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Unlike in developed countries, most public toilets in China do not provide toilet paper onsite and users must bring their toilet paper. Moreover, an open waste bin is placed in each user's cubicle to collect used toilet paper and tissues. Such practices, which are common in East Asia and central America, have induced a dilemma of toilet paper disposal because some municipalities have removed waste bins from public toilets to prevent virus transmission by fecal matter. As a consequence, users were forced to flush down their used toilet paper and tissues. Yet, it is unknown whether standard toilet paper can be flushed easily without causing issues in sewer operations. Here, we surveyed the conditions of toilets in university campus and other public facilities in different regions across China. We also evaluated the disintegration characteristics of toilet paper products both by conducting online surveys and by physical disintegration experiments. We found that only 15% of toilets provided toilet paper, while open waste bins occurred at nearly all sites. Further, our survey indicated that 82% of toilet paper products sold in China did not give any indication on their flushability, whereas 77% of US products did. Disintegration results showed that none of the five popular, best-selling toilet paper products passed the flushability standard. We propose strategies to solve the current toilet paper dilemma in developing communities. [ABSTRACT FROM AUTHOR]

Focusing on the challenges brought about by the devastating COVID-19 pandemic in China and the Chinese government's increasingly stringent regulatory policies, this paper poses the following research questions: During the pandemic and in interactions with the government, why did some non-governmental organizations (NGOs) survive while others were shut down, although most of those that survived were small and medium-sized NGOs? In addressing this question, this paper delves into the strategies adopted by small and medium-sized NGOs to cope with COVID-19. Using data drawn from three years of field observations and research in six major cities in China, this paper finds that four types of small and medium-sized NGOs resorted to different strategies that followed degree of competitiveness and scope of main business. The scarcity of resources resulting to the pandemic plunged NGOs into a competitive survival posture; thus, the strategies for the four types of small and medium-sized NGOs achieved three levels of effectiveness, namely good, medium, and poor. The findings of this paper shed significant light on the diversity and complexity of the survival and development of NGOs under the authoritarian regime of China. [ABSTRACT FROM AUTHOR]

Crises constitute ideal opportunities for authoritarian leaders to promote certain narratives, shaping reality in their favor and crafting their own preferred storylines about current events. In other words: they serve authoritarian leaders on a silver platter the opportunity to instrumentalize these unforeseen circumstances to gain domestic political legitimacy by promoting strategic narratives. The COVID-19 pandemic was no exception in this regard. Ever since its onset in early 2020, the Chinese Communist Party (CCP) was among the most active actors worldwide seeking to capitalize on the global crisis for legitimation purposes. Whether applying narratives of governance supremacy, portraying the People's Republic of China as a "global savior," or promoting emotionally appealing nationalistic narratives, the regime used the pandemic as a window of opportunity to rebrand its international role and enhance its domestic legitimacy. When observing the CCP's communication style over the course of 24 pandemic months (2020–2022), however, major shifts become apparent regarding the main narratives crafted in communication with national audiences. Based on this, the paper focuses on the role of such narratives for legitimation claims. Using exemplary media articles collected between the outbreak of the pandemic in China in late 2019 until the harsh Shanghai lockdown in spring 2022, it thus traces the narratives employed by Chinese state elites and explores how they are intertwined with nationalism and broader power claims. [ABSTRACT FROM AUTHOR]

The coronavirus first appeared in China in 2019, and the World Health Organization (WHO) named it COVID-19. Then WHO announced this illness as a worldwide pandemic in March 2020. The number of cases, infections, and fatalities varied considerably worldwide. Because the main characteristic of COVID-19 is its rapid spread, doctors and specialists generally use PCR tests to detect the COVID-19 virus. As an alternative to PCR, X-ray images can help diagnose illness using artificial intelligence (AI). In medicine, AI is commonly employed. Convolutional neural networks (CNN) and deep learning models make it simple to extract information from images. Several options exist when creating a deep CNN. The possibilities include network depth, layer count, layer type, and parameters. In this paper, a novel Xception-based neural network is discovered using the genetic algorithm (GA). GA finds better alternative networks and parameters during iterations. The best network discovered with GA is tested on a COVID-19 X-ray image dataset. The results are compared with other networks and the results of papers in the literature. The novel network of this paper gives more successful results. The accuracy results are 0.996, 0.989, and 0.924 for two-class, three-class, and four-class datasets, respectively. [ABSTRACT FROM AUTHOR]

