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In this review paper efforts are made to present the Impact of COVID-19 on various economic activities like production, consumption and distribution. This is known to everyone that COVID-19 (Corona Virus Disease) spreadout in December 2019 from Wuhan in China and effected a lot of countries resulting in death of many. Therefore, to minimise the risk, many countries opted lockdown as a precaution to control its spread which effected the lives of ordinary people as well as the economy as a whole, GDP, Functions of Economy and a lot of activities were effected very badly. It had a huge impact on the consumption pattern of population as well as supply chains and production of various goods and services were also impacted as transportation was at halt and movement of people, products, services etc. was not possible from one place to another. There was a heavy down in employment, production, distribution and other activities as well. Hence, this paper will help the readers know the collective impact along with the sector wise effect of COVID-19 on various economic activities. For better understanding we have taken three major economic activities that is Production, Consumption and distribution. Along with these the basic introduction of COVID-19, lockdown and its impact on economy is stated. [ABSTRACT FROM AUTHOR]

Introduction: In December 2019, a cluster of atypical cases of pneumonia was reported in Wuhan, China, which was later designated as Coronavirus disease 2019 (COVID-19) by the World Health Organization (WHO) on Feb 11, 2020. We all are facing a global pandemic, and it is very important to be clear that there is no correct roadmap to navigate this difficult situation. It is imperative to state that this global pandemic impacted the spine care services of our institute. In the present study, we have assessed the spine surgeries performed by orthopedic surgeons in terms of volume and etiologies during the COVID-19 pandemic and compared the data with a pre-COVID period. Materials and Methods: We retrospectively collected data from all patients who underwent spinal surgeries at our institute under the department of orthopedics from August 20, 2019 to August 20, 2020 (a total of 12 months duration). The data was then divided into two groups--pre-COVID period (August 20, 2019-February 19, 2020-6 months) and during the COVID pandemic (February 20, 2020-August 20, 2021-6 months). Results: A total of 140 patients underwent surgery at our institute from August 20, 2019 to August 20, 2020. Of these, 91 patients underwent surgery during the pre-COVID period, and 49 patients underwent surgery during the COVID pandemic. In this devastating phase of the pandemic, our department's total number of surgeries significantly declined to 46.15%. The routine surgeries performed during the pandemic phase show a steep fall from 59.34% in the pre-COVID period to 10.20%. Conclusion: This paper is meant to focus attention on the exorbitant reduction in the operative workflow of the spine patients during the COVID-19 pandemic at a tertiary healthcare institute. It is the need of the hour that orthopedic surgeons maintain equilibrium while providing the best possible treatment to their patients and limiting the spread of the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to ignite a conversation on the challenges of the ongoing 'novel' surrounding the coronavirus (SARS-CoV-2) pandemic in the Southern Africa region. The global COVID-19 pandemic started with the outbreak in Wuhan, China, in December 2019. It is understood that different countries, regions and territories will have different, context-specific challenges in fighting the pandemic. These will differ from country to country and therefore necessitates contextual research. By sharing African perspectives on swift action against this coronavirus, this paper is intended to appraise the nexus between the coronavirus and tourism in order to prepare tourism practitioners for the recovery strategies that will be needed to address the damages caused by the pandemic. This paper points to tourism researchers focusing on the Southern African region and possible key areas for further research as the coronavirus situation unfolds. It draws from published material and document analysis, including from academic research, media reports and social media platforms. [ABSTRACT FROM AUTHOR]

Copyright of Cumhuriyet Medical Journal is the property of Cumhuriyet Medical Journal 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.)

COVID-19 is an infectious disease caused by the virus SARS-CoV-2, a coronavirus variant, which emerged on December 2019 in Wuhan, China. This paper presents an analysis and reflection of emerging research on the efficacy of Chinese medicine (CM) and acupuncture as a treatment for COVID-19. In CM, COVID-19 falls under the category of an epidemic disease. CM places COVID-19 into four stages: early, advanced, critical, and recovery. This research analysis presents the application of immunomodulatory acupuncture points and Chinese herbal prescriptions that may assist the body to defend against epidemic pathogens such as COVID-19 and future epidemic threats. Acupuncture has proven to be able to activate immune cells, at specific entry viral points, to prepare the body's immune system to fend off the viral invasion. The most common Chinese herbs to appear in research are Toujie Quwen granules, Lianhua Qingwen Capsules, Keguan-1, Xuebijing injection, and Chinese syndrome differentiation decoctions. The summary of evidence showed a benefit in the clinical cure, chest image improvement, and a reduction in clinical deterioration, acute respiratory distress syndrome, mechanical ventilation, and mortality with CM plus routine Western treatment compared to routine Western treatment alone. This critical analysis may prepare future generations for prevention, treatment, and integration of varying medical paradigms to effectively respond to a virus with global impact. Traditional Chinese medicine (TCM) is a viable adjunct in supportive care for patients with COVID-19. [ABSTRACT FROM AUTHOR]

