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• 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]

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]

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]

[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]