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A pneumonia outbreak was primarily reported in the fall of 2019 in Wuhan, Hubei province, China, with the identity SARS-CoV-2, a novel coronavirus. It quickly grew from a local epidemic to a global pandemic and was declared a public health emergency by the WHO. A total of three prominent waves were identified across the globe, with a slight temporal variability as per the geographical locations, and has impacted several sectors which connect the world. By March 2022, the coronavirus had infected 444.12 million people and claimed 6.01 million human lives worldwide, and these numbers have not yet stabilized. Our paper enlightens readers on the seven strains of human coronaviruses, with special emphasis on the three severe deadliest outbreaks (SARS-2002, MERS-2012, and COVID-19). This work attempts a comprehensive understanding of the coronavirus and its impact on the possible sectors that link the world through the economic chain, climate conditions, SDGs, recycling of the event, and mitigations. There are many points that are raised by the authors in the possible sectors, which are emerging or are as yet unnoticed and thus have not been taken into consideration. This comprehension will leave sets of new challenges and opportunities for the researchers in various streams, especially in earth sciences. Science-integrated research may help to prevent upcoming disasters as a by-product of (existing) epidemics in the form of coronavirus. [ABSTRACT FROM AUTHOR]

This paper investigates China's leadership over intergovernmental and private actions in tackling the COVID-19 pandemic and identifies sectors, levels, and roles played. The main argument is that successful control over the pandemic depends on effective and integrated leadership at different levels. The conceptual, theoretical, and analytical framework is given by the multi-level governance theory. Through a case study, the actions adopted in China (n = 374) were mapped from reports issued by the Chinese government, the World Health Organization, and media information. Content analysis was adopted to categorize data. Quantitative and qualitative findings show different roles of actors, as well as usefulness of the multi-level governance to provide fast and complex responses required during the pandemic. In the last section, the conclusion and recommendations for future research are addressed. The lack of studies on the application of multi-level governance in non-democratic regimes especially during a pandemic indicates the relevance and contribution of this study to the literature. [ABSTRACT FROM AUTHOR]

Coronavirus outbreak in Wuhan, China, turned into a pandemic in record time. Communication of disease presentation and mechanism of spread remain keys to getting ahead of the virus and limiting its spread beyond the capacity of management. Owing to huge academic focus and pandemic concern around the globe, this bibliometric analysis investigated research productivity related to coronavirus disease (COVID-19) pandemic using the Web of Science database. The relevant data were harvested, and search query was further refined by publication years (2020 OR 2019) and document types (article, book chapter, and proceedings paper). Finally, 6694 records were imported and downloaded in Plaintext and BibTeX formats on August 1, 2020. The data analysis was performed using MS Excel, VOS viewer, and Biblioshiny software. Of the 6694 publications that appeared in that period, the USA and Chinese research institutions topped the numbers. At the same time, the Journal of Medical Virology and CUREUS (Cureus Journal of Medical Science), remained favorite journals for publications. The pattern of multi-author publications has outstripped that of single-authors. Apart from COVID-19 and the novel coronavirus, the important keywords mentioned included pandemic, pneumonia, epidemiology, public health, outbreak, epidemic, China, infection, and treatment. The analysis shows a strong local research response from China, with large teams reporting on the disease outbreak. Subsequent studies will document a global response as the virus spreads worldwide. The initial research related to the current coronavirus outbreak was reported from within China. The data and patterns were supposed to alter as the virus spread globally. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease 2019 (COVID-19) has affected almost all the countries globally, and it is considered a global pandemic. Hence, this study conducted a bibliometric analysis of scientific output on the COVID-19 pandemic outbreak using the Web of Science (WoS) database. A specific search technique was developed based on Corona virus-related keywords from January 1, 2019, until June 24, 2020. A total of 10850 documents related to COVID-19 published from January 1, 2019, to June 24, 2020, were retrieved from the WoS database. The researchers downloaded the data from WoS as a WoS plain text file, and the data were analyzed using R studio (biblioshiny) software. Out of the total of 10850 documents published in 1736 scientific journals, 10845 papers belonging to the year 2020, which constituents 99.954% of the total documents (N=10850). Wang Y is the most productive author who contributed 0.60% (n=65) of the total documents published. USA is the predominant country that produced 2701 documents related to COVID-19, and China follows it with 1937 documents. The most active collaboration held between the USA and China, and it accounted for 2.75% (n=298) of the entire documents (N=10850). [ABSTRACT FROM AUTHOR]

The ongoing pandemic due to SARS CoV2 is really a big one, which would never welcomed from any countries anytime, but knocked the door all of a sudden! The SARS CoV-2 first appeared in China in late December 2019. The virus isolated on January 7, 2020; the disease named as COVID-19 afterwars. Since this was a SARS virus not an influenza, nobody seem to expect it as a pandemic agent, spread so fast and so globally. But actually it did spread to many countries and declared as a pandemic by the World Health Organization on March 11, 2020. The pandemic hit the universities hard. The most important topic for universities during the pandemic was of course distance and remote learning, which become widespread already. The pandemic will cause profound impacts and changes on the higher education system around the world in terms of education-teaching methods, research, internationalization and mobility. The response of Council of Higher Education of Turkey to pandemic could be evaluated in four phases: 1) Close monitoring, 2) Preparing, 3) Action, 4) New normalization. In this paper we will review the response of Turkish higher education institutions and Council of Higher Education to the COVID-19 pandemic. [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]

The sudden outbreak of the COVID-19 pandemic has brought drastic changes to people's daily lives, work, and the surrounding environment. Investigations into these changes are very important for decision makers to implement policies on economic loss assessments and stimulation packages, city reopening, resilience of the environment, and arrangement of medical resources. In order to analyze the impact of COVID-19 on people's lives, activities, and the natural environment, this paper investigates the spatial and temporal characteristics of Nighttime Light (NTL) radiance and Air Quality Index (AQI) before and during the pandemic in mainland China. The monthly mean NTL radiance, and daily and monthly mean AQI are calculated over mainland China and compared before and during the pandemic. Our results show that the monthly average NTL brightness is much lower during the quarantine period than before. This study categorizes NTL into three classes: residential area, transportation, and public facilities and commercial centers, with NTL radiance ranges of 5–20, 20–40 and greater than 40 (nW· cm − 2 · sr − 1 ), respectively. We found that the Number of Pixels (NOP) with NTL detection increased in the residential area and decreased in the commercial centers for most of the provinces after the shutdown, while transportation and public facilities generally stayed the same. More specifically, we examined these factors in Wuhan, where the first confirmed cases were reported, and where the earliest quarantine measures were taken. Observations and analysis of pixels associated with commercial centers were observed to have lower NTL radiance values, indicating a dimming behavior, while residential area pixels recorded increased levels of brightness after the beginning of the lockdown. The study also discovered a significant decreasing trend in the daily average AQI for mainland China from January to March 2020, with cleaner air in most provinces during February and March, compared to January 2020. In conclusion, the outbreak and spread of COVID-19 has had a crucial impact on people's daily lives and activity ranges through the increased implementation of lockdown and quarantine policies. On the other hand, the air quality of mainland China has improved with the reduction in non-essential industries and motor vehicle usage. This evidence demonstrates that the Chinese government has executed very stringent quarantine policies to deal with the pandemic. The decisive response to control the spread of COVID-19 provides a reference for other parts of the world. [ABSTRACT FROM AUTHOR]