Search

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has become pandemic, reaching almost one million death worldwide. At present standard treatment for coronavirus disease 2019 (COVID-19) is not well defined because the evidence, either from randomized or observational studies, with conflicting results, has led to rapid changes in treatment guidelines. Our aim was to narratively summarize the available literature on the management of COVID-19 in order to combine current evidence and interpretation of the data by experts who are treating patients in the frontline setting. The panel conducted a detailed review of the literature and eventual press releases from randomized clinical trials for each possible available treatment. Inductive PubMed search waws performed for publications relevant to the topic, including all clinical trials conducted. The result was a flowchart with treatment indications for patients with COVID-19. After 6 months of a pandemic situation and before a possible second coronavirus wave descends on Europe, it is important to evaluate which drugs proved to be effective while also considering that results from many randomized clinical trials are still awaited. Indeed, among treatments for COVID-19, only glucocorticoids have resulted in an association with a significant decrease in mortality in published randomized controlled trials. New therapeutic strategies are urgently needed. [ABSTRACT FROM AUTHOR]

[Display omitted] • An alternative to vaccination to combat the attack of coronavirus. • First simple model to determine the frequency range for coronavirus inactivation. • First to use the tensegrity concept to model the coronavirus. • Coronavirus inactivation by envelope fracturing is evident in model failure mode. • First natural frequency map for coronavirus vs healthy human cells as reference. COVID-19 is a worldwide health hazard. In discovering a fully efficient vaccination for continually evolving viruses, alternatives such as the sonication treatment methods, which have seen encouraging outcomes in disinfection and medical therapies, are worth exploring. Such treatments incapacitate microbes or diseased cells by selectively invoking large deformation at the resonant frequency that initiates structural failure. Nevertheless, due to biodiversity, the precise range of resonant frequencies for different biological bodies can be widely varying, its determination of which is best achieved with the cost-effective computational simulation approach. The paper proposes, therefore, the numerical determination of the resonant frequency of the coronavirus employing the tensegrity method, due to its efficacy in modeling various biomechanical behaviors of different biological bodies and systems. Initiating from successfully verifying the resonant frequency with existing findings, the model then determines the operative sonication frequencies range for the resonant state of the virus. At these sonication frequencies, the modal analysis exhibits a large structurally destructive deformation of the coronavirus. For practical convenience, the operative range of sonication resonant frequencies of the coronavirus has been mapped alongside healthy human cells. These findings offer an alternative technological avenue in combating the COVID-19 progressive threat. [ABSTRACT FROM AUTHOR]

A novel coronavirus (COVID-19) has taken the world by storm. The disease has spread very swiftly worldwide. A timely clue which includes the estimation of the incubation period among COVID-19 patients can allow governments and healthcare authorities to act accordingly. to undertake a review and critical appraisal of all published/preprint reports that offer an estimation of incubation periods for COVID-19. This research looked for all relevant published articles between the dates of December 1, 2019, and April 25, 2020, i.e. those that were related to the COVID-19 incubation period. Papers were included if they were written in English, and involved human participants. Papers were excluded if they were not original (e.g. reviews, editorials, letters, commentaries, or duplications). COVID-19 Open Research Dataset supplied by Georgetown's Centre for Security and Emerging Technology as well as PubMed and Embase via Arxiv, medRxiv, and bioRxiv. A data-charting form was jointly developed by the two reviewers (NZ and EA), to determine which variables to extract. The two reviewers independently charted the data, discussed the results, and updated the data-charting form. Screening was undertaken 44,000 articles with a final selection of 25 studies referring to 18 different experimental projects related to the estimation of the incubation period of COVID-19. The majority of extant published estimates offer empirical evidence showing that the incubation period for the virus is a mean of 7.8 days, with a median of 5.01 days, which falls into the ranges proposed by the WHO (0–14 days) and the ECDC (2–12 days). Nevertheless, a number of authors proposed that quarantine time should be a minimum of 14 days and that for estimates of mortality risks a median time delay of 13 days between illness and mortality should be under consideration. It is unclear as to whether any correlation exists between the age of patients and the length of time they incubate the virus. [ABSTRACT FROM AUTHOR]

Purpose; The COVID-19 pandemic has necessitated profound adaptations in the delivery of healthcare to manage a rise in critically unwell patients. In an attempt to slow the spread of the virus nationwide lockdown restrictions were introduced. This review aims to scope the literature on the impact of the pandemic and subsequent lockdown on the presentation and management of trauma globally. Methods; A scoping review was conducted in accordance with PRISMA-ScR guidelines. A systematic search was carried out on the Medline, EMBASE and Cochrane databases to identify papers investigating presentation and management of trauma during the COVID-19 pandemic. All studies based on patients admitted with orthopaedic trauma during the COVID-19 pandemic were included. Exclusion criteria were opinion-based reports, reviews, studies that did not provide quantitative data and papers not in English. Results; 665 studies were screened, with 57 meeting the eligibility criteria. Studies reported on the footfall of trauma in the UK, Europe, Asia, USA, Australia and New Zealand. A total of 29,591 patients during the pandemic were considered. Mean age was 43.7 years (range <1–103); 54.8% were male. Reported reductions in trauma footfall ranged from 20.3% to 84.6%, with a higher proportion of trauma occurring secondary to interpersonal violence, deliberate self-harm and falls from a height. A decrease was seen in road traffic collisions, sports injuries and trauma occurring outdoors. There was no significant change in the proportion of patients managed operatively, and the number of trauma patients reported to be COVID-19 positive was low. Conclusion; Whilst the worldwide COVID-19 pandemic has caused a reduction in the number of trauma patients; the services managing trauma have continued to function despite infrastructural, personnel and pathway changes in health systems. The substantial effect of the COVID-19 pandemic on elective orthopaedics is well described, however the contents of this review evidence minimal change in the delivery of effective trauma care despite resource constraints during this global COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

