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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]

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]

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]

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]

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]

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]

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]

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]

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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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]

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]

Background: At the end of December 2019, a new infectious disease was reported in Wuhan, China. It was a new type of coronavirus named COVID-19. The spread of COVID-19 created an emergency in the global health system and the elderly was identified as a vulnerable group to the disease. Materials and Methods: This is a systematic review conducted to manage the vulnerability of the elderly during the COVID-19 pandemic. Accordingly, all articles published in this field from the beginning of March 2019 to the end of June 2021 have been extracted from the following databases: Web of Science, PubMed, Scopus, Cochrane Library, Google Scholar, Irandoc, Magiran, MedLib, and SID. Results: High incidences of COVID-19 are exacerbated in the elderly with cognitive disorders, immunodeficiency, malnutrition, use of various medicines and social problems, anxiety, distance from the family, lack of healthcare, history of falls, multidrug use due to old age during the COVID-19 pandemic, in addition to the elderly with underlying diseases, such as kidney failure, diabetes, high blood pressure, arthritis, heart, and respiratory diseases. Conclusion: The prevalence of vulnerability in the elderly was high during the COVID-19 pandemic, which can be a significant risk factor for health. Suffering from several simultaneous diseases, the number of medicines used, the history of falls, underlying diseases, and living alone were some of the vital determinants of vulnerability and considering the adverse consequences of vulnerability, difficulty in designing and implementing appropriate interventions and self-care education for the elderly and their families to manage drug use, treating chronic diseases, and preventing falls, it seems necessary to observe health protocols and stay at home. [ABSTRACT FROM AUTHOR]

The coronavirus pandemic is a worldwide hazard that poses a threat to millions of individuals throughout the world. This pandemic is caused by the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2), which was initially identified in Wuhan, China's Hubei provincial capital, and has since spread throughout the world. According to the World Health Organization's Weekly Epidemiological Update, there were more than 250 million documented cases of coronavirus infections globally, with five million fatalities. Early detection of coronavirus does not only reduce the spread of the virus, but it also increases the chance of curing the infection. Spectroscopic techniques have been widely used in the early detection and diagnosis of COVID-19 using Raman, Infrared, mass spectrometry and fluorescence spectroscopy. In this review, the reported spectroscopic methods for COVID-19 detection were discussed with emphasis on the practical aspects, limitations and applications. [ABSTRACT FROM AUTHOR]

Coronavirus disease (COVID-19), also known as Severe acute respiratory syndrome (SARS-COV2) and it has imposed deep concern on public health globally. Based on its fast-spreading breakout among the people exposed to the wet animal market in Wuhan city of China, the city was indicated as its origin. The symptoms, reactions, and the rate of recovery shown in the coronavirus cases worldwide have been varied . The number of patients is still rising exponentially, and some countries are now battling the third wave. Since the most effective treatment of this disease has not been discovered so far, early detection of potential COVID-19 patients can help isolate them socially to decrease the spread and flatten the curve. In this study, we explore state-of-the-art research on coronavirus disease to determine the impact of this illness among various age groups. Moreover, we analyze the performance of the Decision tree (DT), K-nearest neighbors (KNN), Naïve bayes (NB), Support vector machine (SVM), and Logistic regression (LR) to determine COVID-19 in the patients based on their symptoms. A dataset obtained from a public repository was collected and pre-processed, before applying the selected Machine learning (ML) algorithms on them. The results demonstrate that all the ML algorithms incorporated perform well in determining COVID-19 in potential patients. NB and DT classifiers show the best performance with an accuracy of 93.70%, whereas other algorithms, such as SVM, KNN, and LR, demonstrate an accuracy of 93.60%, 93.50%, and 92.80% respectively. Hence, we determine that ML models have a significant role in detecting COVID-19 in patients based on their symptoms. [ABSTRACT FROM AUTHOR]