Due to the significant impact of COVID-19, financial markets in various countries have undergone drastic fluctuations. Accurately measuring risk in the financial market and mastering the changing rules of the stock market are of great importance to macro-control and financial market management of the government. This paper focuses on the return rate of the Shanghai Composite Index. Using the SGED-EGARCH(1,1) model as a foundation, a quantile regression is introduced to establish the QR-SGED-EGARCH(1,1) model. Further, the corresponding value at risk (VaR) is calculated for a crisis and stable period within each model. To better compare the models, the Cornish-Fisher expansion model is included for comparison. According to the Kupiec test, VaR values calculated by the QR-SGED-EGARCH(1,1) model are superior to other models at different confidence levels most of the time. In addition, to account for the VaR method's inability to effectively measure tail extreme risk, the expected shortfall (ES) method is introduced. The constructed model is used to calculate the corresponding ES values during different periods. According to the evaluation index, the ES values calculated by the QR-SGED-EGARCH(1,1) model have a better effect during a crisis period with the model showing higher accuracy and robustness. It is of great significance for China to better measure financial risk under the impact of a sudden crisis. [ABSTRACT FROM AUTHOR]

Patients narratives are being recorded increasingly frequently and spontaneously in short user produced content (UGC) films, which may have an impact on the vlogger's health as well as the public's comprehension of the relevant health concerns. This paper addressed three research questions regarding the population characteristics of UGC video publishers, the narrative theme of the videos, and the emotional orientation of the commenters. This study aimed to deepen our understanding of COVID-19 patients' narrative intentions and emotional needs through the theoretical frameworks of theory of planned behavior (TPB) and negative dominance theory (NDT). We collected 335 videos from 28 COVID-19 patients and 572,052 comments as samples on Douyin platform, the largest short-video website in China. Using Latent Semantic Analysis, we analyzed the descriptive information of the video blogs, the narrative textual information of the videos, and the emotional orientation of the comments. Our findings revealled seven categories of narrative themes, with 52.1% of video comments exhibiting a positive emotional orientation. Within a framework integrating TPB and NDT theories, we analyzed the behavioral intentions of vloggers and viewers during COVID-19 epidemic, and interpreted the persistent posting of videos and the active posting of comments as positive actions that counteracted the multiple effects of negative messages. This study contributes to the understanding of individual narratives in macro-risk communication, both theoretically and empirically, and offers policy recommendations in relevant fields. [ABSTRACT FROM AUTHOR]

A better understanding of various patterns in the coronavirus disease 2019 (COVID-19) spread in different parts of the world is crucial to its prevention and control. Motivated by the previously developed Global Epidemic and Mobility (GLEaM) model, this paper proposes a new stochastic dynamic model to depict the evolution of COVID-19. The model allows spatial and temporal heterogeneity of transmission parameters and involves transportation between regions. Based on the proposed model, this paper also designs a two-step procedure for parameter inference, which utilizes the correlation between regions through a prior distribution that imposes graph Laplacian regularization on transmission parameters. Experiments on simulated data and real-world data in China and Europe indicate that the proposed model achieves higher accuracy in predicting the newly confirmed cases than baseline models. [ABSTRACT FROM AUTHOR]

A better understanding of various patterns in the coronavirus disease 2019 (COVID-19) spread in different parts of the world is crucial to its prevention and control. Motivated by the previously developed Global Epidemic and Mobility (GLEaM) model, this paper proposes a new stochastic dynamic model to depict the evolution of COVID-19. The model allows spatial and temporal heterogeneity of transmission parameters and involves transportation between regions. Based on the proposed model, this paper also designs a two-step procedure for parameter inference, which utilizes the correlation between regions through a prior distribution that imposes graph Laplacian regularization on transmission parameters. Experiments on simulated data and real-world data in China and Europe indicate that the proposed model achieves higher accuracy in predicting the newly confirmed cases than baseline models. [ABSTRACT FROM AUTHOR]