This document describes the behavior of the production of academic papers and high circulation newspapers, published under the terms "Coronavirus, Covid-19 or SARS-CoV-2". The purpose of this paper is to employ text mining techniques and qualitative data analysis for pattern identification (Dobrzyński, Dziekoński, Krzysztof, & Jurczuk, 2013; Westergaard, Staerfeldt, Tønsberg, Jensen, & Brunak, 2018). The interest of this study arises from the importance of this topic today and its relationship with the economy, business and society. As a result, the production of scientific papers has been led by the United States, China and Russia. In Latin America, Brazil is the country with the highest scientific output on this subject. The analysis of newspaper articles focusing on economic and business issues showed the work between large companies, the government and the community, as well as companies with businesses and the need to adapt to constant change in the face of the crisis of labor and production. [ABSTRACT FROM AUTHOR]

Since the onset of the new coronavirus pandemic, the world is facing a public health emergency. Repositioning hydroxychloroquine (HQ) seems to be a promising option. Many emerging evidences have converged on the effectiveness of HQ in the treatment of Covid-19 infection. In a recent paper, Gautret et al. suggested that further works are needed to determine if HQ antiviral prophylaxis is useful, especially for healthcare workers. The purpose of this paper is to assess the Covid-19 exposure and risks level among caregivers. For this, we performed research on internet and PubMed by crossing the following keywords: healthcare givers, healthcare workers, doctors, nurses, coronavirus, Covid-19, mortality, infection rate, chloroquine, hydroxychloroquine. Data on healthcare worker's infection and mortality by Covid-19 are partial and are not systematically published. However, it seems that the infection rate varies between 3.8% and 9% depending on the country. Moreover, the mean age of this population is relatively old, especially in the OECD area. Anti-Covid-19 HQ prophylaxis should be urgently accessed, especially for healthcare workers. It is to be hoped that HQ prophylaxis reduces the morbidity and mortality from Covid-19 infection among this population which is particularly exposed and relatively old. [ABSTRACT FROM AUTHOR]

The Coronavirus disease in 2019 (COVID-19), also referred to as the novel 'CoV19 (nCov19)' is caused by a new coronavirus strain similar to Severe Acute Respiratory Syndrome (SARS-CoV-2). SARS-CoV-2 spreads via respiratory droplets, saliva, or direct contact. Therefore it is important to control the viral load in the saliva and respiratory secretions. One of the most simple and cost-effective measures that can be adopted by the public and healthcare professionals to prevent cross-contamination and community transmission, is the implementation of effective oral and throat hygiene. Recent evidence has confirmed that 0.5% povidone iodine (PVP-I) mouthrinse/gargle for 30 s can reduce SARS-CoV-2 virus infectivity to below detectable levels. PVP-I can even interrupt SARS-CoV-2 attachment to oral and nasopharyngeal tissues and lower the viral particles in the saliva and respiratory droplets. Thus, the use of PVP-I mouthrinse as a prophylactic measure has been advocated across the globe to reduce disease transmission. Although the efficacy of PVP-I against SARS-CoV-2 is proven, no review articles have yet discussed the evidence and mechanisms of PVP-I against the SARS-CoV-2. Thus, this paper highlights the rationale, safety, recommendations, and dosage of PVP-I gargle/mouthrinse as an effective method to decrease the viral loads during the pressing times of COVID-19. [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]

[Display omitted] The recent ongoing outbreak of coronavirus disease 2019 (COVID-19), still is an unsolved problem with a growing rate of infected cases and mortality worldwide. The novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is targeting the angiotensin-converting enzyme 2 (ACE2) receptor and mostly causes a respiratory illness. Although acquired and resistance immunity is one of the most important aspects of alleviating the trend of the current pandemic; however, there is still a big gap of knowledge regarding the infection process, immunopathogenesis, recovery, and reinfection. To answer the questions regarding "the potential and probability of reinfection in COVID-19 infected cases" or "the efficiency and duration of SARS-CoV-2 infection-induced immunity against reinfection" we critically evaluated the current reports on SARS-CoV-2 immunity and reinfection with special emphasis on comparative studies using animal models that generalize their finding about protection and reinfection. Also, the contribution of humoral immunity in the process of COVID-19 recovery and the role of ACE2 in virus infectivity and pathogenesis has been discussed. Furthermore, innate and cellular immunity and inflammatory responses in the disease and recovery conditions are reviewed and an overall outline of immunologic aspects of COVID-19 progression and recovery in three different stages are presented. Finally, we categorized the infected cases into four different groups based on the acquired immunity and the potential for reinfection. In this review paper, we proposed a new strategy to predict the potential of reinfection in each identified category. This classification may help to distribute resources more meticulously to determine: who needs to be serologically tested for SARS-CoV-2 neutralizing antibodies, what percentage of the population is immune to the virus, and who needs to be vaccinated. [ABSTRACT FROM AUTHOR]

The Coronavirus (COVID-19) pandemic sparked rapid widespread adoption of telemedicine throughout specialties, including orthopaedics. A succession of factors, starting with payers, followed by provider adoption, and reciprocated by patient approval, created newly accepted telehealth practices that have moved from trial to norm in a short time span. Orthopaedic surgeons believe telehealth will endure as a permanent change to their practice beyond the COVID-19 era, however, many permanent changes must take place by payers, providers, and patients to sustain telehealth into the future. The goal of this paper is to highlight the indelibility of telemedicine adoption in orthopaedic practices globally, characterize the chain of events that led to its large-scale adoption, and catalyze discussion around actionable next steps to sustain its benefits. [ABSTRACT FROM AUTHOR]

