSD) model as a face detector, and replaces the input layer VGG16 of SSD with Deep Residual Network (ResNet) to increase the receptive field. In order to better adapt to the network, we adjust the convolution kernel size of ResNet. In addition, we fine‐tune the Xception network by designing a new fully connected layer, and reduce the training cycle. The weights of the three input samples including anchor, positive and negative are shared and clustered together with triplet network to improve recognition accuracy. Meanwhile, this paper adjusts alpha parameter in triplet loss. A higher value of alpha can improve the accuracy of model recognition. We further adopt a small trick to classify and predict face feature vectors using multi‐layer perceptron (MLP), and a total of 60 neural nodes are set in the three neural layers of MLP to get higher classification accuracy. Moreover, three datasets of MFDD, RMFRD and SMFRD are fused to obtain high‐quality images in different scenes, and we also add data augmentation and face alignment methods for processing, effectively reducing the interference of the external environment in the process of model recognition. According to the experimental results, the accuracy of masked face recognition reaches 98.3%, it achieves better results compared with other mainstream models. In addition, the hyper‐parameters tuning experiment is carried out to improve the utilization of computing resources, which shows better results than the indicators of different networks. [ABSTRACT FROM AUTHOR]

The industry diversity thesis of economic resilience to economic shocks is embedded in community development policy across Australia. The idea being that in the event of an economic shock some industries will prove more recession‐proof than others. The greater the industry diversity, the greater the likelihood of off‐setting industry effects, resulting in greater economic resilience. The COVID‐19 pandemic and the associated restrictions created a unique natural experiment to explore whether the industry diversity thesis holds true under the conditions of a global health pandemic. In this policy paper, we use JobKeeper applications as a proxy for decreased economic resilience. We explore if Australian local government areas (LGAs) with higher industry diversity had less necessity for JobKeeper. We also briefly consider if concentrations of certain industries acted as a better economic buffer to the COVID‐19 economic shock. We observe that as diversity increases, economic resilience strengthens except for Victoria (where the association is inverted). This observation has important implications for current and future policy formation and implementation across all layers of government. [ABSTRACT FROM AUTHOR]

Background: The role of patient participation and representation during crises, such as the COVID‐19 pandemic, has been under‐researched. Existing studies paint a pessimistic picture of patient representation during the pandemic. However, there are indications that patient representatives have adapted to the new situation and can contribute to the resilience of healthcare systems. This paper aims to further explore the potential contribution of patient representatives for healthcare system resilience during the COVID‐19 pandemic. Methods: The study used a qualitative approach. We conducted a thematic analysis on the following data: interviews with client council members (n = 32) and representatives from patient organizations (n = 6) and focus groups (n = 2) to investigate patient representation on both the national policy level and organizational level in the Netherlands. Results: We identified the crisis discourse, the dependent position, the diversity of patient perspectives and the layered decision‐making structure as themes that help to understand what made patient representation in pandemic times a struggle for national and local patient representatives. The analysis of the subjects these representatives put forward during decision‐making shows that their input can play an important role in broadening discussions, challenging decisions, and suggesting alternatives during a crisis. We identified several strategies (e.g., collaborating with other actors, proactively putting subjects on the policy agenda, finding new ways of contacting their 'constituency') used by the patient representatives studied to exert influence despite the difficulties encountered. Conclusions: The struggle for patient representation during pandemic decision‐making is a missed opportunity for resilient healthcare systems as these representatives can play a role in opening up discussions and putting different perspectives to the fore. Moreover, the adaptive strategies used by representatives to influence decision‐making offer lessons for future representation activities. However, adaptations to the crisis decision‐making structure are also needed to enable patient representatives to play their role. Patient Contribution: We conducted interviews with patient representatives and discussed our preliminary findings with patient representatives during the focus groups. Zorgbelang, a patient organization supporting client councils and enabling and organizing patient participation for organizations and municipalities, was partner in this research and contributed to the interview guide, conducting interviews and focus groups. Additionally, the analysis made by the first author was discussed and refined multiple times with the partners of Zorgbelang and one of them co‐authored this paper. [ABSTRACT FROM AUTHOR]

This study systematically reviews applications of three simulation approaches, that is, system dynamics model (SDM), agent‐based model (ABM) and discrete event simulation (DES), and their hybrids in COVID‐19 research and identifies theoretical and application innovations in public health. Among the 372 eligible papers, 72 focused on COVID‐19 transmission dynamics, 204 evaluated both pharmaceutical and non‐pharmaceutical interventions, 29 focused on the prediction of the pandemic and 67 investigated the impacts of COVID‐19. ABM was used in 275 papers, followed by 54 SDM papers, 32 DES papers and 11 hybrid model papers. Evaluation and design of intervention scenarios are the most widely addressed area accounting for 55% of the four main categories, that is, the transmission of COVID‐19, prediction of the pandemic, evaluation and design of intervention scenarios and societal impact assessment. The complexities in impact evaluation and intervention design demand hybrid simulation models that can simultaneously capture micro and macro aspects of the socio‐economic systems involved. [ABSTRACT FROM AUTHOR]

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The world is affected by COVID‐19, an infectious disease caused by the SARS‐CoV‐2 virus. Tests are necessary for everyone as the number of COVID‐19 affected individual's increases. So, the authors developed a basic sequential CNN model based on deep and federated learning that focuses on user data security while simultaneously enhancing test accuracy. The proposed model helps users detect COVID‐19 in a few seconds by uploading a single chest X‐ray image. A deep learning‐aided architecture that can handle client and server sides efficiently has been proposed in this work. The front‐end part has been developed using StreamLit, and the back‐end uses a Flower framework. The proposed model has achieved a global accuracy of 99.59% after being trained for three federated communication rounds. The detailed analysis of this paper provides the robustness of this work. In addition, the Internet of Medical Things (IoMT) will improve the ease of access to the aforementioned health services. IoMT tools and services are rapidly changing healthcare operations for the better. Hopefully, it will continue to do so in this difficult time of the COVID‐19 pandemic and will help to push the envelope of this work to a different extent. [ABSTRACT FROM AUTHOR]