Using Van Gennep's theory of Rite of Passage as its framework, this article examines the impact of Coronavirus (COVID-19) on Chinese culture as depicted through death and mourning in Wang Fang's (penname Fang Fang) recently published Wuhan Diary: Dispatches from a Quarantined City. As part of the efforts to control the outbreak, the Chinese government took over the managing of the deceased, which triggered heated discussions on Chinese social media. Fang Fang's diary, originally written as daily entries on Chinese social media platform Weibo, serves as a voice for those suffering during the pandemic, mediating between personal accounts, accounts of friends, family and those living in Wuhan during the pandemic. These flesh out how the virus has not only been disturbing for Chinese people's lives but also disrupted the death rites and mourning rituals for those who have passed. Our article infuses a digital ontological reading with an anthropological twist that helps to understand how the diary mitigates the disturbances to mourning rituals inside and outside the confines of digital metaphysics. We argue that the digital diary mitigates these disruptions by allowing Chinese people to nourish their sorrow by identifying with the symbolic rites of passage and mourning rituals online at the heart of the COVID-19 pandemic in Wuhan. In doing so, this article examines three stages of rite of passage, including separation, liminality and integration as they unfold in the diary, through which discourses and subjectivities based on collective and individual traumatic experiences are built, as a form of digital mourning that could reconcile both the official and the alternative voices of anonymous narratives about the handling of this crisis. [ABSTRACT FROM AUTHOR]

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

In late 2019, a novel coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) emerged in Wuhan, China. SARS‐CoV‐2 and the disease it causes, coronavirus disease 2019 (COVID‐19), spread rapidly and became a global pandemic in early 2020. SARS‐CoV‐2 spike protein is responsible for viral entry and binds to angiotensin converting enzyme 2 (ACE2) on host cells, making it a major target of the immune system – particularly neutralizing antibodies (nAbs) that are induced by infection or vaccines. Extracellular vesicles (EVs) are small membraned particles constitutively released by cells, including virally‐infected cells. EVs and viruses enclosed within lipid membranes share some characteristics: they are small, sub‐micron particles and they overlap in cellular biogenesis and egress routes. Given their shared characteristics, we hypothesized that EVs released from spike‐expressing cells could carry spike and serve as decoys for anti‐spike nAbs, promoting viral infection. Here, using mass spectrometry and nanoscale flow cytometry (NFC) approaches, we demonstrate that SARS‐CoV‐2 spike protein can be incorporated into EVs. Furthermore, we show that spike‐carrying EVs act as decoy targets for convalescent patient serum‐derived nAbs, reducing their effectiveness in blocking viral entry. These findings have important implications for the pathogenesis of SARS‐CoV‐2 infection in vivo and highlight the complex interplay between viruses, extracellular vesicles, and the immune system that occurs during viral infections. [ABSTRACT FROM AUTHOR]

A new type of coronavirus (COVID-19), detected at the end of December 2019 in Wuhan, China, can pass from person to person, spreading very quickly. The COVID-19 outbreak has created stress among societies. This study aims to evaluate the usability of Google Trends data in predicting and modeling the COVID-19 outbreak and the attitudes of different societies to it by using an infodemiological method. The authors collected the search words related to coronavirus and their relative search volume (RSV) from 11 different countries affected by the COVID-19 outbreak from Google Trends. A positive correlation was found between the trend rate of the words searched on the internet and the number of COVID-19 cases in countries related to the COVID-19 outbreak (p<0.05). There was a significant difference between 11 country societies in the daily RSV for the COVID-19 outbreak (p<0.05). The Turkish, South Korean, Iranian, and Swiss society have searched more intensely on the internet for COVID-19 than others. The research shows that Google Trends data can be used to build the forecast model for case numbers in the COVID-19 outbreak. Besides, Google Trends data provides information about different societies' attitudes in the COVID-19 outbreak. [ABSTRACT FROM AUTHOR]