• Vitamin D serum levels above 35 ng/mL seem to correlate with the mortality rate of COVID-19 patients. • The majority of the Western population is deficient in Vitamin D. • Vitamin D3 supplementation also requires additional vitamin K2 intake. • The immunoregulating function of vitamin D is promising and might decrease the global epidemic mortality. The world is desperately seeking for a sustainable solution to combat the coronavirus strain SARS-CoV-2 (COVID-19). Recent research indicated that optimizing Vitamin D blood levels could offer a solution approach that promises a heavily reduced fatality rate as well as solving the public health problem of counteracting the general vitamin D deficiency. This paper dived into the immunoregulatory effects of supplementing Vitamin D 3 by elaborating a causal loop diagram. Together with D 3 , vitamin K 2 and magnesium should be supplemented to prevent long-term health risks. Follow up clinical randomized trials are required to verify the current circumstantial evidence. [ABSTRACT FROM AUTHOR]

• Oxygen supplementation may disrupt virus replication. • Oxygen therapy can improve the antiviral immune response. • An oxygen-rich environment may down-regulate ACE2 expression. • Nocturnal oxygen therapy may delay the progression of COVID-19. There is currently no effective antiviral therapy or immune-based treatment for coronavirus disease (COVID-19). The urgent challenge is to prevent the transition of COVID-19 from mild to severe infection. This paper discussed nocturnal oxygen therapy as a new option for people with COVID-19 under home quarantine. It suggested that nocturnal oxygen therapy in the early stages may be helpful in preventing disease progression by inhibiting the rapid replication of the virus and improving the body's antiviral ability. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic caused by SARS-CoV-2 remains a significant issue for global health, economics and society. A wealth of data has been generated since its emergence in December 2019, and it is vital for clinicians to keep up with this data from across the world at a time of uncertainty and constantly evolving guidelines and clinical practice. Here we provide an update for clinicians on the recent developments in the virology, diagnostics, clinical presentation, viral shedding, and treatment options for COVID-19 based on current literature. We considered published peer-reviewed papers and non-peer-reviewed pre-print manuscripts on COVID19 and related aspects with an emphasis on clinical management aspects. We describe the virological characteristics of SARS-CoV-2 and the clinical course of COVID-19 with an emphasis on diagnostic challenges, duration of viral shedding, severity markers and current treatment options. The key challenge in managing COVID-19 remains patient density. However, accurate diagnosis as well as early identification and management of high-risk severe cases are important for many clinicians. For improved management of cases, there is a need to understand test probability of serology, qRT-PCR and radiological testing, and the efficacy of available treatment options that could be used in severe cases with a high risk of mortality. [ABSTRACT FROM AUTHOR]

The full impact of the coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), on the field of hematopoietic cell transplantation (HCT) is unknown. This perspective paper reviews the following: current COVID-19 epidemiology, diagnosis, and potential therapies; care considerations unique to HCT recipients; and the concept of a learning network to assimilate emerging guidelines and best practices and to optimize patient outcomes through facilitating shared learning and experience across transplantation centers. [ABSTRACT FROM AUTHOR]

Graph analytics are now considered the state-of-the-art in many applications of communities detection. The combination between the graph's definition in mathematics and the graphs in computer science as an abstract data structure is the key behind the success of graph-based approaches in machine learning. Based on graphs, several approaches have been developed such as shortest path first (SPF) algorithms, subgraphs extraction, social media analytics, transportation networks, bioinformatic algorithms, etc. While SPF algorithms are widely used in optimization problems, Spectral clustering (SC) algorithms have overcome the limits of the most state-of-art approaches in communities detection. The purpose of this paper is to introduce a graph-based approach of communities detection in the novel coronavirus Covid-19 countries' datasets. The motivation behind this work is to overcome the limitations of multiclass classification, as SC is an unsupervised clustering algorithm, there is no need to predefine the output clusters as a preprocessing step. Our proposed approach is based on a previous contribution on an automatic estimation of the k number of the output clusters. Based on dynamic statistical data for more than 200 countries, each cluster is supposed to group countries having similar behaviors of Covid-19 propagation. [ABSTRACT FROM AUTHOR]

At the end of 2019, a new form of Coronavirus, called COVID-19, has widely spread in the world. To quickly screen patients with the aim to detect this new form of pulmonary disease, in this paper we propose a method aimed to automatically detect the COVID-19 disease by analysing medical images. We exploit supervised machine learning techniques building a model considering a data-set freely available for research purposes of 85 chest X-rays. The experiment shows the effectiveness of the proposed method in the discrimination between the COVID-19 disease and other pulmonary diseases. [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]

• This paper investigates changes in persistence caused by the COVID 19 pandemic in the US and EA yield curves. • The long term, short term and medium term factors are extracted and the persistence is proxied by estimating the autoregressive coefficient of each factor. • To examine the time varying effects, we employ a local linear estimation. • Our findings suggest that the US long term and short term factors exhibited explosive behaviour while the EA factors diminished in persistence, making the EA yield curve more predictable This paper investigates changes in persistence caused by the COVID-19 pandemic in the US and EA yield curves. We extract the long-term, short-term and medium-term factors and proxy the persistence by estimating the autoregressive coefficient of each factor. To examine the time-varying effects, we employ a local linear estimation. Our findings suggest that, during the first phases of the pandemic, the US long-term and short-term factors exhibited explosive behaviour while, at the same time, the EA factors diminished in persistence, making the EA yield curve more predictable even though the EA countries were hit by the pandemic somewhat earlier than the US. [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]

[Display omitted] • The existing DWTPs are able to reduce the coronavirus, to date, the drinking water is safe. • The coronaviruses shed in the faeces and urine of infected individuals and then entered wastewater. • SARS-CoV-2 has been detected in a few standard WWTPs and they seem to be enough for removing the virus. • Wastewater monitoring could be a strategic tool for the epidemy evolution in communities. • Intensive studies for SARS-CoV-2 behavior in water and wastewater treatment are highly desirable. By 17 October 2020, the severe acute respiratory syndrome coronavirus (SARS-CoV-2) has caused confirmed infection of more than 39,000,000 people in 217 countries and territories globally and still continues to grow. As environmental professionals, understanding how SARS-CoV-2 can be transmitted via water and air environment is a concern. We have to be ready for focusing our attention to the prompt diagnosis and potential infection control procedures of the virus in integrated water and air system. This paper reviews the state-of-the-art information from available sources of published papers, newsletters and large number of scientific websites aimed to provide a comprehensive profile on the transmission characteristics of the coronaviruses in water, sludge, and air environment, especially the water and wastewater treatment systems. The review also focused on proposing the possible curb strategies to monitor and eventually cut off the coronaviruses under the authors' knowledge and understanding. [ABSTRACT FROM AUTHOR]

