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Background: Since the beginning of the COVID-19 outbreak in December 2019, a substantial body of COVID-19 medical literature has been generated. As of June 2020, gaps and longitudinal trends in the COVID-19 medical literature remain unidentified, despite potential benefits for research prioritisation and policy setting in both the COVID-19 pandemic and future large-scale public health crises.Methods: In this paper, we searched PubMed and Embase for medical literature on COVID-19 between 1 January and 24 March 2020. We characterised the growth of the early COVID-19 medical literature using evidence maps and bibliometric analyses to elicit cross-sectional and longitudinal trends and systematically identify gaps.Results: The early COVID-19 medical literature originated primarily from Asia and focused mainly on clinical features and diagnosis of the disease. Many areas of potential research remain underexplored, such as mental health, the use of novel technologies and artificial intelligence, pathophysiology of COVID-19 within different body systems, and indirect effects of COVID-19 on the care of non-COVID-19 patients. Few articles involved research collaboration at the international level (24.7%). The median submission-to-publication duration was 8 days (interquartile range: 4-16).Conclusions: Although in its early phase, COVID-19 research has generated a large volume of publications. However, there are still knowledge gaps yet to be filled and areas for improvement for the global research community. Our analysis of early COVID-19 research may be valuable in informing research prioritisation and policy planning both in the current COVID-19 pandemic and similar global health crises. [ABSTRACT FROM AUTHOR]

Coronavirus (COVID-19) is the most prevalent coronavirus infection with respiratory symptoms such as fever, cough, dyspnea, pneumonia, and weariness being typical in the early stages. On the other hand, COVID-19 has a direct impact on the circulatory and respiratory systems as it causes a failure to some human organs or severe respiratory distress in extreme circumstances. Early diagnosis of COVID-19 is extremely important for the medical community to limit its spread. For a large number of suspected cases, manual diagnostic methods based on the analysis of chest images are insufficient. Faced with this situation, artificial intelligence (AI) techniques have shown great potential in automatic diagnostic tasks. This paper aims at proposing a fast and precise medical diagnosis support system (MDSS) that can distinguish COVID-19 precisely in chest-X-ray images. This MDSS uses a concatenation technique that aims to combine pre-trained convolutional neural networks (CNN) depend on the transfer learning (TL) technique to build a highly accurate model. The models enable storage and application of knowledge learned from a pre-trained CNN to a new task, viz., COVID-19 case detection. For this purpose, we employed the concatenation method to aggregate the performances of numerous pre-trained models to confirm the reliability of the proposed method for identifying the patients with COVID-19 disease from X-ray images. The proposed system was trialed on a dataset that included four classes: normal, viral-pneumonia, tuberculosis, and COVID-19 cases. Various general evaluation methods were used to evaluate the effectiveness of the proposed model. The first proposed model achieved an accuracy rate of 99.80% while the second model reached an accuracy of 99.71%. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has shown immense potential to fight COVID-19 in many ways. This paper focuses primarily on AI's role in managing COVID-19 using digital images, clinical and laboratory data analysis, and a summary of the most recent articles published last year. We surveyed the use of AI for COVID-19 detection, screening, diagnosis, the progression of severity, mortality, drug repurposing, and other tasks. We started with the technical overview of all models used to fight the COVID-19 pandemic and ended with a brief statement of the current state-of-the-art, limitations, and challenges. [ABSTRACT FROM AUTHOR]

COVID-19 is a rapidly spreading pandemic, and early detection is important to halting the spread of infection. Recently, the outbreak of this virus has severely affected people around the world with increasing death rates. The increased death rates are because of its spreading nature among people, mainly through physical interactions. Therefore, it is very important to control the spreading of the virus and detect people's symptoms during the initial stages so proper preventive measures can be taken in good time. In response to COVID-19, revolutionary automation such as deep learning, machine learning, image processing, and medical images such as chest radiography (CXR) and computed tomography (CT) have been developed in this environment. Currently, the coronavirus is identified via an RT-PCR test. Alternative solutions are required due to the lengthy moratorium period and the large number of false-negative estimations. To prevent the spreading of the virus, we propose the Vehicle-based COVID-19 Detection System to reveal the related symptoms of a person in the vehicles. Moreover, deep extreme machine learning is applied. The proposed system uses headaches, flu, fever, cough, chest pain, shortness of breath, tiredness, nasal congestion, diarrhea, breathing difficulty, and pneumonia. The symptoms are considered parameters to reveal the presence of COVID-19 in a person. Our proposed approach in Vehicles will make it easier for governments to perform COVID-19 tests timely in cities. Due to the ambiguous nature of symptoms in humans, we utilize fuzzy modeling for simulation. The suggested COVID-19 detection model achieved an accuracy of more than 90%. [ABSTRACT FROM AUTHOR]