Acute respiratory Syndrome (SARS) is an uncommon respiratory illness that is transmitted primarily by respiratory droplets or direct contact with an infected person. The COVID-19 initially appeared in late 2019 in Wuhan, China, and spread everywhere around the globe. Earlier in 2003, the coronavirus was known due to the human SARS-CoV virus, which infected 8,000 people and caused 800 deaths in southeast Asia and Canada. It should be mentioned that in 2012, another coronavirus, called Middle East respiratory syndrome coronavirus, infected Saudi Arabia and 27 other countries. The SARS-CoV2 belongs to a family of viruses called Coronaviruses that can infect bats, birds, pigs, cows, and different mammals. Some plant extracts are shown to inhibit microorganism replication and may be utilized in medication to treat infectious and non-infectious diseases, one of which is chamomile. chamomile medicinal plant, that has 120 biologically active compounds, the most necessary of which are flavonoids, like apigenin and luteolin, that are shown to possess antiviral activity. [ABSTRACT FROM AUTHOR]

In December 2019, a novel virus named COVID-19 emerged in the city of Wuhan, China. In early 2020, the COVID-19 virus spread in all continents of the world except Antarctica, causing widespread infections and deaths due to its contagious characteristics and no medically proven treatment. The COVID-19 pandemic has been termed as the most consequential global crisis since the World Wars. The first line of defense against the COVID-19 spread are the non-pharmaceutical measures like social distancing and personal hygiene. The great pandemic affecting billions of lives economically and socially has motivated the scientific community to come up with solutions based on computer-aided digital technologies for diagnosis, prevention, and estimation of COVID-19. Some of these efforts focus on statistical and Artificial Intelligence-based analysis of the available data concerning COVID-19. All of these scientific efforts necessitate that the data brought to service for the analysis should be open source to promote the extension, validation, and collaboration of the work in the fight against the global pandemic. Our survey is motivated by the open source efforts that can be mainly categorized as (a) COVID-19 diagnosis from CT scans, X-ray images, and cough sounds, (b) COVID-19 case reporting, transmission estimation, and prognosis from epidemiological, demographic, and mobility data, (c) COVID-19 emotional and sentiment analysis from social media, and (d) knowledge-based discovery and semantic analysis from the collection of scholarly articles covering COVID-19. We survey and compare research works in these directions that are accompanied by open source data and code. Future research directions for data-driven COVID-19 research are also debated. We hope that the article will provide the scientific community with an initiative to start open source extensible and transparent research in the collective fight against the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

In late 2019, a new virulent coronavirus (CoV) emerged in Wuhan, China and was named as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). This virus spread rapidly, causing the coronavirus disease-2019 (COVID-19) pandemic. Bacillus Calmette–Guérin (BCG) is a live attenuated tuberculosis (TB) vaccine, associated with induction of non-specific cross-protection against unrelated infections. This protection is a memory-like response in innate immune cells (trained immunity), which is caused by epigenetic reprogramming via histone modification in the regulatory elements of specific genes in monocytes. COVID-19 related epidemiological studies showed an inverse relationship between national BCG vaccination policies and COVID-19 incidence and death, suggesting that BCG may induce trained immunity that could confer some protection against SARS-CoV-2. As this pandemic has put most of Earth's population under quarantine, repurposing of the old, well-characterized BCG may ensure some protection against COVID-19. This review focuses on BCG-related cross-protection and acquisition of trained immunity, as well as the correlation between BCG vaccination and COVID-19 incidence and mortality. [ABSTRACT FROM AUTHOR]

Copyright of Polish Journal of Surgery is the property of Fundacja Polski Przedglad Chirurgiczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The pandemic of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) suggests a novel type of disease spread dynamics. We here study the case where infected agents recover and only develop immunity if they are continuously infected for some time τ. For large τ, the disease model is described by a statistical field theory. Hence, the phases of the underlying field theory characterise the disease dynamics: (i) a pandemic phase and (ii) a response regime. The statistical field theory provides an upper bound of the peak rate of infected agents. An effective control strategy needs to aim to keep the disease in the response regime (no 'second' wave). The model is tested at the quantitative level using an idealised disease network. The model excellently describes the epidemic spread of the SARS-CoV-2 outbreak in the city of Wuhan, China. We find that only 30% of the recovered agents have developed immunity. [ABSTRACT FROM AUTHOR]