Introduction: Programme Science (PS) and community‐led monitoring (CLM) intersect in unexpected and promising ways. This commentary examines a CLM initiative in Malawi and South Africa to highlight the crucial role of CLM in bolstering the PS framework. By leveraging data sources often overlooked by conventional research and evaluation approaches, CLM emerges as a pivotal element in enhancing programme effectiveness. This paper delineates the fundamental principles of CLM, presents programme outcomes derived from CLM methodologies and contextualizes these findings within the broader framework of PS. Discussion: The Citizen Science Project implements CLM continuously at 33 health facilities: 14 in Malawi (eight in Kasungu District and six in Dedza District), and 19 in South Africa (all in the West Rand District), representing a total catchment area of 989,848 people. Monitoring indicators are developed in an iterative process with community groups. The indicators are unique to each country, but both focus on the uptake of health services (quantitative) and barriers to access (qualitative). Monthly clinic records surveys capture 34 indicators in Malawi and 20 in South Africa and are supplemented by qualitative interviews with care recipients and healthcare workers. Qualitative interviews provide additional granularity and help confirm and explain the more macro trends in service coverage as described in quantitative data. The resulting data analysis reveals key themes that help stakeholders and decision‐makers to solve problems collaboratively. Noteworthy outcomes include a substantial increase in multi‐month dispensing of antiretroviral therapy (ART) during COVID‐19 (from 6% to 31%) with a subsequent recovery surpassing of HIV service benchmarks in Malawi post‐pandemic. Conclusions: While quantifying direct impact remains challenging due to the project's design, CLM proves to be a robust methodology that generates credible data and produces impactful outcomes. Its potential extends beyond the health sector, empowering community leadership and fostering interventions aligned with community needs. As CLM continues to evolve, its integration into PS promises to improve relevance, quality and impact across diverse disciplines. [ABSTRACT FROM AUTHOR]

The fashion industry makes a sizeable contribution to climate change. Research on grand challenges has now gained momentum in the management literature, given the vast array of grand challenges that are now globally affecting industries, societies and governments, such as the COVID‐19 pandemic, the war in Ukraine and global warming. By coupling paradox theory with the literature on circular economy, this paper investigates the end‐of‐life circular practices implemented by 'born‐circular' Italian fashion companies. The paper focuses on upcycling and remanufacturing, which have been neglected in the literature and whose widespread implementation has been deemed suitable to tackle climate change. The analysis of several in‐depth interviews with companies and industry experts results in the development of a process model. The model provides granular insights into the environmental value created by the implementation of end‐of‐life circular practices by fashion firms in their quest to tackle climate change. The study both suggests that paradox theory is likely to have broader applicability to reconcile a wide set of managerial and societal issues and also supports the argument that overcoming an intrinsic paradox could lead to a strategic pivoting within an industry that might yield a paradigm shift. [ABSTRACT FROM AUTHOR]

We estimate the importance of local labour structure in the spread of COVID‐19 during the first year of the pandemic. We build a unique dataset across 6791 English neighbourhoods that distinguishes between people living (residents) and people working (workers) in a neighbourhood, and differentiate between jobs that can be done from home (homeworkers), jobs that likely continued on‐site (keyworkers), and non‐essential on‐site jobs. We find that a 10 percentage points increase in keyworker jobs among residents is associated with 3.15 more cases per 1000 (4.8% relative to the mean), while a 10 percentage points increase in homeworker jobs among residents is associated with a decrease of 7.74 cases per 1000 (11.8% relative to the mean). Results for the composition of workers show the same sign, but smaller magnitudes. A dynamic analysis of the monthly incidence of reported cases shows that these relationships are particularly strong during lockdown periods. These results are heterogeneous across neighbourhoods, with larger positive effect of keyworkers, and lower protective effect of homeworkers, in higher deprivation areas. We explore the role of occupation skill intensity in driving these neighbourhood differences. These findings highlight important asymmetries in the distributional impact of the policy response to COVID‐19. This paper is part of the Economica 100 Series. Economica, the LSE "house journal" is now 100 years old. To commemorate this achievement, we are publishing 100 papers by former students, as well as current and former faculty. Enrico Vanino is affiliated to the Department of Geography and Environment of the LSE. [ABSTRACT FROM AUTHOR]

Human milk provides essential nutrition for infants and holds many health benefits for infants and mothers. When a mother's own milk is not available for her infant, the World Health Organization recommends feeding donor human milk (DHM) from a human milk banking facility. DHM is human milk produced, collected then donated to a human milk bank (HMB). HMBs serve many vital functions, including screening donor mothers, then collecting, processing, storing, and allocating DHM to recipients. The first HMB opened in 1909, and today there are more than 700 HMBs globally. Unfortunately, HMB facilities are not present in all locales, with notable gaps in South Asia and Africa. Additionally, there are no global standards to guide HMB operational procedures. Even though most HMBs attempt to employ quality control systems to provide safe DHM, differences in community needs, resource availability, and a range of methods and policies to execute processes result in significant variations in DHM quality and HMB operations. Robust and collaborative systems that ensure safe and equitable access to DHM are needed. In this paper, we present a global snapshot of current human milk banking practices; review an interdisciplinary framework to guide and support HMB activities as an integrated part of health and newborn care systems; discuss factors that contribute to HMB sustainability; outline barriers to scaling HMBs worldwide; and highlight knowledge, policy, and research gaps. Developing global HMB guidance and rigorous, adaptable standards would strengthen efforts to improve newborn health. Key messages: Safe donor human milk (DHM) from a human milk bank (HMB) is a WHO recommendation when the mother's own milk is not available.Over 700 HMBs exist globally, with varying operational models and a wide range of practices due to lack of global guidelines and the current practice of adaptation to local needs and health systems.Scaling up of HMBs to meet the global demand for DHM would be facilitated by the development of global standards to guide integration with breastfeeding promotion and quality controlled processing to ensure provision of safe DHM. [ABSTRACT FROM AUTHOR]