This paper investigates, for the first time, the presence of financial contagion among several important Chinese coronavirus concept-based stock indices during the recent COVID-19 global pandemic. We utilize a regime-switching skew-normal (RSSN) methodology to test for contagion through the correlation and coskewness channels while considering structural breaks in the different moments. Our results present evidence of contagion effects, which are robust across identified crisis and non-crisis periods, including that of the Wuhan lockdown. Our empirical results offer investors and policy-makers an additional layer of information when evaluating response mechanisms to major crises through the use of concept-based indices. [ABSTRACT FROM AUTHOR]

A pneumonia outbreak was primarily reported in the fall of 2019 in Wuhan, Hubei province, China, with the identity SARS-CoV-2, a novel coronavirus. It quickly grew from a local epidemic to a global pandemic and was declared a public health emergency by the WHO. A total of three prominent waves were identified across the globe, with a slight temporal variability as per the geographical locations, and has impacted several sectors which connect the world. By March 2022, the coronavirus had infected 444.12 million people and claimed 6.01 million human lives worldwide, and these numbers have not yet stabilized. Our paper enlightens readers on the seven strains of human coronaviruses, with special emphasis on the three severe deadliest outbreaks (SARS-2002, MERS-2012, and COVID-19). This work attempts a comprehensive understanding of the coronavirus and its impact on the possible sectors that link the world through the economic chain, climate conditions, SDGs, recycling of the event, and mitigations. There are many points that are raised by the authors in the possible sectors, which are emerging or are as yet unnoticed and thus have not been taken into consideration. This comprehension will leave sets of new challenges and opportunities for the researchers in various streams, especially in earth sciences. Science-integrated research may help to prevent upcoming disasters as a by-product of (existing) epidemics in the form of coronavirus. [ABSTRACT FROM AUTHOR]

Copyright of Acibadem Saglik Bilimleri Dergisi is the property of Acibadem University Medical School 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.)

Towards the end of 2019, Wuhan experienced an outbreak of novel coronavirus, which soon spread worldwide, resulting in a deadly pandemic that infected millions of people around the globe. The public health agencies followed many strategies to counter the fatal virus. However, the virus severely affected the lives of the people. In this paper, we study the sentiments of people from the top five worst affected countries by the virus, namely the USA, Brazil, India, Russia, and South Africa. We propose a deep language-independent Multilevel Attention-based Conv-BiGRU network (MACBiG-Net), which includes embedding layer, word-level encoded attention, and sentence-level encoded attention mechanisms to extract the positive, negative, and neutral sentiments. The network captures the subtle cues in a document by focusing on the local characteristics of text along with the past and future context information for the sentiment classification. We further develop a COVID-19 Sentiment Dataset by crawling the tweets from Twitter and applying topic modeling to extract the hidden thematic structure of the document. The classification results demonstrate that the proposed model achieves an accuracy of 85%, which is higher than other well-known algorithms for sentiment classification. The findings show that the topics which evoked positive sentiments were related to frontline workers, entertainment, motivation, and spending quality time with family. The negative sentiments were related to socio-economic factors like racial injustice, unemployment rates, fake news, and deaths. Finally, this study provides feedback to the government and health professionals to handle future outbreaks and highlight future research directions for scientists and researchers. [ABSTRACT FROM AUTHOR]

A severe pneumonia of an unknown origin was reported in Wuhan, China in December 2019. The disease, now known as coronavirus disease 2019 (COVID-19), has evolved into a public health emergency of international concern and wreaked worldwide havoc. An unprecedented and vigorous scientific response has allowed the accelerated discovery of the virus and reliable diagnostic methods; a rapid characterization of the disease and its impacts so as to better apply precautionary and public health measures; and resulted in remarkable progress in the development of mitigation strategies, including the development of vaccines at breakneck speed. This paper provides a health perspective of the virus and the pandemic it caused, based on available best evidence. Controversies surrounding the origin of the virus, its incubation period and infectivity, presentation and course of the disease, testing, as well as treatments and vaccinations are highlighted. The pandemic response, including infection control measures, and considerations on mental and economic health, alongside physical health is discussed. Moving forward, it is important that the global community is aware and better informed. More resources are needed to strengthen public health systems and healthcare infrastructure and delivery. This virus has the potential to persist and become endemic and seasonal in communities. Thus, non-pharmaceutical interventions (e.g. wearing masks, frequent hand washing, etc.) might become the new normal in a post-pandemic world. The silver lining in the COVID-19 cloud may be the lessons it provides, so that we may be better prepared to respond to an inevitable next pandemic. [ABSTRACT FROM AUTHOR]

