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The 2019 outbreak of coronavirus disease (COVID‐19) in Wuhan (Hubei province of China) has given rise to a pandemic spread of virus, more than 240 million incidences and a death toll larger than 5 million people. COVID‐19 has set off large efforts in research, therapy and patient care, as well as public and private debates in every imaginable form. A number of scientists used the publication platforms provided by the Federation of the European Biochemical Societies (FEBS) to present their research data, reviews, opinions and other contributions relating to COVID‐19 and severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). Here, I highlight the recent COVID‐19 papers which have been published and collected in a Virtual Issue in FEBS Letters, and discuss their implications towards understanding the molecular, biochemical and cellular mechanisms of SARS‐CoV‐2 infections, vaccine development and antiviral discovery strategies. [ABSTRACT FROM AUTHOR]

Infectious diseases are among the leading causes of mortality worldwide. A new coronavirus named severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) was identified in Wuhan, China in 2019, and the World Health Organization (WHO) declared its outbreak, coronavirus disease 2019 (COVID-19), as a global pandemic in 2020. COVID-19 can spread quickly from person to person. One of the most challenging issues is to identify the infected individuals and prevent potential spread of SARS-CoV-2. Recently, anti-SARS-CoV-2 immunoglobulin M (IgM) and immunoglobulin G (IgG) antibody tests using immunochromatographic methods have been used as a complement to current detection methods and have provided information of the approximate course of COVID-19 infection. However, blood sampling causes pain and poses risks of infection at the needle puncture site. In this study, a novel patch sensor integrating porous microneedles and an immunochromatographic assay (PMNIA) was developed for the rapid detection of anti-SARS-CoV-2 IgM/IgG in dermal interstitial fluid (ISF), which is a rich source of protein biomarkers, such as antibodies. Biodegradable porous microneedles (MNs) made of polylactic acid were fabricated to extract ISF from human skin by capillary effect. The extracted ISF was vertically transported and flowed into the affixed immunoassay biosensor, where specific antibodies could be detected colorimetrically on-site. Anti-SARS-CoV-2 IgM/IgG antibodies were simultaneously detected within 3 min in vitro. Moreover, the limit of detection of anti-SARS-CoV-2 IgM and IgG concentrations was as low as 3 and 7 ng/mL, respectively. The developed device integrating porous MNs and immunochromatographic biosensors is expected to enable minimally invasive, simple, and rapid anti-SARS-CoV-2 IgM/IgG antibody testing. Furthermore, the compact size of the MN and biosensor-integrated device is advantageous for its widespread use. The proposed device has great potential for rapid screening of various infectious diseases in addition to COVID-19 as an effective complementary method with other diagnostic tests. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic originating in the Wuhan province of China in late 2019 has impacted global health, causing increased mortality among elderly patients and individuals with comorbid conditions. During the passage of the virus through affected populations, it has undergone mutations, some of which have recently been linked with increased viral load and prognostic complexities. Several of these variants are point mutations that are difficult to diagnose using the gold standard quantitative real-time PCR (qRT-PCR) method and necessitates widespread sequencing which is expensive, has long turn-around times, and requires high viral load for calling mutations accurately. Here, we repurpose the high specificity of Francisella novicida Cas9 (FnCas9) to identify mismatches in the target for developing a lateral flow assay that can be successfully adapted for the simultaneous detection of SARS-CoV-2 infection as well as for detecting point mutations in the sequence of the virus obtained from patient samples. We report the detection of the S gene mutation N501Y (present across multiple variant lineages of SARS-CoV-2) within an hour using lateral flow paper strip chemistry. The results were corroborated using deep sequencing on multiple wild-type (n = 37) and mutant (n = 22) virus infected patient samples with a sensitivity of 87% and specificity of 97%. The design principle can be rapidly adapted for other mutations (as shown also for E484K and T716I) highlighting the advantages of quick optimization and roll-out of CRISPR diagnostics (CRISPRDx) for disease surveillance even beyond COVID-19. This study was funded by Council for Scientific and Industrial Research, India. [ABSTRACT FROM AUTHOR]

What started as a cluster of patients with a mysterious respiratory illness in Wuhan, China, in December 2019, was later determined to be coronavirus disease 2019 (COVID-19). The pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel Betacoronavirus, was subsequently isolated as the causative agent. SARS-CoV-2 is transmitted by respiratory droplets and fomites and presents clinically with fever, fatigue, myalgias, conjunctivitis, anosmia, dysgeusia, sore throat, nasal congestion, cough, dyspnea, nausea, vomiting, and/or diarrhea. In most critical cases, symptoms can escalate into acute respiratory distress syndrome accompanied by a runaway inflammatory cytokine response and multiorgan failure. As of this article's publication date, COVID-19 has spread to approximately 200 countries and territories, with over 4.3 million infections and more than 290,000 deaths as it has escalated into a global pandemic. Public health concerns mount as the situation evolves with an increasing number of infection hotspots around the globe. New information about the virus is emerging just as rapidly. This has led to the prompt development of clinical patient risk stratification tools to aid in determining the need for testing, isolation, monitoring, ventilator support, and disposition. COVID-19 spread is rapid, including imported cases in travelers, cases among close contacts of known infected individuals, and community-acquired cases without a readily identifiable source of infection. Critical shortages of personal protective equipment and ventilators are compounding the stress on overburdened healthcare systems. The continued challenges of social distancing, containment, isolation, and surge capacity in already stressed hospitals, clinics, and emergency departments have led to a swell in technologically-assisted care delivery strategies, such as telemedicine and web-based triage. As the race to develop an effective vaccine intensifies, several clinical trials of antivirals and immune modulators are underway, though no reliable COVID-19-specific therapeutics (inclusive of some potentially effective single and multi-drug regimens) have been identified as of yet. With many nations and regions declaring a state of emergency, unprecedented quarantine, social distancing, and border closing efforts are underway. Implementation of social and physical isolation measures has caused sudden and profound economic hardship, with marked decreases in global trade and local small business activity alike, and full ramifications likely yet to be felt. Current state-of-science, mitigation strategies, possible therapies, ethical considerations for healthcare workers and policymakers, as well as lessons learned for this evolving global threat and the eventual return to a "new normal" are discussed in this article. [ABSTRACT FROM AUTHOR]

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]