The ongoing fight with Novel Corona Virus, getting quick treatment, and rapid diagnosis reports have become an act of high priority. With millions getting infected daily and a fatality rate of 2%, we made it our motive to contribute a little to solve this real-world problem by accomplishing a significant and substantial method for diagnosing COVID-19 patients. The Exponential growth of COVID-19 cases worldwide has severely affected the health care system of highly populated countries due to proportionally a smaller number of medical practitioners, testing kits, and other resources, thus becoming essential to identify the infected people. Catering to the above problems, the purpose of this paper is to formulate an accurate, efficient, and time-saving method for detecting positive corona patients. In this paper, an Ensemble Deep Convolution Neural Network model "CoVNet-19" is being proposed that can unveil important diagnostic characteristics to find COVID-19 infected patients using X-ray images chest and help radiologists and medical experts to fight this pandemic. The experimental results clearly show that the overall classification accuracy obtained with the proposed approach for three-class classification among COVID-19, Pneumonia, and Normal is 98.28%, along with an average precision and Recall of 98.33% and 98.33%, respectively. Besides this, for binary classification between Non-COVID and COVID Chest X-ray images, an overall accuracy of 99.71% was obtained. Having a high diagnostic accuracy, our proposed ensemble Deep Learning classification model can be a productive and substantial contribution to detecting COVID-19 infected patients. • We have proposed a Stacked Ensemble Deep Learning Model called CoVNet-19. • We collected 6214 chest X-Ray scans from five different datasets. • Time and Cost-effective AI based method to diagnose COVID-19 patients. [ABSTRACT FROM AUTHOR]

Coronavirus is a pandemic that has become a concern for the whole world. This disease has stepped out to its greatest extent and is expanding day by day. Coronavirus, termed as a worldwide disease, has caused more than 8 lakh deaths worldwide. The foremost cause of the spread of coronavirus is SARS-CoV and SARS-CoV-2, which are part of the coronavirus family. Thus, predicting the patients suffering from such pandemic diseases would help to formulate the difference in inaccurate and infeasible time duration. This paper mainly focuses on the prediction of SARS-CoV and SARS-CoV-2 using the B-cells dataset. The paper also proposes different ensemble learning strategies that came out to be beneficial while making predictions. The predictions are made using various machine learning models. The numerous machine learning models, such as SVM, Naïve Bayes, K-nearest neighbors, AdaBoost, Gradient boosting, XGBoost, Random forest, ensembles, and neural networks are used in predicting and analyzing the dataset. The most accurate result was obtained using the proposed algorithm with 0.919 AUC score and 87.248% validation accuracy for predicting SARS-CoV and 0.923 AUC and 87.7934% validation accuracy for predicting SARS-CoV-2 virus. [ABSTRACT FROM AUTHOR]

• A new topic (COVID-19 epidemic) has been introduced in the literature of control theory. In addition, a new robust control algorithm has been applied to the COVID-19 nonlinear system after making some assumptions. • The data of the paper are valid for a certain period of time and the controller could be applied to the same system with different data. • The stability of the system has been achieved using Lyapunov stability analysis and the system performance has been improved much better. Moreover, the parameters of the proposed controller have been optimized using new optimization method called the Most Valuable Player Algorithm (MVPA). The MVPA has introduced for the first time to the literature of control theory. COVID-19 has been a worldwide concern since the outbreak. Many strategies have been involved such as suppression and mitigation strategies to deal with this epidemic. In this paper, a new mathematical-engineering strategy is introduced in order to control the COVID-19 epidemic. Thereby, control theory is involved in controlling the unstable epidemic alongside with the other suggested strategies until the vaccine will hopefully be invented as soon as possible. A new robust control algorithm is introduced to compensate the COVID-19 nonlinear system by propose a proper controller after using necessary assumptions and analysis are made. In addition, the Variable Transformation Technique (VTT) is used to simplify the COVID-19 system. Furthermore, the Most Valuable Player Algorithm (MVPA) is applied in order to optimize the parameters of the proposed controller. The simulation results are based on the daily reports of two cities Hubei (China) and Lazio (Italy) since the outbreak. It can be concluded that the proposed control algorithm can effectively compensate the COVID-19 system. In addition, it can be considered as an effective mathematical-engineering strategy to control this epidemic alongside with the other strategies. [ABSTRACT FROM AUTHOR]

Currently, the "2019-CoV-2" has been raging across the world for months, causing massive death, huge panic, chaos, and immeasurable economic loss. Such emerging epidemic viruses come again and again over years, leading to similar destructive consequences. Air-borne transmission among humans is the main reason for the rapid spreading of the virus. Blocking the air-borne transmission should be a significant measure to suppress the spreading of the pandemic. Considering the hospital is the most probable place to occur massive cross-infection among patients as emerging virus usually comes in a disguised way, an air distribution optimization of a general three-bed hospital ward in China is carried out in this paper. Using the Eulerian-Lagrangian method, sneeze process from patients who are assumed to be the virus carrier, which is responsible for a common event to trigger cross-infection, is simulated. The trajectory of the released toxic particle and the probability of approaching others in the same ward are calculated. Two evaluation parameter, total maximum time (TMT) and overall particle concentration (OPC) to reflect the particle mobility and probability to cause cross-infection respectively, are developed to evaluate the proposed ten air distributions in this paper. A relatively optimized air distribution proposal with the lowest TMT and OPC is distinguished through a three-stage analysis. Results show that a bottom-in and top-out air distribution proposal is recommended to minimize cross-infections. • A ward-based sneeze process from a patient with epidemic disease is modelled. • Air distribution (AD) affects the flow pattern of the sneeze-induced virus particles. • Ten AD proposals are developed to evaluate the their probability of cross-infection. • Two parameters are proposed to judge which proposals is better quantitatively. • The direct probability of cross-infection can be decreased by 95% via optimization. [ABSTRACT FROM AUTHOR]

