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

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

The novel coronavirus 2019 (COVID-19) emerges from the Chinese city Wuhan and its spread to the rest of the world, primarily affected economies and their businesses, leading to a global depression. The explanatory and cross-sectional regression approach assesses the impact of COVID-19 cases on healthcare expenditures, logistics performance index, carbon damages, and corporate social responsibility in a panel of 77 countries. The results show that COVID-19 cases substantially increase healthcare expenditures and decrease corporate social responsibility. On the other hand, an increase in the coronavirus testing capacity brings positive change in reducing healthcare expenditures, increased logistics activities, and corporate social responsibility. The cost of carbon emissions increases when corporate activities begin to resume. The economic affluence supports logistics activities and improves healthcare infrastructure. It linked to international cooperation and their assistance to supply healthcare logistics traded equipment through mutual trade agreements. The greater need to enhance global trade and healthcare logistics supply helps minimize the sensitive coronavirus cases that are likely to provide a safe and healthy environment for living. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak initiated from the Chinese city of Wuhan and eventually affected almost every nation around the globe. From China, the disease started spreading to the rest of the world. After China, Italy became the next epicentre of the virus and witnessed a very high death toll. Soon nations like the USA became severely hit by SARS-CoV-2 virus. The World Health Organisation, on 11th March 2020, declared COVID-19 a pandemic. To combat the epidemic, the nations from every corner of the world has instituted various policies like physical distancing, isolation of infected population and researching on the potential vaccine of SARS-CoV-2. To identify the impact of various policies implemented by the affected countries on the pandemic spread, a myriad of AI-based models have been presented to analyse and predict the epidemiological trends of COVID-19. In this work, the authors present a detailed study of different artificial intelligence frameworks applied for predictive analysis of COVID-19 patient record. The forecasting models acquire information from records to detect the pandemic spreading and thus enabling an opportunity to take immediate actions to reduce the spread of the virus. This paper addresses the research issues and corresponding solutions associated with the prediction and detection of infectious diseases like COVID-19. It further focuses on the study of vaccinations to cope with the pandemic. Finally, the research challenges in terms of data availability, reliability, the accuracy of the existing prediction models and other open issues are discussed to outline the future course of this study. [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]

On 31 December 2019, WHO received inputs of pneumonia cases in Wuhan city of China, but the cause of infection could not be known initially. However, after some time, the medical researchers of China disclosed a novel coronavirus as the cause on 7 January 2020, named "2019-nCoV." The coronavirus (CoV) belongs to Severe Acute Respiratory Syndrome (SARS) species that leads to infection, causing illness, which ranges from the common cold to serious sickness. Finally, on 11 March 2020, the WHO Director-General Dr. Tedros Adhanom Ghebreyesus announced the outbreak as a pandemic. As the fear and ambiguity rose among companies and firms, the profit rate seemed to be lower due to the COVID-19 global impact. Nearly US$6 trillion in wealth from 24th to 28 February 2020 of the stock market has been wiped out. There was a great decrease in value over the S&P index, which abolished over $5 trillion in the same week. However, the largest 10 companies of S&P faced a loss of $1.4 trillion. The investors make an analytical prediction that firms' profits may drop in response to the impact of coronavirus. The prime focus is on the importance of digital business practices and how different sectors have been affected in terms of economic loss during this pandemic outbreak in this paper. [ABSTRACT FROM AUTHOR]

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

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]

The corona virus disease (COVID-19), which has been characterized as a pandemic by world health organization (WHO), is attacking society at its core. Pandemics in general are not merely serious public health concerns, but also trigger catastrophic socio-economic consequences in the infected countries. COVID-19 disease emerged in Wuhan city, the capital of Hubei Province in the People's Republic of China on 31-Dec-2019. The epidemiological profile of the infection has revealed that it has so far affected 216 countries, and has infected over 9 million people globally, as per the World Health Organization (WHO) COVID-19 report. India's total COVID-19 infections as on 02-Feb-2020 were 10, 767, 206 (10.7 million approximately) cases with 1, 54, 522 (1.4%) case fatalities as on 02-Feb-2021. Out of 186,514 active cases, fatalities were 14,989 (8%) as per the Ministry of Health and Family Welfare, Government of India (2021). Genomic analysis revealed that SARS-CoV-2 is phylogenetically related to severe acute respiratory syndrome (SARS) like Bat viruses. Therefore, Bats possibly could be the primary reservoir of COVID-19. The transmission profile has confirmed asymptomatic and human-to- human transmission through close contacts in familial and social settings. The intermediate source of origin and transfer to humans is not known. However, rapid human to human transfer has been confirmed widely and person-to-person transmission of SARS-CoV-2 have occurred between two persons with prolonged, unprotected exposure with the symptomatic patient. It has infected humans in all age groups, of all ethnicities, and both sexes (males and females) while spreading through communities at an alarming rate. Infected patients experience common cold-like symptoms along with raised temperature, non-productive coughing and difficulty to breathe. COVID-19 has rapidly and adversely affected the life and livelihood of mankind. This paper portrays the genetic configuration, epidemiological profile, and mode of transmission of COVID-19 and its impacts on environment in general and society in particular pertaining to health care, education, agriculture, family relation, industry and employment, mental wellbeing, domestic discord, tourism, life and livelihood, social liaison, and delineates global strategies for COVID-19 prevention and control. [ABSTRACT FROM AUTHOR]

The lifecycle of COVID-19 pandemic curves requires timely decisions to protect public health while minimizing the impact to global economy. New models are necessary to predict the effect of mobility suppression/reactivation decisions at a global scale. This research presents an approach to understand such tensions by modeling air travel restrictions during the new coronavirus outbreak. The paper begins with an updated review on the impact of air mobility in infectious disease progression, followed by the adoption of complex networks based on semi-supervised statistical learning. The model can be used to (1) determine the early identification of infectious disease spread via air travel and (2) align the need to keep the economy working with open connections and the different dynamic of national pandemic curves. The approach takes advantage of open data and machine self-supervised statistical learning to develop knowledge networks visualization. Test cases using Hong Kong and Wuhan aerial mobility are discussed in the decisions to (1) restrict and (2) increase mobility. The approach may also be of governments use in their international cooperation policy and commercial companies that need to choose how to prioritize the re-opening of international trade routes. [ABSTRACT FROM AUTHOR]

