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Introduction: The Coronavirus Disease 2019 (COVID 19) epidemic began in December 2019 in China and caused major concern. But no systematic review of COVID-19 infection control has been published. Aim: The aim of this study is to determine the COVID-19 disease control methods. Material and methods : We performed a systematic review of the literature using the keywords: 'coronavirus' and '2020,' 'COVID-19' in databases including ScienceDirect, PubMed, Springer and Scopus from January 1, 2020 to February 23, 2020. All observational studies, as well as case reports and editorials that were published in English were included. Data on the disease control methods of COVID- 19 were extracted by two researchers. Results and discussion: The preliminary search result was about 131 articles; 38 articles were retrieved for full-text screening, after screening articles by title and abstract. Finally, 17 papers having the study inclusion criteria were selected. Disease control is possible at three levels of prevention, diagnosis and treatment. Conclusions : COVID-19 is a new clinical infectious disease that can be controlled by adopting measures to prevent, diagnose and treat the disease. Further studies are needed to elucidate factors that may be effective in controlling the spread of the disease during recovery. [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]

In the last month of 2019, a new virus emerged in China, spreading rapidly and affecting the whole world. This virus, which is called corona, is the most contagious type of virus that humanity has ever encountered. The virus has caused a huge crisis worldwide as it leads to severe infections and eventually death in humans. On March 11, 2020, it was announced by the World Health Organization that a COVID-19 outbreak has occurred. Computer-aided digital technologies, which eliminate many problems and provide convenience in people's lives, did not leave humanity alone in this regard and rushed to provide a solution for this unfortunate event. One of the important aspects in which computer-aided digital technologies can be effective is the diagnosis of the disease. Reverse transcription-polymerase chain reaction (RT-PCR), which is a standard and precise technique for diagnosing the disease, is an expensive and time-consuming method. Moreover, its availability is not the same all over the world. For this reason, it can be very attractive and important to distinguish the COVID-19 disease from a cold or flu through a cough sound analysis via smartphones which have entered into the lives of many people in recent years. In this study, we proposed a machine learning-based system to distinguish patients with COVID-19 from non-COVID-19 patients by analyzing only a single cough sound. Two different data sets were used, one accessible for the public and the other available on request. After combining the data sets, the features were obtained from the cough sounds using the mel-frequency cepstral coefficients (MFCCs) method, and then, they were classified with seven different machine learning classifiers. To determine the optimum values of hyperparameters for MFCCs and classifiers, the leave-one-out cross-validation (LOO-CV) strategy was implemented. Based on the results, the k-nearest neighbors classifier based on the Euclidean distance (kNN Euclidean) with the accuracy rate, sensitivity of COVID-19, sensitivity of non-COVID-19, F-measure, and area under the ROC curve (AUC) of 0.9833, 1.0000, 0.9720, 0.9799, and 0.9860, respectively, is more successful than other classifiers. Finally, the best and most effective features were determined for each classifier using the sequential forward selection (SFS) method. According to the results, the proposed system is excellent compared with similar studies in the literature and can be easily used in smartphones and facilitate the diagnosis of COVID-19 patients. In addition, since the used data set includes reflex and unconscious coughs, the results showed that conscious or unconscious coughing has no effect on the diagnosis of COVID-19 patients based on the cough sound. [ABSTRACT FROM AUTHOR]

The 2019 novel coronavirus disease (COVID-19), with a starting point in China, has spread rapidly among people living in other countries and is approaching approximately 101,917,147 cases worldwide according to the statistics of World Health Organization. There are a limited number of COVID-19 test kits available in hospitals due to the increasing cases daily. Therefore, it is necessary to implement an automatic detection system as a quick alternative diagnosis option to prevent COVID-19 spreading among people. In this study, five pre-trained convolutional neural network-based models (ResNet50, ResNet101, ResNet152, InceptionV3 and Inception-ResNetV2) have been proposed for the detection of coronavirus pneumonia-infected patient using chest X-ray radiographs. We have implemented three different binary classifications with four classes (COVID-19, normal (healthy), viral pneumonia and bacterial pneumonia) by using five-fold cross-validation. Considering the performance results obtained, it has been seen that the pre-trained ResNet50 model provides the highest classification performance (96.1% accuracy for Dataset-1, 99.5% accuracy for Dataset-2 and 99.7% accuracy for Dataset-3) among other four used models. [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]