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The emergence of the Covid-19 pandemic has led to the publication of many scientific papers. The goal of the present research was to analyze these papers using the Altmetric Attention Score (AAS). Statistics for 100 publications with high AAS scores were selected and exported from the Dimension database on May 22nd 2020. The major findings were that these publications were published in 34 different journals or preprint repositories. More than one-third of the total of 657, 350 social media posts were collected from the Twitter platform. The top contributing countries were China, followed by the USA. The paper "The proximal origin of SARS-CoV-2" by Andersen, Kristian G., etal., 2020 had the highest AAS (33 514). These findings may help others to design studies of the AAS in Coronavirus literature and compare them with traditional citations. [ABSTRACT FROM AUTHOR]

The papers in this special section focus on modeling and simulation in disaster and emergency management. The COVID-19 pandemic of 2020 has wrought damage globally to a degree that is totally unprecedented in human history.Within three months that the first known death from the illness caused by the so-called novel Coronavirus was reported by state media in China on the 11th of January 2020, close to 1.8 million people worldwide had tested positive, with more than 100,000 deaths reported. By the time the pandemic will have been declared to be under control, millions more are expected to have been infected and tens (possibly hundreds) of thousands more will have perished as a result of the disease. Not only are the numbers of COVID-19 infected people and deaths staggering. Economies throughout the world have suffered tremendously, with many companies forced to shut down, or at the very least significantly scale down, their production of goods and services as a result of the need for social distancing and self-isolation required to prevent the unmitigated transmission of the disease. All this has led to hundreds of millions worldwide losing their employment or other sources of livelihood. Governments of all nations, large and small, developed and underdeveloped, have needed to pump into their economy and onto their struggling citizens whatever financial resources they could manage to generate. [ABSTRACT FROM AUTHOR]

The appearance of a novel coronavirus in late 2019 radically changed the community of researchers working on coronaviruses since the 2002 SARS epidemic. In 2020, coronavirus-related publications grew by 20 times over the previous two years, with 130,000 more researchers publishing on related topics. The United States, the United Kingdom and China led dozens of nations working on coronavirus prior to the pandemic, but leadership consolidated among these three nations in 2020, which collectively accounted for 50% of all papers, garnering well more than 60% of citations. China took an early lead on COVID-19 research, but dropped rapidly in production and international participation through the year. Europe showed an opposite pattern, beginning slowly in publications but growing in contributions during the year. The share of internationally collaborative publications dropped from pre-pandemic rates; single-authored publications grew. For all nations, including China, the number of publications about COVID track closely with the outbreak of COVID-19 cases. Lower-income nations participate very little in COVID-19 research in 2020. Topic maps of internationally collaborative work show the rise of patient care and public health clusters—two topics that were largely absent from coronavirus research in the two years prior to 2020. Findings are consistent with global science as a self-organizing system operating on a reputation-based dynamic. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease (SARS-CoV-2) has caused many infections and deaths throughout the world; the spread of the coronavirus pandemic is still ongoing and continues to affect healthcare systems and economies of countries worldwide. Mathematical models are used in many applications for infectious diseases, including forecasting outbreaks and designing containment strategies. In this paper, we study two types of SIR and SEIR models for the coronavirus. This study focuses on the discrete-time and fractional-order of these models; we study the stability of the fixed points and orbits using the Jacobian matrix and the eigenvalues and eigenvectors of each case; moreover, we estimate the parameters of the two systems in fractional order. We present a statistical study of the coronavirus model in two countries: Saudi Arabia, which has successfully recovered from the SARS-CoV-2 pandemic, and China, where the number of infections remains significantly high. [ABSTRACT FROM AUTHOR]

The exploration of the dependency structure of the Chinese and EU carbon trading markets is crucial to the construction of a globally harmonized carbon market. In this paper, we studied the characteristics of structural interdependency between China's major carbon markets and the European Union (EU) carbon market before and after the launch of the national carbon emissions trading scheme (ETS) and the occurrence of the new coronavirus (COVID-19) by applying the C-vine copula method, with the carbon trading prices of the EU, Beijing, Shanghai, Guangdong, Shenzhen and Hubei as the research objects. The study shows that there exists a statistically significant dependence between the EU and the major carbon markets in China and their extremal dependences and dependence structures are different at different stages. After the launch of the national carbon ETS, China has become more independent in terms of interdependency with the EU carbon market, and is more relevant between domestic carbon markets. Most importantly, we found that the dependence between the EU and Chinese carbon markets has increased following the outbreak of COVID-19, and tail dependency structures existed before the launch of the national carbon ETS and during the outbreak of the COVID-19. The results of this study provide a basis for the understanding of the linkage characteristics of carbon trading prices between China and the EU at different stages, which in turn can help market regulators and investors to formulate investment decisions and policies. [ABSTRACT FROM AUTHOR]