Ever since China has formally joined the WHO-backed global COVID-19 vaccine initiative known as COVAX, there is a presumed notion that China's vaccine diplomacy will make a significant contribution to the international public good and thus uplift Beijing's role as the rule-maker of international order. To scrutinize this, the paper asks if China succeeded in proliferating its weaponized vaccine policy to obtain maximum diplomatic gains and soft power projection to intensify its international image, geopolitical power, and domestic politico legitimacy. The authors argue that despite its vaccine diplomacy demonstrated the robust governance capacity and responsibility to be a great power. Yet, Beijing's geopolitical influence and international image are significantly overrated and not enough to play a more prominent role in the global power fulcrum/equilibrium. On the contrary, China enjoys a leading position on the domestic political front. Its successful portrayal of China's vaccine provision in the global market and remarkable configuration to leverage a deep-rooted nationalism has fundamentally provided China with a powerful rationale to divert its public's attention from Beijing's earlier inadequate handling of the outbreak. The evaluation of the paper reveals that China's vaccine diplomacy's influence in promoting international image and geopolitics is limited but has successfully stabilized its domestic political environment and enhanced its domestic legitimacy. [ABSTRACT FROM AUTHOR]

Uncertain regression model is a powerful analytical tool for exploring the relationship between explanatory variables and response variables. It is assumed that the errors of regression equations are independent. However, in many cases, the error terms are highly positively autocorrelated. Assuming that the errors have an autoregressive structure, this paper first proposes an uncertain regression model with autoregressive time series errors. Then, the principle of least squares is used to estimate the unknown parameters in the model. Besides, this new methodology is used to analyze and predict the cumulative number of confirmed COVID-19 cases in China. Finally, this paper gives a comparative analysis of uncertain regression model, difference plus uncertain autoregressive model, and uncertain regression model with autoregressive time series errors. From the comparison, it is concluded that the uncertain regression model with autoregressive time series errors can improve the accuracy of predictions compared with the uncertain regression model. [ABSTRACT FROM AUTHOR]

To quantitatively evaluate the impact of domestic aviation control measures on the spread of COVID-19 in China. The number of international flights from March to September 2019 simulated the number of flights from March to September 2020 without implementing aviation control measures. In addition, the proportion of asymptomatic persons and the delay in case reporting were adjusted to estimate the prevalence of each country during the same period and calculate the estimated imported cases. The estimated imported cases were assigned each day with weight, and the estimated daily reported cases were obtained based on the actual daily number of domestic cases in China. Effective Reproduction Number ( R t ) was calculated based on delayed distribution, Basic Reproductive Number ( R 0 ) distribution, and generation time distribution were reported in previous studies. Gaussian Process was used to estimate the effect of time-varying on R t , and the estimated R t was compared with the actual R t . The estimated imported cases increased significantly compared with the actual number of imported cases. The estimated imported cases were mainly concentrated in North America and Europe from March to April and gradually increased in many East Asian countries from May to September. The difference between predicted R t and actual R t was statistically significant. The estimated imported cases and the estimated R t have increased compared to the actual situation. This paper quantitatively proves that Chinese aviation control measures significantly suppress the COVID-19 epidemic, which is conducive to promoting and applying this measure. [ABSTRACT FROM AUTHOR]

