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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]

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 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]

Different industries throughout the world face a tough time confronting the pandemic of novel coronavirus (COVID-19), so does the hotel industry. Hence, this research attempts to determine the impact of the COVID-19 crisis on the hotel industry in terms of guest retention. The crisis which started in Wuhan, China (December 2019) has spread to the whole world and continues to spread. Although in newspapers, journals, and online media there are few published articles, they are mostly based on secondary data. This study is an attempt to examine the perceptions of hotel employees regarding the effect of COVID-19 on the hotel industry through qualitative analysis. Further, the study attempts to evaluate the crisis preparedness and steps taken to combat COVID-19, and eventually determine the positive outcome of the crisis. This paper presents an overview of the advent of COVID-19 in New Delhi, India, and more importantly, reports on the approaches that hotels employ to sustain during this diffcult time. Through the in-depth interviews with senior employees in six hotels in the national capital city New Delhi, India (four interviews before the lockdown and two interviews during the lockdown). Empirical findings showed that at various phases of the crisis, hotels were taking different approaches to survive. The hotels were introducing strategies for guest retention to create stronger guest relationships. This paper presents the impact of this crisis on hotel employees and how the hotels are reacting to this crisis. The practical and theoretical implications of the study are discussed. [ABSTRACT FROM AUTHOR]

In the latter part of 2019, an unknown virus had started to spread, which was first reported from Wuhan city, China. After a few months, it was declared a deadly virus by the WHO and named ‘Coronavirus’ or ‘Covid-19’. Coronavirus is still deadly and hundreds of thousands of human lives are being lost. Since the outbreak became a pandemic, people started to share valuable information. Most people are using the social media platform to share their views, updates, etc. Twitter is one of the most used social media networks in the world. People and other reliable sources are constantly updating every possible information related to coronavirus spread, variants and deaths. One of the possible ways is extracting the information and creating a location-based hotspot to identify the areas of coronavirus spread and coronavirus deaths. This will be more helpful for the government and other authorities to identify potential coronavirus outbreak areas. In this paper, a method is proposed to identify and track the areas of coronavirus spread and deaths using Long Short-Term Memory (LSTM) technique. We have compared the proposed LSTM-based approach with classical machine learning approaches such as Support Vector Machine (SVM), and the proposed approach outperforms the classical methods. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) was first reported in December 2019 in Wuhan, China, and then moved to almost every country showing an unprecedented outbreak. The world health organization declared COVID-19 a pandemic. Since then, millions of people were infected, and millions have lost their lives all around the globe. By the end of 2020, effective vaccines that could prevent the fast spread of the disease started to loom on the horizon. Nevertheless, isolation, social distancing, face masks, and quarantine are the best-known measures, in the time being, to fight the pandemic. On the other hand, contact tracing is an effective procedure in tracking infections and saving others' lives. In this paper, we devise a new approach using a hybrid harmony search (HHS) algorithm that casts the problem of finding strongly connected components (SCCs) to contact tracing. This new approach is named as hybrid harmony search contact tracing (HHS-CT) algorithm. The hybridization is achieved by integrating the stochastic hill climbing into the operators' design of the harmony search algorithm. The HHS-CT algorithm is compared to other existing algorithms of finding SCCs in directed graphs, where it showed its superiority over these algorithms. The devised approach provides a 77.18% enhancement in terms of run time and an exceptional average error rate of 1.7% compared to the other existing algorithms of finding SCCs. [ABSTRACT FROM AUTHOR]

Purpose: This paper analyzes Fang Fang's 2020 Wuhan Diary‐Dispatches from a Quarantined City, to show how the author communicates the coronavirus crisis in Wuhan in a global information ecosystem. The success of the diary showcases how the actual health emergency has been transformed into a communication issue. Design/methodology/approach: This study is exploratory and qualitative in nature. The authors conducted a thematic analysis (TA) of Wuhan Diary, in which we decided to focus on the aspects of sousveillance and solidarity. For the purposes of our paper, we used the English translation of the text by Dr. Michael Berry. Findings: The authors focus on two major themes in their exploration of the corona crisis as a global communication issue: sousveillance and solidarity. The authors argue that the diary's ways of seeing perform a version of "sousveillance" or "undersight" in juxtaposition to surveillance or "oversight" (Mann, 2017). Fang Fang calls for solidarity as an effective measure for individuals, communities and societies to contain the pandemic and a potential misinfodemic. Originality/value: Since Wuhan was the early epicenter of the COVID-19 pandemic, Wuhan Diary presents an unprecedented narrative account of life under quarantine that could function as a litmus test for other cities and countries. Fang Fang's diary provides a countermeasure to official accounts of the pandemic in Wuhan, which has resonated both with people in China and abroad. [ABSTRACT FROM AUTHOR]

Novel coronavirus (COVID-19) is started from Wuhan (City in China), and is rapidly spreading among people living in other countries. Today, around 215 countries are affected by COVID-19 disease. WHO announced approximately number of cases 11,274,600 worldwide. Due to rapidly rising cases daily in the hospitals, there are a limited number of resources available to control COVID-19 disease. Therefore, it is essential to develop an accurate diagnosis of COVID-19 disease. Early diagnosis of COVID-19 patients is important for preventing the disease from spreading to others. In this paper, we proposed a deep learning based approach that can differentiate COVID- 19 disease patients from viral pneumonia, bacterial pneumonia, and healthy (normal) cases. In this approach, deep transfer learning is adopted. We used binary and multi-class dataset which is categorized in four types for experimentation: (i) Collection of 728 X-ray images including 224 images with confirmed COVID-19 disease and 504 normal condition images (ii) Collection of 1428 X-ray images including 224 images with confirmed COVID-19 disease, 700 images with confirmed common bacterial pneumonia, and 504 normal condition images. (iii) Collections of 1442 X- ray images including 224 images with confirmed COVID-19 disease, 714 images with confirmed bacterial and viral pneumonia, and 504 images of normal conditions (iv) Collections of 5232 X- ray images including 2358 images with confirmed bacterial and 1345 with viral pneumonia, and 1346 images of normal conditions. In this paper, we have used nine convolutional neural network based architecture (AlexNet, GoogleNet, ResNet-50, Se-ResNet-50, DenseNet121, Inception V4, Inception ResNet V2, ResNeXt-50, and Se-ResNeXt-50). Experimental results indicate that the pre trained model Se-ResNeXt-50 achieves the highest classification accuracy of 99.32% for binary class and 97.55% for multi-class among all pre-trained models. [ABSTRACT FROM AUTHOR]