Background: In December of 2019, a new disease which is caused by SARS-CoV-2, as an epidemic disease out of Wuhan, China, began to circulate. On March 11, 2020, the Republic of Turkey Ministry of Health had announced the first case from Turkey. The aim of this study is to analyze the scientific publications in the field of COVID-19 included in Science Citation Index Expanded (SCIE) from Turkey and to establish a theoretical background for future studies in the health literature with obtained valuable information about the publications. We searched all papers published in the field of COVID-19 by using the terms of "COVID-19," "2019-n-CoV," "SARS-CoV-2," "Coronavirus disease 19," and "2019 novel coronavirus" as scientific nomenclatures of COVID-19 in the topic search section of the software. Results: Overall, 47,368 papers, indexed by SCI-E, were found related to COVID-19 between January 1, 2020, and December 13, 2020. Of these, 931 were from Turkey. In terms of specialities, the most contribution was from the Medicine General Internal followed by Dermatology. Most of the publications were article. English was the most preferred language in papers. Dermatological Theraphy published the most paper. Conclusions: Applying this kind of analysis on an intermittent basis gives a general perspective for contribution of a countries to scientific publications and useful for the further studies. [ABSTRACT FROM AUTHOR]

Background: Since the COVID-19 pandemic outbreak, diabetes mellitus (DM) has been at the core of the confirmed risk factors for fatal or critical care unittreated COVID-19 and COVID-19 related complications. Although relevant studies on DM have developed rapidly during the COVID-19 pandemic. However, the aforementioned research results have not been systematically quantified by means of bibliometric analysis. Purpose: The purpose of this study is to provide a comprehensive analysis of the current status and trends of publications related to DM research during the COVID19 epidemic. Methods: A bibliometric analysis was performed using the Web of Science database. In this study, we used citespace, R software and R-Bibliometrix to analyze keywords, most-cited authors, most-cited countries, most-cited global documents, and co-occurrence and co-citation networks. Results: A total of 1688 publications was included in this study. Investigators from the United States contributed the most publications. The United States, China and Europe have the most collaboration with the other countries/regions. A total of 3355 institutions made contributions to this study. Of the top 10 institutions with the most publications, N8 Research Partnership showed the most centrality. Among the top 10 journals, Diabetes Research and Clinical Practice published the most articles. Among authors included, Khunti Kamlesh is rated first with 27 papers and has the highest centrality. The most frequently co-cited article is entitled "Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study". The most popular keywords included diabetes, mortality, diabetes, outcome, occurrences, risk, and type 1 diabetes. Conclusion: This bibliometric study provides an overall picture of DM research and research trends during the COVID-19 pandemic and provides a basis for researchers to develop their next research strategies. [ABSTRACT FROM AUTHOR]

Since 2019n-CoV has swept the whole world, people's daily life is affected seriously and the public space system is facing to major challenges. Community public space should be re-evaluated and optimized as people change the way they use it. This research conducted a nationwide questionnaire survey in China, which collected the opinions of residents on community public space from the perspective of epidemic prevention and social needs to select research indicators. The questionnaire data were processed through the frequency analysis method, precedence chart and TOPSIS. We found: (1) under the background of the epidemic, people pay more attention to the satisfaction of leisure activities in community public space and the control of social spacing during activities; (2) the current assessment result of community public space risk of 2019-nCoV exposure is 0.386, which is relatively high; and (3) the exposure risk of the community can be effectively reduced by controlling for the two indicators of social distance and social facility sterilization. When the social distance is 1.8–3 m and the facility sterilization is once a day, the community public space minimizes exposure risk. In view of the above results, this paper selects the Wuhan start-up area of Wuhan, China, as an example for carrying out the optimization design of community public space based on the epidemic and the design the space optimization model for the two indicators of social distance and facility disinfection from both software and hardware aspects. [ABSTRACT FROM AUTHOR]

Objective: Coronavirus disease 19 (COVID-19) is a highly contagious disease characterized by severe respiratory infection by SARS-CoV-2 virus. COVID-19 was first appeared in Wuhan, China, in December 2019 and then rapidly became a global pandemic from a local outbreak. The present study aims to present the rapid increase of scientific productivity on COVID-19 by comparing publications of the first quarter with the first half of 2020. Materials and Methods: Web of Science (WoS) software was used for the search and the analysis. To compare scientific productivity of two periods as the first quarter and the first half of the pandemic era, all scientific papers published about COVID-19 included in Science Citation Index Expanded (SCI-E) from January 1st to April 5th and from January 1st to July 9th of 2020 were searched using the following terms: "COVID-19","2019-n-CoV","SARS-CoV-2","Coronavirus disease 19" and "2019 novel coronavirus" as nomenclatures of COVID-19. Results: Overall, 337 and 11.704 scientific papers related to COVID-19, indexed by SCI-E, were found in the first quarter and the first half of 2020, respectively. While the biggest contribution for publications was from People's Republic of China (PRC) in the first quarter and was from the USA in the first half of 2020 for COVID-19. Conclusion: We found a close correlation between the rapid acceleration of scientific papers and turning the disease from a local outbreak to a global pandemic. Since sharing experiences is as important as struggling with these kinds of novel diseases, we believe that encouraging researchers to make scientific publications for others is more important than ever in the circumstances like this. [ABSTRACT FROM AUTHOR]