The outbreak of a new, potentially fatal virus, SARS‐COV‐2, which started in December 2019 in Wuhan, China, and since developed into a pandemic has stimulated research for an effective treatment and vaccine. For this research to be successful, it is necessary to understand the pathology of the virus. So far, we know that this virus can harm different organs of the body. Although the exact mechanisms are still unknown, this phenomenon may result from the body's secretion of prostaglandin E2 (PGE2), which is involved in several inflammation and immunity pathways. Noticeably, the expression of this molecule can lead to a cytokine storm causing a variety of side effects. In this paper, we discuss those side effects in SARS‐COV‐2 infection separately to determine whether PGE2 is, indeed, an important causative factor. Lastly, we propose a mechanism by which PGE2 production increases in response to COVID‐19 disease and suggest the possible direct relation between PGE2 levels and the severity of this disease. Also see the video abstract here: https://youtu.be/SnPFAcjxxKw. [ABSTRACT FROM AUTHOR]

The whole world is reeling under the brunt of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2 virus or COVID-19 disease) at present. Ever since the first case of this highly contagious virus was detected in Jinyintan Hospital in Wuhan, in December 2019, the disease has spread rapidly in Saudi Arabia and entire world. The current situation is extremely critical and as per the data released by WHO (the World Health Organisation), till 22 May 2020, there were 5,265,750 confirmed cases in the world. In Saudi Arabia alone there were 67,719 COVID-19 patients till 22 May, 2020 and the numbers continue to rise as more people get afflicted by this virus. Generating appropriate, knowledgeable and consistent estimates for the pandemic COVID-19 disease is an enormous challenge. Tackling this virus has become the key objective of both the government as well as the entire medical and scientific community across the world. Responding to the compelling contingency of COVID-19 pandemic, this research pursuit intends to develop a computational model that can be used to gauge the spread of Coronavirus and predict the most vulnerable regions that are likely to be affected by it. Furthermore, the framework of the model also analyzes the impact of Coronavirus on various societal factors. Once identified, these factors can be prioritized so as to device accurate and efficient countermeasures. For developing the expected model, the study has adopted the Fuzzy-AHP technique of computational modeling. Moreover, a decision making technique and severity analysis technique along with the prioritization Fuzzy-AHP approach has been employed to validate the efficacy of the suggested model. Through rigorous study of different models of COVID-19, the proposed framework provides the best model to use according to the social factors prevalent in a country. After evaluation through fuzzy AHP, the priority of proposed model is found to be the highest amongst the seven other models used for comparison. Though the present study is aimed at adding to the government’s initiatives to counter the current health emergency in the Kingdom of Saudi Arabia, the proposed computational model can be an effective aid for the specialists across the world working on containment strategies to minimise the spread of Coronavirus. [ABSTRACT FROM AUTHOR]

In December 2019, the first cases of a new contagious disease were diagnosed in the city of Wuhan, the capital of Hubei province in China. Within a short period of time the outbreak developed exponentially into a pandemic that infected millions of people, with a global death toll of more than 500,000 during its first 6 months. Eventually, the novel disease was named coronavirus disease 2019 (COVID-19), and the new virus was identified as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Similar to all known pandemics throughout history, COVID-19 has been accompanied by a large degree of fear, anxiety, uncertainty, and economic disaster worldwide. Despite multiple publications and increasing knowledge regarding the biological secrets of SARS-CoV-2, as of the writing of this paper, there is neither an approved vaccine nor medication to prevent infection or cure for this highly infectious disease. Past pandemics were caused by a wide range of microbes, primarily viruses, but also bacteria. Characteristically, a significant proportion of them originated in different animal species (zoonoses). Since an understanding of the microbial cause of these diseases was unveiled relatively late in human history, past pandemics were often attributed to strange causes including punishment from God, demonic activity, or volatile unspecified substances. Although a high case fatality ratio was common to all pandemic diseases, some striking clinical characteristics of each disease allowed contemporaneous people to clinically diagnose the infection despite null microbiological information. In comparison to past pandemics, SARS-CoV-2 has tricky and complex mechanisms that have facilitated its rapid and catastrophic spread worldwide. [ABSTRACT FROM AUTHOR]

Coronavirus Disease (COVID-19) is a new disease that emerged in Wuhan, China which spreads through close contact of people, often by small droplets produced during coughing or sneezing. Detail mechanism by which it spreads between people are under investigation. The World Health Organization (WHO) declared this disease as a pandemic after the severity of the disease increased. Many scientific reports gathered have suggested many drugs that could be potential candidates for the treatment. Although, clinical effectiveness has not been fully evaluated. In this review, we have aggregated the data from few research articles, official news websites and few review papers regarding its phylogenetic relation, genomic constitution, transmission, replication and in-silico analysis done by researchers for few potent drugs that are currently used to cure COVID-19. SARS-CoV-2 belongs to Betacoronavirus genus with Genome structure consists 14 Open Reading Frames (ORFs) that encode 27 proteins. Coronavirus replicates into the host cells having unique mechanisms like ribosome frame-shifting and synthesis of genomic and sub genomic RNAs. In-silico methods have the advantage that they can make fast predictions for a large set of compounds in a high-throughput mode and also make their prediction based on the structure of a compound even before it has been synthesized. In-silico softwares have been used to find or to improve a novel bioactive compound, which may exhibit a strong affinity to a particular target in the drug development process. [ABSTRACT FROM AUTHOR]