The magnitude and pace of global affliction caused by Coronavirus Disease-19 (COVID-19) is unprecedented in the recent past. From starting in a busy seafood market in the Chinese city of Wuhan, the virus has spread across the globe in less than a year, infecting over 76 million people and causing death of close to 1.7 million individuals worldwide. As no specific antiviral treatment is currently available, the major strategy in containing the pandemic is focused on early diagnosis and prompt isolation of the infected individuals. Several diagnostic modalities have emerged within a relatively short period, which can be broadly classified into molecular and immunological assays. While the former category is centered around real-time PCR, which is currently considered the gold standard of diagnosis, the latter aims to detect viral antigens or antibodies specific to the viral antigens and is yet to be recommended as a stand-alone diagnostic tool. This review aims to provide an update on the different diagnostic modalities that are currently being used in diagnostic laboratories across the world as well as the upcoming methods and challenges associated with each of them. In a rapidly evolving diagnostic landscape with several testing platforms going through various phases of development and/or regulatory clearance, it is prudent that the clinical community familiarizes itself with the nuances of different testing modalities currently being employed for this condition. [ABSTRACT FROM AUTHOR]

In humans, coronaviruses can cause infections of the respiratory system, with damage of varying severity depending on the virus examined: ranging from mild-to-moderate upper respiratory tract diseases, such as the common cold, pneumonia, severe acute respiratory syndrome, kidney failure, and even death. Human coronaviruses known to date, common throughout the world, are seven. The most common—and least harmful—ones were discovered in the 1960s and cause a common cold. Others, more dangerous, identified in the early 2000s and cause more severe respiratory tract infections. Among these the SARS-CoV, isolated in 2003 and responsible for the severe acute respiratory syndrome (the so-called SARS), which appeared in China in November 2002, the coronavirus 2012 (2012-nCoV) cause of the Middle Eastern respiratory syndrome (MERS) from coronavirus, which exploded in June 2012 in Saudi Arabia, and actually SARS-CoV-2. On December 31, 2019, a new coronavirus strain was reported in Wuhan, China, identified as a new coronavirus beta strain ß-CoV from group 2B, with a genetic similarity of approximately 70% to SARS-CoV, the virus responsible of SARS. In the first half of February, the International Committee on Taxonomy of Viruses (ICTV), in charge of the designation and naming of the viruses (i.e., species, genus, family, etc.), thus definitively named the new coronavirus as SARS-CoV-2. This article highlights the main knowledge we have about the biomolecular and pathophysiologic mechanisms of SARS-CoV-2. [ABSTRACT FROM AUTHOR]

In December 2019, the novel betacoronavirus Severe Acute Respiratory Disease Coronavirus 2 (SARS-CoV-2) was first detected in Wuhan, China. SARS-CoV-2 has since become a pandemic virus resulting in hundreds of thousands of deaths and deep socioeconomic implications worldwide. In recent months, efforts have been directed towards detecting, tracking, and better understanding human humoral responses to SARS-CoV-2 infection. It has become critical to develop robust and reliable serological assays to characterize the abundance, neutralization efficiency, and duration of antibodies in virus-exposed individuals. Here we review the latest knowledge on humoral immune responses to SARS-CoV-2 infection, along with the benefits and limitations of currently available commercial and laboratory-based serological assays. We also highlight important serological considerations, such as antibody expression levels, stability and neutralization dynamics, as well as cross-reactivity and possible immunological back-boosting by seasonal coronaviruses. The ability to accurately detect, measure and characterize the various antibodies specific to SARS-CoV-2 is necessary for vaccine development, manage risk and exposure for healthcare and at-risk workers, and for monitoring reinfections with genetic variants and new strains of the virus. Having a thorough understanding of the benefits and cautions of standardized serological testing at a community level remains critically important in the design and implementation of future vaccination campaigns, epidemiological models of immunity, and public health measures that rely heavily on up-to-date knowledge of transmission dynamics. [ABSTRACT FROM AUTHOR]