After the outbreak of SARS and MERS, the world is now in the grip of another viral disease named COVID-19 caused by a beta Coronavirus – SARS COV-2 which appears to be the only one with a pandemic potential. The case of COVID-19 was reported in the Hubei province of Wuhan city in Central China at the end of December 2019 and it is suspected that the sea food market played a role in this outbreak which was closed abruptly. Subsequently, a Public Health Emergency of International Concern was declared on 30 January 2020 by the World Health Organization. Both SARS and MERS corona viruses had its reservoir in bats and were transferred to humans from palm civets and camels respectively. This virus can be transmitted through airborne droplets. Natural reservoir and intermediate host of COVID-19 is yet to be identified. This paper reviews the occurrences of viral diseases in the recent times including SARS and MERS. As an addition to this, the paper will contain a detailed examination of the COVID-19 Pandemic. [ABSTRACT FROM AUTHOR]

Background: Coronavirus disease (COVID-19), recent pandemic initially reported in Wuhan, would be predicted to affect the mental health of frontline healthcare providers. So, there is an urge need to monitor their mood, sleep rate and other mental health problems to introduce the required mental health interventions when needed. Aim: Reviewing plus evaluating the literatures conducted on mental health problems among healthcare workers (HCW) during the COVID-19 crisis. Methods: A systematic research study of literature databases was performed till to June 1st, 2020. All studies published in the period (January 2020 to May 2020) that related to review analysis subject in these databases (PubMed, Google Scholar, Science Direct, and EBSCO host) were used to perform this review. Upon initial screening, a total of 35 papers were selected but 14 papers were used finally. Results: The meta-analysis of the fourteen studies that based on analysing many facets of HCW mental health concerning COVID-19 revealed the presence of an extensive strain among them due to stress experience, depression, anxiety and sleep disturbance symptoms. Extreme levels of these symptoms were observed in 2.2 % to 14.5% of all participants. Conclusion: As COVID-19 is a global public mental health emergency, every country has to learn from previous epidemics experience and urgently provide multifaceted psychological safeguarding of their healthcare providers' mental health to keep their effectiveness as indispensable resources. [ABSTRACT FROM AUTHOR]

Since December 2019, a series of unexplained pneumonia cases have been reported in Wuhan, China. On 12 January 2020, the World Health Organization (WHO) temporarily named this new virus as the 2019 novel coronavirus (2019‐nCoV). On 11 February 2020, the WHO officially named the disease caused by the 2019‐nCoV as coronavirus disease (COVID‐19). The COVID‐19 epidemic is spreading all over the world, especially in China. Based on the published evidence, we systematically discuss the characteristics of COVID‐19 in the hope of providing a reference for future studies and help for the prevention and control of the COVID‐19 epidemic. Research Highlights: Currently, the world is watching the progress of Corona Virus Disease 2019 (COVID‐19) epidemic. The purpose of our article is to give people a comprehensive understanding of COVID‐19, so that they can better prevent it. We review the latest published papers on COVID‐19. In this paper, the origin of the virus, the mechanism of virus infection, the clinical characteristics of COVID‐19 and the treatment and prevention of COVID‐19 are comprehensively elaborated. We hope this article will help for the prevention and control of the COVID‐19 epidemic. [ABSTRACT FROM AUTHOR]

The emergence of an unusual Corona virus (COVID-19) flu pandemic starting in China in December 2019, spreading all around the globe is a major threat to public health. The investigations have shown this virus originated from a seafood market in Wuhan. However, the unavailability of medicines for the new disease is a big challenge all around. An attempt has been made in the present article to familiarise about the morphology of the virus. The study of effect of pH, temperature and relative humidity is also depicted. Various preventive measures have also been discussed. The natural dietary measures suggested in the paper would be very beneficial to improve and boost the immunity of the mankind. [ABSTRACT FROM AUTHOR]

Coronaviruses (CoVs) are a large family of viruses and are endemic in humans and animals, causing respiratory and intestinal infections. Coronavirus has become a challenge in China region due to its recent outbreak at the start of the year 2020. The current outbreak of coronavirus disease (COVID) has resulted in many fatalities and has forced the people of Wuhan Province in China to remain confined in their homes. Two other two forms of CoV were epidemic in 2003 when Severe Acute Respiratory Syndrome (SARS-CoV) spread in Hong Kong and Middle East Respiratory Syndrome (MERS-CoV) spread in the Middle East region. This bibliometric study is an attempt to trace the trends of research associated with "Coronavirus" for a period of 50 years using the SCOPUS database. The study was carried out using the keyword Coronavirus and analyzed for annual growth, productive countries, institutes, authors, journals, highly cited papers, and research focus using keywords. There were two peaks when a large number of publications appeared. Most of the research publications were from the USA (31.67%), and the University of Hong Kong was the most productive institute. The Journal of Virology has published the most number of articles on CoV. [ABSTRACT FROM AUTHOR]

The novel Coronavirus COVID-19 emerged in Wuhan, China in December 2019. COVID-19 has rapidly spread among human populations and other mammals. The outbreak of COVID-19 has become a global challenge. Mathematical models of epidemiological systems enable studying and predicting the potential spread of disease. Modeling and predicting the evolution of COVID-19 epidemics in near real-time is a scientific challenge, this requires a deep understanding of the dynamics of pandemics and the possibility that the diffusion process can be completely random. In this paper, we develop and analyze a model to simulate the Coronavirus transmission dynamics based on Reservoir-People transmission network. When faced with a potential outbreak, decision-makers need to be able to trust mathematical models for their decision-making processes. One of the most considerable characteristics of COVID-19 is its different behaviors in various countries and regions, or even in different individuals, which can be a sign of uncertain and accidental behavior in the disease outbreak. This trait reflects the existence of the capacity of transmitting perturbations across its domains. We construct a stochastic environment because of parameters random essence and introduce a stochastic version of the Reservoir-People model. Then we prove the uniqueness and existence of the solution on the stochastic model. Moreover, the equilibria of the system are considered. Also, we establish the extinction of the disease under some suitable conditions. Finally, some numerical simulation and comparison are carried out to validate the theoretical results and the possibility of comparability of the stochastic model with the deterministic model. [ABSTRACT FROM AUTHOR]