At the beginning of 2020, a suddenly appearing novel coronavirus (COVID-19) rapidly spread around the world. The outbreak of the COVID-19 pandemic in China occurred during the Spring Festival when a large number of migrants traveled between cities, which greatly increased the infection risk of COVID-19 across the country. Financially supported by the Wuhan government, and based on cellphone signaling data from Unicom (a mobile phone carrier) and Baidu location-based data, this paper analyzed the effects that city dwellers, non-commuters, commuters, and people seeking medical services had on the transmission risk of COVID-19 in the early days of the pandemic in Wuhan. The paper also evaluated the effects of the city lockdown policy on the spread of the pandemic outside and inside Wuhan. The results show that although the daily business activities in the South China Seafood Wholesale Market and nearby commuters' travel behaviors concentrated in the Hankou area, a certain proportion of these people were distributed in the Wuchang and Hanyang areas. The areas with relatively high infection risks of COVID-19 were scattered across Wuhan during the early outbreak of the pandemic. The lockdown in Wuhan closed the passageways of external transport at the very beginning, largely decreasing migrant population and effectively preventing the spread of the pandemic to the outside. However, the Wuhan lockdown had little effect on preventing the spread of the pandemic within Wuhan at that time. During this period, a large amount of patients who went to hospitals for medical services were exposed to a high risk of cross-infection without precaution awareness. The pandemic kept dispersing in three towns until the improvement of the capacity of medical treatment, the management of closed communities, the national support to Wuhan, and the implementation of a series of emergency responses at the same time. The findings in this paper reveal the spatiotemporal features of the dispersal of infection risk of COVID-19 and the effects of the prevention and control measures during the early days of the pandemic. The findings were adopted by the Wuhan government to make corresponding policies and could also provide supports to the control of the pandemic in the other regions and countries. [ABSTRACT FROM AUTHOR]

The new SARS-CoV-2 coronavirus has spread rapidly around the world since it was first reported in humans in Wuhan, China, in December 2019 after being contracted from a zoonotic source. This new virus produces the so-called coronavirus 2019 or COVID-19. Although several studies have supported the epidemiological hypothesis that weather patterns may affect the survival and spread of droplet-mediated viral diseases, the most recent have concluded that summer weather may offer partial or no relief of the COVID-19 pandemic to some regions of the world. Some of these studies have considered only meteorological variables, while others have included non-meteorological factors. The statistical and modelling techniques considered in this research line have included correlation analyses, generalized linear models, generalized additive models, differential equations, or spatio-temporal models, among others. In this paper we provide a systematic review of the recent literature on the effects of climate on COVID-19's global expansion. The review focuses on both the findings and the statistical and modelling techniques used. The disparate findings reported seem to indicate that the estimated impact of hot weather on the transmission risk is not large enough to control the pandemic, although the wide range of statistical and modelling approaches considered may have partly contributed to the inconsistency of the findings. In this regard, we highlight the importance of being aware of the limitations of the different mathematical approaches, the influence of choosing geographical units and the need to analyse COVID-19 data with great caution. The review seems to indicate that governments should remain vigilant and maintain the restrictions in force against the pandemic rather than assume that warm weather and ultraviolet exposure will naturally reduce COVID-19 transmission. [ABSTRACT FROM AUTHOR]

2019 Coronavirus disease (COVID-19) had a big impact in Italy, mainly concentrated in the northern part of the Country. All this was mainly due to similarities of this area with Wuhan in Hubei Province, according to geographical, environmental and socio-economic points of view. The basic hypothesis of this research was that the presence of atmospheric pollutants can generate stress on health conditions of the population and determine pre-conditions for the development of diseases of the respiratory system and complications related to them. In most cases the attention on environmental aspects is mainly concentrated on pollution, neglecting issues such as land management which, in some way, can contribute to reducing the impact of pollution. The reduction of land take and the decrease in the loss of ecosystem services can represent an important aspect in improving environmental quality. In order to integrate policies for environmental change and human health, the main factors analyzed in this paper can be summarized in environmental, climatic and land management. The main aim of this paper was to produce three different hazard scenarios respectively related to environmental, climatic and land management-related factors. A Spatial Analytical Hierarchy Process (AHP) method has been applied over thirteen informative layers grouped in aggregation classes of environmental, climatic and land management. The results of the health hazard maps show a disparity in the distribution of territorial responses to the pandemic in Italy. The environmental components play an extremely relevant role in the definition of the red zones of hazard, with a consequent urgent need to renew sustainable development strategies. The comparison of hazard maps related to different scenarios provides decision makers with tools to orient policy choices with a different degree of priority according to a place-based approach. In particular, the geospatial representation of risks could be a tool for legitimizing the measures chosen by decision-makers, proposing a renewed approach that highlights and takes account of the differences between the spatial contexts to be considered - Regions, Provinces, Municipalities - also in terms of climatic and environmental variables. • COVID-19 had a big impact in Italy, mainly in the northern part of the Country. • Reduction of land take and ecosystem services' loss improve environmental quality. • Environmental, climatic and land management can be leveraged for pandemic policies. • Geospatial representation of risks is a useful approach to support decision-makers. [ABSTRACT FROM AUTHOR]

COVID-19 pandemic erupted from Wuhan/China, has impacted the whole world in many ways drastically. Understanding such a pandemic, taking necessary precautions, and controlling a potential epidemic is essential. Modules and communities are typically and frequently represented in networks and analyzed with specific methods. Epidemic network analysis is a powerful method that provides us different information to interpret and make accurate decisions for pandemic events. The randomness of epidemic networks is considerably high; hence, it is essential to obtain consistent information from such networks. In this study, we proposed the network-based community detection of the early-stage COVID-19 associated with the basic reproduction number and the geographical distance between country locations. Worldwide confirmed cases between 22/01/2020 and 08/06/2020 had been analyzed for tenday periods. For this purpose, the Community Detection method was used. Therefore, (i) the community regions, (ii) the change of these regions and weighted nodal degrees of confirmed cases during the COVID-19, (iii) the relations between locations, and (iv) locations or regions which played an important role during the spread of this disease were obtained. The results of this study may help to reduce the reproduction number by lowering the average rate of contact in the early stage of a new pandemic. [ABSTRACT FROM AUTHOR]

In December 2019, the SARS-CoV-2 virus was first detected in Wuhan, China. Soon after, China became the first country in the world to enforce strict lockdown protocols in an effort to mitigate the spread of the disease. This study examines the perceptions of pandemic rules enforcement during the first COVID-19 lockdown in 2020 among 600 Chinese police officers. The analyses reveal that police departments' prompt and adequate adjustment to the pandemic, police officers' procedural justice in interacting with citizens, and police perceptions of police trustworthiness among the citizens all had direct and indirect effects on public compliance to lockdown enforcement during the pandemic. These findings affirm the role of police procedural justice in public compliance to law enforcement in China and stress the importance of organizational adjustment and public trust during an emergency situation such as the COVID-19 pandemic. Implications to pandemic-related policymaking and enforcement are also discussed. [ABSTRACT FROM AUTHOR]