Since the end of 2019, the world has faced a major crisis because of the outbreak of COVID-19 disease which has created a severe threat to humanity. To control this pandemic, the World Health Organization gave some guidelines like wearing PPE (personal protective equipment) (e.g., face masks, overshoes, gloves), social distancing, hand hygiene and shutting down all modes of public transport services. During this pandemic, plastic products (e.g., household plastics, PPE and sanitizer bottles) have substantially prevented the spread of this virus. Since the outbreak, approximately 1.6 million tons of plastic waste have been generated daily. However, single-use PPE like face masks (N95), surgical masks and hand gloves contain many non-biodegradable plastics materials. These abandoned products have created a huge number of plastic debris which ended up as microplastics (MPs) followed by nanoplastics (NPs) in nature that are hazardous to the eco-system. These MPs and NPs also act as vectors for the various pathogenic contaminants. The goal of this review is to offer an extensive discussion on the formation of NPs and MPs from all of these abandoned plastics and their long-term impact on the environment as well as human health. This review paper also attempts to assess the present global scenario and the main challenge of waste management to reduce the potential NP/MPs pollution to improve the eco-systems. [Display omitted] • Majority of Covid-19 plastic waste ends up in landfills in underdeveloped nations. • Impacts of nano/microplastics (NPs/MPs) on humans and organisms were reviewed. • NP/MPs release from personal protective equipment (PPE) because of weathering effects. • Novel research methods are needed to assess the toxicity of NP/MPs. [ABSTRACT FROM AUTHOR]

This paper investigates the correlation between the high level of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) lethality and the atmospheric pollution in Northern Italy. Indeed, Lombardy and Emilia Romagna are Italian regions with both the highest level of virus lethality in the world and one of Europe's most polluted area. Based on this correlation, this paper analyzes the possible link between pollution and the development of acute respiratory distress syndrome and eventually death. We provide evidence that people living in an area with high levels of pollutant are more prone to develop chronic respiratory conditions and suitable to any infective agent. Moreover, a prolonged exposure to air pollution leads to a chronic inflammatory stimulus, even in young and healthy subjects. We conclude that the high level of pollution in Northern Italy should be considered an additional co-factor of the high level of lethality recorded in that area. [ABSTRACT FROM AUTHOR]

• Comprehensive review of health monitoring based sensors. • Categorize the sensors into homogeneous, dual and heterogeneous. • Discuss their strengths, drawbacks and application domains. • Outline major issues and significant solutions. • Highlights future prospect of health monitoring sensors. Mobile and wearable devices embedded with multiple sensors for health monitoring and disease diagnosis are growing fields with the potential to provide efficient means for remote health management. A sensor-based health monitoring system offers an essential mechanism for real-time diagnosis and management to detect/predict, recommend treatment and prevent the onset of diseases. This paper aims to synthesize the research efforts on mobile and wearable sensors for health monitoring. It will investigate sensors, components of health monitoring systems, major application areas, challenges, and solutions faced during the implementation of health monitoring systems by researchers and practitioners. It was observed that sensors embedded in mobile and wearable devices for health monitoring are broadly categorized into homogeneous, dual, and heterogeneous sensors. In health monitoring, heterogeneous sensor-based is widely implemented and the most effective due to its ability to combine multiple sensors from various domains. The fusion of multiple sensors provides reliability, credibility, and better accuracy for monitoring multiple health parameters. We observed that researchers follow established procedures such as data collection, data transmission, preprocessing, feature extraction and development, data analysis, and evaluation of different algorithms for implementation of the health monitoring system. Supervised machine learning algorithms such as support vector machine, decision tree, k-nearest neighbors, and deep learning methods were the most implemented methods, while accuracy was the favored evaluation measure for health monitoring. Generally, we found that a health monitoring system is implemented to resolve health issues in the areas of human activity and posture monitoring, sleep disorder, sleep stage detection, fall monitoring in the elderly, depression, and mood swing detection. Other important areas include Parkinson's disease management, cardiac diseases monitoring, disease diagnosis, and well-being, and Corona virus detection and contact tracing to minimize infection rate. Furthermore, the review succinctly highlights various challenges impeding the development of sensor-based health monitoring systems with significant solutions that were recommended in the literature to ameliorate these challenges discussed. From the review, it can be acknowledged that various research efforts have been conducted to develop effective health monitoring systems, and many new systems have been implemented. However, there is still much work to be done which we have also discussed under future prospects. [ABSTRACT FROM AUTHOR]

Radiological methodologies, such as chest x-rays and CT, are widely employed to help diagnose and monitor COVID-19 disease. COVID-19 displays certain radiological patterns easily detectable by X-rays of the chest. Therefore, radiologists can investigate these patterns for detecting coronavirus disease. However, this task is time-consuming and needs lots of trial and error. One of the main solutions to resolve this issue is to apply intelligent techniques such as deep learning (DL) models to automatically analyze the chest X-rays. Nevertheless, fine-tuning of architecture and hyperparameters of DL models is a complex and time-consuming procedure. In this paper, we propose an effective method to detect COVID-19 disease by applying convolutional neural network (CNN) to the chest X-ray images. To improve the accuracy of the proposed method, the last Softmax CNN layer is replaced with a K -nearest neighbors (KNN) classifier which takes into account the agreement of the neighborhood labeling. Moreover, we develop a novel evolutionary algorithm by improving the basic version of competitive swarm optimizer. To this end, three powerful evolutionary operators: Cauchy Mutation (CM), Evolutionary Boundary Constraint Handling (EBCH), and tent chaotic map are incorporated into the search process of the proposed evolutionary algorithm to speed up its convergence and make an excellent balance between exploration and exploitation phases. Then, the proposed evolutionary algorithm is used to automatically achieve the optimal values of CNN's hyperparameters leading to a significant improvement in the classification accuracy of the proposed method. Comprehensive comparative results reveal that compared with current models in the literature, the proposed method performs significantly more efficient. • A novel deep neuroevolution algorithm for detecting COVID-19 from X-ray images. • Proposing a modified version of the Competitive Swarm Optimizer algorithm. • Tuning the hyperparameters and architecture of Convolutional Neural Networks. • Producing excellent results by proposed framework comparing to 23 benchmarks. [ABSTRACT FROM AUTHOR]