The coronavirus pandemic started in Wuhan, China in December 2019, and put millions of people in a difficult situation. This fatal virus spread to over 227 countries and the number of infected patients increased to over 400 million cases, causing over 6 million deaths worldwide. Due to the serious consequence of this virus, it is necessary to develop a detection method that can respond quickly to prevent the spreading of COVID-19. Using chest X-ray images to detect COVID-19 is one of the promising techniques; however, with a large number of COVID-19 infected cases every day, the number of radiologists available to diagnose the chest X-ray images is not sufficient. We must have a computer aid system that helps doctors instantly and automatically determine COVID-19 cases. Recently, with the emergence of deep learning methods applied for medical and biomedical uses, using convolutional neural net and transformer applications for chest X-ray images can be a supplement for COVID-19 testing. In this paper, we attempt to classify three types of chest X-ray, which are normal, pneumonia, and COVID-19 using deep learning methods on a customized dataset. We also carry out an experiment on the COVID-19 severity assessment task using a tailored dataset. Five deep learning models were obtained to conduct our experiments: DenseNet121, ResNet50, InceptionNet, Swin Transformer, and Hybrid EfficientNet-DOLG neural networks. The results indicated that chest X-ray and deep learning could be reliable methods for supporting doctors in COVID-19 identification and severity assessment tasks. [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]

In this paper, the mathematical modeling of five different classes for coronavirus disease-19 (COVID-19) is considered using the fractional arbitrary order derivative in Atangana–Baleanu sense. We use nonlinear analysis for the existence theory of the solution for the suggested model. Additionally, the modified Adam–Bashforth method is used for the numerical approximation of the assumed model. Finally, we simulate the results for 100 days with the help of data from the literature to display the excellency of the suggested model. [ABSTRACT FROM AUTHOR]

"Novel Coronavirus", also known as COVID-19 has spread globally which is sparked in Wuhan, the capital city of Hubei in China. It is a big problem for the world's health organization as the number of affected cases as well as deaths increases in each country. Several Machine Learning models are designed to predict and forecast the COVID-19 outbreak. In this paper, we are designed to understand the current scenario of coronavirus, and predicting and forecasting the total number of confirmed cases of novel COVID-19 in India using linear regression, polynomial regression, and support vector machine regression models. The decision-making process of an organization depends on the data analysis and it is presenting with interactive visual representation. It is not possible to predict without having necessary information from the past and the present. Trends over time tell us where we were and where we can go with our model. The model is predicting the number of confirmed cases from 30th May 2021 to 15th June 2021on the data available from 22 January 2020 to 29 May 2021. Forecasting the future of confirmed cases can help health organizations to plan medical facilities. Moreover, the time series forecasting technique is being engaged to forecast the overall total of confirmed cases in the upcoming. [ABSTRACT FROM AUTHOR]

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]

COVID-19 is a highly contagious disease caused by the so-called coronavirus. The causative agent of the disease, the SARS-CoV-2 virus, was firstly discovered in the late 2019 in the city of Wuhan, in the Chinese province of Hubei. From there, it spread rapidly around the world causing a worldwide pandemic of COVID-19 disease. In Croatia, the first case of infection was confirmed on February 25, 2020. This paper describes the movement of the number of infected, the number of deaths and the number of active and cured persons from COVID-19 disease in the Republic of Croatia in the period from 25 February 2020 to 25 February 2021. The paper also presents the movement of the values of variables associated with COVID-19 disease at the county level in the Republic of Croatia. This paper provides the basis for future statistical modelling related to the movement of COVID-19 variables, which should result in solutions to curb this pandemic. [ABSTRACT FROM AUTHOR]

Background and Objective: Coronavirus is spreading across the globe since December 2019. As India's first case was reported on January 30, 2020, the spread of this virus is experiencing by our population. As a result, there are extensive emotional distress and anxiety among populations due to this pandemic. Screening mental health is important for psychological well-being of any individual during this pandemic. The objective of this study was to validate the Coronavirus Anxiety Scale (CAS) in an Indian population. Materials and Methods: The research proposal was laid out before an ethical clearance committee, which approved the conduct of the research and consequent publication of the paper. Data were collected from 246 respondents through online. CAS, developed by Lee (2020), was adopted for the study. Informed consent was given by all the respondents, and their participation was voluntary. AMOS and SPSS were used to calculate confirmatory factor analysis and other statistical analyses. Bartlett's test of sphericity and Kaiser-Meyer-Olkin (KMO) test, and Cronbach's alpha, were also calculated. Convergent validity was calculated through average variance extracted (AVE) and composite reliability (CR) in Microsoft Excel. Results, and Conclusions: Bartlett's test of sphericity was highly significant measuring Chi-square = 494.004, df = 10, and P < 0.001. The KMO was acceptable at 0.805. Fit indices (P < 0.001, Chi-square/df = 3.24, goodness of fit index = 0.976, Tucker-Lewis index = 0.954, and comparative fit index = 0.977) are statistically significant. Cronbach's alpha coefficient (0.822), AVE (0.526), and CR (0.745) were adequate. The overall findings of the analyses demonstrate that the CAS is a reliable and valid scale that evaluates the severity levels of dysfunctional anxiety linked to COVID-19 in an Indian sample. CAS is applicable to measure the level of dysfunctional coronavirus anxiety in the Indian population. [ABSTRACT FROM AUTHOR]