The novel Coronavirus disease of 2019 (nCOV-19) is a viral outbreak noted first in Wuhan, China. This disease is caused by Severe Acute Respiratory Syndrome (SARS) Coronavirus (CoV)-2. In the past, other members of the coronavirus family, such as SARS and Middle East Respiratory Syndrome (MERS), have made an impact in China and the Arabian peninsula respectively. Both SARS and COVID-19 share similar symptoms such as fever, cough, and difficulty in breathing that can become fatal in later stages. However, SARS and MERS infections were epidemic diseases constrained to limited regions. By March 2020 the SARS-CoV-2 had spread across the globe and on March 11th, 2020 the World Health Organization (WHO) declared COVID-19 as pandemic disease. In severe SARS-CoV-2 infection, many patients succumbed to pneumonia. Higher rates of deaths were seen in older patients who had co-morbidities such as diabetes mellitus, hypertension, cardiovascular disease (CVD), and dementia. In this review paper, we discuss the effect of SARS-CoV-2 on CNS diseases, such as Alzheimer's-like dementia, and diabetes mellitus. We also focus on the virus genome, pathophysiology, theranostics, and autophagy mechanisms. We will assess the multiorgan failure reported in advanced stages of SARS-CoV-2 infection. Our paper will provide mechanistic clues and therapeutic targets for physicians and investigators to combat COVID-19. Unlabelled Image • SARS-CoV-2 and Dementia, Multiple Sclerosis, Encephalitis, and Parkinson's disease • SARS-CoV-2 and Diabetes Mellitus Coronaviruses and Autophagy • Protease Inhibitors and Repurposed Drugs for SARS-CoV-2 USFDA approved drugs-HCQ and Remdesivir. • SARS-CoV-2 diagnostics [ABSTRACT FROM AUTHOR]

The emergence of a novel human coronavirus (SARS-CoV-2) causing severe contagious respiratory tract infections presents a serious threat to public health worldwide. To date, there are no specific antiviral agents available for this disease, currently known as COVID-19. Therefore, genomic sequencing and therapeutic clinical trials are being conducted to develop effective antiviral agents. Several reports have investigated FDA-approved drugs as well as in silico virtual screening approaches such as molecular docking and modeling to find novel antiviral agents. Until now, antiparasitic drugs such as chloroquine have shown the most relevant results. Furthermore, there is an urgent need to understand the pathogenesis of this novel coronavirus, its transmission routes, surface survival and evolution in the environment. So far, the scientific community has indicated a possible transmission of COVID-19 via blood transfusion which is challenging in the case of asymptomatic individuals. Protocols for pathogen inactivation are also needed. In this paper, we reviewed recent findings about this life-threatening pandemic. [ABSTRACT FROM AUTHOR]

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

Currently, the world is facing a devastating pandemic of a novel coronavirus, which started as an outbreak of pneumonia of unknown cause in Wuhan city of China in December of 2019. According to the recent report of the World Health Organization (WHO), 210 countries convicted badly 1.8 million infections and almost 200,000 causalities. Due to the non-availability of the vaccination, delay strategies such as community distancing, travel restrictions, extension in breaks, use of face-mask, and self-quarantine are the effective treatments to control the pandemic of coronavirus. So, we proposed the delayed susceptible-exposed-infected-recovered model with a nonlinear incidence rate to study the effective role of control strategies. For this analysis, we discussed three types of equilibria of the model such as trivial, coronavirus free, and coronavirus existence with delay terms. The local and global stabilities are investigated by using well-posed notations like the Lasalle invariance principle, Routh–Hurwitz criterion, and Lyapunov function. In the end, some useful replications are presented. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in Wuhan, China, has led to the rapid development of Coronavirus disease 2019 (COVID-19) pandemic. COVID-19 represents a fatal disease with a great global public health importance. This study aims to develop a three-parameter Weibull mathematical model using continuous functions to represent discrete COVID-19 data. Subsequently, the model was applied to quantitatively analyze the characteristics for the mortality of COVID-19, including the age, sex, the length of symptom time to hospitalization time (SH), hospitalization date to death time (HD) and symptom time to death time time (SD) and others. A three-parameter mathematical model was developed by combining the reported cases in the Data Repository from the Center for Systems Science and Engineering at Johns Hopkins University and applied to estimate and analyze the characteristics for mortality of COVID-19. We found that the scale parameters of males and females were 5.85 and 5.45, respectively. Probability density functions in both males and females were negative skewness. 5% of male patients died under the age of 43.28 (44.37 for females), 50% died under 69.55 (73.25 for females), and 95% died under 86.59 (92.78 for females). The peak age of male death was 67.45 years, while that of female death was 71.10 years. The peak and median values of SH, HD and SD in male death were correspondingly 1.17, 5.18 and 10.30 days, and 4.29, 11.36 and 16.33 days, while those in female death were 1.19, 5.80 and 12.08 days, and 4.60, 12.44 and 17.67 days, respectively. The peak age of probability density in male and female deaths was 69.55 and 73.25 years, while the high point age of their mortality risk was 77.51 and 81.73 years, respectively. The mathematical model can fit and simulate the impact of various factors on IFR. From the simulation results of the model, we can intuitively find the IFR, peak age, average age and other information of each age. In terms of time factors, the mortality rate of the most susceptible population is not the highest, and the distribution of male patients is different from the distribution of females. This means that Self-protection and self-recovery in females against SARS-CoV-2 virus might be better than those of males. Males were more likely to be infected, more likely to be admitted to the ICU and more likely to die of COVID-19. Moreover, the infection fatality ration (IFR) of COVID-19 population was intrinsically linked to the infection age. Public health measures to protect vulnerable sex and age groups might be a simple and effective way to reduce IFR. [ABSTRACT FROM AUTHOR]