Background In present study, we aimed to identify and evaluate the trends of the 100 most-cited research articles on Covid-19 using bibliometric techniques. Methods Scopus online database hosted by Elsevier was used to extract relevant articles for this study. We have identified the top 100 research papers on Covid-19 published across the globe based on their citations data. Further analysis was made to find the countries of origin, journal wise distribution, author cluster network, keyword analysis and inter-citation map to understand and to establish the links among them. Results The T100 most cited articles were published between January-August, 2020 and their citations ranged from 304 to 5295. The T100 articles were contributed by 24 countries, with more than half is originated from China (n=63). Scientific publications originated from china received highest citations (55,688) followed by USA (13,996) and Hong Kong (9,501). Although 'New England Journal of Medicine' published the most papers (n=15) with the highest impact factor value of (74.699) but studies published in 'The Lancet' has received highest citations (n=18,431). The top five journals hold 44% of these influential studies. The Tongji medical college, Huazhong University of science & technology, Wuhan, China is the top institution with the most T100 articles in the field of Covid-19. Conclusions We analyzed the 100 most-cited articles in the field of Covid-19. China and USA are the dominant countries in terms of the number of T100 articles, scientists and institutions. Scientific publications originated from China also had the highest mean number of citations. The leading institutions with the most productive articles were Tongji medical college, Jin yin-tan hospital, Tsinghua university school of medicine (China) and University of North Carolina (US). The study authored by Huang C on "Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China" has received maximum citations (5295). [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic was an unprecedented global event in recent history. Beginning with an initial outbreak in Wuhan, China, in December 2019, the virus spread rapidly across the globe, causing millions of deaths and triggering an unprecedented health, economic, and social crisis. The initial response to the outbreak in many countries was the implementation of social distancing measures, including the closure of schools and businesses, the cancellation of mass events, and the banning of travel. These measures were aimed at reducing the virus' spread and preventing health systems from being overwhelmed by the numerous severe COVID-19 cases. However, these measures also had a devastating economic impact, especially on precarious workers and freelancers, as well as those who were unable to work from home. As the pandemic (also considered a syndemic or synergistic epidemic) dragged on, countries adopted more flexible approaches to dealing with the virus, adopting mitigation measures rather than social distancing measures. These included the use of masks, testing and contact tracing, and the opening of businesses and schools with the implementation of additional safety measures. This paper highlights the social consequences of the pandemic, ultimately arguing that it is a total social fact (from the French fait social total), based on Marcel Mauss' categorization, since it encompassed and impacted all facets of human life. [ABSTRACT FROM AUTHOR]

Background: In early infected or severe coronavirus disease 2019 (COVID-19) patients, circulating NK cells are consistently reduced, despite being highly activated or exhausted. The aim of this paper was to establish whether severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein (SP) may directly trigger NK cells and through which receptor(s). Methods: SP-stimulated human NK cells have been evaluated for the expression of activation markers, cytokine release, and cytotoxic activity, as well as for gene expression profiles and NF-κB phosphorylation, and they have been silenced with specific small interfering RNAs. Results: SPs from the Wuhan strain and other variants of concern (VOCs) directly bind and stimulate purified NK cells by increasing activation marker expression, cytokine release, and cytolytic activity, prevalently in the CD56brightNK cell subset. VOC-SPs differ in their ability to activate NK cells, G614, and Delta-Plus strains providing the strongest activity in the majority of donors. While VOC-SPs do not trigger ACE2, which is not expressed on NK cells, or other activating receptors, they directly and variably bind to both Toll-like receptor 2 (TLR2) and TLR4. Moreover, SP-driven NK cell functions are inhibited upon masking such receptors or silencing the relative genes. Lastly, VOC-SPs upregulate CD56dimNK cell functions in COVID-19 recovered, but not in non-infected, individuals. Conclusions: TLR2 and TLR4 are novel activating receptors for SP in NK cells, suggesting a new role of these cells in orchestrating the pathophysiology of SARS-CoV-2 infection. The pathogenic relevance of this finding is highlighted by the fact that free SP providing NK cell activation is frequently detected in a SARS-CoV-2 inflamed environment and in plasma of infected and long-COVID-19 subjects. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has dramatically influenced urban mobility patterns. This paper explores the impact of the pandemic on the travel patterns and flow structure of bike-sharing, in Wuhan, China. We find that during the pandemic, bike-sharing became a major mode of transport and helped for the travel to hospitals. In post-pandemic Wuhan, more cycling neighbourhoods emerged in suburban areas, and the percentage of bike-sharing ridership in denser areas tends to decrease, especially in workplaces, indicating there is a decentralised trend of bike-sharing flow structure. These findings imply that bike-sharing plays a significant role in promoting urban resilience, and are helpful to build bike-friendly cities and communities in the post-pandemic era. [ABSTRACT FROM AUTHOR]

The rise of e-commerce, bolstered by advances in information and communication technology (ICT), has made it possible for consumers to shop online without the need to physically travel. The unexpected emergence of COVID-19 further accelerated this shift towards online shopping. This paper compares virtual versus physical access to goods, drawing from dual access theory. It aims to offer a comprehensive understanding of the disparities in accessibility between digital and brick-and-mortar shopping experiences. Our results indicate that, when considering the complete private costs — including the intrinsic costs of shopping and those incurred en-route like travel and delivery fees — online shopping typically offers greater accessibility and is more cost-effective than its in-store counterpart. While physical access to shopping displays a pattern where the central city has a distinct advantage over the suburbs, virtual access presents a more uniform distribution throughout the city. [ABSTRACT FROM AUTHOR]

First described in December 2019 in Wuhan (China), COVID-19 disease rapidly spread worldwide, constituting the biggest pandemic in the last 100 years. Even if SARS-CoV-2, the agent responsible for COVID-19, is mainly associated with pulmonary injury, evidence is growing that this virus can affect many organs, including the heart and vascular endothelial cells, and cause haemostasis, CNS, and kidney and gastrointestinal tract abnormalities that can impact in the disease course and prognosis. In fact, COVID-19 may affect almost all the organs. Hence, SARS-CoV-2 is essentially a systemic infection that can present a large number of clinical manifestations, and it is variable in distribution and severity, which means it is potentially life-threatening. The goal of this comprehensive review paper in the series is to give an overview of non-pulmonary involvement in COVID-19, with a special focus on underlying pathophysiological mechanisms and clinical presentation. [ABSTRACT FROM AUTHOR]

Since the outbreak of the global COVID-19 pandemic in Wuhan, China, in 2019, its impact has been seen worldwide. Early identification of COVID-19 is very crucial, as it keeps the infected people isolated from other people, thus minimizing the risk of further transmission. The standard diagnostic approach is based on RT-PCR. However, due to the scarcity of PCR kits in some regions and the costs associated with this technique, there is a growing demand for alternative solutions. Recently, diagnosis of COVID-19 by medical imaging has been recognized as a valid clinical practice. Meanwhile, the massive increase in COVID-19 cases has put considerable pressure on radiologists responsible for interpreting these scans. This paper introduces an automated detection approach as a rapid alternative for COVID-19 diagnosis. We present a deep CNN model to differentiate between normal and pneumonia cases, as well as patients with COVID-19. Our approach is based on EfficientNet-B7 architecture and improved with Squeeze and Excitation block as an attention mechanism. In addition, we propose an innovative architecture that combines CNN with SVM to achieve the best performance. Experimental results show that the proposed framework provides better performance than existing SOTA methods, with an average accuracy of 97.50%, while the precision and recall of COVID-19 are both 100%. [ABSTRACT FROM AUTHOR]