Coronaviruses are a family of viruses that can be transmitted from one person to another. Earlier strains have only been mild viruses, but the current form, known as coronavirus disease 2019 (COVID-19), has become a deadly infection. The outbreak originated in Wuhan, China, and has since spread worldwide. The symptoms of COVID-19 include a dry cough, sore throat, fever, and nasal congestion. Antimicrobial drugs, pathogen--host interaction, and 2 weeks of isolation have been recommended for the treatment of the infection. Safe operating procedures, such as the use of face masks, hand sanitizer, hand washing with soap, and social distancing, are also suggested. Moreover, travel bans for cities, states, and countries have been put in place, along with lockdowns to control the outbreak. Travel restrictions, mask use, sanitizer or soap use, and avoidance of touching the face and nose have produced encouraging results, whereas the effectiveness of antibiotics has not been proved. The results of isolation for the recovery of infected people have also been promising. Travel bans and lockdowns have caused a slump in economies, and unemployment has risen sharply, resulting in an increase in mental health cases globally. To date, vaccines have been developed and are in use in certain countries, but following standard operating procedures remain critical. The countries following the guidelines can eradicate this virus. New Zealand was the first country to eliminate the virus from their territory. [ABSTRACT FROM AUTHOR]

Coronavirus is spread in end of December, 2019 in Wuhan and in March, 2020 World Health Organization declared it as a pandemic. Coronavirus affect world in many ways like socially, economically and in many other. In early January, 2021 India make their vaccine and not only vaccinated their own people but also help other countries and provide vaccine to their neighbouring countries on grant and commercial basis and this will help India to make their reputation in world platform. In this paper, Vaccine Maîtri an initiative by India to provide vaccine to their neighbouring countries and also other needy countries and how this will enhance the image of India in world stage. [ABSTRACT FROM AUTHOR]

The outbreak of the novel coronavirus disease (COVID-19), caused by the SARS-CoV-2 virus, occurred in Wuhan, China in December 2019 and quickly spread to both the developed and developing countries. The virus will continue to infect humans and animals symptomatically or asymptomatically, and humans will be expected to coexist with SARS-CoV-2 by preventing infections and its spread. In this paper, we report on the occurrence of the COVID-19 epidemic in Japan, including the city of Kobe. [ABSTRACT FROM AUTHOR]

The outbreak of the novel COVID-19, which began silently in Wuhan City, China, has now taken the form of a pandemic, with its claws spreading rapidly in many countries, with new and new cases emerging rapidly. The COVID-19-associated CoV is a beta coronavirus, which spreads at such a deadly rate that the World Health Organization (WHO) has to declare it a Public Health Emergency of International Concern (PHEIC). The objective of the narrative review is to describe what is COVID-19-related coronavirus (CoV), its structure and particle size, potential transmission routes, the risk of infection in patients undergoing blood transfusion or in patients with diabetes and cancer, and recommendations to prevent its spread in office settings, travel / recreation settings, residential and health facilities. This paper also discusses several groundbreaking approaches that are used to counter COVID-19. With this narrative review, we hope to raise awareness of the usual and unusual pathways of transmission and prevent the spread of this pandemic disease. [ABSTRACT FROM AUTHOR]

Featured Application: In this work, the prediction of the spread of COVID-19 in Saudi Arabia was analyzed and evaluated using different epidemiological models in order to choose the most appropriate model. Additionally, the impacts of social distancing and treatment were modeled using two modified versions of the susceptible–infectious–recovered (SIR) model The first case of COVID-19 originated in Wuhan, China, after which it spread across more than 200 countries. By 21 July 2020, the rapid global spread of this disease had led to more than 15 million cases of infection, with a mortality rate of more than 4.0% of the total number of confirmed cases. This study aimed to predict the prevalence of COVID-19 and to investigate the effect of awareness and the impact of treatment in Saudi Arabia. In this paper, COVID-19 data were sourced from the Saudi Ministry of Health, covering the period from 31 March 2020 to 21 July 2020. The spread of COVID-19 was predicted using four different epidemiological models, namely the susceptible–infectious–recovered (SIR), generalized logistic, Richards, and Gompertz models. The assessment of models' fit was performed and compared using four statistical indices (root-mean-square error (RMSE), R squared ( R 2 ), adjusted R2 (   R adj 2 ), and Akaike's information criterion (AIC)) in order to select the most appropriate model. Modified versions of the SIR model were utilized to assess the influence of awareness and treatment on the prevalence of COVID-19. Based on the statistical indices, the SIR model showed a good fit to reported data compared with the other models (RMSE = 2790.69, R 2 = 99.88%, R adj 2 = 99.98%, and AIC = 1796.05). The SIR model predicted that the cumulative number of infected cases would reach 359,794 and that the pandemic would end by early September 2020. Additionally, the modified version of the SIR model with social distancing revealed that there would be a reduction in the final cumulative epidemic size by 9.1% and 168.2% if social distancing were applied over the short and long term, respectively. Furthermore, different treatment scenarios were simulated, starting on 8 July 2020, using another modified version of the SIR model. Epidemiological modeling can help to predict the cumulative number of cases of infection and to understand the impact of social distancing and pharmaceutical intervention on the prevalence of COVID-19. The findings from this study can provide valuable information for governmental policymakers trying to control the spread of this pandemic. [ABSTRACT FROM AUTHOR]

A novel coronavirus, called 2019-nCoV, was recently found in Wuhan, Hubei Province of China, and now is spreading across China and other parts of the world. Although there are some drugs to treat 2019-nCoV, there is no proper scientific evidence about its activity on the virus. It is of high significance to develop a drug that can combat the virus effectively to save valuable human lives. It usually takes a much longer time to develop a drug using traditional methods. For 2019-nCoV, it is now better to rely on some alternative methods such as deep learning to develop drugs that can combat such a disease effectively since 2019-nCoV is highly homologous to SARS-CoV. In the present work, we first collected virus RNA sequences of 18 patients reported to have 2019-nCoV from the public domain database, translated the RNA into protein sequences, and performed multiple sequence alignment. After a careful literature survey and sequence analysis, 3C-like protease is considered to be a major therapeutic target and we built a protein 3D model of 3C-like protease using homology modeling. Relying on the structural model, we used a pipeline to perform large scale virtual screening by using a deep learning based method to accurately rank/identify protein–ligand interacting pairs developed recently in our group. Our model identified potential drugs for 2019-nCoV 3C-like protease by performing drug screening against four chemical compound databases (Chimdiv, Targetmol-Approved_Drug_Library, Targetmol-Natural_Compound_Library, and Targetmol-Bioactive_Compound_Library) and a database of tripeptides. Through this paper, we provided the list of possible chemical ligands (Meglumine, Vidarabine, Adenosine, d-Sorbitol, d-Mannitol, Sodium_gluconate, Ganciclovir and Chlorobutanol) and peptide drugs (combination of isoleucine, lysine and proline) from the databases to guide the experimental scientists and validate the molecules which can combat the virus in a shorter time. [ABSTRACT FROM AUTHOR]