Recently, most countries have entered the outbreak period of the novel coronavirus epidemic. This sudden outbreak has caused a huge impact on the global economy, which has intensified the division of globalization and the recession of the global economy. Although the epidemic situation in China has gradually stabilized, the severe situation in the world still inevitably impacts China's economy. Based on the uncertainty of future epidemic, this paper sets up three scenarios to analyze the impact of the epidemic on China's economy. The first is that in June, the epidemic both at home and abroad is under control without rebound; the second is that the domestic epidemic is basically controlled but the foreign situation is not effectively controlled; the third is that the epidemic situation in China has a serious rebound due to the influence of the imported cases from abroad, which destroy the economy again. At the same time, some corresponding guidelines are put forward for the recovery of economy, and to minimize the economic losses as well as accelerate the pace of national economic recovery. In addition, it is believed that these suggestions may have certain reference value to other countries. [ABSTRACT FROM AUTHOR]

This project is funded by the US National Science Foundation (NSF) through their NSF RAPID program under the title "Modeling Corona Spread Using Big Data Analytics." The project is a joint effort between the Department of Computer & Electrical Engineering and Computer Science at FAU and a research group from LexisNexis Risk Solutions. The novel coronavirus Covid-19 originated in China in early December 2019 and has rapidly spread to many countries around the globe, with the number of confirmed cases increasing every day. Covid-19 is officially a pandemic. It is a novel infection with serious clinical manifestations, including death, and it has reached at least 124 countries and territories. Although the ultimate course and impact of Covid-19 are uncertain, it is not merely possible but likely that the disease will produce enough severe illness to overwhelm the worldwide health care infrastructure. Emerging viral pandemics can place extraordinary and sustained demands on public health and health systems and on providers of essential community services. Modeling the Covid-19 pandemic spread is challenging. But there are data that can be used to project resource demands. Estimates of the reproductive number (R) of SARS-CoV-2 show that at the beginning of the epidemic, each infected person spreads the virus to at least two others, on average (Emanuel et al. in N Engl J Med. 2020, Livingston and Bucher in JAMA 323(14):1335, 2020). A conservatively low estimate is that 5 % of the population could become infected within 3 months. Preliminary data from China and Italy regarding the distribution of case severity and fatality vary widely (Wu and McGoogan in JAMA 323(13):1239–42, 2020). A recent large-scale analysis from China suggests that 80 % of those infected either are asymptomatic or have mild symptoms; a finding that implies that demand for advanced medical services might apply to only 20 % of the total infected. Of patients infected with Covid-19, about 15 % have severe illness and 5 % have critical illness (Emanuel et al. in N Engl J Med. 2020). Overall, mortality ranges from 0.25 % to as high as 3.0 % (Emanuel et al. in N Engl J Med. 2020, Wilson et al. in Emerg Infect Dis 26(6):1339, 2020). Case fatality rates are much higher for vulnerable populations, such as persons over the age of 80 years (> 14 %) and those with coexisting conditions (10 % for those with cardiovascular disease and 7 % for those with diabetes) (Emanuel et al. in N Engl J Med. 2020). Overall, Covid-19 is substantially deadlier than seasonal influenza, which has a mortality of roughly 0.1 %. Public health efforts depend heavily on predicting how diseases such as those caused by Covid-19 spread across the globe. During the early days of a new outbreak, when reliable data are still scarce, researchers turn to mathematical models that can predict where people who could be infected are going and how likely they are to bring the disease with them. These computational methods use known statistical equations that calculate the probability of individuals transmitting the illness. Modern computational power allows these models to quickly incorporate multiple inputs, such as a given disease's ability to pass from person to person and the movement patterns of potentially infected people traveling by air and land. This process sometimes involves making assumptions about unknown factors, such as an individual's exact travel pattern. By plugging in different possible versions of each input, however, researchers can update the models as new information becomes available and compare their results to observed patterns for the illness. In this paper we describe the development a model of Corona spread by using innovative big data analytics techniques and tools. We leveraged our experience from research in modeling Ebola spread (Shaw et al. Modeling Ebola Spread and Using HPCC/KEL System. In: Big Data Technologies and Applications 2016 (pp. 347-385). Springer, Cham) to successfully model Corona spread, we will obtain new results, and help in reducing the number of Corona patients. We closely collaborated with LexisNexis, which is a leading US data analytics company and a member of our NSF I/UCRC for Advanced Knowledge Enablement. The lack of a comprehensive view and informative analysis of the status of the pandemic can also cause panic and instability within society. Our work proposes the HPCC Systems Covid-19 tracker, which provides a multi-level view of the pandemic with the informative virus spreading indicators in a timely manner. The system embeds a classical epidemiological model known as SIR and spreading indicators based on causal model. The data solution of the tracker is built on top of the Big Data processing platform HPCC Systems, from ingesting and tracking of various data sources to fast delivery of the data to the public. The HPCC Systems Covid-19 tracker presents the Covid-19 data on a daily, weekly, and cumulative basis up to global-level and down to the county-level. It also provides statistical analysis for each level such as new cases per 100,000 population. The primary analysis such as Contagion Risk and Infection State is based on causal model with a seven-day sliding window. Our work has been released as a publicly available website to the world and attracted a great volume of traffic. The project is open-sourced and available on GitHub. The system was developed on the LexisNexis HPCC Systems, which is briefly described in the paper. [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]