Copyright of Eurasian Journal of Veterinary Sciences is the property of Eurasian Journal of Veterinary Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background COVID-19 infection has become a pandemic and a global health issue since its origin in Wuhan, China in December 2019. The present systematic review and meta-analysis aim to assess hematological changes seen in COVID-19 infection and their association with the severity of the disease. Methods Pooled proportions were calculated using both fixed effects model and random effects model. Weighted mean difference and 95% CI were calculated and reported. Results Initial search identified 84 reference articles, 23 relevant articles were selected and reviewed. Compared to general population, the weighted mean difference of WBC count in all COVID-19 patients was lower by 0.97 × 109 mm3 (95% CI = –1.29 to –0.66). In severe COVID-19 patients, the weighted mean difference of platelet count was lower by 23.85 × 109/liter (95% CI = –35.18 to –9.53), as compared to general population. The weighted mean difference of prothrombin time, D-Dimer, and fibrinogen in severe COVID-19 patients was higher by 1.92 seconds (95% CI = 0.01 to 3.84), 6.23 mg/liter (95% CI = 0.11 to 12.36) and 1.88 g/liter (95% CI = 1.18 to 2.48) respectively, as compared to general population. Pooled proportion showed D-Dimer to be elevated in 80.00% (95 % CI = 50.00 to 99.00) of severe patients. Conclusions Our meta-analysis shows that patients with COVID-19 have significant thrombocytopenia, leukopenia along with elevated D-dimer, fibrinogen and prothrombin time. These laboratory findings are marked in severe COVID–19 infections and could be helpful in early recognition of severe infection. [ABSTRACT FROM AUTHOR]

Coronavirus Disease 2019 (COVID-19) has been classified as a global threat, affecting millions of people and killing thousands. It is caused by the SARS-CoV-2 virus, which emerged at the end of 2019 in Wuhan, China, quickly spreading worldwide. COVID-19 is a disease with symptoms that range from fever and breathing difficulty to acute respiratory distress and death, critically affecting older patients and people with previous comorbidities. SARS-CoV-2 uses the angiotensin-converting enzyme 2 (ACE2) receptor and mainly spreads through the respiratory tract, which it then uses to reach several organs. The immune system of infected patients has been demonstrated to suffer important alterations, such as lymphopenia, exhausted lymphocytes, excessive amounts of inflammatory monocytes and macrophages, especially in the lungs, and cytokine storms, which may contribute to its severity and difficulty of establishing an effective treatment. Even though no specific treatment is currently available, several studies have been investigating potential therapeutic strategies, including the use of previously approved drugs and immunotherapy. In this context, this review addresses the interaction between SARS-CoV-2 and the patient's host immune system during infection, in addition to discussing the main immunopathological mechanisms involved in the development of the disease and potential new therapeutic approaches. [ABSTRACT FROM AUTHOR]

At the end of 2019, a novel coronavirus (CoV) was found at the seafood market of Hubei province in Wuhan, China, and this virus was officially named coronavirus diseases 2019 (COVID-19) by World Health Organization (WHO). COVID-19 is mainly characterized by severe acute respiratory syndrome coronavirus-2 (SARS-CoV2) and creates public health concerns as well as significant threats to the economy around the world. Unfortunately, the pathogenesis of COVID-19 is unclear and there is no effective treatment of this newly life-threatening and devastating virus. Therefore, it is crucial to search for alternative methods that alleviate or inhibit the spread of COVID-19. In this review, we try to find out the etiology, epidemiology, symptoms as well as transmissions of this novel virus. We also summarize therapeutic interventions and suggest antiviral treatments, immune-enhancing candidates, general supplements, and CoV specific treatments that control replication and reproduction of SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV). [ABSTRACT FROM AUTHOR]