Wuhan, a city of China, is the epicenter for the pandemic outbreak of coronavirus disease‐2019 (COVID‐19). It has become a severe public health challenge to the world and established a public health emergency of international worry. This infectious disease has pulled down the economy of almost all top developed nations. The coronaviruses (CoVs) known for various epidemics caused time to time. Infectious diseases such as severe acute respiratory syndrome (SARS) and middle east respiratory syndrome (MERS), followed by COVID‐19, are all coronaviruses led outbreaks that scourged the history of mankind. CoVs evolved themselves to more infectious, transmissible, and more pandemic with time. To prevent the spread of the SARS‐CoV‐2, many countries have ordered the complete lockdown to combat the outbreak. This paper briefly discussed the historical background of CoVs and the evolution of human coronaviruses (HCoVs), the case studies and the development of their antiviral medications. The viral infection encountered with present‐day challenges and futuristic approaches with the help of nanotechnology to minimize the spread of infectious viruses. The antiviral drugs and their clinical advances, along with herbal medicines for viral inhibition and immunity boosters, are described. Elaboration of tables related to CoVs for the compilation of the literature has been adopted for the better understanding. [ABSTRACT FROM AUTHOR]

Nobody can be fully prepared to a pandemic. Of course there are signs of it, the scientists can predict, alarming speeches can be made. But there are always alarmist people around, maybe that is why sometimes even the most serious warnings may be not considered by the authorities on time. The first patients may be lost without a proper diagnosis. When everybody realizes that there may be a big problem in the horizon, sometimes it is too late. That is why it is very important to monitor contagious diseases and follow the warnings and releases of national and international disease control centers and other related organizations. China celebrated Lunar New Year with more than 40 thousand families on the 18 of January 2020. Nobody seem to be expecting this emerging new viral pneumonia outbreak appeared in Wuhan, in the last days of 2019, will break the chains and turn out to be a pandemic! But maybe this time it was not too late. There were four important pandemics within the last century: Spanish Flu, Hong Kong Flu, Asian Flu and Swine Flu. Each left different story behind. Millions of people had infected, hundreds, thousands of people died. This time, the Modern World had different tools to limit the SARS CoV2 outbreak. The national and international institutions of our globe were all communicating and taking precautions in a very fast manner than ever. However, this time, unexpectedly, the SARS-CoV-2 contagion was also faster. Besides the international organizations like WHO, UNESCO and UNICEF, the roles of local authorities, health ministries, disease control centers, health protection agencies, research centers and universities are all very important in different operational levels to control and survive from the pandemic. This paper will review the immediate response of different national and international institutions and authorities to COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Accurate analysis of medical data supports early disease identification, patient treatment, and community services through data development in biomedical and healthcare populations. In addition, various regions exhibit specific features of some regional diseases that that undermine the prediction of disease outbreaks. In this paper, we streamline machine learning algorithms to provide efficient coronavirus prediction. Coronavirus or called SARS-CoV-2 is RNA virus discovered in Wuhan-China the last 2019 than transmitted to worldwide. COVID-19 become global pandemic in 2020 than World Health Organization (WHO) works on quarantine people and reducing contact even in hospitals and health centers to control this pandemic. Experimented in this study with the updated models of prediction over COVID-19 disease data gathered from recent studies. Compared to many traditional prediction algorithms, our proposed algorithm's prediction accuracy achieves a convergence level that is faster than that of the COVID-19 unimodal disease risk prediction algorithm. This scientific application aimed to help predicted COVID-19 and work reducing number of people for visiting hospitals and health centers to check up about coronavirus with spreading health awareness about this critical virus. [ABSTRACT FROM AUTHOR]

COVID-19 is a kind of coronavirus that appeared in China in the Province of Wuhan in December 2019. The most significant influence of this virus is its very highly contagious characteristic which may lead to death. The standard diagnosis of COVID-19 is based on swabs from the throat and nose, their sensitivity is not high enough and so they are prone to errors. Early diagnosis of COVID-19 disease is important to provide the chance of quick isolation of the suspected cases and to decrease the opportunity of infection in healthy people. In this research, a framework for chest X-ray image classification tasks based on deep learning is proposed to help in early diagnosis of COVID-19. The proposed framework contains two phases which are the pre-processing phase and classification phase which uses pre-trained convolution neural network models based on transfer learning. In the pre-processing phase, different image enhancements have been applied to full and segmented X-ray images to improve the classification performance of the CNN models. Two CNN pre-trained models have been used for classification which are VGG19 and EfficientNetB0. From experimental results, the best model achieved a sensitivity of 0.96, specificity of 0.94, precision of 0.9412, F1 score of 0.9505 and accuracy of 0.95 using enhanced full X-ray images for binary classification of chest X-ray images into COVID-19 or normal with VGG19. The proposed framework is promising and achieved a classification accuracy of 0.935 for 4-class classification. [ABSTRACT FROM AUTHOR]

The world is going through the most unprecedented time with the outbreak of novel Coronavirus disease (COVID-19), which has become a threat to millions. A Coronavirus is a group of viruses that cause a variety of diseases in mammals and birds leading to a range of illnesses in humans including common cold and more severe forms like severe acute respiratory syndrome Coronavirus (SARS-CoV), Middle East respiratory syndrome Coronavirus (MERS-CoV) and COVID-19, which are life-threatening. The virus gets its name from its shape which takes the form of a crown with protrusions around it. In December 2019, a pneumonia outbreak was reported in the Wuhan City of China, which was later traced to a novel strain of Coronavirus and termed as Novel COVID-19. It typically causes flu-like symptoms including fever, cough and shortness of breath and is transmitted through human-to-human and there is no cure for it till now. Thus, this bibliometric study has been carried out to analyze the research progress in Coronavirus and literature published during a period of 30 years (1989-2019). Data for the study were fetched from Web of Science(WoS) multidisciplinary database and the publication trends in terms of total articles, productive countries, institutions, journals, productive authors, most cited articles and authors, etc have been analyzed. In total, 4917 articles were retrieved; these were from 711 sources and were contributed by 14442 authors. The collaboration index was 3.11, which clearly indicates that there has been a lot of collaboration in this field. The most preferred journal for the study period was "Journal of Virology" and the maximum contribution has been from the University of Hong Kong. [ABSTRACT FROM AUTHOR]