The COVID-19 (Coronavirus Disease 2019), caused by the SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) began in December 2019 in Wuhan, China. Until February 2021, there are 110 million of infected people, 60 million have recovered and approximately 2.5 million have passed away worldwide according to WHO. The coronavirus pandemic is evolving very rapidly and represents a risk for health care workers and society in general. Moreover, pandemic has tested the limits of health systems by raising questions about forms of prevention, management of infections with conventional therapies and the use of diagnostic tools. In this article we discussed the possible role of the nanostructured-graphene based materials as aid tools for preventing the spread and infection of SARS-CoV-2. In this regard, nanotechnology could take part in the fight against the spread of future diseases caused by deadly viruses. However, its use should be well founded in terms of biocompatibility. Therefore, we have proposed an approach based on graphene nanomaterials as possible allies for the fight against the COVID-19 spread based on the physicochemical features that present these novel nanomaterials. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID‐19) is a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). The disease was first reported in December 2019 in Wuhan, China, but now more than 200 countries have been affected and the coronavirus pandemic is still ongoing. The severity of COVID‐19 symptoms can range from mild to severe. FDA approved remdesivir as a treatment of COVID‐19 so far. Various clinical trials are underway to find an effective method to treat patients with COVID‐19. This review aimed at summarizing 219 registered clinical trials in the ClinicalTrials.gov database with possible mechanisms, and novel findings of them, and other recent publications related to COVID‐19. According to our analyses, various treatment approaches and drugs are being investigated to find an effective drug to cure COVID‐19 and among all strategies, three important mechanisms are suggested to be important against COVID‐19 including antiviral, anti‐inflammatory, and immunomodulatory properties. Our review can help future studies get on the way to finding an effective drug for COVID‐19 treatment by providing ideas for similar researches. [ABSTRACT FROM AUTHOR]

An ongoing global outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; previously temporarily named 2019-nCoV) was reported in Wuhan, China at the end of 2019. SARS-CoV-2 has become a pandemic and a major public health concern. As of 18 January 2021, the virus has spread all over the world, resulting in over 99,026 cases in China and more than 95 million cases in another 216 countries. After three months of efforts, Chinese authorities have managed to control the outbreak by implementing aggressive and effective measures; by striking contrast, the number of confirmed patients outside China is still rapidly climbing following an exponential growth trend, especially in some European and American countries. To date, no specific therapeutic drugs still exist for COVID-19. Therefore, it is of utmost importance to understand the updated comprehensive summary regarding COVID-19, in the hopes of providing a reference for the intervention and prevention of the COVID‐19 epidemic for public health authorities and healthcare workers around the world. [ABSTRACT FROM AUTHOR]

Résumé: Une coïncidence entre les foyers les plus importants de Covid-19 et la pollution atmosphérique a été observée dès la première vague de la pandémie, en Chine dans la région de Wuhan puis dans le nord de l'Italie. Ceci a conduit des chercheurs à associer l'observation de cas graves avec les niveaux élevés de polluants atmosphériques dans ces régions industrielles très peuplées. Les particules atmosphériques fines et ultrafines ont été tout particulièrement suspectées de servir à la fois de support à la propagation du virus et de facteur d'aggravation de la maladie. Les données acquises au cours de ces derniers mois montrent que la pollution chronique par les particules fines (PM) et le dioxyde d'azote (NO2), plus que les pics de pollution, est bien associée au nombre de cas et aux formes graves de la maladie. Deux hypothèses explicatives sont formulées : une augmentation de l'inflammation pulmonaire chez des sujets déjà fragilisés avec une surexpression du récepteur ACE2 sous l'effet d'une exposition chronique à la pollution et le rôle de support de transport des particules atmosphériques pour le SARS-CoV-2, qui n'a en revanche pas été démontré. On observing a coincidence between larger COVID-19 clusters and atmospheric pollution in the first pandemic wave, in Wuhan, China, then in Northern Italy, researchers began to associate severe cases with elevated levels of atmospheric pollutants in heavily populated industrial regions. Fine and ultrafine particles in particular were suspected of carrying and spreading the virus and aggravating the disease. Data acquired in recent months show that chronic PM and NO2 pollution, rather than their peaks, are associated with case numbers and severe forms of the disease. There are two possible explanations: an increase in pulmonary inflammation among individuals already weakened by an overexpression of the ACE2 receptor under the effect of chronic pollution, and the idea that atmospheric particles transport SARS-CoV-2, which has not yet been demonstrated. [ABSTRACT FROM AUTHOR]

Virus diseases are a continued threat to human health in both community and healthcare settings. The current virus disease COVID-19 outbreak raises an unparalleled public health issue for the world at large. Wuhan is the city in China from where this virus came first and, after some time the whole world was affected by this severe disease. It is a challenge for every country's people and higher authorities to fight with this battle due to the insufficient number of resources. On-going assessment of the epidemiological features and future impacts of the COVID-19 disease is required to stay up-to-date of any changes to its spread dynamics and foresee needed resources and consequences in different aspects as social or economic ones. This paper proposes a prediction model of confirmed and death cases of COVID-19. The model is based on a deep learning algorithm with two long short-term memory (LSTM) layers. We consider the available infection cases of COVID-19 in India from January 22, 2020, till October 9, 2020, and parameterize the model. The proposed model is an inference to obtain predicted coronavirus cases and deaths for the next 30 days, taking the data of the previous 260 days of duration of the pandemic. The proposed deep learning model has been compared with other popular prediction methods (Support Vector Machine, Decision Tree and Random Forest) showing a lower normalized RMSE. This work also compares COVID-19 with other previous diseases (SARS, MERS, h1n1, Ebola, and 2019-nCoV). Based on the mortality rate and virus spread, this study concludes that the novel coronavirus (COVID-19) is more dangerous than other diseases. • A predictive model of confirmed and death cases of COVID-19 is proposed. • The predictive model is a deep learning model based on LSTM networks. • COVID-19 cases and deaths for the next 30 days are predicted. • The spread and mortality rates of five different virus diseases are compared. • It is concluded that COVID-19 is more dangerous than SARS, MERS, h1n1, and Ebola. [ABSTRACT FROM AUTHOR]

Background: The coronavirus disease-19 (COVID-19) was first reported in Wuhan, China, with Korea being subsequently exposed. In Korea, COVID-19 screening guidelines have been established in every hospital as an attempt to prevent its spread. There has been a previous report of a successful cesarean section of a confirmed mother; however, there remain no guidelines for suspected mothers. Cesarean section is often urgently operated without sufficient infection evaluations. We would like to suggest anesthetic management guidelines for cesarean section patients suspected of COVID-19. Case: Our hospital, which is located in Daegu, Korea, was designated as a quarantine and delivery facility for suspected mothers. We performed the cesarean section on seven suspected mothers and one confirmed mother. Conclusions: This case report presents guidelines for infection control during surgery and anesthesia for cesarean section of mothers with suspected COVID-19 involving operating room preparation and protection strategy. [ABSTRACT FROM AUTHOR]