Simple Summary: The COVID-19 pandemic showcases the impact of mitigation and elimination strategies across the globe, including the development of effective vaccines, antiviral drugs and diagnostic tools. However, the virus changes rapidly over time. Consequently, control strategies have been limited by time-consuming experimental acquisition of three-dimensional atomic protein structures of the fast-developing mutant 'variants' of the virus, which remains an unviable strategy for fast and effective disease control. Here, we use AlphaFold2 to model the atomic structure of the ever-changing SARS-CoV-2 spike protein in silico. AlphaFold2 is an artificial intelligence (AI) deep learning computational tool capable of producing models at experimental resolution in only a few hours. Structural models for major Variants of Concern (Alpha, Delta, and Omicron) and latitude-delimited haplotypes, sets of genetically linked and highly prevalent mutations that impact the epidemic calendar of the virus, were compared to the structure of the reference Wuhan strain. We find that patterns of structural change triggered by seasonal haplotype diversification could help predict the changing face of the virus, understand seasonal behavior, and develop more resilient vaccines and drugs. The slow experimental acquisition of high-quality atomic structures of the rapidly changing proteins of the COVID-19 virus challenges vaccine and therapeutic drug development efforts. Fortunately, deep learning tools such as AlphaFold2 can quickly generate reliable models of atomic structure at experimental resolution. Current modeling studies have focused solely on definitions of mutant constellations of Variants of Concern (VOCs), leaving out the impact of haplotypes on protein structure. Here, we conduct a thorough comparative structural analysis of S-proteins belonging to major VOCs and corresponding latitude-delimited haplotypes that affect viral seasonal behavior. Our approach identified molecular regions of importance as well as patterns of structural recruitment. The S1 subunit hosted the majority of structural changes, especially those involving the N-terminal domain (NTD) and the receptor-binding domain (RBD). In particular, structural changes in the NTD were much greater than just translations in three-dimensional space, altering the sub-structures to greater extents. We also revealed a notable pattern of structural recruitment with the early VOCs Alpha and Delta behaving antagonistically by suppressing regions of structural change introduced by their corresponding haplotypes, and the current VOC Omicron behaving synergistically by amplifying or collecting structural change. Remarkably, haplotypes altering the galectin-like structure of the NTD were major contributors to seasonal behavior, supporting its putative environmental-sensing role. Our results provide an extensive view of the evolutionary landscape of the S-protein across the COVID-19 pandemic. This view will help predict important regions of structural change in future variants and haplotypes for more efficient vaccine and drug development. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic, which outbroke in Wuhan (China) in December 2019, severely hit almost all sectors of activity in the world as a consequence of the restrictive measures imposed. Two years later, Africa still emerges as the least affected continent by the pandemic. This study analyzed COVID-19 prevalence across African countries through country-level variables prior to clustering. Using Spearman-rank correlation, multicollinearity analysis and univariate filtering, 9 country-level variables were identified from an initial set of 34 variables. These variables relate to socioeconomic status, population structure, healthcare system and environment and the climatic setting. A clustering of the 54 African countries is further carried out through the use of agglomerative hierarchical clustering (AHC) method, which generated 3 distinctive clusters. Cluster 1 (11 countries) is the most affected by COVID-19 (median of 63,508.6 confirmed cases and 946.5 deaths per million) and is composed of countries with the highest socioeconomic status. Cluster 2 (27 countries) is the least affected (median of 4473.7 confirmed cases and 81.2 deaths per million), and mainly features countries with the least socioeconomic features and international exposure. Cluster 3 (16 countries) is intermediate in terms of COVID-19 prevalence (median of 2569.3 confirmed cases and 35.7 deaths per million) and features countries the least urbanized and geographically close to the equator, with intermediate international exposure and socioeconomic features. These findings shed light on the main features of COVID-19 prevalence in Africa and might help refine effectively coping management strategies of the ongoing pandemic. [ABSTRACT FROM AUTHOR]

A novel coronavirus, SARS-CoV-2, first identified in Wuhan, China, in late 2019, caused a global pandemic resulting in significant morbidity and mortality in humans worldwide. Disease presentations in humans range from asymptomatic virus carriers to fatal systemic disease. A historic effort is underway to develop therapeutics and vaccines to slow or stop virus spread and treat those infected with coronavirus disease (COVID-19). Animal models play vital roles in understanding the pathogenesis of SARS-CoV-2 and evaluating therapeutics and vaccines. We compare the pathologic findings, including macroscopic, microscopic, and molecular findings, in three nonhuman primate species (cynomolgus macaques, rhesus macaques, and African green monkeys), ferrets, guinea pigs, hamsters, and mice following experimental exposure to SARS-CoV-2 by the intranasal, intratracheal or small particle aerosol routes to the disease as it occurs in humans who died during the current pandemic. [ABSTRACT FROM AUTHOR]