• We are achieving High Performance COVID-19 Detection compared to existing methods. • This method is less time consuming that RTPCR. The Coronavirus Disease 2019 (COVID-19) outbreak has a devastating impact on health and the economy globally, that's why it is critical to diagnose positive cases rapidly. Currently, the most effective test to detect COVID-19 is Reverse Transcription-polymerase chain reaction (RT-PCR) which is time-consuming, expensive and sometimes not accurate. It is found in many studies that, radiology seems promising by extracting features from X-rays. COVID-19 motivates the researchers to undergo the deep learning process to detect the COVID- 19 patient rapidly. This paper has classified the X-rays images into COVID- 19 and normal by using multi-model classification process. This multi-model classification incorporates Support Vector Machine (SVM) in the last layer of VGG16 Convolution network. For synchronization among VGG16 and SVM we have added one more layer of convolution, pool, and dense between VGG16 and SVM. Further, for transformations and discovering the best result, we have used the Radial Basis function. CovXmlc is compared with five existing models using different parameters and metrics. The result shows that our proposed CovXmlc with minimal dataset reached accuracy up to 95% which is significantly higher than the existing ones. Similarly, it also performs better on other metrics such as recall, precision and f-score. [ABSTRACT FROM AUTHOR]

Using daily data on policy rates from 28 advanced economies and 32 emerging markets, this paper investigates the monetary policy reaction function of central banks during the Coronavirus Disease 2019 (COVID-19) outbreak. The results show that emerging markets or countries without a zero bound on their interest rates were able to reduce interest rates as a reaction to reduced economic activity and to the volatility in their exchange rates, whereas advanced economies or countries with a zero bound on their interest rates were not. Several policy implications follow for countries with a zero bound on their interest rates amid COVID-19. [ABSTRACT FROM AUTHOR]

Almost 80% of people confronting COVID-19 recover from COVID-19 disease without any particular treatments. They experience heterogeneous symptoms; a wide range of respiratory symptoms, cough, dyspnea, fever, and viral pneumonia. However, some others need urgent intervention and special treatment to get rid of this widespread disease. So far, there isn't any unique drug for the potential treatment of COVID 19. However, some available therapeutic drugs used for other diseases seem beneficial for the COVID-19 treatment. On the other hand, there is a robust global concern for developing an efficient COVID-19 vaccine to control the COVID-19 pandemic sustainably. According to the WHO report, since 8 October 2021, 320 vaccines have been in progress. 194 vaccines are in the pre-clinical development stage that 126 of them are in clinical progression. Here, in this paper, we have comprehensively reviewed the most recent and updated information about coronavirus and its mutations, all the potential therapeutic approaches for treating COVID-19, developed diagnostic systems for COVID- 19 and the available COVID-19 vaccines and their mechanism of action. [Display omitted] [ABSTRACT FROM AUTHOR]

Auto-detection of diseases has become a prime issue in medical sciences as population density is fast growing. An intelligent framework for disease detection helps physicians identify illnesses, give reliable and consistent results, and reduce death rates. Coronavirus (Covid-19) has recently been one of the most severe and acute diseases in the world. An automatic detection framework should therefore be introduced as the fastest diagnostic alternative to avoid Covid-19 spread. In this paper, an automatic Covid-19 identification in the CT scan and chest X-ray is obtained with the help of a combined deep learning and multi-level feature extraction methodology. In this method, the multi-level feature extraction approach comprises GIST, Scale Invariant Feature Transform (SIFT), and Convolutional Neural Network (CNN) extract features from CT scans and chest X-rays. The objective of multi-level feature extraction is to reduce the training complexity of CNN network, which significantly assists in accurate and robust Covid-19 identification. Finally, Long Short-Term Memory (LSTM) along the CNN network is used to detect the extracted Covid-19 features. The Kaggle SARS-CoV-2 CT scan dataset and the Italian SIRM Covid-19 CT scan and chest X-ray dataset were employed for testing purposes. Experimental outcomes show that proposed approach obtained 98.94% accuracy with the SARS-CoV-2 CT scan dataset and 83.03% accuracy with the SIRM Covid-19 CT scan and chest X-ray dataset. The proposed approach helps radiologists and practitioners to detect and treat Covid-19 cases effectively over the pandemic. • We propose a combined deep learning and multi-level features extraction-based approach for automatic Covid-19 detection with severity classification. • The multi-level features extraction-based strategy is proposed to reduce learning network training complexity. • The sparsity of the fused feature set is validated using t-distributed Stochastic Neighbor Embedding (t-SNE). • A combined CNN-LSTM based deep learning strategy is proposed for automatic Covid-19 detection with severity classification. [ABSTRACT FROM AUTHOR]

