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Novel coronavirus (COVID-19) is started from Wuhan (City in China), and is rapidly spreading among people living in other countries. Today, around 215 countries are affected by COVID-19 disease. WHO announced approximately number of cases 11,274,600 worldwide. Due to rapidly rising cases daily in the hospitals, there are a limited number of resources available to control COVID-19 disease. Therefore, it is essential to develop an accurate diagnosis of COVID-19 disease. Early diagnosis of COVID-19 patients is important for preventing the disease from spreading to others. In this paper, we proposed a deep learning based approach that can differentiate COVID- 19 disease patients from viral pneumonia, bacterial pneumonia, and healthy (normal) cases. In this approach, deep transfer learning is adopted. We used binary and multi-class dataset which is categorized in four types for experimentation: (i) Collection of 728 X-ray images including 224 images with confirmed COVID-19 disease and 504 normal condition images (ii) Collection of 1428 X-ray images including 224 images with confirmed COVID-19 disease, 700 images with confirmed common bacterial pneumonia, and 504 normal condition images. (iii) Collections of 1442 X- ray images including 224 images with confirmed COVID-19 disease, 714 images with confirmed bacterial and viral pneumonia, and 504 images of normal conditions (iv) Collections of 5232 X- ray images including 2358 images with confirmed bacterial and 1345 with viral pneumonia, and 1346 images of normal conditions. In this paper, we have used nine convolutional neural network based architecture (AlexNet, GoogleNet, ResNet-50, Se-ResNet-50, DenseNet121, Inception V4, Inception ResNet V2, ResNeXt-50, and Se-ResNeXt-50). Experimental results indicate that the pre trained model Se-ResNeXt-50 achieves the highest classification accuracy of 99.32% for binary class and 97.55% for multi-class among all pre-trained models. [ABSTRACT FROM AUTHOR]

The emergence of an unusual Corona virus (COVID-19) flu pandemic starting in China in December 2019, spreading all around the globe is a major threat to public health. The investigations have shown this virus originated from a seafood market in Wuhan. However, the unavailability of medicines for the new disease is a big challenge all around. An attempt has been made in the present article to familiarise about the morphology of the virus. The study of effect of pH, temperature and relative humidity is also depicted. Various preventive measures have also been discussed. The natural dietary measures suggested in the paper would be very beneficial to improve and boost the immunity of the mankind. [ABSTRACT FROM AUTHOR]

• The latest summary of the COVID-19 outbreak in China; • There might be an oral-fecal transmission of the virus; • Aggregates and consolidates the epidemiology, clinical manifestations, diagnosis, treatments and preventions of this new type of coronavirus. There is a current worldwide outbreak of the novel coronavirus Covid-19 (coronavirus disease 2019; the pathogen called SARS-CoV-2; previously 2019-nCoV), which originated from Wuhan in China and has now spread to 6 continents including 66 countries, as of 24:00 on March 2, 2020. Governments are under increased pressure to stop the outbreak from spiraling into a global health emergency. At this stage, preparedness, transparency, and sharing of information are crucial to risk assessments and beginning outbreak control activities. This information should include reports from outbreak site and from laboratories supporting the investigation. This paper aggregates and consolidates the epidemiology, clinical manifestations, diagnosis, treatments and preventions of this new type of coronavirus. [ABSTRACT FROM AUTHOR]

The recent emergence of a new coronavirus (COVID-19) has gained a high cover in public media and worldwide news. The virus has caused a viral pneumonia in tens of thousands of people in Wuhan, a central city of China. This short paper gives a brief introduction on how the demand for information on this new epidemic is reported through Google Trends. The reported period is 31 December 2020 to 20March 2020. The authors draw conclusions on current infodemiological data on COVID-19 using three main search keywords: coronavirus, SARS and MERS. Two approaches are set. First is the worldwide perspective, second - the Chinese one, which reveals that in China this disease in the first days was more often referred to SARS then to general coronaviruses, whereas worldwide, since the beginning, it is more often referred to coronaviruses. [ABSTRACT FROM AUTHOR]

• 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]

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

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

The outbreak of coronavirus disease (COVID-19) was first reported from Wuhan, China, on December 31st, 2019. As the number of coronavirus infections has exceeded 100,000 with toll deaths of about 5000 worldwide as of early March, 2020, scientists and researchers are racing to investigate the nature of this virus and evaluate the short and long term effects of this disease. Despite its negative impacts that obliged the World Health Organization to declare COVID-19 epidemic as a Public Health Emergency of International Concern, the rate of mortality of this infection has not exceeded 3.4% globally. On the other hand, the mortality rate caused by ambient air pollution has contributed to 7.6% of all deaths in 2016 worldwide. The outbreak of COVID-19 has forced China to lockdown its industrial activities and hence dropped its NO2 and carbon emissions by 30 and 25%, respectively. This work reports on the first case study that compares the air quality status before and after the crisis. It sheds light on the facts related to the demographics of deaths by gender, age and health status before infection. The historical data on air quality, estimates of annual deaths and its economic burden have been presented and analyzed. The actual daily deaths due to COVID-19 have been obtained from the official records of the daily Situation Reports published by World Health Organization as of March 11th. The rate of mortality due to COVID-19 was impacted by two factors: age and health status. Results show that 75% of deaths were related to cases that had underlying present diseases with the majority aged of 80+ years. The reported figures were compared with the average daily mortality due to poor air quality which reached up to 3287 deaths due to high levels of NO2, O3 and PM. The air quality status before the crisis was compared with the current situation showing that COVID-19 forced-industrial and anthropogenic activities lockdown may have saved more lives by preventing ambient air pollution than by preventing infection. [ABSTRACT FROM AUTHOR]