A novel coronavirus member was reported in Wuhan City, Hubei Province, China, at the end of the year 2019. Initially, the infection spread locally, affecting the Wuhan people, and then expanded rapidly throughout the world. On 11 March 2020, the World Health Organization (WHO) proclaimed it a global pandemic. The virus is a new strain most closely related to a bat coronavirus (RaTG13) which was not previously discovered in humans and is now formally known as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Coronavirus disease 2019 (COVID-19) is the disease syndrome that the SARS-CoV-2 virus triggers. It is suggested that SARS-CoV-2 can be transmitted through aerosols, direct/indirect contact, and also during medical procedures and specimen handling. The infection is characterized by isolated flu-like symptoms, but there may be specific signs of fever, fatigue, cough, and shortness of breath, as well as the loss of smell and breathing difficulty. Within this report, we tried to review the most current scientific literature published by January 2021 on various aspects of the outbreak, including virus structure, pathogenesis, clinical presentation, epidemiology, diagnostic approaches, potential therapeutics and vaccines, and prospects. We hope this article makes a beneficial impact on public education to better deal with the SARS-CoV-2 crisis and push a step forward in the near term towards its prevention and control. [ABSTRACT FROM AUTHOR]

COVID-19 is a Severe Acute Respiratory Syndrome (SARS), caused by SARS-CoV-2, a novel virus which belongs to the family Coronaviridae. It was first reported in December 2019 in the Wuhan city of China and soon after, the virus and hence the disease got spread to the entire world. As of February 26, 2021, SARS-CoV-2 has infected ~112.20 million people and caused ~2.49 million deaths across the globe. Although the case fatality rate among SARS-CoV-2 patient is lower (~2.15%) than its earlier relatives, SARS-CoV (~9.5%) and MERS-CoV (~34.4%), the SARS-CoV-2 has been observed to be more infectious and caused higher morbidity and mortality worldwide. As of now, only the knowledge regarding potential transmission routes and the rapidly developed diagnostics has been guiding the world for managing the disease indicating an immediate need for a detailed understanding of the pathogen and the disease-biology. Over a very short period of time, researchers have generated a lot of information in unprecedented ways in the key areas, including viral entry into the host, dominant mutation, potential transmission routes, diagnostic targets and their detection assays, potential therapeutic targets and drug molecules for inhibiting viral entry and/or its replication in the host including cross-neutralizing antibodies and vaccine candidates that could help us to combat the ongoing COVID-19 pandemic. In the current review, we have summarized the available knowledge about the pathogen and the disease, COVID-19. We believe that this readily available knowledge base would serve as a valuable resource to the scientific and clinical community and may help in faster development of the solution to combat the disease. [ABSTRACT FROM AUTHOR]

The new type of corona virus (SARS-COV-2) emerging in Wuhan, China has spread rapidly to the world and has become a pandemic. In addition to having a significant impact on daily life, it also shows its effect in different areas, including public health and economy. Currently, there is no vaccine or antiviral drug available to prevent the COVID-19 disease. Therefore, determination of protein interactions of new types of corona virus is vital in clinical studies, drug therapy, identification of preclinical compounds and protein functions. Protein–protein interactions are important to examine protein functions and pathways involved in various biological processes and to determine the cause and progression of diseases. Various high-throughput experimental methods have been used to identify protein–protein interactions in organisms, yet, there is still a huge gap in specifying all possible protein interactions in an organism. In addition, since the experimental methods used include cloning, labeling, affinity purification mass spectrometry, the processes take a long time. Determining these interactions with artificial intelligence-based methods rather than experimental approaches may help to identify protein functions faster. Thus, protein–protein interaction prediction using deep-learning algorithms has been employed in conjunction with experimental method to explore new protein interactions. However, to predict protein interactions with artificial intelligence techniques, protein sequences need to be mapped. There are various types and numbers of protein-mapping methods in the literature. In this study, we wanted to contribute to the literature by proposing a novel protein-mapping method based on the AVL tree. The proposed method was inspired by the fast search performance on the dictionary structure of AVL tree and was used to verify the protein interactions between SARS-COV-2 virus and human. First, protein sequences were mapped by both the proposed method and various protein-mapping methods. Then, the mapped protein sequences were normalized and classified by bidirectional recurrent neural networks. The performance of the proposed method was evaluated with accuracy, f1-score, precision, recall, and AUC scores. Our results indicated that our mapping method predicts the protein interactions between SARS-COV-2 virus proteins and human proteins at an accuracy of 97.76%, precision of 97.60%, recall of 98.33%, f1-score of 79.42%, and with AUC 89% in average. [ABSTRACT FROM AUTHOR]

An unusual episode of pneumonia happened in Hubei Province, Wuhan City(China) in December 2019.A novel coronavirus was recognized as the causative agent. The situation was declared a pandemic by WHO (World Health Organisation) and the disease was named as COVID-19. The outbreak progresses faster than SARS and MERS coronaviruses but has less mortality than the two precursors. COVID-19 cases continue to intensify, despite meticulous global containment and quarantine efforts. Gathered from the evidences currently published, this review deliberately summarizes the findings of COVID-19 disease transmission, pathogenesis, manifestations, analysis, treatment, and counteraction. [ABSTRACT FROM AUTHOR]

Motivation The outbreak of novel severe acute respiratory syndrome coronavirus (SARS-CoV-2, also known as COVID-19) in Wuhan has attracted worldwide attention. SARS-CoV-2 causes severe inflammation, which can be fatal. Consequently, there has been a massive and rapid growth in research aimed at throwing light on the mechanisms of infection and the progression of the disease. With regard to this data science is playing a pivotal role in in silico analysis to gain insights into SARS-CoV-2 and the outbreak of COVID-19 in order to forecast, diagnose and come up with a drug to tackle the virus. The availability of large multiomics, radiological, bio-molecular and medical datasets requires the development of novel exploratory and predictive models, or the customisation of existing ones in order to fit the current problem. The high number of approaches generates the need for surveys to guide data scientists and medical practitioners in selecting the right tools to manage their clinical data. Results Focusing on data science methodologies, we conduct a detailed study on the state-of-the-art of works tackling the current pandemic scenario. We consider various current COVID-19 data analytic domains such as phylogenetic analysis, SARS-CoV-2 genome identification, protein structure prediction, host–viral protein interactomics, clinical imaging, epidemiological research and drug discovery. We highlight data types and instances, their generation pipelines and the data science models currently in use. The current study should give a detailed sketch of the road map towards handling COVID-19 like situations by leveraging data science experts in choosing the right tools. We also summarise our review focusing on prime challenges and possible future research directions. Contact hguzzi@unicz.it , sroy01@cus.ac.in [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]