• A deep learning COVID-19 diagnostic system is developed using state-of-the-art deep learning architectures. • A detailed analysis of different methods used for detecting COVID-19 is presented in this paper. • Chest X-rays from different sources are collected to build a robust classification model. • The developed diagnostic model provided efficient results, even with a limited number of input images. • The results produced in this work are validated by an expert radiologist. In recent months, a novel virus named Coronavirus has emerged to become a pandemic. The virus is spreading not only humans, but it is also affecting animals. First ever case of Coronavirus was registered in city of Wuhan, Hubei province of China on 31st of December in 2019. Coronavirus infected patients display very similar symptoms like pneumonia, and it attacks the respiratory organs of the body, causing difficulty in breathing. The disease is diagnosed using a Real-Time Reverse Transcriptase Polymerase Chain reaction (RT-PCR) kit and requires time in the laboratory to confirm the presence of the virus. Due to insufficient availability of the kits, the suspected patients cannot be treated in time, which in turn increases the chance of spreading the disease. To overcome this solution, radiologists observed the changes appearing in the radiological images such as X-ray and CT scans. Using deep learning algorithms, the suspected patients' X-ray or Computed Tomography (CT) scan can differentiate between the healthy person and the patient affected by Coronavirus. In this paper, popular deep learning architectures are used to develop a Coronavirus diagnostic systems. The architectures used in this paper are VGG16, DenseNet121, Xception, NASNet, and EfficientNet. Multiclass classification is performed in this paper. The classes considered are COVID-19 positive patients, normal patients, and other class. In other class, chest X-ray images of pneumonia, influenza, and other illnesses related to the chest region are included. The accuracies obtained for VGG16, DenseNet121, Xception, NASNet, and EfficientNet are 79.01%, 89.96%, 88.03%, 85.03% and 93.48% respectively. The need for deep learning with radiologic images is necessary for this critical condition as this will provide a second opinion to the radiologists fast and accurately. These deep learning Coronavirus detection systems can also be useful in the regions where expert physicians and well-equipped clinics are not easily accessible. [ABSTRACT FROM AUTHOR]

Copyright of Çankırı Karatekin University Journal of the Faculty of Economics & Administrative Sciences is the property of Cankiri Karatekin University, Faculty of Economics & Administrative Sciences 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.)

This study presents internet of things (IOT) and artificial intelligence technologies that are critical in reducing the harmful effects of this illness and assisting its recovery. It explores COVID-19's economic impacts before learning about new technologies and potential solutions. The research objective was to propose a solution for self-diagnosis, self-monitoring, and self-management of COVID-19 with personal mobiles and personal data using cloud solutions and mobile applications with the help of an intelligent IoT system, artificial intelligence, machine learning, and 5G technologies. The proposed solution based on self-diagnosis without any security risk for users' data with low cost of cloud-based data analytics by using handsets only is an innovative approach. Since the COVID-19 outbreak, the global social, economic, religious, and cultural frameworks and schedules have been affected adversely. The fear and panic associated with the new disease, which the world barely knew anything about, amplified the situation. Scientists and epidemiologists have traced the first outbreak of COVID-19 at Wuhan, China. A close examination of the genetic makeup of the virus showed that the virus is zoonotic, meaning that the virus changed hosts from animals to humans. The uncertainty associated with the above features and characteristics of the virus, as well as the high mortality rates witnessed in many parts of the globe, significantly contributed to the widespread global panic that brought the world to a standstill. Different authorities and agencies associated with securing the public have implemented different means and methods to try and mitigate the transmission of the infection as scientists and medical practitioners work on remedies to curb the spread of COVID-19. Owing to different demographics, different parts of the globe have attempted to effectively implement locally available resources to efficiently fight and mitigate the adverse effects of the COVID-19 pandemic. The general framework provided by the World Health Organization (WHO) has been implemented or enhanced in different parts of the globe by locally available resources and expertise to effectively mitigate the impact of COVID-19. There is currently no effective vaccine for COVID-19, but new technology can be available within weeks to reduce the spread of the disease; current approaches such as contact tracing and testing are not secure, and the cost of testing is high for end users. The proposed solution based on self-diagnosis without any security risk for users' data with low cost of cloud-based data analytics functions by using an intelligent internet of things (IOT) system for collecting sensors data and processing them with artificial intelligence to improve efficiency and reduce the spread of COVID-19. [ABSTRACT FROM AUTHOR]

The formulation of mathematical models using differential equations has become crucial in predicting the evolution of viral diseases in a population in order to take preventive and curative measures. In December 2019, a novel variety of Coronavirus (SARS-CoV-2) was identified in Wuhan, Hubei Province, China, which causes a severe and potentially fatal respiratory syndrome. Since then, it has been declared a pandemic by the World Health Organization and has spread around the globe. A reaction–diffusion system is a mathematical model that describes the evolution of a phenomenon subjected to two processes: a reaction process, in which different substances are transformed, and a diffusion process, which causes their distribution in space. This article provides a mathematical study of the Susceptible, Exposed, Infected, Recovered, and Vaccinated population model of the COVID-19 pandemic using the bias of reaction–diffusion equations. Both local and global asymptotic stability conditions for the equilibria were determined using a Lyapunov function, and the nature of the stability was determined using the Routh–Hurwitz criterion. Furthermore, we consider the conditions for the existence and uniqueness of the model solution and show the spatial distribution of the model compartments when the basic reproduction rate R 0 < 1 and R 0 > 1 . Thereafter, we conducted a sensitivity analysis to determine the most sensitive parameters in the proposed model. We demonstrate the model's effectiveness by performing numerical simulations and investigating the impact of vaccination, together with the significance of spatial distribution parameters in the spread of COVID-19. The findings indicate that reducing contact with an infected person and increasing the proportion of susceptible people who receive high-efficacy vaccination will lessen the burden of COVID-19 in the population. Therefore, we offer to the public health policymakers a better understanding of COVID-19 management. [ABSTRACT FROM AUTHOR]