Introduction: The COVID‐19 pandemic has taken many forms and continues to evolve, now around the Omicron wave, raising concerns over the globe. With COVID‐19 being declared no longer a "public health emergency of international concern (PHEIC)," the COVID pandemic is still far from over, as new Omicron subvariants of interest and concern have risen since January of 2023. Mainly with the XBB.1.5 and XBB.1.16 subvariants, the pandemic is still very much "alive" and "breathing." Methods: This review consists of five highly concerning questions about the current state of the COVID Omicron peak. We searched four main online databases to answer the first four questions. For the last one, we performed a systematic review of the literature, with keywords "Omicron," "Guidelines," and "Recommendations." Results: A total of 31 articles were included. The main symptoms of the current Omicron wave include a characteristically high fever, coughing, conjunctivitis (with itching eyes), sore throat, runny nose, congestion, fatigue, body ache, and headache. The median incubation period of the symptoms is shorter than the previous peaks. Vaccination against COVID can still be considered effective for the new subvariants. Conclusion: Guidelines recommend continuation of personal protective measures, third and fourth dose boosters, along with administration of bivalent messenger RNA vaccine boosters. The consensus antiviral treatment is combination therapy using Nirmatrelvir and Ritonavir, and the consensus for pre‐exposure prophylaxis is Tixagevimab and Cilgavimab combination. We hope the present paper raises awareness for the continuing presence of COVID and ways to lower the risks, especially for at‐risk groups. Key points: The COVID pandemic is far from over, with new variants of interest and concern rising since January of 2023 (mainly XBB.1.5 and XBB.1.16 subvariants).The main symptoms of the current Omicron wave include a high fever, coughing, conjunctivitis (with itching of the eyes), sore throat, runny nose and congestion, fatigue, body ache, and headache.The median incubation period for the current peak was calculated to be 3.8 days which is shorter than the incubation period calculated for most of the previous peaks; and the median duration of the symptoms was calculated to be 5 days for the current peak which is shorter than the duration calculated for the previous peaks by 3–5 days.A cumulative 64.42% of the population worldwide are fully vaccinated against COVID‐19 as of June 16, 2023. Vaccination against COVID can still be considered mostly effective against new symptoms, severe disease, long COVID, and death for the new subvariants.Guidelines recommend continuation of personal protective measures, third and fourth dose boosters, particularly in individuals above 60, immunocompromised patients, and healthcare personnel, along with administration of bivalent messenger RNA vaccine boosters.The consensus option for antiviral treatment is combination therapy using Nirmatrelvir and Ritonavir, and the consensus for pre‐exposure prophylaxis is Tixagevimab and Cilgavimab combination. [ABSTRACT FROM AUTHOR]

This paper argues that a lonely death is, by definition, a bad death and that society as a whole, as well as individuals in society are obligated to assure a certain degree of well‐being, flourishing, or care among and for fellow individuals. Individuals can then be said to have a right against dying a lonely death. Such a right has corresponding duties. The paper further specifies what such duties may entail based on what individuals may need on their deathbed, specifically focusing on the need for human touch. Assuming that human touch might indeed be needed by some individuals in order to avoid a lonely death, the paper explores potential implications from a public health perspective, particularly during infectious disease outbreaks such as COVID. [ABSTRACT FROM AUTHOR]

Background and Aims: The precise prediction of COVID‐19 prognosis remains a clinical challenge. In this regard, early identification of severe cases facilitates the triage and management of COVID‐19 cases. The present paper aims to explore the prognosis of COVID‐19 patients based on routine laboratory tests taken when patients are admitted. Methods: A data set including 1455 COVID‐19 patients (727 male, 728 female) and their routine laboratory tests conducted upon hospital admission, age, Intensive Care Unit (ICU) admission, and outcome were gathered. The data set was randomly split into the train (75% of the data) and test data set (25% of the data). The explainable boosting machine (EBM) and extreme gradient boosting (XGBoost) were used for predicting the mortality and ICU admission of COVID‐19 cases. Also, feature importance was extracted using EBM and XGBoost. Results: The EBM and XGBoost achieved 86.38% and 88.56% accuracy in the test data set, respectively. In addition, EBM and XGBoost predicted the ICU admission with an accuracy of 89.37%, and 79.29% in the test data set for COVID‐19 patients, respectively. Also, obtained models indicated that aspartate transaminase (AST), lymphocyte, blood urea nitrogen (BUN), and age are the most significant predictors of COVID‐19 mortality. Furthermore, the lymphocyte count, AST, and BUN level were the most significant ICU admission predictors of COVID‐19 patients. Conclusions: The current study indicated that both EBM and XGBoost could predict the ICU admission and mortality of COVID‐19 cases based on routine hematological and clinical chemistry evaluation at the time of admission. Also, based on the results, AST, lymphocyte count, and BUN levels could be used as early predictors of COVID‐19 prognosis. [ABSTRACT FROM AUTHOR]

This paper explores the realm of university education after COVID‐19, in which there is a perceived need to manage the 'old' and the 'new'. The paper brings two different systems approaches to that end: the systems theory of autopoiesis and the systems identity technology of profilicity. In their own way, both explain the phenomena of 'blindness' to social situations, potentially enabling meaningful conversations to occur. However, they require a broader, common ground so that they could also coexist with each other. This could lead us to consider the inclusion of rituals in education. Two perspectives about ritual are brought forth to start developing such common ground. With these, the paper proposes a number of questions and implications to further develop systemic coexistence. [ABSTRACT FROM AUTHOR]

Objectives: The World Mental Health Qatar (WMHQ) study, the first national general population mental health survey in Qatar, was conducted as part of the World Health Organization (WHO) World Mental Health (WMH) Survey Initiative. It was one of the few WMH survey conducted during the COVID‐19 pandemic. This paper presents the methodological advances and challenges encountered while conducting the survey by telephone during the pandemic. Methods: Disproportionate stratified sampling using a national‐level cellular telephone frame selected a representative sample of Arabic‐speaking adults. Participants were initially contacted via Short Message Service text, followed by telephone interviews. WMH training materials supported a comprehensive training program, and data quality was ensured through a quality control indicator system and extensive monitoring. Results: Over 234 days, 5195 interviews in Arabic were completed, averaging 77 min each. In line with Qatar's population, the majority of participants were non‐Qatari residents living in Qatar (72.2%). Conclusions: A distributed remote Computer Assisted Telephone Interviewing system facilitated centralized quality monitoring and data security. However, the pandemic intensified challenges such as remote management of interviewer productivity, low response rates, and rising survey costs. The findings will inform Qatar's mental health policymakers, and the strategies used to address these challenges offer valuable insights for researchers worldwide. [ABSTRACT FROM AUTHOR]