In December 2019, COVID-19 appeared for the first time in Wuhan (Hubei Province, China), after which it quickly spread over the entire earth. The World Health Organization quickly designated COVID-19 a pandemic because of the high number of deaths and rapid global spread of the disease. Because of this, many facets of society have been impacted, and those effects may last for years to come. Therefore, COVID-19 detection methods were the focus of numerous research projects in the past. This has led to the development of the COVID-19 AI Detector, a specialized area of artificial intelligence-based research. In this paper, we survey all significant current efforts that have taken advantage of machine learning to COVID-19 detection and prediction. We first surveyed all datasets used in relevant research, and then summarized them in a table containing the link to that data. Then, we mention all the methodologies employed to detect the presence of COVID-19 using such datasets. Later, the challenges and difficulties that facing the concerned researches are reported, while the results of all interesting related work that lay ahead in this field before concluding this paper were discussed fairly. [ABSTRACT FROM AUTHOR]

COVID-19 is a contagious disease caused by a virus, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The first known case was identified in Wuhan, China, in December 2019. The disease quickly spread worldwide, resulting in the COVID-19 pandemic. The outburst of COVID-19 pandemic had tremendous effect on the whole world and analysis of the data can be meaningful in many ways to better understand the effect it had in our society. This paper aims in the direction of analysis of COVID-19 daily information based on country and continent level in terms of understanding the number of cases and deaths and their relationship, besides this is aims to better understand the vaccination number by country and effect of cases/death how they have affected these numbers. The solution was created on analysis of a dataset that contains daily information on each country, and using MySQL, SQL and PowerBi to generate the results for this work in way of query results which have been transformed to visuals using PowerBi for better understanding for further research work on this topic. [ABSTRACT FROM AUTHOR]

The Covid-19 first occurs in Wuhan, China in December 2019. After that, the virus has spread all over the world and at the time of writing this paper the total number of confirmed cases are above 11 million with over 600,000 deaths. The pattern recognition of complex environment can be used to determine if a COVID-19 breath pattern can be established with accuracy. The traditional decorative pattern detection method has a high degree of recognition in simple scene. However, the efficiency of decorative pattern detection in complex scenes is low and the recognition accuracy is not high. Firstly, the evaluation index of target detection method is designed. Through this paper, it is found that the success rate of some targets is naturally better than other targets, and easy to distinguish from the background. In order to improve the recognition success rate of the object in the complex environment and determine the position and attitude of the object, the pattern as the artificial identification in the environment is proposed. The interior art decoration pattern is selected as the experimental pattern and the pattern classification evaluation index is designed. The experimental results show that the method proposed in this paper can optimize the pattern subsets which are confused with each other and easy to distinguish from the background. It has a certain reference value for decorative pattern recognition in complex environment for COVID-19 epidemic. [ABSTRACT FROM AUTHOR]

This paper addresses the reactions of domestic helpers to the Wuhan (Hubei Province) lockdown that began on January 23, 2020. We use a novel dataset containing the information of over 40,000 Chinese domestic helpers registered on a leading professional website from November 2019 to June 2020. The results indicate a declining pattern of short-term labor supply of domestic helpers across 11 major Chinese cities, which shows an increase in the expected monthly wage of domestic helpers in these cities. More importantly, using a difference-in-difference (DID) model, this paper provides some evidence on the existence of labor market discrimination against domestic helpers born in Hubei Province due to employers' fear of infection. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19 in 2020 caused extensive impact on household income and foods consumption. However, little attention has been paid to the immediate impact of income loss on animal-source foods consumption in the early stage of the COVID-19 pandemic. This paper aims to narrow this gap, and a total of 1301 valid samples of household food consumption surveys in Wuhan and Nanjing were obtained through specially designed online questionnaires. The surveys show that there were 69.6% (Wuhan) and 42.2% (Nanjing) of surveyed households whose animal-source foods consumption were affected, and there were 47.4% (Wuhan) and 18.9% (Nanjing) of surveyed households who suffered income loss. Furthermore, this paper makes an empirical study on the linkage between income loss and animal-source foods consumption. The results show that the pandemic affected household income, resulting in an immediate impact on animal-source foods consumption. This immediate impact may have been due to the combination of price increases, income loss and insufficient savings, which led to a "perfect storm" for animal-source foods consumption. Moreover, household income loss affected various animal-source foods consumption differently. For households suffering income losses, the odds of pork, beef and mutton, poultry, aquatic products, eggs and dairy products consumption being affected were increased by a factor of 1.894, 2.140, 2.773, 2.345, 1.802, 2.835, respectively, holding other variables constant. The results may be related to residents' consumption habits and food prices. During the COVID-19 pandemic, the reduction of animal-source foods consumption may have led to a state of tension concerning an increase in the development of nutrition intake and health, which may have led to increased food security risks. [ABSTRACT FROM AUTHOR]

When COVID-19 broke out in Wuhan in 2019, the world looked at it as a 'Chinese' disease and no international efforts were made to assist China. Countries that supported China just offered solidarity messages. China adopted a raft of measures that included prescribing a lockdown on Wuhan and construction of infrastructure such as hospitals. In 2020, COVID-19 extended its tentacles across the globe. A majority of countries adopted the lockdown as a mitigatory measure. The lockdown activated an extraordinary instant emergency in the education sector as schools, colleges and universities shut down. What worsened the situation was that no solution was in sight as the medical researchers dithered from one suggestion to the other. This paper examines possible ways to deal with the emergency in the education sector by suggesting alternative learning solutions. The major argument of this paper is that countries should not simply copy and paste solutions that are not in sync with their local settings. Using the multistage designs sampling technique, three universities from a target of eighteen were selected. Convenience sampling was used to select the three universities and analytic rubrics were used to analyse clarity of policy and disaster preparedness by universities in Zimbabwe. For comparative purposes, four international webinars on education and COVID-19 were selected. This paper contributes towards addressing the lacunae created by global lockdown and subsequent shutdown of learning institutions due to COVID-19. The findings were that the lockdown approach was adopted and implemented without adaptation. Learning institutions were closed indefinitely despite the economic environment, the digital divide and the rural-urban divide militating against lockdown's entire adoption. Key proposals to deal with the lockdown include scaling up distance education based on mixed technologies, a paradigm shift on perceptions on digital education and resuscitation of postal services. [ABSTRACT FROM AUTHOR]