Coronavirus refers to a group of widespread viruses. The name refers to the specific morphology of these viruses because their spikes look like a crown under an electron microscope. The outbreak of coronavirus disease 2019 (COVID-19) that has been reported in Wuhan, China, in December 2019, was proclaimed an international public health emergency (PHEIC) on 30 January 2020, and on 11 March 2020, it was declared as a pandemic (World Health Organization 2020). The official name of the virus was declared by the WHO as "COVID-19 virus", formerly known as "2019-nCoV", or "Wuhan Coronavirus". The International Committee on Virus Taxonomy's Coronavirus Research Group has identified that this virus is a form of coronavirus that caused a severe outbreak of acute respiratory syndrome in 2002–2003 (SARS). As a result, the latest severe acute respiratory syndrome has been classified as a corona virus 2 (SARS-CoV-2) pathogen by this committee. This disease spread quickly across the country and the world within the first 3 months of the outbreak and became a global pandemic. To stop COVID-19 from spreading, the governing agencies used various chemicals to disinfect different commercial spaces, streets and highways. However, people used it aggressively because of panic conditions, anxiety and unconsciousness, which can have a detrimental impact on human health and the environment. Our water bodies, soil and air have been polluted by disinfectants, forming secondary products that can be poisonous and mutagenic. In the prevention and spread of COVID-19, disinfection is crucial, but disinfection should be carried out with sufficient precautions to minimize exposure to harmful by-products. In addition, to prevent inhalation, adequate personal protective equipment should be worn and chemical usage, concentrations, ventilation in the room and application techniques should be carefully considered. In the USA, 60% of respondents said they cleaned or disinfected their homes more often than they had in the previous months. In addition to the robust use of disinfection approaches to combat COVID-19, we will explore safe preventative solutions here. [ABSTRACT FROM AUTHOR]

COVID-19 ushered in almost unprecedented socioeconomic and political challenges. A typical social reaction during such emergencies is rumormongering, which has intensified since the advent of social media. This study explored factors affecting users' willingness to spread pandemic-related rumors in Wuhan, China and Israel. We tested a multi-variant model of factors affecting the forwarding of COVID-19 rumors. In an online survey conducted in April–May 2020, users of each country's leading social media platform (WeChat and WhatsApp, respectively) reported on patterns of exposure to and spread of COVID-19 rumors, as well as on their motives for doing so. Despite major differences between the two societies, interesting similarities were found: in both cases, individual drives, shaped by personal needs and degree of negative feelings, were the leading factors behind rumormongering. Exposure to additional sources of information regarding the rumors was also a significant predictor, but only in the Chinese case. [ABSTRACT FROM AUTHOR]

The 2019 outbreak of corona virus disease began from Wuhan (China), transforming into a leading pandemic, posing an immense threat to the global population. The WHO coined the term nCOVID-19 for the disease on 11th February, 2020 and the International Committee of Taxonomy of Viruses named it SARS-CoV-2, on account of its similarity with SARS-CoV-1 of 2003. The infection is associated with fever, cough, pneumonia, lung damage, and ARDS along with clinical implications of lung opacities. Brief understanding of the entry target of virus, i.e., ACE2 receptors has enabled numerous treatment options as discussed in this review. The manuscript provides a holistic picture of treatment options in COVID-19, such as non-specific anti-viral drugs, immunosuppressive agents, anti-inflammatory candidates, anti-HCV, nucleotide inhibitors, antibodies and anti-parasitic, RNA-dependent RNA polymerase inhibitors, anti-retroviral, vitamins and hormones, JAK inhibitors, and blood plasma therapy. The text targets to enlist the investigations conducted on all the above categories of drugs, with respect to the COVID-19 pandemic, to accelerate their significance in hindering the disease progression. The data collected primarily targets recently published articles and most recent records of clinical trials, focusing on the last 10-year database. The current review provides a comprehensive view on the critical need of finding a suitable treatment for the currently prevalent COVID-19 disease, and an opportunity for the researchers to investigate the varying possibilities to find and optimized treatment approach to mitigate and ameliorate the chaos created by the pandemic worldwide. [ABSTRACT FROM AUTHOR]

COVID-19 (C19) was first recognized and described in November 2019 in Wuhan. In January 2020, WHO declared a public health emergency of international concern as a result of the spreading epidemic. By March of the same year, C19 was declared a pandemic. In connection with the pandemic, individual countries around the world enacted a variety of policies designed to slow the spread of C19; policies which dramatically impacted visits to museums of all kinds. Lockdowns selected institutional closures and restrictions and declines in tourism all contributed to precipitous declines in the use of museums. In response, museum significantly increased their digital presence, attempting to replace in-person experiences with virtual experiences of all kinds. However, the inherent limitations of the digital realm, including and particularly financial limitations, suggest an uncertain future for museums. We use data from visitations to the worlds' ten largest art museums over the period 2007-2020 as a vehicle for tangibly describing and discussing these issues. [ABSTRACT FROM AUTHOR]