The severe acute respiratory syndrome (SARS) is an infectious disease developed in Wuhan, China, at first. It involves the respiratory system and other organs like kidney, gastrointestinal tract and nervous system as well. The recent reports indicated that renal disorder is prevalent in coronavirus patients. The aim of this study was to provide a review of nephropathy caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) and its mechanisms. The Web of Science, Scopus, and PubMed databases were systematically searched. Articles reporting nephropathy, coronavirus disease (COVID-19), coronavirus and the renal injury were included for assessment. Study designs, contrast agents, case reports and results were assessed. Of the assessed studies, suggested mechanisms include sepsis which caused cytokine storm syndrome or perhaps direct cellular injury due to the virus. In patients who were studied, albuminuria, proteinuria, and hematuria as well as an elevation in blood urea nitrogen and serum creatinine were observed. Additionally CT scan of the kidneys showed a decrease in tissue density suggestive of inflammation and interstitial edema. On the other hand, dialysis patients are a high-risk group than the general population. The current treatment for COVID-19 in acute kidney injury includes supportive management or kidney replacement therapy. All patients need to be quarantined. An N95 fit-tested mask and protective clothing and proper equipment are necessary. Some drugs can be effective to inhibit the outcome of this infection such as lopinavir/ritonavir, remdesvir, Chloroquine phosphate, convalescent plasma, tocilizumab, ACEi/ARBs (angiotensin-converting enzyme inhibitor/angiotensin receptor blockers), and hrsACE2 (human recombinant soluble angiotensin-converting-enzyme 2). [ABSTRACT FROM AUTHOR]

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) outbreak that started in Wuhan, China, in 2019 resulted in a pandemic not seen for a century, and there is an urgent need to develop safe and efficacious vaccines. The scientific community has made tremendous efforts to understand the disease, and unparalleled efforts are ongoing to develop vaccines and treatments. Toxicologists and pathologists are involved in these efforts to test the efficacy and safety of vaccine candidates. Presently, there are several SARS-CoV-2 vaccines in clinical trials, and the pace of vaccine development has been highly accelerated to meet the urgent need. By 2021, efficacy and safety data from clinical trials are expected, and potentially a vaccine will be available for those most at risk. This review focuses on the ongoing SARS-CoV-2 vaccine development efforts with emphasis on the nonclinical safety assessment and discusses emerging preliminary data from nonclinical and clinical studies. It also provides a brief overview on vaccines for other coronaviruses, since experience gained from these can be useful in the development of SARS-CoV-2 vaccines. This review will also explain why, despite this unprecedented pace of vaccine development, rigorous standards are in place to ensure nonclinical and clinical safety and efficacy. This is a review article submitted to the Toxicologic Pathology Forum. It represents the views of the authors. It does not constitute an official position of the Society of Toxicologic Pathology, British Society of Toxicological Pathology, or European Society of Toxicologic Pathology, and the views expressed might not reflect the best practices recommended by these Societies. This article should not be construed to represent the policies, positions, or opinions of their respective organizations, employers, or regulatory agencies. [ABSTRACT FROM AUTHOR]

A highly contagious viral infection emerged in Wuhan city; China had increased mortality with uncertain pathogenesis spreads throughout the world to become a pandemic. It is reported to be caused by a member of β coronaviruses and named it as COVID-19 by the World Health Organization (WHO). The disease is caused by a mutant strain of coronavirus SARS-COV-2 that affects the respiratory tract causing mild to severe respiratory tract illness. The clinical manifestation ranges from mild, moderate, severe and very severe signs and symptoms result in death due to severe hypoxia or multi-organ dysfunction. Also, the affected persons were capable of infecting others through various modes of transmission through respiratory droplets (aerosol spread). A definite investigation protocol has followed to diagnose COVID 19 disease but mainly confirmed with reverse transcription polymerase chain reaction. Computerized tomography scan plays a significant role in the diagnosis and prognosis of affected individuals. The major problem with COVID-19 is due to its novelty and lack of vaccination or treatment. This review focuses on the mutation, pathogenesis, various diagnostic tests adopted and autopsy findings in patients affected with COVID-19. [ABSTRACT FROM AUTHOR]