On 31 December 2019, WHO received inputs of pneumonia cases in Wuhan city of China, but the cause of infection could not be known initially. However, after some time, the medical researchers of China disclosed a novel coronavirus as the cause on 7 January 2020, named "2019-nCoV." The coronavirus (CoV) belongs to Severe Acute Respiratory Syndrome (SARS) species that leads to infection, causing illness, which ranges from the common cold to serious sickness. Finally, on 11 March 2020, the WHO Director-General Dr. Tedros Adhanom Ghebreyesus announced the outbreak as a pandemic. As the fear and ambiguity rose among companies and firms, the profit rate seemed to be lower due to the COVID-19 global impact. Nearly US$6 trillion in wealth from 24th to 28 February 2020 of the stock market has been wiped out. There was a great decrease in value over the S&P index, which abolished over $5 trillion in the same week. However, the largest 10 companies of S&P faced a loss of $1.4 trillion. The investors make an analytical prediction that firms' profits may drop in response to the impact of coronavirus. The prime focus is on the importance of digital business practices and how different sectors have been affected in terms of economic loss during this pandemic outbreak in this paper. [ABSTRACT FROM AUTHOR]

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]

Background: The global spread of the novel coronavirus pneumonia is still continuing, and a new round of more serious outbreaks has even begun in some countries. In this context, this paper studies the dynamics of a type of delayed reaction-diffusion novel coronavirus pneumonia model with relapse and self-limiting treatment in a temporal-spatial heterogeneous environment.Methods: First, focus on the self-limiting characteristics of COVID-19, incorporate the relapse and self-limiting treatment factors into the diffusion model, and study the influence of self-limiting treatment on the diffusion of the epidemic. Second, because the traditional Lyapunov stability method is difficult to determine the spread of the epidemic with relapse and self-limiting treatment, we introduce a completely different method, relying on the existence conditions of the exponential attractor of our newly established in the infinite-dimensional dynamic system to determine the diffusion of novel coronavirus pneumonia. Third, relapse and self-limiting treatment have led to a change in the structure of the delayed diffusion COVID-19 model, and the traditional basic reproduction number [Formula: see text] no longer has threshold characteristics. With the help of the Krein-Rutman theorem and the eigenvalue method, we studied the threshold characteristics of the principal eigenvalue and found that it can be used as a new threshold to describe the diffusion of the epidemic.Results: Our results prove that the principal eigenvalue [Formula: see text] of the delayed reaction-diffusion COVID-19 system with relapse and self-limiting treatment can replace the basic reproduction number [Formula: see text] to describe the threshold effect of disease transmission. Combine with the latest official data and the prevention and control strategies, some numerical simulations on the stability and global exponential attractiveness of the diffusion of the COVID-19 epidemic in China and the USA are given.Conclusions: Through the comparison of numerical simulations, we find that self-limiting treatment can significantly promote the prevention and control of the epidemic. And if the free activities of asymptomatic infected persons are not restricted, it will seriously hinder the progress of epidemic prevention and control. [ABSTRACT FROM AUTHOR]