Actually, COVID-19 and its variants present a big challenge for the public health security. COVID-19 is a new form of the coronaviruses characterized by a set of symptoms like laboratory and radiological symptoms, when the first case has confirmed in December 2019 in Wuhan City, as well as a new variant of this form has appeared in December 2020 in the United Kingdom. Internet of things (IoT) is a technological revolution employed in different areas in the aim to serve the asked purposes. The implementation of IoT solutions in healthcare area has several benefits such as reducing the cost of services and improving treatment results. In this paper, we present a review on the impact of IoT on this new health challenge (COVID-19 and its variants), we will focus this study on the impact of the use of IoT devices to reduce transmissions of COVID-19 and its variants. [ABSTRACT FROM AUTHOR]

The rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) infections that were first detected in Wuhan, China, at the end of 2019 led to the state of the global COVID-19 pandemic declared by the World Health Organization in March 2020. Thus, the world faces a new challenge to control the spread of the virus. The pandemic highlighted the need for rapid detection of infections that would be able to accurately screen the population much more rapidly and economically than the gold standard molecular biology-based tools such as quantitative real-time reverse transcription polymerase chain reaction. Such screening measures would be able to isolate infected individuals, including those showing no symptoms, and provide early treatments before more serious complications develop, thus allowing for the spread of infections to be better controlled without the implementation of socioeconomically crippling lockdowns. In this review, we present a comprehensive overview of the major advances in the use of electrolyte-gated field effect transistor-based biosensors for the detection of viruses, including their use for SARS-CoV-2 detection. We describe the main types of bioreceptors used for the detection of viruses in general and those used for COVID-19 in detail. [ABSTRACT FROM AUTHOR]

Background and Aims: One of the main responsibilities of health systems impacted by the global Coronavirus disease 2019 (COVID‐19) pandemic, where the first case was discovered in Wuhan, China, in December 2019, is the provision of medical services. The current study looked into the impact of vaccination on the utilization of services provided to COVID‐19 patients. Methods: This study was conducted in Iran between 2021 and 2022, utilizing a cross‐sectional research design. The research team collected data on the utilization of provided services and the number of COVID‐19 vaccines administered to 1000 patients in Iran through a random sampling approach. The data were analyzed with statistical methods, including the mean difference test, and multiple linear regression. Results: Regression estimates show that after controlling for confounding variables like age, type of admission, and comorbidities, vaccination reduces the utilization of healthcare services in the general majority of services. The study's results reveal a fall in COVID‐19 patients' utilization of services, specifically in patients administered two or three doses of the vaccine. However, the reduction is not statistically significant. Regression models are in contrast to univariate analysis findings that vaccination increases the mean utilization of healthcare services for COVID‐19 patients in general. Comorbidities are a crucial factor in determining the utilization of diagnostic and treatment services for COVID‐19 patients. Conclusion: Full COVID‐19 vaccination and other implementations, including investing in public health, cooperating globally, and vaccinating high‐risk groups for future pandemics, are essential as a critical response to this pandemic as they reduce healthcare service utilization to alleviate the burden on healthcare systems and allocate resources more efficiently. [ABSTRACT FROM AUTHOR]

The ongoing Coronavirus Disease 2019 (COVID-19) pandemic has infected over 58 million people and claimed over 1.58 millions deaths globally (as of 11th December 2020) since its first outbreak in Wuhan, China in December 2019. Initially, the numbers of infected patients and death was largely contained in China with 98% of all confirmed infected cases. However, the increased rate of new infected cases outside of China like United States, Italy, and Spain raises questions on the virus characteristics and its routes of transmission. Although the main transmission modes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are through direct contact and respiratory droplet/aerosol inhalation, current studies stipulate that SARS-CoV-2 RNA is found in sewerage, suggesting the potential transmission of SARS-COV-2 through wastewater systems. This paper seeks to review potential exposure routes of SARS-COV-2 in urban environments, the survival rate of coronaviruses that pose human health risks, and to provide relevant safety recommendations to reduce the impact of ongoing COVID-19 pandemic. There is an urgent need for wastewater effluent and water treatment supply epidemiology surveillance, especially in developing countries with subpar wastewater treatment systems and infrastructure to reduce human and ecological risks to protect populations from infectious diseases outbreak. [ABSTRACT FROM AUTHOR]

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When 2020 began, we had no idea what was to unfold globally as we learnt about the Novel-Coronavirus in Wuhan, in the Hubei province of China. As this virus spread rapidly, it became a matter of time before many countries began to implement measures to try and contain the spread of the disease. COVID-19 as it is referred to, resulted in two main approaches to fighting the viral pandemic, either through a progressive set of measures to slow down the number of identified cases designed to 'flatten the curve' over time (anticipated to be at least six months), or to attack it by the severest of measures including a total lock-down and/or herding exposure to fast track 'immunisation' while we await a vaccine. The paper reports the findings from the first phase of an ongoing survey designed to identify the changing patterns in travel activity of Australian residents as a result of the stage 2 restrictions imposed by the Australian government. The main restrictions, in addition to social distancing of at least 1.5 m, are closure of entry to Australia (except residents returning), and closure of non-essential venues such as night clubs, restaurants, mass attendee sporting events, churches, weddings, and all social gatherings in any circumstance. With some employers encouraging working from home and others requiring it, in addition to job losses, and many children attending school online from home, the implications on travel activity is extreme. We identify the initial impacts associated with the first month of stricter social distancing measures introduced in Australia. • General support for the actions of government and business. • Widespread suppression of travel demand for all trip purposes across all modes. • Sizeable shift to working from home by those who can. • Public transport will face the largest hurdles in regaining confidence. • Retail and food logistics will need to examine distribution processes • The aviation sector will likely need to restart with a heavy focus on domestic travel. • Public transport will need to take overt measures to restore confidence. • The dominance of the car is further enforced in the context of biosecurity concerns. • Active transport is a viable option for short inner-city trips. • Infrastructure investment should be carefully considered. • Flexible working arrangements are perhaps the biggest policy lever available to governments. [ABSTRACT FROM AUTHOR]