Owing to the lockdowns associated with the Coronavirus Disease 2019(COVID-19) pandemic, carbon emissions were significantly reduced. However, the accurate impacts on the personal transport sector since then remain unclear. To further investigate the influence of sudden public health emergencies on actual carbon emissions from personal electric vehicles, this paper examined the travel patterns and corresponding carbon emissions of plug-in hybrid electric vehicles (PHEVs) operating in Chongqing, China, before and after COVID-19. The results revealed that the pandemic has reshaped the travel patterns of vehicle drivers, with a 9 % reduction in the postpandemic fleet average daily travel mileage. Currently, the total daily carbon emissions of a PHEV with a range of 80 km (PHEV80) are 6.24 kg, which is 13 % lower than emissions from conventional vehicles and 32 % higher than those from electric battery-powered vehicles before the pandemic. Since COVID-19, there has been a 24 % decrease in carbon emissions from PHEV80 vehicles for the fleet and a 30 % maximum increase for individuals. Furthermore, considering the integration of 50 % renewable energy into China's power grid by 2025, PHEVs can better mitigate the fluctuations in carbon emissions associated with sudden public health emergencies compared with conventional vehicles. [ABSTRACT FROM AUTHOR]

The COVID-19 disease spreads swiftly, and nearly three months after the first positive case was confirmed in China, Coronavirus started to spread all over the United States. Some states and counties reported high number of positive cases and deaths, while some reported lower COVID-19 related cases and death. In this paper, the factors that could affect the risk of COVID-19 infection and death were analyzed in county level. An innovative method by using K-means clustering and several classification models is utilized to determine the most critical factors. Results showed that longitudinal coordinate and population density, latitudinal coordinate, percentage of non-white people, percentage of uninsured people, percent of people below poverty, percentage of Elderly people, number of ICU beds per 10,000 people, percentage of smokers were the most significant attributes. [ABSTRACT FROM AUTHOR]

The aviation industry has become one of the top ten greenhouse gas emission industries in the world. China's aviation carbon emissions continue to increase, but the analysis of its influencing factors at the provincial level is still incomplete. This paper firstly uses Stochastic Impacts by Regression on Population, Affluence and Technology model (STIRPAT) model to analyze the time series evolution of China's aviation carbon emissions from 2000 to 2019. Secondly, it uses the Logarithmic Mean Divisia Index (LDMI) model to analyze the influencing characteristics and degree of four factors on China's aviation carbon emissions, which are air transportation revenue, aviation route structure, air transportation intensity and aviation energy intensity. Thirdly, it determines the various factors' influencing direction and evolution trend of 31 provinces' aviation carbon emissions in China (not including Hong Kong, Macao, Taiwan of China due to incomplete data). Finally, it derives the decoupling effort model and analyzes the decoupling relationship and decoupling effort degree between air carbon emissions and air transportation revenue in different provinces. The study found that from 2000 to 2019, China's total aviation carbon emissions continued to grow, while the growth rate of aviation carbon emissions showed a fluctuating downward trend. Air transportation revenue and aviation route structure promote the growth of total aviation carbon emissions, and air transportation intensity and aviation energy intensity have a restraining effect on the growth of total aviation carbon emissions. The scope of negative driving effect of air transportation revenue and air transportation intensity on total aviation carbon emissions in various provinces has increased. While the scope of positive driving influence of aviation route structure on total aviation carbon emissions of various provinces has increased, aviation energy intensity mainly has negative driving influence on total aviation carbon emissions of each province. Overall, the emission reduction trend in the areas to the west and north of the Qinling-Huaihe River Line is obvious. The decoupling mode between air carbon emissions and air transportation revenue in 31 provinces is mainly expansion negative decoupling. The air transportation intensity effect shows strong decoupling efforts in most provinces, the decoupling effort of aviation route structure effect and aviation energy intensity effect is not prominent. [ABSTRACT FROM AUTHOR]

A new epidemic of acute respiratory viral pneumonia was discovered in central China at the end of 2019. The disease was given the name coronavirus disease 2019 (COVID-19), and the virus that caused this disease was known as severe acute respiratory syndrome coronavirus (SARS-CoV-2). So far, diagnostic methods have been focused on (a) human antibody detection, (b) viral antigen detection and (c) viral gene detection, the latter using RT-PCR being the most accurate approach. In this paper, we present a summary of the COVID-19 pandemic, clinical features and epidemiology and pathogenesis. Also, we focus on the recent advances in bioanalytical diagnostic methods based on various techniques for SARS-CoV-2 sensing that have recently been published (2020–2021). Furthermore, we present the mechanisms, advantages and disadvantages of the most common biosensors for COVID-19 detection, which include optical, electrochemical and piezoelectric biosensors as well as wearable and smart nanobiosensors, immunosensors, aptasensors and genosensors. [ABSTRACT FROM AUTHOR]