Background: The coronavirus disease 2019 (COVID-19) is not just a disease of the respiratory system. The virus can affect the gastrointestinal (GI) tract as well. Recognizing the various manifestations in every organ system is important because these manifestations can contribute to community-based transmission. Methods:We outline the evidence of the pathophysiology of COVID-19 in the GI tract, the effects of the virus on the gut and liver, the presence of the virus in stool samples, and the potential for fecal-oral transmission of COVID-19. Most of the literature sources used in this paper are case studies from China following the surge of COVID-19 infection. Results: In patients with COVID-19, GI symptoms such as anorexia, nausea, vomiting, diarrhea, and abdominal pain have presented in conjunction with respiratory symptoms such as fever, shortness of breath, and cough. Evidence also shows acute hepatocellular injury, indicated by elevated liver enzymes such as alanine aminotransferase, aspartate aminotransferase, and gamma-glutamyl transferase. Fecal-oral transmission of COVID-19 is suspected because of the presence of COVID-19 RNA in stool samples of COVID-19-positive patients. Conclusion: Even without the presence of respiratory symptoms, several GI symptoms are associated with COVID-19 infection, as well as possible fecal-oral transmission. Therefore, COVID-19 infection should be considered for patients presenting with primarily GI symptoms. [ABSTRACT FROM AUTHOR]

Public health lessons from one pandemic become the planning assumptions for the next one. Aotearoa New Zealand's 2017 pandemic plan was derived from past experience of influenza. When Covid-19 emerged as a major global health threat, it took time for the realisation to crystallise that this infection was so different from influenza that it required a completely new pandemic response strategy. In this paper we describe how early evidence about SARS-CoV-2 transmission from China led to the adoption of an elimination strategy in Aotearoa New Zealand, making it the first country to choose elimination as a specific policy response. We discuss how further evidence has shaped the selection and design of Covid-19 pandemic control measures such as border restrictions, case and contact management, hygiene practices and use of face masks, physical distancing, and vaccines. This experience demonstrates the need for a different approach to the design of the next national pandemic plan. We identify key early evidence that will be required to develop a flexible and appropriate public health response to a new pandemic threat. We present a framework for a new pandemic plan that aims to learn from the Covid-19 experience by making as few limiting assumptions as possible. [ABSTRACT FROM AUTHOR]

Background: Preprints are preliminary reports that have not been peer-reviewed. In December 2019, a novel coronavirus appeared in China, and since then, scientific production, including preprints, has drastically increased. In this study, we intend to evaluate how often preprints about COVID-19 were published in scholarly journals and cited. Methods: We searched the iSearch COVID-19 portfolio to identify all preprints related to COVID-19 posted on bioRxiv, medRxiv, and Research Square from January 1, 2020, to May 31, 2020. We used a custom-designed program to obtain metadata using the Crossref public API. After that, we determined the publication rate and made comparisons based on citation counts using non-parametric methods. Also, we compared the publication rate, citation counts, and time interval from posting on a preprint server to publication in a scholarly journal among the three different preprint servers. Results: Our sample included 5,061 preprints, out of which 288 were published in scholarly journals and 4,773 remained unpublished (publication rate of 5.7%). We found that articles published in scholarly journals had a significantly higher total citation count than unpublished preprints within our sample (p < 0.001), and that preprints that were eventually published had a higher citation count as preprints when compared to unpublished preprints (p < 0.001). As well, we found that published preprints had a significantly higher citation count after publication in a scholarly journal compared to as a preprint (p < 0.001). Our results also show that medRxiv had the highest publication rate, while bioRxiv had the highest citation count and shortest time interval from posting on a preprint server to publication in a scholarly journal. Conclusions: We found a remarkably low publication rate for preprints within our sample, despite accelerated time to publication by multiple scholarly journals. These findings could be partially attributed to the unprecedented surge in scientific production observed during the COVID-19 pandemic, which might saturate reviewing and editing processes in scholarly journals. However, our findings show that preprints had a significantly lower scientific impact, which might suggest that some preprints have lower quality and will not be able to endure peer-reviewing processes to be published in a peer-reviewed journal. [ABSTRACT FROM AUTHOR]