Both diabetes mellitus (DM) and tuberculosis (TB) are prevalent all across in India. TB‐DM comorbidity has emerged as a syndemic and needs more attention in India considering gaps in screening, clinical care, and research. This paper is intended to review published literature on TB and DM in India to understand the burden of the dual epidemic and its trajectory and to obtain perspectives on the gaps, constraints, and challenges in care and treatment of this dual epidemic. A literature search was carried out on PubMed, Scopus, and Google Scholar, using the key words 'Tuberculosis' OR 'TB' AND 'Diabetes' OR 'Diabetes Mellitus' AND 'India', focusing on the research published between the year 2000 to 2022. The prevalence of DM is high in patients with TB. Quantitative data on the epidemiological situation of TB/DM in India such as incidence, prevalence, mortality, and management are lacking. During the last 2 years convergence of TB‐DM syndemic with the COVID‐19 pandemic has increased cases with uncontrolled DM but also made coordinated control of TB‐DM operationally difficult and of low effectiveness. Research regarding TB‐DM comorbidity is required in the context of epidemiology and management. Detection and bidirectional screening are aggressively warranted. Management of DM in those with TB‐DM comorbidity needs more efforts, including training and supervision of frontline workers. [ABSTRACT FROM AUTHOR]

COVID-19 E-health digital certificates: security and privacy As a response to the COVID-19 crisis, the EU designed and implemented digital Covid certificates to ease the movements of EU citizens across Europe. The COVID19 pandemic is having a worldwide impact on the way business is conducted, people interact, work is organised, and more. COVID-19 E-HEALTH DIGITAL CERTIFICATES: SECURITY AND PRIVACY The paper, "AI BOT to detect fake COVID19 Vaccine certificates" by Muhammad Arif, Shermin Shamsudheen, Ajesh F, Guojun Wang, and Jianer Chen, intends to thwart the plague related to fake COVID-19 vaccination certificates. [Extracted from the article]

Background and Objectives: The coronavirus disease‐19 (COVID‐19) pandemic, which began in 2019, is threatening millions of people around the world. Coronavirus, a severe acute respiratory syndrome, made it mandatory to wear masks, it was carried out through public awareness and review of changes in cosmetics. Methods: This literature review paper was written by referring to keywords such as "Eyebrow," "Permanent Make‐up," "Microblading," "Make‐up," and "COVID‐19." The study selected a total of 485 references using representative journal search sites such as PubMed, Google Scholar, ResearchGate, RISS, DBPia, and CrossRef, of which a total of 43 papers were selected at the final stage from 2000 to 2022 using PRISMA flow diagram. Results: With the wearing of a mask due to COVID‐19, we are paying attention to the change in the makeup trend caused by the preference for easy eye makeup. Conclusions: This narrative review understands that eyebrow makeup has a significant impact on human images due to changes in makeup methods after the COVID‐19 pandemic. It is expected to be used as important data for the rapidly growing semi‐permanent makeup market. [ABSTRACT FROM AUTHOR]

The emergence of the COVID‐19 pandemic has brought radical changes in consumer spending patterns. One aspect of this change is food stockpiling detected in several countries. Using a univariate probit model, this paper relies on the COVID‐19 Impact Survey (2020) for American households to assess the likelihood of consumers stockpiling food in response to the stringent lockdown measures imposed by the government's pandemic regulations. Our findings reveal a set of significant correlations between marital status, age, race, occupation, household structure, and the propensity of stockpiling food during the pandemic. Furthermore, the results show that residents in urban areas are more likely to engage in food stockpiling compared with residents in rural and suburban areas. The paper also examines the nexus between residence areas, lockdown measures, and the probability of stockpiling food. This research reveals a significant association between psychological factors and the likelihood of stockpiling food in response to the COVID‐19 pandemic [EconLit Citations: D12, H12, I18, P25]. [ABSTRACT FROM AUTHOR]

Background: When the coronavirus pandemic 2019 (COVID‐19) emerged, concerns were also raised regarding the safety of allergen immunotherapy (AIT). The German Society for Allergology and Clinical Immunology (DGAKI) conducted a survey to collect real‐world data on the daily routine of administering subcutaneous AIT (SCIT) and sublingual AIT (SLIT) during the COVID‐19 pandemic. Methods: A web‐based retrospective survey using the online platform survio with 26 standardized questions was used to survey physicians treating allergic patients during the pandemic. Results: Three hundred and forty‐five physicians who regularly offer and perform AIT in German‐speaking countries responded to the questions. 70.4% of the respondents stated that they regularly initiated and dosed up SCIT for inhalant allergies (41.4% venom‐SCIT, 73.6% SLIT), and 85.2% of the respondents stated that they continued SCIT for inhalant allergies during the maintenance phase in a regular way (59.1% venom‐SCIT, 90.4% SLIT) in healthy patients without current symptoms indicating an infection with COVID‐19. With regard to tolerability, there was no evidence for increased occurrence of adverse events in patients without current symptoms of COVID‐19 infection during the pandemic. Conclusions: This retrospective study demonstrated adherence to national and international position papers of AIT during the COVID‐19 pandemic in German‐speaking countries. Besides, the survey has confirmed a good tolerability of AIT for both SCIT and SLIT. [ABSTRACT FROM AUTHOR]