The pneumonia cases in Wuhan, China reported by the Chinese authorities foreshadow an outbreak caused by a new type of coronavirus. Shortly afterwards, the World Health Organization decleared the outbreak as a pandemic due to its high infection rate and worldwide prevalence. The first diagnosis of coronavirus in Turkey was announced to the public in March, 2020. After then, several precautional meaures in social, economic, legal, religious and military areas have been adopted. In the period of the pandemic disease, several memes, urban legends, rumors, jokes, and myths concerning both the virus itself and its constrained lifestyle released into the social media. Minstrels who are the significant actors of traditional culture have written poems on the pandemic and its effects. The sensitive approaches of the minstrels to the contemporary social problems is closely related to their social role which is loaded them by the minstrelsy tradition. It is seen that the roles of the minstrels such as leading and guiding the society both affects the reasoning of writing poems and has an influence on the contents of these poems. In the quarantine period, minstrels have shared their poems especially through social media networks and Youtube. In this article, mainly the Youtube videos of the minstrels are discussed based on the motivation that Youtube provides them to reach a set of diversified audience relatively to those of existing ones. When evaluating the public opinion on the minstrelsy tradition, the posted comments of the minstrels' videos are also utilized. The large parts of the minstrels' poems refer to how and why the coronavirus appears, how it affects the daily lives, and helplessness of the mankind against to the virus and the pandemic. The poems that advise the public to stay at home and give supporting messages to the healthcare staff have also written by the minstrels. Minstrels have succeeded to be visible to the public by means of performing their poems in electronic platforms inparticular during the period of quarantine. However, considering the historical progress of minstrelsy tradition, it is quite obvious that the minstrels and their products have relatively depreciated and their roles of leading and guiding the public are becoming restricted to a certain set of audience compared to the past. In the context of social roles of minstrels, this paper also discusses the question of what are the reasons that interrupt the efforts of the minstrels who are dealing with the contemporary problems and using the contemporary technology. The audiences of the minstrels' poems have the opinion that the minstrels' wish of eradication of the cruel persons by the virus and being the virus as a kind of divine retribution contradicts to the role of minstrels as leading and guiding the public. It is inferred that the language and the style of the interpretions as well as sometimes the outlook of the minstrels upon the coronavirus pandemic and its effects are reviewed as obsolete by the audiences. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic is having a severe effects on both workers and employers in different countries and industries worldwide. In this paper we analyzed the impact of the corona crisis on the global economic growth and the main questions addressed here are the effects of corona crisis on the global manufacturing activity and service industry. The research results showed that at the beginning of 2020 when the coronavirus emerged in Wuhan, China and has spread around the world causing a pandemic, it brought the global economy on its knees in a very short period of time. Also, the results indicate that the global economy will take years to recover from the corona crisis. The research is done by exploring of the latest economic analysis related to the COVID-19 pandemic in order to identify its effects on world economy throughout the first half of 2020. [ABSTRACT FROM AUTHOR]

• The latest summary of the COVID-19 outbreak in China; • There might be an oral-fecal transmission of the virus; • Aggregates and consolidates the epidemiology, clinical manifestations, diagnosis, treatments and preventions of this new type of coronavirus. There is a current worldwide outbreak of the novel coronavirus Covid-19 (coronavirus disease 2019; the pathogen called SARS-CoV-2; previously 2019-nCoV), which originated from Wuhan in China and has now spread to 6 continents including 66 countries, as of 24:00 on March 2, 2020. Governments are under increased pressure to stop the outbreak from spiraling into a global health emergency. At this stage, preparedness, transparency, and sharing of information are crucial to risk assessments and beginning outbreak control activities. This information should include reports from outbreak site and from laboratories supporting the investigation. This paper aggregates and consolidates the epidemiology, clinical manifestations, diagnosis, treatments and preventions of this new type of coronavirus. [ABSTRACT FROM AUTHOR]

The recent emergence of a new coronavirus (COVID-19) has gained a high cover in public media and worldwide news. The virus has caused a viral pneumonia in tens of thousands of people in Wuhan, a central city of China. This short paper gives a brief introduction on how the demand for information on this new epidemic is reported through Google Trends. The reported period is 31 December 2020 to 20March 2020. The authors draw conclusions on current infodemiological data on COVID-19 using three main search keywords: coronavirus, SARS and MERS. Two approaches are set. First is the worldwide perspective, second - the Chinese one, which reveals that in China this disease in the first days was more often referred to SARS then to general coronaviruses, whereas worldwide, since the beginning, it is more often referred to coronaviruses. [ABSTRACT FROM AUTHOR]

Since its discovery in Wuhan, China, in December 2019, Coronavirus (COVID-19) diseaseis considered one of the fastest spreading viral infections. The virus spreads between humans by droplets or by direct contact with an incubation period reaching up to 14 days, and isolation is the best way to prevent its spread. The symptoms of COVID-19 infection include dry cough, fever, and shortness of breath. No vaccine or treatment is available at the moment. Precautionary measures have been taken by the government of Saudi Arabia to control the spread. This study aimed to assess the knowledge of Male King Abdulaziz University students in Rabigh branch about the new emerging coronavirus COVID-19 by distributing a questionnaire among them. This survey was conducted between March 1 till March 5, 2020, before the suspension of all the educational activities at King Abdulaziz University. Three hundred seventy-five students filled the questionnaire. 51% believed that COVID-19 is the deadliest virus in the world. Around 47% knew about hand washing and COVID-19 symptoms. In conclusion, at the time of the study, our students had average knowledge of the virus with many misconceptions and moderate attitude. [ABSTRACT FROM AUTHOR]