The availability of accurate information has proved fundamental to managing health crises. This research examined pandemic data provided by 198 countries worldwide two years after the outbreak of the deadly Coronavirus in Wuhan, China. We compiled and reevaluated the consistency of daily COVID-19 infections with Benford's Law. It is commonly accepted that the distribution of the leading digits of pandemic data should conform to Newcomb-Benford's expected frequencies. Consistency with the law of leading digits might be an indicator of data reliability. Our analysis shows that most countries have disseminated partially reliable data over 24 months. The United States, Israel, and Spain spread the most consistent COVID-19 data with the law. In line with previous findings, Belarus, Iraq, Iran, Russia, Pakistan, and Chile published questionable epidemic data. Against this trend, 45 percent of countries worldwide appeared to demonstrate significant BL conformity. Our measures of Benfordness were moderately correlated with the Johns Hopkins Global Health Security Index, suggesting that the conformity to Benford's law may also depend on national health care policies and practices. Our findings might be of particular importance to policymakers and researchers around the world. [ABSTRACT FROM AUTHOR]

The world is currently struggling with a new type of coronavirus (2019-nCoV) pandemic that first appeared in Wuhan, China, and then spread to almost all countries. As in other countries of the world, public authorities in Turkey are implementing many preventive and mitigating partial lockdown (PL) actions against the virus's effects. Some decisions and policies implemented before and after March 11, 2020, when the first virus case has been identified, have reduced people and traffic circulation, which has also turned into some improvements in air quality. At this point, this study aims to investigate how this pandemic affects the air quality of a metropolis. A case study of the city of Istanbul, the most affected city with more than half of Turkey's cases, is performed. In our analysis, we observe, compare, and discuss the impact of the COVID-19 pandemic and PL decisions on Istanbul city's air quality. We consider the particulate matter (PM10), sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), nitrogen oxide (NO), nitrogen oxides (NOx), and ozone (O3) concentrations. We used data from 19 air monitoring stations (AMSs) and obtained improvements in the air quality for the pandemic period. In summary, the concentration levels in PM10, NO2, NO, and NOx result in a clear decline in pandemic times compared to the normal times in Istanbul. On the other hand, a non-homogenous trend for SO2 and CO concentrations is observed for different AMSs. A partial increase in O2 concentration is obtained in the comparison of before and during the PL period. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this research is to compare over the developing trend in Covid-19 research publications between 2020 and 2021 in India, overall in respect of age groups, health conditions, funding support, institutions, and research design. Background: Covid-19 is a contagious disease caused by severe acute respiratory syndrome coronavirus (SARS-CoV), first cited in Wuhan, China in December 2019. And has affected the entire world rapidly and still ongoing. The symptoms are fever, cough, weakness, and breathlessness; the infected individual develops pneumonia that sometimes leads to respiratory failure. The older population with co-morbidities is at higher risk. Methodology: This is a cross-sectional study done by Scopus, Web of Science, and Pubmed Indexed journals, with the Covid-19, SARS-CoV, Pandemic, Coronavirus, India, and Outburst as keywords. Yearly publication data were extracted through 'Bibliometrix R studio' and relative percentage was computed and linear or exponential regressions examined the yearly growth in the proportion to research publications on Covid-19. [ABSTRACT FROM AUTHOR]

A sequence of unexplained pneumonia cases was reported in December 2019 in Wuhan, China. After several analyses, in January 2020, the World Health Organization temporarily named this new virus as the 2019 novel coronavirus (2019-nCoV). It is found that 2019-nCoV is abundantly present in nasopharyngeal and salivary secretions of affected patients and it is found to be transmitted through the saliva and the feco–oral routes since its spread is through potential person-to-person transmission route which is predominantly thought to be respiratory droplet/contact in nature. Dental surgeons are at tremendous risk of 2019-nCoV infection due to the face-to-face communication and the exposure to aerosols, saliva, blood, and other body fluids and the handling of sharp instruments and also may encounter patients with suspected or confirmed severe acute respiratory syndrome coronavirus 2 infection and will have to act diligently not only to provide care but also at the same time prevent nosocomial spread of infection. The significance of this review is that oral health providers must be aware and be prepared to tackle any imminent infectious diseases like the present coronavirus disease 2019 outbreak, which can be life threatening to susceptible patients. This review provides a brief outline of the epidemiology, symptoms, mode of transmission, specific recommendations for dental practice that are suggested for patient screening, infection control strategies to block the person-to-person transmission routes in dental clinics and hospitals, and patient management protocol since there are certain chances of encountering oral emergencies such as acute pulpitis, periapical periodontitis, abscess, dental trauma, children's oral emergencies, and oro-maxillofacial infections during this period. [ABSTRACT FROM AUTHOR]