To treat the novel COronaVIrus Disease (COVID), comparatively fewer medicines have been approved. Due to the global pandemic status of COVID, several medicines are being developed to treat patients. The modern COVID medicines development process has various challenges, including predicting and detecting hazardous COVID medicine responses. Moreover, correctly predicting harmful COVID medicine reactions is essential for health safety. Significant developments in computational models in medicine development can make it possible to identify adverse COVID medicine reactions. Since the beginning of the COVID pandemic, there has been significant demand for developing COVID medicines. Therefore, this paper presents the transfer‐learning methodology and a multilabel convolutional neural network for COVID (MLCNN‐COV) medicines development model to identify negative responses of COVID medicines. For analysis, a framework is proposed with five multilabel transfer‐learning models, namely, MobileNetv2, ResNet50, VGG19, DenseNet201, and Inceptionv3, and an MLCNN‐COV model is designed with an image augmentation (IA) technique and validated through experiments on the image of three‐dimensional chemical conformer of 17 number of COVID medicines. The RGB color channel is utilized to represent the feature of the image, and image features are extracted by employing the Convolution2D and MaxPooling2D layer. The findings of the current MLCNN‐COV are promising, and it can identify individual adverse reactions of medicines, with the accuracy ranging from 88.24% to 100%, which outperformed the transfer‐learning model's performance. It shows that three‐dimensional conformers adequately identify negative COVID medicine responses. [ABSTRACT FROM AUTHOR]

Background: The oral glucose tolerance test (OGTT) is the gold standard for detecting gestational diabetes mellitus (GDM). However, during the COVID‐19 pandemic, screening practices were reevaluated due to the risk of infection associated with the prolonged hospital visit required for the OGTT. Some countries have published novel screening protocols for GDM, suggesting the utilization of hemoglobin A1c (HbA1c), random plasma glucose (RPG), or fasting plasma glucose (FPG) in favor of OGTTs during the pandemic. Therefore, differences in screening methods before and after the epidemic need to be examined. Methods: A systematic search was carried out across five electronic databases (Cinahl, Medline, Embase, Pubmed, and Web of Science) between 2016 and 2023. The Critical Appraisal Skills Programme (CASP) checklist for cohort studies was used to evaluate the quality of included papers. Results: A total of 13 eligible studies were included. Prior to the COVID‐19 pandemic, the OGTT was the recommended measure to screen GDM, internationally based on various official guidelines. During the pandemic, it was recommended that HbA1c or FPG, or RPG be used as a substitute for OGTTs. However, the new methods have low sensitivity, may not reflect accurately the prevalence of GDM, and may lead to many false‐negative results in women and to adverse pregnancy and neonatal outcomes. Conclusion: The new screening methods for GDM have poor accuracy and a high risk of adverse pregnancy outcomes. Comparatively, targeted screening tests to detect GDM according to the risk level are more effective in an emergency. In the future, the alternatives to OGTTs still need to be further explored in more depth. This study aims to compare the screening methods for gestational diabetes mellitus before and during the COVID‐19 pandemic. The new screening methods for GDM had poor sensitivity, which resulted in many misdiagnosed pregnant women. It also prompts reflection on the post‐pandemic era. OGTTs remain the gold standard, and there must be future planning so that future pandemics or emergencies do not significantly alter the effectiveness of screening practices for GDM. [ABSTRACT FROM AUTHOR]

This paper provides a general framework for conceptualizing triage for intensive care unit admissions in public health emergencies such as the COVID‐19 pandemic. It applies this framework to some of the guidelines issued during the pandemic and addresses some controversial issues, including the role of age, the use of lives or life years, and the relevance of quality of life considerations. The paper defends a view on which triage protocols for public health emergencies should aim to maximize the number of life years saved, may take into account age as a proxy, and should ignore quality of life considerations. [ABSTRACT FROM AUTHOR]

Introduction: Telepractice has the potential to align with the directive to reduce inequalities by United Nations Sustainable Development Goal 10. Telepractice additionally addresses a national digital health strategic plan for accessible digitally enabled models of care. To plan improvements, it is essential to understand the experience of telepractice for people with disability, which may be achieved through an approach such as journey mapping. The current article provides both a disability‐specific case study and a methodological guide for the inclusion of customers and clinicians in the meaningful redevelopment of services. The Perth, Australia‐based case study aimed to gain insights into the experience of telepractice for people with disability. The methodological aim describes using co‐design to produce a journey map in collaboration with customers and clinicians, for potential replication in a wide range of health and social care contexts. Method: Interview transcripts gathered from a cohort of customer participants (n = 17) were used to inform the journey map. A group of customers (n = 5) and clinicians plus one manager (n = 5) distributed the findings onto a customer experience journey map during a co‐design workshop. The journey map describes the emotional experience and actions taken, along five phases of a timeline through telepractice service interactions: (1) before, (2) selecting telepractice, (3) telepractice preparation, (4) during telepractice sessions and (5) after. Results: A journey map visualisation of customer experiences was produced that identified strengths of telepractice service delivery (flexibility) while noting challenges (with technology) as opportunities for improvement. The consensus of participants was the desire to have access to telepractice currently and in the future, in addition to in‐person delivery. Conclusion: These findings are valuable in the context of advocating for the incorporation of customers and clinicians through co‐design workshops in the content analysis and creation of a journey map that is representative of the lived experience of accessing telepractice services. Patient or Public Contribution: The paper forms part of a larger co‐design process that included customer participants throughout the design and planning of the project, inclusion of a peer researcher and the co‐designers in the workshops, journey map and this article production. [ABSTRACT FROM AUTHOR]

Background: Young people have the right to be informed and consulted about decisions affecting their lives. Patient and public involvement (PPI) ensures that research is carried out 'with' or 'by' young people rather than 'to', 'about' or 'for' them. The aim of this paper is to outline how youth PPI can be embedded within a physical activity intervention, reflect on the impact of PPI and provide recommendations for future PPI in a similar context. Methods: A Youth Advisory Group (YAG) was set up within the Walking In ScHools (WISH) Study to involve adolescent girls in the delivery, implementation and dissemination of a physical activity intervention targeted at adolescents. Schools invited pupils aged 12–14 years and 15–18 years to YAG meetings (n3, from 2019 to 2023). Participative methods were used to inform recruitment strategies and data collection methods for the WISH Study. Results: Across the three YAG meetings, n51 pupils from n8 schools were involved. Pupils enjoyed the YAG meetings, felt that their feedback was valued and considered the meetings a good way to get young people involved in research. The YAG advised on specific issues and although measuring impact was not the primary aim of the YAG meetings, over the course of the study there were many examples of the impact of PPI. Recruitment targets for the WISH Study were exceeded, the attrition rate was low and pupils were engaged in data collection. Conclusion: Youth PPI is a developing field and there are few physical activity studies that report the PPI work undertaken. Within the WISH Study, three YAG meetings were held successfully, and the views of adolescent girls were central to the development of the study. Considering the specific issues that the YAG advised on (study recruitment, attrition and data collection), there was evidence of a positive impact of PPI. Patient or Public Contribution: Pupils from post‐primary schools interested/participating in the WISH Study were invited to attend YAG meetings. YAG meetings were set up to consult adolescent girls on the delivery, implementation and dissemination of the WISH intervention. [ABSTRACT FROM AUTHOR]