The new coronavirus infection (COVID-19) has spread to numerous countries around the world since several cases of the disease were first reported in late December 2019 in China. Currently, the WHO strongly recommends infection prevention measures such as wearing masks, hand washing, and frequent disinfection of high-touch surfaces, but there were many arguments against infection prevention policy in March 2020. For example, the WHO did not recommend the use of masks for the healthy general public. In Japan, wearing a mask was required before the habit of wearing a mask was established, which gave additional works of checking whether customer was wearing a mask to the employees. To reduce the workloads of employee and ensure mask-wearing, we started providing the mask-wearing system free of charge on March 5, 2020. We also developed hand washing time estimation and disinfection support system. It is useful to accumulate data on the status of implementation of countermeasures by our application, which leads to gain useful knowledge regarding countermeasures against COVID-19 as well as other infectious diseases. In this paper, we describe the development and introduction impacts of these systems in a pandemic emergencies. In addition, because of the security and privacy issues in running these image analysis applications, we discuss the delivery methods suitable for each service. [ABSTRACT FROM AUTHOR]

Coping with the outbreak of Coronavirus disease 2019 (COVID-19), many countries have implemented public-health measures and movement restrictions to prevent the spread of the virus. However, the strict mobility control also brought about production stagnation and market disruption, resulting in a severe worldwide economic crisis. Quantifying the economic stagnation and predicting post-pandemic recovery are imperative issues. Besides, it is significant to examine how the impact of COVID-19 on economic activities varied with industries. As a reflection of enterprises' production output, high-frequency electricity-consumption data is an intuitive and effective tool for evaluating the economic impact of COVID-19 on different industries. In this paper, we quantify and compare economic impacts on the electricity consumption of different industries in eastern China. In order to address this problem, we conduct causal analysis using a difference-in-difference (DID) estimation model to analyze the effects of multi-phase public-health measures. Our model employs the electricity-consumption data ranging from 2019 to 2020 of 96 counties in the Eastern China region, which covers three main economic sectors and their 53 sub-sectors. The results indicate that electricity demand of all industries (other than information transfer industry) rebounded after the initial shock, and is back to pre-pandemic trends after easing the control measures at the end of May 2020. Emergency response, the combination of all countermeasures to COVID-19 in a certain period, affected all industries, and the higher level of emergency response with stricter movement control resulted in a greater decrease in electricity consumption and production. The pandemic outbreak has a negative-lag effect on industries, and there is greater resilience in industries that are less dependent on human mobility for economic production and activities. [ABSTRACT FROM AUTHOR]

In the fight against the COVID-19 pandemic, many brilliant results have been achieved, but the thermodynamics of the novel SARS-CoV-2 coronavirus has been completely neglected. This is a serious systematic error, which can compromise the results of the entire pandemic virus SARS-CoV-2 research. In the present work, we therefore study the thermodynamics of SARS-CoV-2 in its environment, from air to endosome and endosome-independent cell entry pathways. In the study of the thermodynamics of the new coronavirus SARS-CoV-2 in air, the presence of pollen, bacteria, other viruses, spores, dust, but more particularly, that of nanoparticles of health interest at the same scale threshold as the spike proteins of the pandemic virus, such as particulate matter, cannot be neglected. This work therefore starts from a comparative study of the air environments in China and Italy, the first countries affected by the infection. Currently, a correlation between the spread of infection and pollution is still very controversial. But our paper is not concerned with this. We propose some methodological notes which lead us to the formulation of a general mathematical apparatus (an energy landscape theory), suitable to explain at the molecular level the energetic configurations of the quasi-species of the pandemic virus SARS-CoV-2 in its environment. We focus on complexes between the viral particle and other objects in its environment at the scale threshold of the spikes of the viral particle. Then, we wondered if such complexes can lead to the generation of more aggressive viral variants and how to predict their populations and energy configurations, in order to plan an adequate prophylaxis. [ABSTRACT FROM AUTHOR]

COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), spread rapidly and affected most of the world since its outbreak in Wuhan, China, which presents a major challenge to the emergency response mechanism for sudden public health events and epidemic prevention and control in all countries. In the face of the severe situation of epidemic prevention and control and the arduous task of social management, the tremendous power of science and technology in prevention and control has emerged. The new generation of information technology, represented by big data and artificial intelligence (AI) technology, has been widely used in the prevention, diagnosis, treatment and management of COVID-19 as an important basic support. Although the technology has developed, there are still challenges with respect to epidemic surveillance, accurate prevention and control, effective diagnosis and treatment, and timely judgement. The prevention and control of sudden infectious diseases usually depend on the control of infection sources, interruption of transmission channels and vaccine development. Big data and AI are effective technologies to identify the source of infection and have an irreplaceable role in distinguishing close contacts and suspicious populations. Advanced computational analysis is beneficial to accelerate the speed of vaccine research and development and to improve the quality of vaccines. AI provides support in automatically processing relevant data from medical images and clinical features, tests and examination findings; predicting disease progression and prognosis; and even recommending treatment plans and strategies. This paper reviews the application of big data and AI in the COVID-19 prevention, diagnosis, treatment and management decisions in China to explain how to apply big data and AI technology to address the common problems in the COVID-19 pandemic. Although the findings regarding the application of big data and AI technologies in sudden public health events lack validation of repeatability and universality, current studies in China have shown that the application of big data and AI is feasible in response to the COVID-19 pandemic. These studies concluded that the application of big data and AI technology can contribute to prevention, diagnosis, treatment and management decision making regarding sudden public health events in the future. [ABSTRACT FROM AUTHOR]

This paper develops a new grey prediction model with quadratic polynomial term. Analytical expressions of the time response function and the restored values of the new model are derived by using grey model technique and mathematical tools. With observations of the confirmed cases, the death cases and the recovered cases from COVID-19 in China at the early stage, the proposed forecasting model is developed. The computational results demonstrate that the new model has higher precision than the other existing prediction models, which show the grey model has high accuracy in the forecasting of COVID-19. [ABSTRACT FROM AUTHOR]

In confronting the sudden epidemic of COVID-19, China and other countries have been under great deal of pressure to block virus transmission and reduce death cases. Fangcang shelter hospital, which is converted from large-scale public venue, is proposed and proven to be an effective way for administering medical care and social isolation. This paper presents the practice in information technology support for a Fangcang shelter hospital in Wuhan, China. The experiences include the deployment strategy of IT infrastructure, the redesign of function modules in the hospital information system (HIS), equipment maintenance and medical staff training. The deployment strategy and HIS modules have ensured smoothness and efficiency of clinical work. The team established a quick response mechanism and adhered to the principle of nosocomial infection control. Deployment of network and modification of HIS was finished in the 48 hours before patient admittance. A repair hotline and remote support for equipment and software were available whenever medical workers met with any questions. No engineer ever entered the contaminated areas and no one was infected by the coronavirus during the hospital operation. Up to now, Fangcang shelter hospital is adopted by many regions around the world facing the collapse of their medical systems. This valuable experience in informatization construction and service in Wuhan may help participators involving in Fangcang shelter hospital get better information technology support, and find more practical interventions to fight the epidemic. [ABSTRACT FROM AUTHOR]

Utilizing national migration data regarding the outbreak of the novel coronavirus (2019-nCoV), this paper employs a difference-in-differences approach to empirically analyze the relationship between human mobility and the transmission of infectious diseases in China. We show that national human mobility restrictions ascribed to the first-level public health emergency response policy effectively reduce both intercity and intracity migration intensities, thus leading to a declining scale of human mobility, which improves the effectiveness in controlling the epidemic. Human mobility restrictions have greater influences on cities with better economic development, denser populations, or larger passenger volumes. Moreover, mobility restriction measures are found to be better implemented in regions with increased public awareness, or with provincial leaders who have healthcare crisis management experience, local administrative experience, or the opportunity to serve a consecutive term. [ABSTRACT FROM AUTHOR]