• The association between crypto-market and global sentiment indicators are studied from 2015 to 2021. • Results indicate crypto-assets have consistently remained uncoupled from prominent global indicators representing risk and uncertainty. • Among the first papers to study the link between crypto-assets and risk aversion • Pandemic sample results hint at potential safe haven capabilities of cryptoassets Employing high-dimensional stochastic-volatility commonality tests on crypto-assets against a basket of global investor sentiment proxies, we report new evidence that the cryptocurrency market is decoupled from global sentiments. Our approach's novelty resides in employment of appropriate sources of risk and uncertainty and two comprehensive indices (CRIX and VCRIX) that permit treating cryptocurrencies as a united pool from 2016 to 2021. Our consolidated findings suggest nugatory association between cryptocurrencies and global risk, risk aversion, and uncertainty. Further COVID-19 resampling reinforces long-horizon results. These findings bolster the growing wave of support for recognizing crypto-assets as an independent asset class. [ABSTRACT FROM AUTHOR]

Coronavirus is an infectious life-threatening disease and is mainly transmitted through infected person coughs, sneezes, or exhales. This disease is a global challenge that demands advanced solutions to address multiple dimensions of this pandemic for health and wellbeing. Different types of medical and technological-based solutions have been proposed to control and treat COVID-19. Machine learning is one of the technologies used in Magnetic Resonance Imaging (MRI) classification whereas nature-inspired algorithms are also adopted for image optimization. In this paper, we combined the machine learning and nature-inspired algorithm for brain MRI images of COVID-19 patients namely Machine Learning and Nature Inspired Model for Coronavirus (MLNI-COVID-19). This model improves the MRI image classification and optimization for better diagnosis. This model will improve the overall performance especially the area of brain images that is neglected due to the unavailability of the dataset. COVID-19 has a serious impact on the patient brain. The proposed model will help to improve the diagnosis process for better medical decisions and performance. The proposed model is evaluated with existing algorithms and achieved better performance in terms of sensitivity, specificity, and accuracy. [ABSTRACT FROM AUTHOR]

This paper uses data at the trading day frequency and the method of local projections to quantify the dynamic responses of U.S. airline stock prices to a COVID-19 shock. We show that airline stock prices decline immediately by 0.1 percentage point in response to a 1% COVID-19 shock. In addition, the effect of the shock persists beyond the day on which it occurs, with most airline stock prices falling by as much as 0.6 percentage points after fifteen days. This negative response of airline stock prices to a COVID-19 shock is not explained by a COVID-19-induced increase in airlines' variable costs, but rather by a COVID-19-induced decrease in air travel, which, in turn decreases revenues, profitability, and stock prices of U.S. airlines. • We quantify the impact the COVID-19 pandemic on U.S. airline industry stock prices. • We use high frequency (daily) data from January 21, 2020 to March 15, 2021. • We employ the method of local projections to estimate impulse response functions. • A COVID-19 shock has negative effects on airline stock prices that persist beyond the day on which the shock occurs. • We find no evidence that mask mandates or vaccine administration moderated the effect of the COVID-19 shock. [ABSTRACT FROM AUTHOR]

One of the recognized causes of acute pancreatitis is viral-induced pancreatitis. The SARS-COV-2 virus has been linked to pancreatic injury and hence the causation of acute pancreatitis. This paper reports three cases of acute pancreatitis linked to COVID-19 infection adding to serving to further consolidate evidence. Three patients aged between 63 and 87 years were diagnosed with acute pancreatitis and concomitant or previous COVID-19 infection. Criteria for diagnosis of pancreatitis were according to the revised Atlanta criteria. None of the patients had had previous episodes of pancreatitis and other aetiologies were not suggestive. The patients were not vaccinated against SARS-CoV-2. Supportive treatment was instituted for the three patients, and all made an uneventful recovery. Mean hospital stay was 4 days. The diagnosis of acute pancreatitis in the presented cases is being linked to COVID-19 infection, as other causes were not evident. There is however a confounding factor, as the patient in case 2 had slightly elevated triglyceride levels and had been on long-term low dose atorvastatin, both of which are associated with a low risk of acute pancreatitis. However, she had never had pancreatitis prior to this presentation. The novel virus SARS-COV-2 has also been linked to pancreatic damage and thus a possible causative factor in acute pancreatitis. This mini-case series presents three cases of acute pancreatitis in COVID-19 positive patients, in the absence of other risk factors. This phenomenon linking COVID-19 and pancreatitis has been expounded by other case reports and cohort studies from around the world. It is reasonable to acknowledge that, like other viruses, SARS CoV-2 may cause acute pancreatitis, although sounder evidence from the international community needs to be compiled. • The virus SARS-COV-2 has been linked to pancreatic damage, and thus a possible causative factor in acute pancreatitis. • ACE-2 receptors expressed in the pancreas, act as viral receptors thought to be responsible for COVID-19 mediated pancreatitis. • Increased international cooperation to gather empiric and clinical evidence about COVID-19 associated pancreatitis is needed. [ABSTRACT FROM AUTHOR]

The current ongoing outbreak of Coronavirus Disease 2019 (COVID-19) has globally affected the lives of more than one hundred million people. RT-PCR based molecular test is recommended as the gold standard method for diagnosing current infections. However, transportation and processing of the clinical sample for detecting virus require an expert operator and long processing time. Testing device enables on-site virus detection could reduce the sample-to-answer time, which plays a central role in containing the pandemic. In this work, we proposed an intelligent face mask, where a flexible immunosensor based on high density conductive nanowire array, a miniaturized impedance circuit, and wireless communication units were embedded. The sub-100 nm size and the gap between the neighbored nanowires facilitate the locking of nanoscale virus particles by the nanowire arrays and greatly improve the detection efficiency. Such a point-of-care (POC) system was demonstrated for coronavirus 'spike' protein and whole virus aerosol detection in simulated human breath. Detection of viral concentration as low as 7 pfu/mL from the atomized sample of coronavirus aerosol mimic was achieved in only 5 min. The POC systems can be readily applied for preliminary screening of coronavirus infections on-site and may help to understand the COVID-19 progression while a patient is under prescribed therapy. • This paper presents an intelligent face mask for the exhaled coronavirus aerosols screening within a few minutes. • It integrates a high density conductive nanowire array-based immunosensor and a miniaturized wireless impedance circuit. • The matching relation between the geometry of the nanowires and the target virus aerosol are designed for the indentification directly. • Through the enrichment of the exhaled viral aerosols by the face mask, it canbe achieve the preliminary screening virus infections. [ABSTRACT FROM AUTHOR]