In the advent of the novel coronavirus epidemic since December 2019, governments and authorities have been struggling to make critical decisions under high uncertainty at their best efforts. In computer science, this represents a typical problem of machine learning over incomplete or limited data in early epidemic Composite Monte-Carlo (CMC) simulation is a forecasting method which extrapolates available data which are broken down from multiple correlated/casual micro-data sources into many possible future outcomes by drawing random samples from some probability distributions. For instance, the overall trend and propagation of the infested cases in China are influenced by the temporal–spatial data of the nearby cities around the Wuhan city (where the virus is originated from), in terms of the population density, travel mobility, medical resources such as hospital beds and the timeliness of quarantine control in each city etc. Hence a CMC is reliable only up to the closeness of the underlying statistical distribution of a CMC, that is supposed to represent the behaviour of the future events, and the correctness of the composite data relationships. In this paper, a case study of using CMC that is enhanced by deep learning network and fuzzy rule induction for gaining better stochastic insights about the epidemic development is experimented. Instead of applying simplistic and uniform assumptions for a MC which is a common practice, a deep learning-based CMC is used in conjunction of fuzzy rule induction techniques. As a result, decision makers are benefited from a better fitted MC outputs complemented by min–max rules that foretell about the extreme ranges of future possibilities with respect to the epidemic. • Composite Monte-Carlo (CMC) simulation is a forecasting method. • A case study of using CMC through deep learning network is developed. • Decision makers are benefited from a better fitted Monte Carlo outputs. • Novel Coronavirus Epidemic is studied. [ABSTRACT FROM AUTHOR]

The severe acute respiratory syndrome coronavirus 2 originated in Wuhan, China at the end of 2019 and rapidly spread in more than 100 countries. Researchers in different fields have been working on finding explanations for the unequal impact of the virus and deaths from the associated coronavirus disease 2019 (COVID-19) across geographical areas. Demographers and other social scientists have hinted at the importance of demographic factors, such as age structure and intergenerational relationships. Our aim is to reflect on the possible link between intergenerational relationships and spread and lethality of COVID-19 in a critical way. We show that with available aggregate data it is not possible to draw robust evidence to support these links. In fact, despite a higher prevalence of intergenerational coresidence and contacts that is broadly positively associated with COVID-19 case fatality rates at the country level, the opposite is generally true at the subnational level. While this inconsistent evidence demonstrates neither the existence nor the absence of a causal link between intergenerational relationships and the severity of COVID-19, we warn against simplistic interpretations of the available data, which suffer from many shortcomings. We conclude by arguing that intergenerational relationships are not only about physical contacts between family members. Theoretically, different forms of intergenerational relationships may have causal effects of opposite sign on the diffusion of COVID-19. Policies should also take into account that intergenerational ties are a source of instrumental and emotional support, which may favor compliance to the lockdown and “phase-2” restrictions and may buffer their negative consequences on mental health. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the most recently discovered severe acute respiratory syndrome coronavirus 2. The outbreak of COVID-19 which was first detected in Wuhan, China, has evolved swiftly into a worldwide health crisis. The disease started spreading globally from 2019 and has resulted in the 2019–2020 coronavirus pandemic. Being a recently discovered disease, several aspects about it are unknown. Infection control measures are needed to control this pandemic situation. The characteristics of dental settings may give rise to the risk of cross-infection for both patients and dentists. Thus, certain guidelines should be followed by dental practitioners, and infection control protocols are urgently needed. Literature review was performed in PubMed, PubMed Central, EBSCO, and Google Scholar using keywords to identify and gather data. This article can serve as a guidance for dentists to deal with coronavirus disease. [ABSTRACT FROM AUTHOR]