Background: A novel coronavirus (COVID-19) outbreak has occurred in China, and national medical workers have been thrown into this silent battle. Paediatric medical workers have been an important part of this battle and under enormous pressure. This paper evaluates the depression, anxiety, and stress of paediatric medical staff during the epidemic and examines related impact factors. Methods: We conducted this study using online questionnaires via social networking software during the week of Feb. 17 to Feb. 23, 2020. The 21-item Depression Anxiety Stress Scale (DASS), which is a revised, simplified version of the original DASS developed by Lovibond et al., was used in this study. Results: Among all 2,031 respondents, 14.81%, 18.3%, and 9.98% had depression, anxiety and stress symptoms, respectively. Males, doctors, individuals aged between 31‑60 years, those with senior job titles, those who had contact with patients with confirmed or suspected cases of COVID-19, those who worked on the clinical frontlines fighting the epidemic and those who had experience combating similar outbreaks were more likely to have depression, anxiety or stress symptoms. Respondents in Beijing and Chongqing had lower negative psychological symptom scores than the national average. Conclusion: During the COVID-19 outbreak, depression, anxiety and stress are present to varying degrees among paediatric medical workers across the country. Psychological crisis interventions should be implemented to protect the mental health of paediatric medical workers during and after the epidemic. [ABSTRACT FROM AUTHOR]

Coronavirus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been reported as a worldwide emergency. Due to the extensiveness of spread and death, it has been declared as a pandemic. This review focused on the current pandemic situation and understanding the prevention and control strategies of COVID-19. Data presented here was by April 3, 2020. A total of 1,016,399 cases of COVID-19 with 53,238 deaths was reported from 204 countries and territories including two international conveyances over the world. After China, most of the new cases were from Europe, particularly Italy acting as the source of importation to many of the other countries around the world. China has obtained success by ascribing control strategies against COVID-19. The implementation of China's strategy, as well as the development of a vaccine, may control the pandemic of COVID-19. Further robust studies are required for a clear understanding of transmission parameters, prevention, and control strategies of SARSCoV-2. This review paper describes the nature of COVID-19 and the possible ways for the effective controlling of the COVID-19 or similar viral diseases that may come in the future. [ABSTRACT FROM AUTHOR]

The World Health Organization (WHO) recognized COVID-19 as the cause of a global pandemic in 2019. COVID-19 is caused by SARS-CoV-2, which was identified in China in late December 2019 and is indeed referred to as the severe acute respiratory syndrome coronavirus-2. The whole globe was hit within several months. As millions of individuals around the world are infected with COVID-19, it has become a global health concern. The disease is usually contagious, and those who are infected can quickly pass it on to others with whom they come into contact. As a result, monitoring is an effective way to stop the virus from spreading further. Another disease caused by a virus similar to COVID-19 is pneumonia. The severity of pneumonia can range from minor to life-threatening. This is particularly hazardous for children, people over 65 years of age, and those with health problems or immune systems that are affected. In this paper, we have classified COVID-19 and pneumonia using deep transfer learning. Because there has been extensive research on this subject, the developed method concentrates on boosting precision and employs a transfer learning technique as well as a model that is custom-made. Different pretrained deep convolutional neural network (CNN) models were used to extract deep features. The classification accuracy was used to measure performance to a great extent. According to the findings of this study, deep transfer learning can detect COVID-19 and pneumonia from CXR images. Pretrained customized models such as MobileNetV2 had a 98% accuracy, InceptionV3 had a 96.92% accuracy, EffNet threshold had a 94.95% accuracy, and VGG19 had a 92.82% accuracy. MobileNetV2 has the best accuracy of all of these models. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease 2019 (COVID-19) has placed tremendous pressure on public health systems across the world. Compared with the healthy population, cancer patients are more prone to developing psychological problems, including depression and anxiety, because of worries about cancer recurrence, cancer symptoms, treatment-related discomfort, a lack of social interaction and the impact on their financial well-being. This paper aimed to identify existing evidence on psychological symptoms and their associated factors among cancer patients in the Chinese Mainland during the COVID-19 pandemic, and on interventions to effectively manage these symptoms. Articles related to the prevalence, the risk factors and interventions of psychological symptoms among cancer patients in the Chinese Mainland during COVID-19 published between December 2019 and August 2020 were searched in two English (PubMed and Embase) and two Chinese (CNKI and Wan Fang Data) databases. A total of 180 studies were identified, and 18 studies were included in the review after removing duplicates and screening for relevancy. The results suggest that patients with cancer in the Chinese Mainland have suffered psychological pressure during COVID-19, with a high prevalence of psychological distress, depression and anxiety reported across most of the reviewed studies. Pandemic-related factors such as treatment discontinuation and worry about being infected are associated with these symptoms. Nurses may help to relieve these symptoms by identifying stressors, providing relevant information through mass and social media and referring patients to specialists for psychological support. However, evidence about treatments and interventions for these symptoms is limited, and additional research is warranted to identify effective interventions to promote resilience in this patient population. [ABSTRACT FROM AUTHOR]