By employing discourse-historical approach and corpus linguistics, this paper examines media reports to analyze the Chinese official discourse in the context of the COVID-19 outbreak. The results demonstrate that a paradox of globalism and nationalism has been simultaneously reflected when reporting the global pandemic. Based on a polarizing discursive construction of positive "self" and negative "others," on many occasions, the globalist and nationalist arguments have been closely intertwined and complement each other to reinforce the legitimacy of the ruling party at home and the international reputation of China under the leadership of the ruling party. [ABSTRACT FROM AUTHOR]

The spread of COVID-19 is closely related to the structure of human social networks. Based on 237 cases, by using epidemiological retrospective statistics, data visualization, and social network analysis methods, this paper summarized characteristics of patients with COVID-19 in Shaanxi, China, and analyzed these patients' dynamic contact network structure. The study found that there are many clustered infections through strong ties, about one-third of cases are caused by relatives' infection. In early stages of the epidemic, imported cases were the most, and in the later stages, local infection cases were the most. The infected people were mostly middle-aged men. Symptoms of imported cases occurred on average of 3 days after they arrived, and medical measures were taken 5 days later on average. All cases showed symptoms in less than 2 days on average and were then taken to medical treatment. The contact network can be divided into multiple disconnected components. The largest component has 12 patients. The average degree centrality in the network is 0.987, average betweenness degree is 0, average closeness degree is 0.452, and average PageRank index is 0.0042. The number of contacts of patients is unevenly distributed in the network. [ABSTRACT FROM AUTHOR]

In December 2019, corona virus disease 2019 (COVID-19) has broken out in China. Understanding the distribution of disease at the national level contributes to the formulation of public health policies. There are several studies that investigating the influencing factors on distribution of COVID-19 in China. However, more influencing factors need to be considered to improve our understanding about the current epidemic. Moreover, in the absence of effective medicine or vaccine, the Chinese government introduced a series of non-pharmaceutical interventions (NPIs). However, assessing and predicting the effectiveness of these interventions requires further study. In this paper, we used statistical techniques, correlation analysis and GIS mapping expression method to analyze the spatial and temporal distribution characteristics and the influencing factors of the COVID-19 in mainland China. The results showed that the spread of outbreaks in China's non-Hubei provinces can be divided into five stages. Stage I is the initial phase of the COVID-19 outbreak; in stage II the new peak of the epidemic was observed; in stage III the outbreak was contained and new cases decreased; there was a rebound in stage IV, and stage V led to level off. Moreover, the cumulative confirmed cases were mainly concentrated in the southeastern part of China, and the epidemic in the cities with large population flows from Wuhan was more serious. In addition, statistically significant correlations were found between the prevalence of the epidemic and the temperature, rainfall and relative humidity. To evaluate the NPIs, we simulated the prevalence of the COVID-19 based on an improved SIR model and under different prevention intensity. It was found that our simulation results were compatible with the observed values and the parameter of the time function in the improved SIR model for China is a = − 0.0058. The findings and methods of this study can be effective for predicting and managing the epidemics and can be used as an aid for decision makers to control the current and future epidemics. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) is a new human infectious disease. The etiology for this outbreak is a novel coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Thus far, related research on COVID-19 is still in preliminary stage. This paper summarized the latest outcomes of corresponding study from Chinese centers and clarified the hematopoietic abnormality caused by SARS-CoV-2 and potential mechanism. Lymphopenia was common in the early stage after the onset of COVID-19. A significant decrease was observed in peripheral CD4+ and CD8+ T lymphocytes. As the illness progressed, neutrophilia emerged in several cases, and patients with severe critical pulmonary conditions showed higher neutrophils than common type. Thrombocytopenia was resulting from the consumption and/or the reduced production of platelets in damaged lungs. Anemia was not observed notably, but the decrease in hemoglobin was frequent. The activation of monocyte-macrophage system aggravates the immune damage of lung and other tissues, which leads to the increase of D-dimer, prothrombin time, and platelet consumption. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) pneumonia, firstly reported in Wuhan, Hubei province, China, has rapidly spread around the world with high mortality rate among critically ill patients. The use of corticosteroids in COVID-19 remains a major controversy. Available evidences are inconclusive. According to WHO guidance, corticosteroids are not recommended to be used unless for another reason. Chinese Thoracic Society (CTS) proposes an expert consensus statement that suggests taking a prudent attitude of corticosteroid usage. In our clinical practice, we do not use corticosteroids routinely; only low-to-moderate doses of corticosteroids were given to several severely ill patients prudently. In this paper, we will present two confirmed severe COVID-19 cases admitted to isolation wards in Optical Valley Campus of Tongji hospital, Tongji Medical College, Huazhong University of Science and Technology. We will discuss questions related to corticosteroids usages. [ABSTRACT FROM AUTHOR]