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The causative agent of COVID-19 is a novel betacoronavirus or severe acute respiratory syndrome coronavirus (SARS-CoV-2), which has emerged as a pandemic of global concern. Considering its rapid transmission, WHO has declared public health emergency on 11th March 2020 worldwide. SARS-CoV-2 is a genetically diverse positive sense RNA virus that typically exhibit high rates of mutation than DNA viruses. Higher rates of mutation bring higher genomic variability which may lead to viral evolution and enabling viruses to evade the pre-existing immunity of host and quickly acquire drug resistance properties. The objective of our study was to compare the SARS-CoV-2 RdRp sequences of Indian SARS-CoV-2 isolates with those of Wuhan type virus. A total of 384 point mutations were detected from 488 sequence of the RdRp protein of Indian SARS-CoV-2 genome, out of which seven were used for subsequent study. Furthermore, prediction of secondary structure, protein modeling and its dynamics were performed which revealed that seven mutations (R118C, T148I, Y149C, E802A, Q822H, V880I and D893Y) significantly altered the stability and flexibility of RdRp protein. Present study was therefore, undertaken to analyze the variations occurring in RdRp due to multiple mutations leading to the alterations in the structure and function of RNA-dependent RNA polymerase which is essential for the replication /transcription of this virus and hence can be utilized as a promising therapeutic target to curb SARS-CoV-2 infections. [ABSTRACT FROM AUTHOR]

SARS-CoV-2, a novel strain of coronavirus' also called coronavirus disease 19 (COVID-19), a highly contagious pathogenic respiratory viral infection emerged in December 2019 in Wuhan, a city in China's Hubei province without an obvious cause. Very rapidly it spread across the globe (over 200 countries and territories) and finally on 11 March 2020 World Health Organisation characterized it as a "pandemic". Although it has low mortality of around 3% as of 18 May 2021 it has already infected 164,316,270 humans with 3,406,027 unfortunate deaths. Undoubtedly the world was rocked by the COVID-19 pandemic, but researchers rose to all manner of challenges to tackle this pandemic by adopting the shreds of evidence of ML and AI in previous epidemics to develop novel models, methods, and strategies. We aim to provide a deeper insight into the convolutional neural network which is the most notable and extensively adopted technique on radiographic visual imagery to help expert medical practitioners and researchers to design and finetune their state-of-the-art models for their applicability in the arena of COVID-19. In this study, a deep convolutional neural network, its layers, activation and loss functions, regularization techniques, tools, methods, variants, and recent developments were explored to find its applications for COVID-19 prognosis. The pipeline of a general architecture for COVID-19 prognosis has also been proposed. This paper highlights recent studies of deep CNN and its applications for better prognosis, detection, classification, and screening of COVID-19 to help researchers and expert medical community in multiple directions. It also addresses a few challenges, limitations, and outlooks while using such methods for COVID-19 prognosis. The recent and ongoing developments in AI, MI, and deep learning (Deep CNN) has shown promising results and significantly improved performance metrics for screening, prediction, detection, classification, forecasting, medication, treatment, contact tracing, etc. to curtail the manual intervention in medical practice. However, the research community of medical experts is yet to recognize and label the benchmark of the deep learning framework for effective detection of COVID-19 positive cases from radiology imagery. [ABSTRACT FROM AUTHOR]

The SARS-CoV2 pandemic that began in December 2019 in Wuhan, China caused a major change in all areas of interest in our contemporary world, the medical system being particularly affected by the large number of infections, hospitalizations and deaths daily recorded. The impact of COVID-19 disease has affected the majority of medical specialities, dermatology and psychiatry being directly involved in the multitudeconsequences of this viral infection. Skin lesions related to the systemic disease are multiple, nonspecific and varied, reflecting the range of host immune responses to SARS-CoV2 infection. It appears that maculopapular, vesicular and urticarial eruptions are the most common lesions associated to COVID-19 disease. From a psychological point of view, both medical workers and patients were equally affected. On one hand, symptoms such as depression, anxiety, stress and posttraumatic stress disorder (PTSD) have been frequently reported among COVID-19 positive individuals and on the other hand, physical and mental exhaustion as well as burnout syndrome have affected the entire medical community. [ABSTRACT FROM AUTHOR]

The novel-coronavirus (2019-nCOV) emerged in Wuhan City in December 2019, this resulted in a quick and catastrophic health problem all over the world but our country is slightly less affected by the pandemic. There could be a number of reasons for less numbers of Covid positive cases and lack of awareness and reduced Covid testing capacity and hence less mortality in Pakistan. In order to provide assistance to an ever increasing number of infected patients and, at the same time taking care of urgent maxillofacial conditions. This manuscript gives the reader in a nutshell the overall surgical experience of oral and maxillofacial practice at Aga Khan University Hospital, Karachi, Pakistan in the COVID-19 pandemic and would like to provide a number of recommendations that would assist the scheduling process of surgical management during the COVID-19 pandemic and reduce the risk of infection among healthcare workers and others involved with the service. [ABSTRACT FROM AUTHOR]