Between 7 December 2022 and 28 February 2023, China experienced a new wave of COVID-19 that swept across the entire country and resulted in an increasing amount of respiratory infections and hospitalizations. The purpose of this study is to reveal the intensity and composition of coinfecting microbial agents. In total, 196 inpatients were recruited from The Third People's Hospital of Shenzhen, and 169 respiratory and 73 blood samples were collected for metagenomic next-generation sequencing. The total "Infectome" was characterized and compared across different groups defined by the SARS-CoV-2 detection status, age groups, and severity of disease. Our results revealed a total of 22 species of pathogenic microbes (4 viruses, 13 bacteria, and 5 fungi), and more were discovered in the respiratory tract than in blood. The diversity of the total infectome was highly distinguished between respiratory and blood samples, and it was generally higher in patients that were SARS-CoV-2-positive, older in age, and with more severe disease. At the individual pathogen level, HSV-1 seemed to be the major contributor to these differences observed in the overall comparisons. Collectively, this study reveals the highly complex respiratory infectome and high-intensity coinfection in patients admitted to the hospital during the period of the 2023 COVID-19 pandemic in China. [ABSTRACT FROM AUTHOR]

The novel coronavirus (COVID-19) pandemic spread globally from its outbreak in China in early 2020, negatively affecting economies and industries on a global scale. In line with historic crises and shock events including the 2002-04 SARS outbreak, the 2011 Christchurch earthquake and 2017 Hurricane Irma, COVID-19 has significantly impacted global economic conditions, causing significant economic downturns, company and industry failures, and increased unemployment. To understand how conditions created by the pandemic to date compare to the aforementioned shock events, we conducted a thorough literature review focusing on the presentation of panic buying and herd mentality behaviours, changes to discretionary consumer spending as defined by Maslow's Hierarchy of Needs, and the impact of global media on these behaviours. The methodology utilised to analyse panic buying, herd mentality and altered patterns of consumer discretionary spending (according to Maslow's theory) involved an analysis of consumer spending data, largely focused on Australian and American markets. Here, we analysed the volume and timing of consumer spending patterns; the volumes of spending on specific, highly-demanded consumer goods during the investigative period; and the distribution of spending on luxury and non-durable goods to identify the occurrence of these consumer behaviours. Moreover, to identify the presence of the media in influencing consumer behaviour we focused on web traffic to media sites, alongside keyword and phrase data mining. We conclude that, to date, consumer behaviour during the COVID-19 crisis appears to align with behaviours exhibited during historic shock events. We hope to contribute to the body of research on the early months of this pandemic before longer-term studies are available. [ABSTRACT FROM AUTHOR]

Background: The novel coronavirus diseases 2019 (COVID-19) caused over 1.7 million confirmed cases and cumulative mortality up to over 110,000 deaths worldwide as of 14 April 2020. A total of 57 Macao citizens were obligated to stay in Hubei province, China, where the highest COVID-19 prevalence was noted in the country and a "lockdown" policy was implemented for outbreak control for more than one month. They were escorted from Wuhan City to Macao via a chartered airplane organized by Macao SAR government and received quarantine for 14 days with none of the individual being diagnosed with COVID-19 by serial RNA tests from the nasopharyngeal specimens and sera antibodies. It was crucial to identify common characteristics among these 57 uninfected individuals. Methods: A questionnaire survey was conducted to extract information such as behavior, change of habits and preventive measures. Results: A total of 42 effective questionnaires were analyzed after exclusion of 14 infants and children with age under fifteen as ineligible for the survey and missing of one questionnaire, with a response rate of 97.7% (42 out of 43). The proportion of female composed more than 70% of this group of returners. The main reason for visiting Hubei in 88.1% of respondents was to visit relatives. Over 88% of respondents did not participate in high-risk activities due to mobility restriction. All (100%) denied contact with suspected or confirmed COVID-19 cases. Comparison of personal hygiene habits before and during disease outbreak showed a significant increase in practice including wearing a mask when outdoor (16.7% and 95.2%, P < 0.001) and often wash hands with soap or liquid soap (85.7% and 100%, P = 0.031). [ABSTRACT FROM AUTHOR]