Since December 2019, a novel coronavirus pneumonia (COVID-19) has broken out in Wuhan, China and spread rapidly. Recent studies have found that ⁓ 15.7% of patients develop severe pneumonia, and cytokine storm is an important factor leading to rapid disease progression. Currently, there are no specific drugs for COVID-19 and the cytokine storm it causes. IL-6 is one of the key cytokines involved in infection-induced cytokine storm. Tocilizumab, which is the IL-6 receptor antagonist, has been approved by the US FDA for the treatment of cytokine release syndrome (CRS), is expected to treat cytokine storm caused by COVID-19 and is now in clinical trials. In this paper, we will elaborate the role of cytokine storm in COVID-19, the mechanism of tocilizumab on cytokine storm and the key points of pharmaceutical care based on the actual clinical application for COVID-19 in our hospital, the latest research reports, clinical trial progress of tocilizumab, drug instruction from the US FDA, and "Diagnosis and Treatment Plan of Novel Coronavirus Pneumonia (seventh trial edition)" in China, so as to provide reference for the treatment of COVID-19. [ABSTRACT FROM AUTHOR]

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that emerged in China at the end of 2019 has become a pandemic infection that has now involved 200 countries with 465,915 confirmed cases and 21,031 confirmed deaths. Unfortunately, many data have shown that the high number of undocumented infections could have a major role in the rapid diffusion of the disease. In most of the nations involved, non-urgent, non-cancer procedures have been stopped to reallocate medical and paramedical staff to face the emergency. Moreover, concerns have been raised that minimally invasive surgery could be a procedure that carries the risk of virus diffusion in the operating theater during surgery. This paper reports clinical recommendations and scientific studies to assist clinicians in this field. [ABSTRACT FROM AUTHOR]

A correction to this paper has been published: https://doi.org/10.1007/s10902-021-00406-0 [ABSTRACT FROM AUTHOR]

The present outbreak of the novel coronavirus SARS‐CoV‐2, epicentered in China in December 2019, has spread to many other countries. The entire humanity has a vital responsibility to tackle this pandemic and the technologies are being helpful to them to a greater extent. The purpose of the work is to precisely bring scientific and general awareness to the people all around the world who are currently fighting the war against COVID-19. It's visible that the number of people infected is increasing day by day and the medical community is tirelessly working to maintain the situation under control. Other than the negative effects caused by COVID-19, it is also equally important for the public to understand some of the positive impacts it has directly or indirectly given to society. This work emphasizes the various impacts that are created on society as well as the environment. As a special additive, some important key areas are highlighted namely, how the modernized technologies are aiding the people during the period of social distancing. Some effective technological implications carried out by both information technology and educational institutions are highlighted. There are also several steps taken by the state government and central government in each country in adopting the complete lockdown rule. These steps are taken primarily to prevent the people from COVID-19 impact. Moreover, the teachings we need to learn from the quarantine situation created to prevent further spread of this global pandemic is discussed in brief and the importance of carrying them to the future. Finally, the paper also elucidates the general preventive measures that have to be taken to prevent this deadly coronavirus, and the role of technology in this pandemic situation has also been discussed. [ABSTRACT FROM AUTHOR]