• Stock markets around the world reacted to the Covid-19 pandemic with negative returns • However, stock markets' reaction was not uniform across countries • National-level uncertainty avoidance moderates the stock markets' reaction to the pandemic. • Decline in stock market returns due to the Covid-19 confirmed cases is stronger in countries with higher uncertainty avoidance Recent literature reports stock markets around the world reacted to the Covid-19 pandemic with negative returns. However, this reaction was not uniform across countries. In this paper, we postulate that the national-level uncertainty avoidance, which determines how sensitive members of a nation are to uncertainty, moderates the stock markets' reaction to the pandemic. Using daily data of Covid-19 confirmed cases and stock market returns from 43 countries, we find robust evidence that the decline in stock market returns in response to one percent increase in growth in confirmed cases is stronger for the countries with higher national-level uncertainty aversion. [ABSTRACT FROM AUTHOR]

The study of search plans has found considerable interest between searchers due to its interesting applications in our real life like searching for located and moving targets. This paper develops a method for detecting moving targets. We propose a novel strategy based on weight function W (Z) , W (Z) = λ H (Z) + (1 - λ) L (Z) , where H (Z) , L (Z) are the total probabilities of un-detecting, and total effort respectively, is searching for moving novel coronavirus disease (COVID-19) cells among finite set of different states. The total search effort will be presented in a more flexible way, so it will be presented as a random variable with a given distribution. The objective is searching for COVID-19 which hidden in one of n cells in each fixed number of time intervals m and the detection functions are supposed to be known to the searcher or robot. We look in depth for the optimal distribution of the total effort which minimizes the probability of undetected the target over the set of possible different states. The effectiveness of this model is illustrated by presenting a numerical example. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) was classified as a pandemic by the World Health Organization in March 2020. Given that this novel virus most notably affects the human respiratory system, early detection may help prevent severe lung damage, save lives, and help prevent further disease spread. Given the constraints on the healthcare facilities and staff, the role of artificial intelligence for automatic diagnosis is critical. The automatic diagnosis of COVID-19 based on medical images is, however, not straightforward. Due to the novelty of the disease, available X-ray datasets are very limited. Furthermore, there is a significant similarity between COVID-19 X-rays and other lung infections. In this paper, these challenges are addressed by proposing an approach consisting of a bag of visual words and a neural network classifier. The proposed method can classify X-ray chest images into non-COVID-19 and COVID-19 with high performance. Three public datasets are used to evaluate the proposed approach. Our best accuracy on the first, second, and third datasets is 96.1, 99.84, and 98 percent. Since detection of COVID-19 is important, sensitivity is used as a criterion. The proposed method's best sensitivities are 90.32, 99.65, and 91 percent on these datasets, respectively. The experimental results show that extracting features with the bag of visual words results in better classification accuracy than the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

• A new topic (COVID-19 pandemic) has been introduced in the literature of control theory. In addition, a new strategy (lead/lag compensator) been applied to the COVID-19 nonlinear. • The data of the paper are valid for a certain period of time and the controller could be applied to the same system with different data. • The stability of the system has been achieved and the system performance has been improved. Moreover, the parameters of the proposed controller have been optimized using new method called the Most Valuable Player Algorithm (MVPA). COVID-19 is still the main worldwide issue since the outbreak. Many strategies were implemented such as suppression, mitigation, and mathematical-engineering strategies, to control this pandemic. In this work, a lead/lag compensator is proposed to control an unstable Covid-19 nonlinear system after using some required assumptions. The control theory is involved with the unstable pandemic and other existing strategies until the invention of the vaccine is approved. In addition, the Most Valuable Player Algorithm (MVPA) is used to optimize the parameters of the proposed controller and to determine whether it is a lead or lag compensator. Finally, the simulation results are based on the daily reports of two pandemic cities: Hubei (China), and Lazio (Italy) since the outbreak began. It can be concluded that the lead/lag compensator can effectively control the COVID-19 system. [ABSTRACT FROM AUTHOR]

[Display omitted] • We introduce and share a new and large dataset of original CT scans. • We introduce a fully automated system for detecting COVID-19 cases that acts with high accuracy and speed. • We propose a new architecture to improve the classification accuracy of images containing important objects in various scales (especially in small scales), which has shown very good improvement. • We evaluated our model in two ways: one based on single-image classification (tested on more than 7,996 images) and the other one for evaluating the automated diagnosis system (tested on 235 patients and 41,892 images). • We have segmented the infection areas of the CT scan images. This paper aims to propose a high-speed and accurate fully-automated method to detect COVID-19 from the patient's chest CT scan images. We introduce a new dataset that contains 48,260 CT scan images from 282 normal persons and 15,589 images from 95 patients with COVID-19 infections. At the first stage, this system runs our proposed image processing algorithm that analyzes the view of the lung to discard those CT images that inside the lung is not properly visible in them. This action helps to reduce the processing time and false detections. At the next stage, we introduce a novel architecture for improving the classification accuracy of convolutional networks on images containing small important objects. Our architecture applies a new feature pyramid network designed for classification problems to the ResNet50V2 model so the model becomes able to investigate different resolutions of the image and do not lose the data of small objects. As the infections of COVID-19 exist in various scales, especially many of them are tiny, using our method helps to increase the classification performance remarkably. After running these two phases, the system determines the condition of the patient using a selected threshold. We are the first to evaluate our system in two different ways on Xception, ResNet50V2, and our model. In the single image classification stage, our model achieved 98.49% accuracy on more than 7996 test images. At the patient condition identification phase, the system correctly identified almost 234 of 245 patients with high speed. Our dataset is accessible at https://github.com/mr7495/COVID-CTset. [ABSTRACT FROM AUTHOR]