Coronavirus is a potentially fatal disease that normally occurs in mammals and birds. Generally, in humans, the virus spreads through aerial droplets of any type of fluid secreted from the body of an infected person. Coronavirus is a family of viruses that is more lethal than other unpremeditated viruses. In December 2019, a new variant, i.e., a novel coronavirus (COVID-19) developed in Wuhan province, China. Since January 23, 2020, the number of infected individuals has increased rapidly, affecting the health and economies of many countries, including Pakistan. The objective of this research is to provide a system to classify and categorize the COVID-19 outbreak in Pakistan based on the data collected every day from different regions of Pakistan. This research also compares the performance of machine learning classifiers (i.e., Decision Tree (DT), Naive Bayes (NB), Support Vector Machine, and Logistic Regression) on the COVID-19 dataset collected in Pakistan. According to the experimental results, DT and NB classifiers outperformed the other classifiers. In addition, the classified data is categorized by implementing a BayesianRegularizationArtificialNeuralNetwork (BRANN) classifier. The results demonstrate that the BRANN classifier outperforms state-of-the-art classifiers. [ABSTRACT FROM AUTHOR]

Since it was first identified, the epidemic scale of the recently emerged novel coronavirus (2019-nCoV) in Wuhan, China, has increased rapidly, with cases arising across China and other countries and regions. Using a transmission model, we estimate a basic reproductive number of 3.11 (95% CI, 2.39-4.13), indicating that 58-76% of transmissions must be prevented to stop increasing. We also estimate a case ascertainment rate in Wuhan of 5.0% (95% CI, 3.6-7.4). The true size of the epidemic may be significantly greater than the published case counts suggest, with our model estimating 21 022 (prediction interval, 11 090-33 490) total infections in Wuhan between 1 and 22 January. We discuss our findings in the light of more recent information. [ABSTRACT FROM AUTHOR]

This article discusses the impact of the COVID-19 pandemic from the perspective of a large academic health system's Employee Assistance Program (EAP). It builds on a growing literature documenting the psychological effects experienced by Health Care Workers (HCW) during extreme and adverse circumstances. Although it is an observational study based on recent events in the United States, it reviews survey data collected in Wuhan China and New York City. The article discusses the operational responses of an EAP as it responded to the pandemic by adopting virtual communication platforms and developing innovative engagement strategies. It discusses the differential impacts experienced by front line and remote health care workers. A pre and post COVID-19 comparison of EAP activities will be offered. Lastly, the article explores practice implications for other EAPs. [ABSTRACT FROM AUTHOR]

Purpose: With the rapid spread of COVID-19 across the world, the consideration of effective communication strategies from Wuhan can provide valuable insight to other countries in how to manage their risk response. This study analyzes the building of a risk communication system in Wuhan, China, to aid cross-country comparison from a policy and academic perspective. Methods: We use complex adaptive systems theory (CAS) to theorize the communication strategy adopted by the government – led by the Hubei Province COVID-19 Epidemic Prevention and Control Headquarters. Using ethnographic fieldwork and discourse analysis, we observed and analyzed the online and offline communication process to formulate an overview of the communications platforms used in Wuhan. Results: Wuhan's adaptive crisis communication system was backed by digital and offline infrastructure, human resources support, policy development, as well as access to scientific and technological expertize. The Wuhan municipal government adapted its communication strategy in response to public feedback, and created mechanisms to ensure that two-way communication was used to drive policy and integrate feedback from the government, enterprises, social organizations and the public. Wuhan's risk and crisis communication strategy aimed to meet emergency commitments, recover trust, regulate the public's emotions and build consensus, operating within a complex adaptive system (CAS). Conclusion: By using complex adaptive system (CAS) theory, we argue that Wuhan quickly built an adaptive communication system consisting of five elements: 1) non-linear information output, 2) online and offline continuous support systems, 3) a public emotional support system, 4) multi-subject information interaction platforms and 5) a context-based approach. [ABSTRACT FROM AUTHOR]

What started with 41 hospitalized patients identified as having laboratory-confirmed coronavirus disease 2019 (COVID-19) in Wuhan, China, by January 2, 2020, turned into an unprecedented pandemic with more than 113 million confirmed cases and a mortality exceeding 2.5 million deaths worldwide by the beginning of March 2021. Although the course of the disease is uneventful in most cases, there is a percentage of patients who become critically ill and need admission in the intensive care unit for severe respiratory failure. Numerous of these patients undergo invasive mechanical ventilation and have an extremely high mortality rate. For these patients, extracorporeal membrane oxygenation (ECMO) has emerged as a last standing resource. In the present study, the literature was reviewed to evaluate the worldwide data regarding the use of ECMO in the management of critically ill COVID-19 patients. ISI Thomson Web of Science was searched for articles with English language abstracts from inception to March 1, 2021, with 'ECMO in COVID-19' as key words. A total of 214 abstracts were screened (case reports, guidelines, reviews) and the most relevant articles were included in the present review. The use of ECMO in the management of critically ill patients with COVID-19-induced acute respiratory distress syndrome refractory to conventional mechanical invasive ventilation is increasing. By increasing the survival rate from less than 20% to more than 50%, ECMO proved to be a valuable resource in the management of the most challenging critically ill COVID-19 patients. [ABSTRACT FROM AUTHOR]