The recently discovered coronavirus, SARS-CoV-2, which was detected in Wuhan, China, has spread worldwide and is still being studied at the end of 2019. Detection of COVID-19 at an early stage is essential to provide adequate healthcare to affected patients and protect the uninfected community. This paper aims to design and develop a novel ensemble-based classifier to predict COVID-19 cases at a very early stage so that appropriate action can be taken by patients, doctors, health organizations, and the government. In this paper, a synthetic dataset of COVID-19 is generated by a dataset generation algorithm. A novel ensemble-based classifier of machine learning is employed on the COVID-19 dataset to predict the disease. A convex hull-based approach is also applied to the data to improve the proposed novel, ensemble-based classifier's accuracy and speed. The model is designed and developed through the python programming language and compares with the most popular classifier, i.e., Decision Tree, ID3, and support vector machine. The results indicate that the proposed novel classifier provides a more significant precision, kappa static, root means a square error, recall, F-measure, and accuracy. [ABSTRACT FROM AUTHOR]

Aim: Coronavirus disease, caused by severe acute respiratory syndrome coronavirus 2, originated in December 2019 from Wuhan, China. This virus has spread worldwide, with over 16 million cases and over 650,000 deaths. The novel coronavirus disease (COVID‐19) has resulted in significant impact on the livelihoods of the British public and has had implications for healthcare delivery. The cancellation of elective procedures is likely to affect oral surgery (OS) specialty training. This paper aims to ascertain the extent of any impact of COVID‐19 on OS specialty training. Material and Methods: A survey was created for OS specialty trainees in the United Kingdom. A variety of questions were used, including multiple choice, yes/no, Likert scales and free text answers. All questions were related to the impact of COVID‐19 on training. Results: A total of 34 full responses were recorded. Results showed that COVID‐19 has had an immense impact on OS training, with most trainees recording high anxiety levels regarding the future of their training. The overall experience of most trainees involved a reduction in logbook procedures, cancelled study days, courses or conferences. Conclusion: Although OS training has been deficient during this period, some trainees reported positive experiences while redeployed in other fields or specialties. In addition, we highlight the significant effect on trainees' mental health. Most trainees suggested a training period extension to remedy deficiencies. From this paper, we identify the wide‐ranging effects of the pandemic, and OS trainees now await decisions on the future of specialty training. [ABSTRACT FROM AUTHOR]

BACKGROUND: Coronaviruses (CoVs) belong to the RNA viruses family. The viruses in this family are known to cause mild respiratory disease in humans. The origin of the novel SARSCOV2 virus that caused the coronavirus-19 disease (COVID-19) is the Wuhan city in China from where it disseminated to cause a global pandemic. Although lungs are the predominant target organ for Coronavirus Disease-19 (COVID-19), since its outbreak, the disease is known to affect heart, blood vessels, kidney, intestine, liver and brain. This review aimed to summarize the catastrophic impacts of Coronavirus disease-19 on heart and liver along with its mechanisms of pathogenesis. METHODS: The information used in this review was obtained from relevant articles published on PubMed, Google Scholar, Google, WHO website, CDC and other sources. Key searching statements and phrases related to COVID-19 were used to retrieve information. Original research articles, review papers, research letters and case reports were used as a source of information. RESULTS: Besides causing severe lung injury, COVID-19 has also been reported to affect and cause dysfunction of many other organs. COVID-19 infection can affect people by downregulating membrane-bound active angiotensin-converting enzyme (ACE). People who have deficient ACE2 expression are more vulnerable to COVID-19 infection. The patients' pre-existing co-morbidities are major risk factors that predispose individuals to severe COVID-19. CONCLUSION: The disease severity and its broad spectrum phenotype is a result of combined direct and indirect pathogenic factors. Therefore, protocols that harmonize many therapeutic preferences should be the best alternatives to de-escalate the disease and obviate deaths caused as a result of multiple organ damage and dysfunction induced by the disease. [ABSTRACT FROM AUTHOR]

The search-engine-based surveillance methods for the early warning and prediction of infectious diseases cannot achieve search engine keywords automatic filtering and real-time updating, lead to powerless for the early warning of emerging infectious diseases. The aim of this study is to develop an artificial intelligence (AI) method for search-engine-based surveillance to improve the early warning ability for emerging infectious diseases. The 32 keywords (444 million search queries) that may be related to the coronavirus disease (COVID-19) outbreak was collected from December 18, 2019 to February 11, 2020 from Baidu's search engine database. The graph convolution network (GCN) model was used to select search engine keywords automatically, and then, multiple linear regression was performed to explore the relationship between the daily query frequencies of keywords and daily new cases. The GCN model was used to automatically select keywords. The prediction trend of the GCN model was highly consistent with the true curve with a mean absolute error of 81.65. Three keywords including "epidemic", "mask" and "coronavirus" were selected. The selection keywords in the search queries were highly correlated with the daily number of confirmed cases (r = 0.96, 0.94, and 0.89; P < 0.01). An abnormal initial peak (3.05 times the normal volume) in queries appeared on December 31, 2019, which could have served as an early warning signal for an outbreak. Of particular concern, 17.5% of query volume originated from the Hubei Province, 51.15% of which was from Wuhan City. The coefficients of determination (R2) of our constructed model were 0.88, 0.88, 0.84, 0.77, 0.77, 0.75, 0.73, and 0.73 for a time lag of 0–7 days, respectively, using selection keywords. The model we constructed was used in the Beijing Xinfadi outbreak as an independent test dataset, which successfully predicted the daily numbers of cases for the following days and detected an early signal during the Beijing Xinfadi outbreak (R2 = 0.79). In this paper search-engine-based surveillance based on the AI method was established for the early detection of the COVID-19 epidemic for the first time. The model achieves automatic filtering and real-time updating of search engine keywords and can effectively detect the early signals of emerging infectious diseases. [ABSTRACT FROM AUTHOR]