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 the midst of a global pandemic, hospitals around the world are working to meet the demand for patients ill with the 2019 coronavirus disease (COVID-19) caused by the novel coronavirus first identified in Wuhan, China. As the crisis unfolds, several countries have reported lower numbers as well as less morbidity and mortality for pediatric patients. Thus, pediatric centers find themselves pivoting from preparing for a patient surge to finding ways to support the regional response for adults. This study describes the response from 2 West Coast freestanding academic children's hospitals that were among the first cities in the United States impacted during this pandemic. [ABSTRACT FROM AUTHOR]

Early in the COVID-19 pandemic, predictions of international outbreaks were largely based on imported cases from Wuhan, China, potentially missing imports from other cities. We provide a method, combining daily COVID-19 prevalence and flight passenger volume, to estimate importations from 18 Chinese cities to 43 international destinations, including 26 in Africa. Global case importations from China in early January came primarily from Wuhan, but the inferred source shifted to other cities in mid-February, especially for importations to African destinations. We estimate that 10.4 (6.2 – 27.1) COVID-19 cases were imported to these African destinations, which exhibited marked variation in their magnitude and main sources of importation. We estimate that 90% of imported cases arrived between 17 January and 7 February, prior to the first case detections. Our results highlight the dynamic role of source locations, which can help focus surveillance and response efforts. Sparse testing early in the SARS-CoV-2 pandemic hinders estimation of the dates and origins of initial case importations. Here, the authors show that the main source of cases imported from China shifted from Wuhan to other Chinese cities by mid-February, especially for African locations. [ABSTRACT FROM AUTHOR]

During the outbreak of the novel coronavirus disease (COVID‐19) in Wuhan, the gastroenterology department of our hospital performed gastrointestinal endoscopy procedures using strict infection control measures. Thorough screening of incoming patients, separation of diagnostic and treatment areas, regional management, hierarchical protection, disinfection protocols, and other measures were enforced to prevent virus transmission during endoscopic treatments. During the COVID‐19 pandemic, between February and March 2020, 159 endoscopic examinations and treatments were performed, including emergency endoscopy for 17 patients. Among these, seven patients were either previously infected with or were suspected carriers of the virus. Using the aforementioned control measures, we did not encounter a single case of cross‐infection or infection among the patients or staff. The presented protocols may provide valuable insight regarding how to protect gastroenterology endoscopy units during the novel coronavirus disease pandemic. [ABSTRACT FROM AUTHOR]

In December 2019, an outbreak of a novel coronavirus (SARS-CoV-2) in Wuhan, China resulted in the current COVID-19 global pandemic. The human immune system has not previously encountered this virus, raising the important question as to whether or not protective immunity is generated by infection. Growing evidence suggests that protective immunity can indeed be acquired post-infection—although a handful of reinfection cases have been reported. However, it is still unknown whether the immune response to SARS-CoV-2 leads to some degree of long-lasting protection against the disease or the infection. This review draws insights from previous knowledge regarding the nature and longevity of immunity to the related virus, SARS-CoV, to fill the gaps in our understanding of the immune response to SARS-CoV-2. Deciphering the immunological characteristics that give rise to protective immunity against SARS-CoV-2 is critical to guiding vaccine development and also predicting the course of the pandemic. Here we discuss the recent evidence that characterises the adaptive immune response against SARS-CoV-2 and its potential implications for the generation of memory responses and long-term protection. [ABSTRACT FROM AUTHOR]

Background The new coronavirus (COVID-19) rapidly resulted in a pandemic. We report the characteristics of patients with severe or critical severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in Wuhan city, China, and the risk factors related to infection severity and death. Methods We extracted the demographic and clinical data of 7283 patients with severe COVID-19 infection from designated Wuhan hospitals as of 25 February 2020. Factors associated with COVID-19 critical illness and mortality were analysed using logistic- and Cox-regression analyses. Results We studied 6269 patients with severe COVID-19 illness and 1014 critically ill patients. The median (IQR) age was 64 (53–71) years; 51.2% were male, 38.9% were retirees and 7.4% had self-reported histories of chronic disease. Up to the end of the study, 1180 patients (16.2%) recovered and were discharged, 649 (8.9%) died and the remainder were still receiving treatment. The number of daily confirmed critical cases peaked between 23 January and 1 February 2020. Patients with advanced age [odds ratio (OR), 1.03; 95% confidence intervals (CIs), 1.03–1.04], male sex (OR, 1.57; 95% CI, 1.33–1.86) and pre-existing diabetes (OR, 2.11), hypertension (OR, 2.72), cardiovascular disease (OR, 2.15) or respiratory disease (OR, 3.50) were more likely to be critically ill. Compared with those who recovered and were discharged, patients who died were older [hazard ratio (HR), 1.04; 95% CI, 1.03–1.05], more likely to be male (HR, 1.74; 95% CI, 1.44–2.11) and more likely to have hypertension (HR, 5.58), cardiovascular disease (HR, 1.83) or diabetes (HR, 1.67). Conclusion Advanced age, male sex and a history of chronic disease were associated with COVID-19 critical illness and death. Identifying these risk factors could help in the clinical monitoring of susceptible populations. [ABSTRACT FROM AUTHOR]

COVID-19 disease has emerged as one of the life threatening threat to the society. A novel beta coronavirus causes it. It began as unidentified pneumonia of unknown etiology in Wuhan City, Hubei province in China emerged in December 2019. No vaccine has been produced till now. Mathematical models are used to study the impact of different measures used to decrease pandemic. Mathematical models have been designed to estimate the numbers of spreaders in different scenarios in the present manuscript. In the present manuscript, three different mathematical models have been proposed with different scenarios, such as screening, quarantine, andNPIs, to estimate the number of virus spreaders. The analysis shows that the numbers of COVID-19 patients will be more without screening the peoples coming from other countries. Since every people suffering fromCOVID-19 disease are spreaders. The screening and quarantine with NPIs have been implemented to study their impact on the spreaders. It has been found that NPI measures can reduce the number of spreaders. The NPI measures reduce the spread function's growth and provide decision makers more time to prepare with in dealing with the disease. [ABSTRACT FROM AUTHOR]