The novel coronavirus pneumonia (COVID-19) pandemic has caused enormous impacts around the world. Characterizing the risk dynamics for urgent epidemics such as COVID-19 is of great benefit to epidemic control and emergency management. This article presents a novel approach to characterizing the space-time risks of the COVID-19 epidemic. We analyzed the heavy-tailed distribution and spatial hierarchy of confirmed COVID-19 cases in 367 cities from 20 January to 12 April 2020, and population density data for 2019, and modelled two parameters, COVID-19 confirmed cases and population density, to measure the risk value of each city and assess the epidemic from the perspective of spatial and temporal changes. The evolution pattern of high-risk areas was assessed from a spatial and temporal perspective. The number of high-risk cities decreased from 57 in week 1 to 6 in week 12. The results show that the risk measurement model based on the head/tail breaks approach can describe the spatial and temporal evolution characteristics of the risk of COVID-19, and can better predict the risk trend of future epidemics in each city and identify the risk of future epidemics even during low incidence periods. Compared with the traditional risk assessment method model, it pays more attention to the differences in the spatial level of each city and provides a new perspective for the assessment of the risk level of epidemic transmission. It has generality and flexibility and provides a certain reference for the prevention of infectious diseases as well as a theoretical basis for government implementation strategies. [ABSTRACT FROM AUTHOR]

Background: An international pandemic of severe coronavirus disease (COVID-19) has been caused by the novel coronavirus SARS-CoV-2. A large number of patients with chest trauma were infected with COVID-19. The COVID-19 pandemic had a significant impact on the management of chest trauma. Objective: The present observational study was conducted to evaluate the clinical characteristics and outcomes of chest trauma patients with or without COVID-19 infection. Methods: A multicenter observational study was performed at three Chinese hospitals between November 1, 2022, and January 31, 2023. All enrolled patients were diagnosed with chest trauma. We analyzed data from existing medical records, including all baseline data and prognostic follow-up data, such as 30-day mortality, hospital stays, hospitalization costs, and complications. Results: All 375 eligible patients completed the follow-up. There was no significant difference in baseline characteristics between chest trauma combined with COVID-19 (p > 0.05). Chest trauma combined with COVID-19 infection may lead to higher 30-day mortality (16.36% vs. 7.14%, p = 0.005), longer hospital stays (22.5 ± 5.9 vs. 12.8 ± 4.2, p < 0.001), larger hospitalization costs (p < 0.001), and increased hospitalization complications, such as pulmonary embolism (10.30% vs. 4.76%, p = 0.039), deep vein thrombosis (DVT, 33.33% vs. 18.57%, p = 0.001), the incidence of 7-day delirium (69.70% vs. 46.19%, p < 0.001), and respiratory failure (38.18% vs. 24.77%, p = 0.005). Conclusions: Compared to chest trauma alone, it may lead to higher mortality, larger hospitalization costs, and more complications. To better respond to the future of COVID-19 or other similar virus-borne disease pandemics, it is important to understand the clinical characteristics and complications, such as pulmonary embolism, DVT, and respiratory failure after COVID-19 infection. To guide the future treatment of chest trauma combined with COVID-19 infection or other infectious diseases. [ABSTRACT FROM AUTHOR]

COVID-19 pandemic erupted from Wuhan/China, has impacted the whole world in many ways drastically. Understanding such a pandemic, taking necessary precautions, and controlling a potential epidemic is essential. Modules and communities are typically and frequently represented in networks and analyzed with specific methods. Epidemic network analysis is a powerful method that provides us different information to interpret and make accurate decisions for pandemic events. The randomness of epidemic networks is considerably high; hence, it is essential to obtain consistent information from such networks. In this study, we proposed the network-based community detection of the early-stage COVID-19 associated with the basic reproduction number and the geographical distance between country locations. Worldwide confirmed cases between 22/01/2020 and 08/06/2020 had been analyzed for tenday periods. For this purpose, the Community Detection method was used. Therefore, (i) the community regions, (ii) the change of these regions and weighted nodal degrees of confirmed cases during the COVID-19, (iii) the relations between locations, and (iv) locations or regions which played an important role during the spread of this disease were obtained. The results of this study may help to reduce the reproduction number by lowering the average rate of contact in the early stage of a new pandemic. [ABSTRACT FROM AUTHOR]