The SARS-CoV-2 coronavirus, also known as the disease-causing agent for COVID-19, is a virulent pathogen that may infect people and certain animals. The global spread of COVID-19 and its emerging variation necessitates the development of rapid, reliable, simple, and low-cost diagnostic tools. Many methodologies and devices have been developed for the highly sensitive, selective, cost-effective, and rapid diagnosis of COVID-19. This review organizes the diagnosis platforms into four groups: imaging, molecular-based detection, serological testing, and biosensors. Each platform's principle, advancement, utilization, and challenges for monitoring SARS-CoV-2 are discussed in detail. In addition, an overview of the impact of variants on detection, commercially available kits, and readout signal analysis has been presented. This review will expand our understanding of developing advanced diagnostic approaches to evolve into susceptible, precise, and reproducible technologies to combat any future outbreak. [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]

[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]

Purpose: To clarify the prevalence and determinants of fear of COVID-19 among homeless individuals during the COVID-19 pandemic. Patients and Methods: Data were taken from the Hamburg survey of homeless individuals (n=150) covering homeless adults in Hamburg, Germany. Multiple linear regressions were used. Beyond sociodemographic and lifestyle-related independent variables, COVID-19-related factors were also used as independent variables. Results: In sum, 56.2% of homeless individuals reported no fear of COVID-19, 23.8% reported a little fear of COVID-19, 9.2% reported some fear of COVID-19 and 10.8% reported severe fear of COVID-19. Regressions showed that an increased fear of COVID-19 was significantly associated with younger age (β=−.01, p< 0.05), absence of chronic alcohol consume (β=−.55, p< 0.01), an increased perceived own risk of getting infected with the coronavirus one day (β=0.42, p< 0.001) and a higher agreement that a diagnosis of the coronavirus would ruin his/her life (β=0.13, p< 0.01). Conclusion: Given the poor hygienic conditions and their increased vulnerability, data surprisingly suggest a quite low fear of COVID-19 among homeless individuals. Efforts may be beneficial which contribute to thinking rationally about COVID-19 among homeless people, since low levels of fear of COVID-19 may contribute to careless behavior and can lead to super-spreading events. [ABSTRACT FROM AUTHOR]

After a century, the whole world fighting against the pandemic viral infection: a novel coronavirus, COVID-19. Currently, more than 210 countries are suffering from COVID-19 with the number of affected countries and patients are exponentially increasing day by day. It became a global health issue where more than 2.7 million cases were reported with a death ratio of approximate 7% globally by World Health Organization (WHO) (as of 24 April 2020) which is a 22 times higher numbers in 1.5 month and this figure increasing day by day at an alarming rate. The maximum infected cases reported from the most developed country and the world leader America however, the maximum death cases are from the world's second health service provider country Italy. China, the origin country of COVID-19, has taken serious actions in terms of prevention, control against the spreading of this coronavirus through lockdown, sanitation, medication, and social distancing. The risk of transmissions of coronavirus from humans to humans is more and thus a social distancing is the best way for its persistence and precautions. Thus, the COVID-19 outbreak continues must explore and evolve, certain strict and mandatory precautions to stop this dangerous devil virus. Also, it is a major challenge for all global scientists to find out an effective remedial drug to control this deadly coronavirus before uncontrolled conditions. Thus, considering the depth of the spreading of coronavirus and its impact on global health, it is necessitating to know the dos and don'ts for persistence, precautions, and diagnostic strategies against the challenging COVID-19. [ABSTRACT FROM AUTHOR]

The outbreak of novel coronavirus disease (2019-nCoV or COVID-19) is responsible for severe health emergency throughout the world. The attack of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is found to be responsible for COVID-19. The World Health Organization has declared the ongoing global public health emergency as a pandemic. The whole world fights against this invincible enemy in various capacities to restore economy, lifestyle, and safe life. Enormous amount of scientific research work(s), administrative strategies, and economic measurements are in place to create a successful step against COVID-19. Furthermore, differences in opinion, facts, and implementation methods laid additional layers of complexities in this battle against survival. Thus, a timely overview of the recent, important, and overall inclusive developments against this pandemic is a pressing need for better understanding and dealing with COVID-19. In this review, we have systematically summarized the epidemiological studies, clinical features, biological properties, diagnostic methods, treatment modalities, and preventive measurements related to COVID-19. [ABSTRACT FROM AUTHOR]