This study explores the evolution of publication practices associated with the SARS‐CoV‐2 research papers, namely, peer‐reviewed journal and review articles indexed in PubMed and their associated preprints posted on bioRxiv and medRxiv servers: a total of 4,031 journal article‐preprint pairs. Our assessment of various publication delays during the January 2020 to March 2021 period revealed the early bird effect that lies beyond the involvement of any publisher policy action and is directly linked to the emerging nature of new and 'hot' scientific topics. We found that when the early bird effect and data incompleteness are taken into account, COVID‐19 related research papers show only a moderately expedited speed of dissemination as compared with the pre‐pandemic era. Medians for peer‐review and production stage delays were 66 and 15 days, respectively, and the entire conversion process from a preprint to its peer‐reviewed journal article version took 109.5 days. The early bird effect produced an ephemeral perception of a global rush in scientific publishing during the early days of the coronavirus pandemic. We emphasize the importance of considering the early bird effect in interpreting publication data collected at the outset of a newly emerging event. [ABSTRACT FROM AUTHOR]

The latest epidemic of COVID‐19 has significantly impacted both human capital and the global economy, contributing to pandemics and severe global crises. Research into the creation and propagation of the disease is desperately needed. The Internet of Things, cloud computing, and artificial intelligence offer modern technology for real‐time processing for multiple applications such as healthcare applications, transport, traffic control, and so on blockchain is an evolving technology that will dramatically boost transaction protection in finance, supply chain, and other transaction networks. A stable and latency‐sensitive Quality of Service framework for COVID‐19 is the need of an hour. The purpose of this paper is to combine Fog computing and Artificial Intelligence with smart health to establish a reliable platform for early‐stage detection of COVID‐19 infection. A new ensemble‐based classifier is proposed to detect COVID‐19 patients. This research offers a blockchain platform to analyse how the unrelated cases of the COVID‐19 virus can be tracked and identified using peer‐to‐peer, time stamping, and the shared storage advantages of blockchain. In addition to growing patient loyalty, this would effectively enhance the consistency, flexibility, productivity, performance, and effectiveness of healthcare services. The idea of blockchain is used to establish security for the whole framework. Different implementations measure the efficiency of the suggested system. The performance of the proposed framework is evaluated in terms of delay, network usages, RAM usages, and energy consumption. On the other hand, the classifier is evaluated in terms of classifier accuracy, recall, precision, kappa static, and root mean square error. The result shows the performance of the proposed framework and classifier is always better than the traditional frameworks and classifiers. [ABSTRACT FROM AUTHOR]

This paper argues that identity economics and social psychology provide a useful frame of reference to interpret supererogatory actions and suggests that identity of companies can be a driving force behind these actions. Companies may perform actions against the narrow sense of economic rationality if those actions serve purposes of high importance for them. The climate crisis and the more recent COVID‐19 crisis call for supererogatory actions by companies more than ever before. [ABSTRACT FROM AUTHOR]

Responses to the COVID‐19 pandemic have been widely criticized for being too delayed and indecisive. As a result, the precautionary principle has been endorsed, applauded, and proposed to guide future responses to global public health emergencies. Drawing from controversial issues in response to COVID‐19, especially in Vietnam, this paper critically discusses some key ethical and legal issues of employing the precautionary principle in public health emergencies. Engaging with discussions concerning this principle, especially in environmental law where the precautionary principle first appeared as a guiding principle with objective content(s), this paper formulates the precautionary principle as 'in dubio pro salus', which is about advising, justifying and demanding states to proactively prepare for scenarios arising out of any public health emergency. It distinguishes the precautionary principle into moderate and hard versions. A moderate version largely takes a holistic approach and fulfils a series of criteria specified in this paper, while a hard version either permits restrictive measures to be deployed primarily on a hypothetic basis or expresses an instrumental mentality. The hard version should be rejected because of the ethical and legal problems it raises, including risk‐risk tradeoffs, internal paradoxes, unjustified causing of fear and unreasonable presupposition. Ultimately, this paper defends the moderate version. [ABSTRACT FROM AUTHOR]

The Pacific Small Island Developing State (SIDS) of Kiribati has met the formal, minimal criteria for graduation from the Least Developed Country (LDC) category of the United Nations on multiple occasions from 2003 to 2018. Nevertheless, in light of both structural, long‐standing constraints and severe more recent challenges – such as the COVID‐19 pandemic, the Russia‐Ukraine conflict and the exacerbation of the climate crisis – that past assessments took into only partial consideration, the country still appears, at present, unready to lose the support measures that come with the LDC inclusion and to graduate, once and for all, with sustained "momentum." The analysis of strengths, weaknesses, opportunities and threats conducted in this paper suggests the need to further delay any decisions on graduation until more holistic, thorough readiness assessments can be conducted on the basis of new, additional indicators closely reflecting the full range of vulnerabilities that Kiribati, and other similar SIDS, currently face. [ABSTRACT FROM AUTHOR]