Corona virus disease (Covid-19) has out broken in Wuhan, China in Dec-2019 latter was declared Pandemic by WHO on 11 March 2020. The pandemic has caused distressing and devastating economic, health and social impact worldwide. The pandemic has caused one of the most serious Economic and public health crises faced by South Asian Countries. The present paper aims to analyse the impact of Covid-19 on India's trade with South Asian Countries based on the reports and data collected from Ministry of Industries and Trade Govt. of India. Out of the total exports of India to all the countries of the world the share of export to South Asian countries were 7.681% in 2018-19 just before Covid-19 which has fallen to 7.0036% in 2019-20 which gradually rises to 7.5495% in 2020-21 and 8.1451% in 2021-22. Similarly imports has also been affected by the pandemic out of the total imports of India to all the countries of the world the share of import to South Asian countries were 0.8502% in 2018-19 and deteriorated to .8094% in 2019-20 which gradually improves and started to rise in 2020-21 it was 0.8555% which has further increased to 0.9174% in 2021-22. [ABSTRACT FROM AUTHOR]

A new mathematical model of Coronavirus (2019-nCov) using piecewise hybrid fractional order derivatives is given in this paper. Moreover, in order to be consistent with the physical model problem, a new parameter μ is presented. The boundedness, existence, and positivity of the solutions for the proposed model are discussed. Two improved numerical methods are presented in this paper. The Caputo proportional constant nonstandard modified Euler–Maruyama method is introduced to study the fractional stochastic model, and the Grünwald–Letnikov nonstandard finite difference method is presented to study the hybrid fractional order deterministic model. Comparative studies with real data from Spain and Wuhan are presented. [ABSTRACT FROM AUTHOR]

Corona disease outbreak occurred in 2019 from Wuhan, China and the virus was identified to be a member of the Coronaviridae family. the disease was named as COVID-19 by WHO. It started as an epidemic and later turned into pandemic affecting world wide. the present study was aimed to conduct a systematic review on most prevalent symptoms associated with COVID-19 and to perform a Meta analysis on Comorbidities associated with COVID-19 and assess its severity in underlying conditions. A literature search was conducted using search engines like Pubmed, Web of Science, elsevier COVID-19 and litCovid databases between January 2020 and August 2020. Forest plot analysis was done using Random effects model to estimate odds ratio and 95% confidence interval estimate. 83 papers with a sample size of 19,454 were analysed statistically after adopting inclusion and exclusion criteria. Our results showed the most prevalent symptoms to be Cough, fever, dyspnoea and myalgia in interval plot analysis. When assessed for comorbidities, Hypertension (27.02%), Diabetes (16.61%), Cardiovascular disease (12.75%) and Chronic pulmonary obstructive diseases(19.9%) are found to be the most risk factors in comparison with others. However a finer experimental research needs to be confirmed with the findings. [ABSTRACT FROM AUTHOR]

The recent COVID-19 pandemic, which broke at the end of the year 2019 in Wuhan, China, has infected more than 98.52 million people by today (January 23, 2021) with over 2.11 million deaths across the globe. To combat the growing pandemic on urgent basis, there is need to design effective solutions using new techniques that could exploit recent technology, such as machine learning, deep learning, big data, artificial intelligence, Internet of Things, for identification and tracking of COVID-19 cases in near real time. These technologies have offered inexpensive and rapid solution for proper screening, analyzing, prediction and tracking of COVID-19 positive cases. In this paper, a detailed review of the role of AI as a decisive tool for prognosis, analyze, and tracking the COVID-19 cases is performed. We searched various databases including Google Scholar, IEEE Library, Scopus and Web of Science using a combination of different keywords consisting of COVID-19 and AI. We have identified various applications, where AI can help healthcare practitioners in the process of identification and monitoring of COVID-19 cases. A compact summary of the corona virus cases are first highlighted, followed by the application of AI. Finally, we conclude the paper by highlighting new research directions and discuss the research challenges. Even though scientists and researchers have gathered and exchanged sufficient knowledge over last couple of months, but this structured review also examined technological perspectives while encompassing the medical aspect to help the healthcare practitioners, policymakers, decision makers, policymakers, AI scientists and virologists to quell this infectious COVID-19 pandemic outbreak. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) is a novel harmful respiratory disease that has rapidly spread worldwide. At the end of 2019, COVID-19 emerged as a previously unknown respiratory disease in Wuhan, Hubei Province, China. The world health organization (WHO) declared the coronavirus outbreak a pandemic in the second week of March 2020. Simultaneous deep learning detection and classification of COVID-19 based on the full resolution of digital X-ray images is the key to efficiently assisting patients by enabling physicians to reach a fast and accurate diagnosis decision. In this paper, a simultaneous deep learning computer-aided diagnosis (CAD) system based on the YOLO predictor is proposed that can detect and diagnose COVID-19, differentiating it from eight other respiratory diseases: atelectasis, infiltration, pneumothorax, masses, effusion, pneumonia, cardiomegaly, and nodules. The proposed CAD system was assessed via five-fold tests for the multi-class prediction problem using two different databases of chest X-ray images: COVID-19 and ChestX-ray8. The proposed CAD system was trained with an annotated training set of 50,490 chest X-ray images. The regions on the entire X-ray images with lesions suspected of being due to COVID-19 were simultaneously detected and classified end-to-end via the proposed CAD predictor, achieving overall detection and classification accuracies of 96.31% and 97.40%, respectively. Most test images from patients with confirmed COVID-19 and other respiratory diseases were correctly predicted, achieving average intersection over union (IoU) greater than 90%. Applying deep learning regularizers of data balancing and augmentation improved the COVID-19 diagnostic performance by 6.64% and 12.17% in terms of the overall accuracy and the F1-score, respectively. It is feasible to achieve a diagnosis based on individual chest X-ray images with the proposed CAD system within 0.0093 s. Thus, the CAD system presented in this paper can make a prediction at the rate of 108 frames/s (FPS), which is close to real-time. The proposed deep learning CAD system can reliably differentiate COVID-19 from other respiratory diseases. The proposed deep learning model seems to be a reliable tool that can be used to practically assist health care systems, patients, and physicians. [ABSTRACT FROM AUTHOR]

The general remit of this paper is the role of wildlife trade in pathogen spillover. Its underlying assumption is that, so far from being the exclusive domain of the life sciences, the study of pathogen spillover will be greatly enhanced by multi-perspectival approaches, including One Health and those employed here, namely, non-speciesist green criminology and critical animal studies. The paper moves from discussions of zoonosis, anthroponosis and wildlife trade to the emergence of COVID-19 in Wuhan, China. The paper recommends the abolition of all wildlife trade and the reclamation of wildlife habitat and broaches discussion of the extension of legal personhood to wild animals. [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]