Background: SARS-CoV-2 began spreading in December 2019 and has since become a pandemic that has impacted many aspects of human society. Several issues concerning the origin, time of introduction to humans, evolutionary patterns, and underlying force driving the SARS-CoV-2 outbreak remain unclear. Method: Genetic variation in 137 SARS-CoV-2 genomes and related coronaviruses as of 2/23/2020 was analyzed. Result: After correcting for mutational bias, the excess of low frequency mutations on both synonymous and nonsynonymous sites was revealed which is consistent with the recent outbreak of the virus. In contrast to adaptive evolution previously reported for SARS-CoV during its brief epidemic in 2003, our analysis of SARS-CoV-2 genomes shows signs of relaxation. The sequence similarity in the spike receptor binding domain between SARS-CoV-2 and a sequence from pangolin is probably due to an ancient intergenomic introgression that occurred approximately 40 years ago. The current outbreak of SARS-CoV-2 was estimated to have originated on 12/11/2019 (95% HPD 11/13/2019–12/23/2019). The effective population size of the virus showed an approximately 20-fold increase from the onset of the outbreak to the lockdown of Wuhan (1/23/2020) and ceased to increase afterwards, demonstrating the effectiveness of social distancing in preventing its spread. Two mutations, 84S in orf8 protein and 251 V in orf3 protein, occurred coincidentally with human intervention. The former first appeared on 1/5/2020 and plateaued around 1/23/2020. The latter rapidly increased in frequency after 1/23/2020. Thus, the roles of these mutations on infectivity need to be elucidated. Genetic diversity of SARS-CoV-2 collected from China is two times higher than those derived from the rest of the world. A network analysis found that haplotypes collected from Wuhan were interior and had more mutational connections, both of which are consistent with the observation that the SARS-CoV-2 outbreak originated in China. Conclusion: SARS-CoV-2 might have cryptically circulated within humans for years before being discovered. Data from the early outbreak and hospital archives are needed to trace its evolutionary path and determine the critical steps required for effective spreading. [ABSTRACT FROM AUTHOR]

Background: Compared to adults, there are relatively few studies on COVID-19 infection in children, and even less focusing on the unique features of COVID-19 in children in terms of laboratory findings, locations of computerized tomography (CT) lesions, and the role of CT in evaluating clinical recovery. The objective of this study is to report the results from patients at Wuhan Children's Hospital, located within the initial center of the outbreak.Methods: Clinical, imaging, and laboratory data of 76 children were collected retrospectively and analyzed with the Fisher exact test and Cox regression statistical methods.Results: Among 50 children with a positive COVID-19 real-time reverse-transcriptase polymerase chain reaction (PCR), five had negative PCR results initially but showed positive results in subsequent tests. Eight (16%) patients had lymphopenia, seven (14%) with thrombocytopenia, four (8%) with lymphocytosis, two (4%) with thrombocytosis, ten (20%) with elevated C-reactive protein, four (8%) with hemoglobin above, and six (12%) with below standard reference values. Seven (14%) of the 50 had no radiologic evidence of disease on chest CT. For the 43 patients who had abnormal CT findings, in addition to previously reported patterns of ground-glass opacity (67%), local patchy shadowing (37%), local bilateral patchy shadowing (21%), and lesion location of lower lobes (65%), other CT features include that an overwhelming number of pediatric patients had lesions in the subpleural area (95%) and 22 of the 28 lower lobe lesions were in the posterior segment (78%). Lesions in most of the 15 patients (67%) who received chest CT at discharge were not completely absorbed, and 26% of these pediatric patients had CT lesions that were either unchanged or worse.Conclusions: There were a few differences between COVID-19 children and COVID-19 adults in terms of laboratory findings and CT characteristics. CT is a powerful tool to detect and characterize COVID-19 pneumonia but has little utility in evaluating clinical recovery for children. These results oppose current COVID-19 hospital discharge criteria in China, as one requirement is that pulmonary imaging must show significant lesion absorption prior to discharge. These differences between pediatric and adult cases of COVID-19 may necessitate pediatric-specific discharge criteria. [ABSTRACT FROM AUTHOR]