AI and machine learning are increasingly often applied in the medical industry. The COVID‐19 epidemic will start to spread quickly over the planet around the start of 2020. At hospitals, there were more patients than there were beds. It was challenging for medical personnel to identify the patient who needed treatment right away. A machine learning approach is used to predict COVID‐19 pandemic patients at high risk. To provide input data and output results that execute the machine learning model on the backend, a straightforward Python Flask web application is employed. Here, the XGBoost algorithm, a supervised machine learning method, is applied. In order to predict high‐risk patients based on their current underlying health issues, the model uses patient characteristics as well as criteria like age, sex, health issues including diabetes, asthma, hypertension, and smoking, among others. The XGBoost model predicts the patient's severity with an accuracy of about 98% after data pre‐processing and training. The most important factors to the models are chosen to be age, diabetes, sex, and obesity. Patients and hospital personnel will benefit from this project's assistance in making timely choices and taking appropriate action. This will let medical personnel decide how much time and space to devote to the COVID‐19 high‐risk patients. providing a treatment that is both efficient and ideal. With this programme and the necessary patient data, hospitals may decide whether a patient need immediate care or not. [ABSTRACT FROM AUTHOR]

The literature on Covid‐19 has demonstrated that frontline workers use different coping strategies and engage in sense‐making to address negative emotions. However, we know little about the underlying process of sense‐making. Thus, this paper uses institutional logics to investigate how sense‐making of negative emotions is enabled and constrained. This analysis draws on a diary written by a nurse at an Italian hospital, which represents an account of the emotions experienced by medical staff. The analysis identifies a set of enablers and disablers of sense‐making, as well as, the patterns that alleviate and intensify frontline workers' emotions. Based on these findings and evidence of the Covid‐task force at Papa Giovanni XXIII hospital in Bergamo, Italy, this paper illustrates Critical Systems Heuristics as a means to address the disablers of sense‐making through participatory conversations that consider different institutional logics. [ABSTRACT FROM AUTHOR]

Introduction: The United States has been unsuccessful in containing the rapid spread of COVID‐19. The complex epidemiology of the disease and the fragmented response to it has resulted in thousands of ways in which spread has occurred, creating a situation where each community needs to create its own local, context‐specific learning model while remaining compliant to county or state mandates. Methods: In this paper, we demonstrate how cross sector collaborations can use the Cynefin Framework, a tool for decision‐making in complex systems, to guide community response to the COVID‐19 pandemic. Results: We explore circumstances under which communities can inhabit each of the four domains of systems complexity represented in the Cynefin framework: simple, complicated, chaotic, and complex, and describe the decision‐making process in each domain that balances health, economic, and social well‐being. Conclusion: This paper serves as a call to action for the creation of community learning systems to improve community resilience and capacity to make better‐informed decisions to address complex public health problems during the pandemic and beyond. [ABSTRACT FROM AUTHOR]

A smart and scalable system is required to schedule various machine learning applications to control pandemics like COVID‐19 using computing infrastructure provided by cloud and fog computing. This paper proposes a framework that considers the use case of smart office surveillance to monitor workplaces for detecting possible violations of COVID effectively. The proposed framework uses deep neural networks, fog computing and cloud computing to develop a scalable and time‐sensitive infrastructure that can detect two major violations: wearing a mask and maintaining a minimum distance of 6 feet between employees in the office environment. The proposed framework is developed with the vision to integrate multiple machine learning applications and handle the computing infrastructures for pandemic applications. The proposed framework can be used by application developers for the rapid development of new applications based on the requirements and do not worry about scheduling. The proposed framework is tested for two independent applications and performed better than the traditional cloud environment in terms of latency and response time. The work done in this paper tries to bridge the gap between machine learning applications and their computing infrastructure for COVID‐19. [ABSTRACT FROM AUTHOR]

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Copyright of Regional Science Policy & Practice is the property of Wiley-Blackwell 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 abstract 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 abstract. (Copyright applies to all Abstracts.)

How should the government support small and medium‐sized enterprises amid a pandemic crisis while balancing the trade‐off between short‐run stabilization and long‐run allocative efficiency? We develop a two‐sector equilibrium model featuring small businesses with private information on their likely future success and a screening contract. Businesses in the sector adversely affected by a pandemic can apply for government loans to stay afloat. A pro‐allocation government sets a harsh default sanction to deter entrepreneurs with poorer projects, thereby improving long‐run productivity at the cost of persistent unemployment, whereas a pro‐stabilization government sets a lenient default sanction. Interest rate effective lower bound leads to involuntary unemployment in the other open sector and shifts the optimal default sanction to a lenient stance. The rise in firm markups exerts the opposite effect. A high creative destruction wedge polarizes the government's hawkish and dovish stances, and optimal default sanction is more lenient, exacerbating resource misallocation. The model illuminates how credit guarantees might be structured in future crises. This paper is part of the Economica 100 Series. Economica, the LSE "house journal" is now 100 years old. To commemorate this achievement, we are publishing 100 papers by former students, as well as current and former faculty. Charles Goodhart, CBE, FBA was the Norman Sosnow Professor of Banking and Finance at the London School of Economics and Political Science until 2002. [ABSTRACT FROM AUTHOR]

In the wake of the COVID‐19 global pandemic, self‐administered microsampling tools have reemerged as an effective means to maintain routine healthcare assessments without inundating hospitals or clinics. Finger‐stick collection of blood is easily performed at home, in the workplace, or at the point‐of‐care, obviating the need for a trained phlebotomist. While the initial collection of blood is facile, the diagnostic or clinical utility of the sample is dependent on how the sample is processed and stored prior to transport to an analytical laboratory. The past decade has seen incredible innovation for the development of new materials and technologies to collect low‐volume samples of blood with excellent precision that operate independently of the hematocrit effect. The final application of that blood (i.e., the test to be performed) ultimately dictates the collection and storage approach as certain materials or chemical reagents can render a sample diagnostically useless. Consequently, there is not a single microsampling tool that is capable of addressing every clinical need at this time. In this review, we highlight technologies designed for patient‐centric microsampling blood at the point‐of‐care and discuss their utility for quantitative sampling as a function of collection material and technique. In addition to surveying methods for collecting and storing whole blood, we emphasize the need for direct separation of the cellular and liquid components of blood to produce cell‐free plasma to expand clinical utility. Integrating advanced functionality while maintaining simple user operation presents a viable means of revolutionizing self‐administered microsampling, establishing new avenues for innovation in materials science, and expanding access to healthcare. [ABSTRACT FROM AUTHOR]