In 2019, the infectious coronavirus disease 2019 (COVID-19) was first reported in Wuhan, China. It has then become a public health problem in the world. This pandemic is having a heavy impact on the lives of people in our country. All countries are trying to control the spread of this disease. To solve the problem, each person needs to wear masks in a public place. Therefore, we propose a model capable of distinguishing between masked and nonmasked faces using a convolutional neural network (CNN) based on deep learning (DL)—MobileNetV2 in this paper. The model can detect people who are not wearing masks. It has an accuracy of up to 99.37%. The model will be applied in places such as schools, offices, and so on to monitor the wearing masks. [ABSTRACT FROM AUTHOR]

An outbreak of coronavirus disease 2019 (COVID-19) occurred in Wuhan and it has rapidly spread to almost all parts of the world. In the field of Medical Science, concerning the definition of the topological index on the molecular structure and corresponding medical, biological, chemical, pharmaceutical properties of drugs can be studied by the topological index calculation. In this paper, we compute some of the general topological properties of chloroquine and hydroxychloroquine used to inhibit the outbreak of coronavirus disease-19. The results in this paper may be useful in finding new drug and vaccine for the treatment and prevention of COVID-19. [ABSTRACT FROM AUTHOR]

The new emerged infectious disease that is known the coronavirus disease (COVID-19), which is a high contagious viral infection that started in December 2019 in China city Wuhan and spread very fast to the rest of the world. This infection caused millions of infected cases globally and still poses an alarming situation for human lives. Pakistan in Asian countries is considered the third country with higher number of cases of coronavirus with more than 649824. Recently, some mathematical models have been constructed for better understanding the coronavirus infection. Mostly, these models are based on classical integer-order derivative using real numbers which cannot capture the fading memory. So at the current position it is a challenge for the world to understand and control the spreading of COVID-19. Therefore, the aim of our paper is to develop some novel techniques, namely complex Pythagorean fuzzy weighted averaging (abbreviated as CPFWA) operator, complex Pythagorean fuzzy ordered weighted averaging (abbreviated as CPFOWA) operator, complex Pythagorean fuzzy hybrid averaging (abbreviated as CPFHA) operator, induced complex Pythagorean fuzzy ordered weighted averaging (abbreviated as I-CPFOWA) operator and induced complex Pythagorean fuzzy hybrid averaging (abbreviated as I-CPFHA) operator to analysis the spreading of COVID-19. At the end of the paper, an illustrative the emergency situation of COVID-19 is given for demonstrating the effectiveness of the suggested approach along with a sensitivity analysis, showing the feasibility and reliability of its results. [ABSTRACT FROM AUTHOR]

In December 2019, a group of people in Wuhan city of Hubei province of China were found to be affected by an infection called dark etiology pneumonia. The outbreak of this pneumonia infection was declared a deadly disease by the China Center for Disease Control and Prevention on January 9, 2020, named Novel Coronavirus 2019 (nCoV-2019). This nCoV- 2019 is now known as COVID-19. There is a big list of infections of this coronavirus which is present in the form of a big family. This virus can cause several diseases that usually develop with a serious problem. According to the World Health Organization (WHO), 2019-nCoV has been placed as the modern generation of Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) coronaviruses, so COVID-19 can repeatedly change its internal genome structure to extend its existence. Understanding and accurately predicting the mutational properties of the genome structure of COVID-19 can forma good leadership role in preventing and fighting against coronavirus. In this research paper, an analytical approach has been presented which is based on the k-means cluster technique of machine learning to find the clusters over the mutational properties of the COVID-19 viruses' complete genome. This method would be able to act as a promising tool to monitor and track pathogenic infections in their stable and local genetics/hereditary varieties. This paper identifies five main clusters of mutations with k = 5 as best in most cases in the coronavirus that could help scientists and researchers develop disease control vaccines for the transformation of coronaviruses. [ABSTRACT FROM AUTHOR]

Simple Summary: This paper proposes a modified SEIR model to study COVID-19 in Wuhan. The modified model is calibrated by the public number of COVID-19 hospitalization cases in Wuhan. The paper further uses this model to estimate the earliest date of COVID-19 infection in Wuhan, which is in agreement with some existing results. Based on SEIR (susceptible–exposed–infectious–removed) epidemic model, we propose a modified epidemic mathematical model to describe the spread of the coronavirus disease 2019 (COVID-19) epidemic in Wuhan, China. Using public data, the uncertainty parameters of the proposed model for COVID-19 in Wuhan were calibrated. The uncertainty of the control basic reproduction number was studied with the posterior probability density function of the uncertainty model parameters. The mathematical model was used to inverse deduce the earliest start date of COVID-19 infection in Wuhan with consideration of the lack of information for the initial conditions of the model. The result of the uncertainty analysis of the model is in line with the observed data for COVID-19 in Wuhan, China. The numerical results show that the modified mathematical model could model the spread of COVID-19 epidemics. [ABSTRACT FROM AUTHOR]

The novel Coronavirus, first appeared in Wuhan province of China in late December 2019, and gripped the whole world in 2020, growing to be a full-blown pandemic and changed every individual’s life completely. India recorded its first Novel Coronavirus case on 30th January 2020 in Kerala, a student from Wuhan, China. In this article, the question about whether India is ready to tackle the crisis, i.e., Covid-19, was answered. Three mathematical models were reviewed and then compared to the number of beds available in the country. The third mathematical model seemed to model the country’s curve much closer than the second one. The first one’s prediction actually came out to be true. Furthermore, the paper discusses the health infrastructure in the country and the strategies adopted by states to prevent the spread. It talks about two specific states Kerala and Maharashtra and how the country needs to deal with the crisis. It is important to note that according to the models and numbers currently, India is geared, However, if precautions are not taken the situation can get much worse. This paper also discusses the fatality rates currently in India and where we stand for treatment. Currently, the treatment protocols keep changing from time to time. [ABSTRACT FROM AUTHOR]