Coronavirus Disease 2019 (COVID-19) is the official name of a respiratory infectious disease caused by a new coronavirus that started first in Wuhan, China, and outspread worldwide with an unexpectedly fast speed. Flights have been canceled worldwide and transportation has been closed nationwide and across international borders. As a consequence, the economic activity has been stopped and stock markets have been dropped. The COVID-19 lockdown has several social and economic effects. Additionally, COVID-19 has caused several impacts on global migration. On the other hand, such lockdown, along with minimal human mobility, has impacted the natural environment somewhat positively. Overall carbon emissions have dropped, and the COVID-19 lockdown has led to an improvement in air quality and a reduction in water pollution in many cities around the globe. A summary of the existing reports of the environmental impacts of COVID-19 pandemic are discussed and the important findings are presented focusing on several aspects: air pollution, waste management, air quality improvements, waste fires, wildlife, global migration, and sustainability. [ABSTRACT FROM AUTHOR]

A novel coronavirus recently identified in Wuhan, China (2019-nCoV) has resulted in an increasing number of patients globally, and has become a highly lethal pathogenic member of the coronavirus family affecting humans. 2019-nCoV has established itself as one of the most threatening pandemics that human beings have faced, and therefore analysis and evaluation of all possible responses against infection is required. One such strategy includes utilizing the knowledge gained from the SARS and MERS outbreaks regarding existing antivirals. Indicating a potential for success, one of the drugs, remdesivir, under repurposing studies, has shown positive results in initial clinical studies. Therefore, in the current work, the authors have attempted to utilize the remdesivir–RdRp complex – RdRp (RNA-dependent RNA polymerase) being the putative target for remdesivir – to screen a library of the already reported RdRp inhibitor database. Further clustering on the basis of structural features and scoring refinement was performed to filter out false positive hits. Finally, molecular dynamics simulation was carried out to validate the identification of hits as RdRp inhibitors against novel coronavirus 2019-nCoV. The results yielded two putative hits which can inhibit RdRp with better potency than remdesivir, subject to further biological evaluation. [ABSTRACT FROM AUTHOR]

Novel coronavirus, COVID-19, erupted in Wuhan, China, in 2019 and has now spread to almost all countries in the world. Until the end of November 2020, there were over 50 × 106 people diagnosed with COVID-19 worldwide and it caused at least 1 × 106 deaths. These numbers are still increasing. To control the spread of the pandemic and to choose a suitable treatment plan, a fast, accurate, effective, and ready-to-use diagnostic method has become an important prerequisite. In this Review, we introduce the principles of multiple off-site and on-site detection methods for virus diagnosis, including qPCR-based, ELISA-based, CRISPR-based methods, etc. All of these methods have been successfully implanted on the microfluidic platform for rapid screening. We also summarize currently available diagnostic methods for the detection of SARS, MERS, and COVID-19. Some of them not only can be used to analyze the SARS and MERS but also have the potential for COVID-19 detection after modifications. Finally, we hope that understanding of current microfluidic-based detection approaches can help physicians and researchers to develop advanced, rapid, and appropriate clinical detection techniques that reduce the financial expenditure of the society, accelerate the examination process, increase the accuracy of diagnosis, and eventually suppress the worldwide pandemic. [ABSTRACT FROM AUTHOR]

COVID-19 (SARS-CoV-2) is currently a global pandemic. This paper will attempt to estimate global infection rates and potential resultant mortality in the absence of effective treatment and/or vaccination. Calculations are based on World Health Organisation data from Wuhan in China: 14% of infected cases are severe, 5% require intensive care and 4% die. Estimated infection rates and mortality rates at the level of continents and some individual countries (when these are of sufficient size) are tabulated. This pandemic may cause close to half a billion deaths, i.e. 6% of the global population - and potentially more. At the risk of sounding sensational, but with a sober sense of realism, healthcare risks being plunged into the Middle-Ages if the public do not do their part. Infection cannot occur in the absence of contact. The only way to mitigate these numbers is to apply social distancing and take the standard precautions so frequently reiterated by Public Health: hand washing, avoid touching the face and so on. These measures are crucial as the human cost is going to be unthinkable even in the best-case scenarios that epidemiologists are modelling. [ABSTRACT FROM AUTHOR]

The discovery of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) and the outbreak of coronavirus disease 2019 (COVID‐19) are causing public health emergencies. A handful pieces of literature have summarized its clinical and radiologic features, whereas therapies for COVID‐19 are rather limited. To evaluate the efficacy of convalescent plasma therapy in COVID‐19 patients, we did this timely descriptive study. Six laboratory‐confirmed COVID‐19 patients were enrolled and received the transfusion of ABO‐compatible convalescent plasma. The efficacy of this intervention was determined by the alleviation of symptoms, changes in radiologic abnormalities and laboratory tests. No obvious adverse effect observed during the treatment. Transfusion of convalescent plasma led to a resolution of ground‐glass opacities and consolidation in patients #1, #2, #3, #4, and #6. In patients #1 and #5 who presented with SARS‐CoV‐2 in throat swab, convalescent plasma therapy elicited an elimination of the virus. Serologic analysis indicated an immediate increase in anti‐SARS‐CoV‐2 antibody titers in patients #2 and #3, but not in patient #1. This study indicates that convalescent plasma therapy is effective and specific for COVID‐19. This intervention has a special significance for eliminating SARS‐CoV‐2 and is believed to be a promising state‐of‐the‐art therapy during COVID‐19 pandemic crisis. Highlights: Convalescent plasma is a specific therapy for COVID‐19. Convalescent plasma therapy is safe and effective in COVID‐19 patients. The SARS‐CoV‐2 neutralizing antibody in the convalescent plasma may directly lead to virus clearance. Convalescent plasma therapy improves both clinical symptoms and radiologic abnormalities. [ABSTRACT FROM AUTHOR]