Loneliness is a major public health concern, particularly during pandemics such as Covid. It is extremely common, and it poses a major risk to human health. Technological solutions including social media, robots, and virtual reality have been advocated and implemented to relieve loneliness, and their use will undoubtedly increase in the near future. This paper explores the use of technological solutions from a normative perspective, asking whether and to what extent such measures should indeed be relied upon. The conclusion is that technological solutions are unquestionably part of the solution to loneliness, but that they cannot and should not constitute the whole solution. It is important to note that this is not a straw‐man argument, as several organizations and scholars have strictly focused on such technological solutions for loneliness. [ABSTRACT FROM AUTHOR]

This paper investigates the resilience of environmentally friendly companies in an overwhelming economic and social environment that has been generated after the outbreak of the novel coronavirus disease (COVID‐19) pandemic. To this respect, we have investigated the cointegration between the Standard & Poor's (S&P) 500 Carbon Efficiency Index (CEI) with COVID‐19 cases, supplemented with covariates such as government response stringency to the pandemic, economic policy uncertainty, oil prices and global markets fluctuations. We have used daily data from 2nd January to 5th October 2020 and have employed a robust estimator within a Fourier approach to accommodate both sharp and smooth breaks. Our results suggest that green companies have been positively affected by the outbreak of COVID‐19. Our paper provides practical implications for companies that wish to furnish themselves with resilience during rough times and stakeholders who wish to invest in safe, long‐lasting returns. [ABSTRACT FROM AUTHOR]

Coronavirus disease (COVID‐19) is a pandemic that has caused thousands of casualties and impacts all over the world. Most countries are facing a shortage of COVID‐19 test kits in hospitals due to the daily increase in the number of cases. Early detection of COVID‐19 can protect people from severe infection. Unfortunately, COVID‐19 can be misdiagnosed as pneumonia or other illness and can lead to patient death. Therefore, in order to avoid the spread of COVID‐19 among the population, it is necessary to implement an automated early diagnostic system as a rapid alternative diagnostic system. Several researchers have done very well in detecting COVID‐19; however, most of them have lower accuracy and overfitting issues that make early screening of COVID‐19 difficult. Transfer learning is the most successful technique to solve this problem with higher accuracy. In this paper, we studied the feasibility of applying transfer learning and added our own classifier to automatically classify COVID‐19 because transfer learning is very suitable for medical imaging due to the limited availability of data. In this work, we proposed a CNN model based on deep transfer learning technique using six different pre‐trained architectures, including VGG16, DenseNet201, MobileNetV2, ResNet50, Xception, and EfficientNetB0. A total of 3886 chest X‐rays (1200 cases of COVID‐19, 1341 healthy and 1345 cases of viral pneumonia) were used to study the effectiveness of the proposed CNN model. A comparative analysis of the proposed CNN models using three classes of chest X‐ray datasets was carried out in order to find the most suitable model. Experimental results show that the proposed CNN model based on VGG16 was able to accurately diagnose COVID‐19 patients with 97.84% accuracy, 97.90% precision, 97.89% sensitivity, and 97.89% of F1‐score. Evaluation of the test data shows that the proposed model produces the highest accuracy among CNNs and seems to be the most suitable choice for COVID‐19 classification. We believe that in this pandemic situation, this model will support healthcare professionals in improving patient screening. [ABSTRACT FROM AUTHOR]

Objectives: The COVID‐19 Psychological Research Consortium (C19PRC) Study was established in March 2020 to monitor the psychological and socio‐economic impact of the pandemic in the UK and other countries. This paper describes the protocol for Wave 5 (March–April 2021). Methods: The survey assessed: COVID‐19 related experiences; experiences of common mental health disorders; psychological characteristics; and social and political attitudes. Adults who participated in any previous wave (N = 4949) were re‐invited to participate. Weights were calculated using a survey raking algorithm to ensure the longitudinal panel was nationally representative in terms of gender, age, and household income, amongst other factors. Results: Overall, 2520 adults participated. A total of 2377 adults who participated in the previous survey wave (November–December 2020) were re‐interviewed at Wave 5 (61.5% retention rate). Attrition between these two waves was predicted by younger age, lower household income, children living in the household, and treatment for mental health difficulties. Of the adults recruited into the C19PRC study at baseline, 57.4% (N = 1162) participated in Wave 5. The raking procedure re‐balanced the longitudinal panel to within 1.5% of population estimates for selected socio‐demographic characteristics. Conclusion: This paper outlines the growing strength of the publicly available C19PRC Study data for COVID‐19‐related interdisciplinary research. [ABSTRACT FROM AUTHOR]

Background: Consumer and advanced consumption culture in modern society is an era that focuses on individual personality and value, and "my own customized products", or customized marketing strategies, are actively being developed throughout the industry. Recently, IT technologies that can support personalized services such as artificial intelligence, ubiquitous systems, and marketing automation have been recognized for their potential, directly or indirectly affecting distribution industries affected by personal consumption culture. Accordingly, customized products or services, i.e., customization, are attracting attention as an effective methodology to cope with such market changes. Objectives: Among the necessities used by modern women, cosmetics account for an endless interest in beauty and maintaining physical and mental health, and as the cosmetics market expands, it is considered that the cosmetics industry needs a clearer and in‐depth study on the cosmetics submarket to satisfy consumers' diverse needs. Methods: This review paper is a literature review, and a narrative review approach has been used for this study. A total of 300 to 400 references were selected using representative journal search websites such as PubMed, Google Scholar, Scopus, ResearchGate, LitCovid, DBPia, and RISS, of which a total of 42 papers were selected in the final stage based on 2013 to 2022 using PRISMA flow diagram. Results: This study suggested to indicate the changes in the cosmetics market due to the emergence of cosmetics curation services after the coronavirus disease‐19 pandemic, advanced changes in consumer purchase patterns following the 4th Industrial Revolution, and significant future prospects of cosmetics curation services. Conclusion: As the beauty and cosmetology industry is expected to develop in the future, it will grow as a centerpiece of the beauty industry and symbolizes nationalized cultural pride. Therefore, this review article will be continuing to promote customization as a premium beauty service for dermatological condition in Republic of Korea through corporate analysis. [ABSTRACT FROM AUTHOR]