Background: Since its first report in Wuhan, China, in December 2019, COVID-19 has become a pandemic, affecting millions of people worldwide. Although the virus primarily affects the respiratory tract, gastrointestinal symptoms are also common. The aim of this narrative review is to provide an overview of the pathophysiology and clinical manifestations of gastrointestinal COVID-19. Methods: We conducted a systematic electronic search of English literature up to January 2023 using Medline, Scopus, and the Cochrane Library, focusing on papers that analyzed the role of SARS-CoV-2 in the gastrointestinal tract. Results: Our review highlights that SARS-CoV-2 directly infects the gastrointestinal tract and can cause symptoms such as diarrhea, nausea/vomiting, abdominal pain, anorexia, loss of taste, and increased liver enzymes. These symptoms result from mucosal barrier damage, inflammation, and changes in the microbiota composition. The exact mechanism of how the virus overcomes the acid gastric environment and leads to the intestinal damage is still being studied. Conclusions: Although vaccination has increased the prevalence of less severe symptoms, the long-term interaction with SARS-CoV-2 remains a concern. Understanding the interplay between SARS-CoV-2 and the gastrointestinal tract is essential for future management of the virus. [ABSTRACT FROM AUTHOR]

In this paper, the mathematical modeling of the novel corona virus (COVID-19) is considered. A brief relationship between the unknown hosts and bats is described. Then the interaction among the seafood market and peoples is studied. After that, the proposed model is reduced by assuming that the seafood market has an adequate source of infection that is capable of spreading infection among the people. The reproductive number is calculated and it is proved that the proposed model is locally asymptotically stable when the reproductive number is less than unity. Then, the stability results of the endemic equilibria are also discussed. To understand the complex dynamical behavior, fractal-fractional derivative is used. Therefore, the proposed model is then converted to fractal-fractional order model in Atangana-Baleanu (AB) derivative and solved numerically by using two different techniques. For numerical simulation Adam-Bash Forth method based on piece-wise Lagrangian interpolation is used. The infection cases for Jan-21, 2020, till Jan-28, 2020 are considered. Then graphical consequences are compared with real reported data of Wuhan city to demonstrate the efficiency of the method proposed by us. [ABSTRACT FROM AUTHOR]

Purpose: Taking a global perspective, this paper aims to examine the impact of COVID-19 on Airbnb booking activities through three critical perspectives – the initial Wuhan lockdown, local COVID-19 cases and local lockdowns. Design/methodology/approach: Using Airbnb reviews and cancellations as proxies for Airbnb bookings on a global scale, econometrics was used to examine the impacts of the initial Wuhan lockdown, local COVID-19 cases and local lockdowns on Airbnb bookings. Findings: The authors find that local lockdowns result in a 57.8% fall in global booking activities. Every doubling of newly infected cases is associated with a 4.16% fall in bookings. The sensitivity of bookings to COVID-19 decreases with geographic distance to Wuhan and increases with government stringency of lockdown policies and human mobility within a market. Practical implications: The empirical evidence from this research can provide governments with insights into more accurate assessment of the financial loss of Airbnb hosts so that proper support can be offered based on the financial needs because of due to sudden lockdown. Originality/value: This research contributes to new knowledge on peer-to-peer accommodation during a time of crisis and provides much needed global evidence to understand the impacts of COVID-19 on the accommodation industry. [ABSTRACT FROM AUTHOR]

Thee coronavirus which appeared in December 2019 in Wuhan has spread out worldwide and caused the death of more than 330,000 people (as of May 29, 2020, submission date for the present article). Since February 2020, doubts were raised about the numbers of confirmed cases and deaths reported by the Chinese government. In this paper, we examine data available from China at the city and provincial levels and we compare them with Canadian provincial data, US state data and French regional data. We consider cumulative and daily numbers of confirmed cases and deaths and examine these numbers through the lens of their first two digits and in particular we measure departures of these first two digits to the Newcomb-Benford distribution, often used to detect frauds. Our finding is that there is no evidence that cumulative and daily numbers of confirmed cases and deaths for all these countries have different first or second digit distributions. We also show that the Newcomb-Benford distribution cannot be rejected for these data. [ABSTRACT FROM AUTHOR]

The objective of this paper is to investigate three new methods to regulate and analyze the spreading of coronavirus disease (COVID-19). COVID-19 started in the city of Wuhan, China at the end of 2019 and spread to the whole world in a short time. This infection caused millions of infected cases globally and still poses a disturbing situation for the people. Recently, some mathematical models have been constructed for better understanding of the coronavirus infection. Mostly, these models are based on classical integer-order derivative using real numbers which cannot capture the fading memory. So, at the current position, it is a challenge for the world to control the spreading of COVID-19. Therefore, the aim of this paper is to utilize fuzzy logic to control the transmission and spreading of COVID-19. Here, we develop three new methods such as the generalized interval-valued intuitionistic fuzzy Einstein weighted geometric (GIVIFEWG) operator, the generalized interval-valued intuitionistic fuzzy Einstein ordered weighted geometric (GIVIFEOWG) operator, and the generalized interval-valued intuitionistic fuzzy Einstein hybrid geometric (GIVIFEHG) operator. Intuitionistic fuzzy information for interval-valued is the moral and decent method to precise the fuzzy information for judgment or decision and Einstein operations are the best approximations, and the generalized aggregation operators are a generalization of most aggregation operators so, in these notes, we can spread the Einstein operations to aggregate the interval-valued intuitionistic fuzzy information based on the generalized aggregation operators. At the end of the paper, an illustration of the emergency situation of COVID-19 is given for demonstrating the effectiveness of the suggested approach, showing the feasibility and reliability of the new methods. [ABSTRACT FROM AUTHOR]

This paper displays a Systemic Functional Linguistic and Critical Discourse Analysis of Boris Johnson's first public speech on COVID-19. Covid-19 is a very dangerous infectious disease caused by the last discovered virus of the Coronavirus strain. This virus began in Wuhan's Chinese city in December 2019. COVID-19 has spread from Wuhan to the rest of the world. It has now turned into a pandemic affecting the whole world. Halliday's (2004) model of systemic functional linguistics (meta-functions), relying on interpersonal and ideational metafunctions, and Van Dijk's ideology and discourse (2000) model depending on the argumentation categories, are the adopted models of analysis. The paper's main objectives are to analyze the speech of Prime Minister Boris critically to uncover the used ideologies to advise, persuade and control the people's beliefs and actions. In addition, this paper aims to identify the interpersonal and ideational meta-functions in the selected speeches of the chosen figure stating their frequencies, then finding out how these features uncover the ideological strategies used to affect all people; Finding the argumentation categories that are used by the prime minister to support the ideas and actions presented. The paper presents a theoretical background of discourse, Critical discourse analysis, dominant, and ideology. Explains the adopted models; Analyzes the speech critically. [ABSTRACT FROM AUTHOR]