Background: The emergence of a novel strain of Coronavirus, now named SARS-CoV-2, in Wuhan city, China, in late 2019, resulted in a global pandemic that spread to every region of the world. Various measures like social distancing, wearing masks including closure of schools became the new normal. The decision to close schools is to bring stability between the risk associated with transmission in the school environment and the educational and welfare impact upon children of shutting down education establishments. While countries in the region are at various phases of evolution of the COVID-19 pandemic, facing a combination of common and unique challenges, each has begun to prepare and effectuate the safe reopening of schools. Objective: To assess community accordance to the essential actions at school level and support for school reopening in covid era and factors responsible for it. Methodology: A community based cross-sectional study was conducted among adults who were parents/ care takers/guardians of school children aged 5-15years residing in urban area. The proforma comprised of sociodemographic profile and the 21 Actions at School level as per WHO recommendation. Data was collected using Google Forms. Responses were presented as frequencies, percentages and Chi-square test. Results: A total of 615 adults were enrolled for the study. 44.44% of study participants accepted the 21 actions at school level and were in favour of school reopening. 60% were 32-45 years, 63.25% were literates and maximum were females. Significant associations were observed with study participants with younger age (p<0.0001), residing in nuclear families and children enrolled in private schools (p<0.00001), were more in favour of accepting the 21 actions at school level and willing to send the children back to school. Conclusion: There is a need to revaluate purpose, content and modes of delivery of education and to make adjustments that in the future strengthen multiple flexible learning pathway introduce innovative pedagogical models, and incorporate crisis-sensitive planning. [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]

Coronavirus Disease 2019 (COVID-19) is an infectious respiratory illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The disease, first identified in the Chinese city of Wuhan in November 2019, has since spread worldwide, is the latest human pandemic and has officially infected over 800 million people and has caused nearly seven million deaths to date. Although SARS-CoV-2 belongs to the large family of coronaviruses, it has some unique biological characteristics in its interplay with the human host. Therefore, this narrative review aims to provide an up-to-date overview of the structure of the virus, incubation and shedding in the human host, infectivity and biological evolution over time, as well as the main mechanisms for invading human host cells and replicating within. We also proffer that ongoing epidemiological surveillance of newly emerged variants must always be accompanied by biological studies aimed at deciphering new advantageous traits that may contribute to increasing virulence and pathogenicity, such that the most appropriate strategies for establishing a (relatively) safe coexistence with the human host can be implemented. [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]

• Use Machine Learning models to predict the mortality of COVID19 patients. • Analyse patient case studies which may help in better personalised treatment. • Apply Shapley Values to interpret the Machine Learning models predictions. • Perform what-if analysis to see how patient mortality outcomes change. In this paper we apply a series of Machine Learning models to a recently published unique dataset on the mortality of COVID19 patients. We use a dataset consisting of blood samples of 375 patients admitted to a hospital in the region of Wuhan, China. There are 201 patients who survived hospitalisation and 174 patients who died whilst in hospital. The focus of the paper is not only on seeing which Machine Learning model is able to obtain the absolute highest accuracy but more on the interpretation of what the Machine Learning models provides. We find that age , days in hospital , Lymphocyte and Neutrophils are important and robust predictors when predicting a patients mortality. Furthermore, the algorithms we use allows us to observe the marginal impact of each variable on a case-by-case patient level, which might help practicioneers to easily detect anomalous patterns. This paper analyses the global and local interpretation of the Machine Learning models on patients with COVID19. [ABSTRACT FROM AUTHOR]

COVID-19 was first identified in December 2019 in Wuhan, China. It mainly affects the respiratory system and can lead to the death of the patient. The motivation for this study was the current pandemic situation and general deficiency of emergency mechanical ventilators. The paper presents the development of a mechanical ventilator and its control algorithm. The main feature of the developed mechanical ventilator is AmbuBag compressed by a pneumatic actuator. The control algorithm is based on an adaptive neuro-fuzzy inference system (ANFIS), which integrates both neural networks and fuzzy logic principles. Mechanical design and hardware design are presented in the paper. Subsequently, there is a description of the process of data collecting and training of the fuzzy controller. The paper also presents a simulation model for verification of the designed control approach. The experimental results provide the verification of the designed control system. The novelty of the paper is, on the one hand, an implementation of the ANFIS controller for AmbuBag pressure control, with a description of training process. On other hand, the paper presents a novel design of a mechanical ventilator, with a detailed description of the hardware and control system. The last contribution of the paper lies in the mathematical and experimental description of AmbuBag for ventilation purposes. [ABSTRACT FROM AUTHOR]

COVID-19 is the common name of the disease caused by the novel coronavirus (2019-nCoV) that appeared in Wuhan, China in 2019. Discovering the infected people is the most important factor in the fight against the disease. The gold-standard test to diagnose COVID-19 is polymerase chain reaction (PCR), but it takes 5-6 h and, in the early stages of infection, may produce false-negative results. Examining Computed Tomography (CT) images to diagnose patients infected with COVID-19 has become an urgent necessity. In this study, we propose a residual attention deep support vector data description SVDD (RADSVDD) approach to diagnose COVID-19. It is a novel approach combining residual attention with deep support vector data description (DSVDD) to classify the CT images. To the best of our knowledge, we are the first to combine residual attention with DSVDD in general, and specifically in the diagnosis of COVID-19. Combining attention with DSVDD naively may cause model collapse. Attention in the proposed RADSVDD guides the network during training and enables quick learning, residual connectivity prevents vanishing gradients. Our approach consists of three models, each model is devoted to recognizing one certain disease and classifying other diseases as anomalies. These models learn in an end-to-end fashion. The proposed approach attained high performance in classifying CT images into intact, COVID-19, and non-COVID-19 pneumonia. To evaluate the proposed approach, we created a dataset from published datasets and had it assessed by an experienced radiologist. The proposed approach achieved high performance, with the normal model attained sensitivity (0.96-0.98), specificity (0.97-0.99), F1-score (0.97-0.98), and area under the receiver operator curve (AUC) 0.99; the COVID-19 model attained sensitivity (0.97-0.98), specificity (0.97-0.99), F1-score (0.97-0.99), and AUC 0.99; and the non-COVID pneumoniamodel attained sensitivity (0.97-1), specificity (0.98-0.99), F1-score (0.97-0.99), and AUC 0.99. [ABSTRACT FROM AUTHOR]