In this paper, we utilize the Internet big data tool, namely Baidu Index, to predict the development trend of the new coronavirus pneumonia epidemic to obtain further data. By selecting appropriate keywords, we can collect the data of COVID-19 cases in China between 1 January 2020 and 1 April 2020. After preprocessing the data set, the optimal sub-data set can be obtained by using random forest feature selection method. The optimization results of the seven hyperparameters of the LightGBM model by grid search, random search and Bayesian optimization algorithms are compared. The experimental results show that applying the data set obtained from the Baidu Index to the Bayesian-optimized LightGBM model can better predict the growth of the number of patients with new coronary pneumonias, and also help people to make accurate judgments to the development trend of the new coronary pneumonia. [ABSTRACT FROM AUTHOR]

Acute macular neuroretinopathy (AMN) has been associated with several conditions. The aim of this study is to report a surge of AMN cases diagnosed since the easing of epidemic control for COVID-19 in China, in early December 2022.Case Report: Four cases presented with paracentral or central scotomas, or blurred vision soon after SARS-CoV-2 coronavirus infection. Fundus manifestations were recorded, including typical hyper-reflective segments of the outer plexiform layer (OPL) and outer nuclear layer (ONL), and associated disruption of ellipsoid, interdigitation zones, and retinal pigment epithelium (RPE) layers on optical coherence tomography (OCT). Oral prednisone was administered and gradually tapered. Slight scotoma persisted with hyper-reflective segments fading and irregularity of outer retina on OCT during the follow-up. Case 4 was lost to follow-up.Conclusion: With the ongoing pandemic and extensive vaccination programs, it is expected that cases of AMN will surge. It is important for ophthalmologists to be aware of the possibility of COVID-19-induced AMN. [ABSTRACT FROM AUTHOR]

Background: The rapid worldwide spread of COVID-19 has caused a global health challenge with high mortality of severe or critically ill patients with COVID-19. To date, there is no specific efficient therapeutics for severe or critically ill patients with COVID-19. It has been reported that androgen is related to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Proxalutamide as an androgen receptor antagonist has shown potential treatment effects on COVID-19 patients. Thus, this trial is designed to investigate the efficacy and safety of proxalutamide in severe or critically ill patients with COVID-19. Methods: This single-arm, open-label, single-center prospective exploratory trial is planned to recruit 64 severe or critically ill patients with COVID-19 in China. Recruitment started on 16 May 2022 and is foreseen to end on 16 May 2023. Patients will be followed-up until 60 days or death, whichever comes first. The primary outcome is the 30-day all-cause mortality. Secondary endpoints included 60-day all-cause mortality, rate of clinical deterioration within 30 days after administration, time to sustain clinical recovery (determined using an 8-point ordinal scale), mean change in the Acute Physiology and Chronic Health Evaluation II scores, change in oxygenation index, changes in chest CT scan, percentage of patients confirmed negative for SARS-CoV-2 by nasopharyngeal swab, change in Ct values of SARS-CoV-2 and safety. Visits will be performed on days 1 (baseline), 15 or 30, 22, and 60. Discussion: The trial is the first to investigate the efficacy and safety of proxalutamide in severe or critically ill patients with COVID-19. The findings of this study might lead to the development of better treatment for COVID-19 and provide convincing evidence regarding the efficacy and safety of proxalutamide. Trial registration: This study was registered on 18 June 2022 at the Chinese Clinical Trial Registry (ChiCTR2200061250). [ABSTRACT FROM AUTHOR]

Background: Multinational studies have reported that the implementation of nonpharmaceutical interventions (NPIs) to control severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission coincided with the decline of other respiratory viruses, such as influenza viruses and respiratory syncytial virus. Objective: To investigate the prevalence of common respiratory viruses during the coronavirus disease 2019 (COVID-19) pandemic. Methods: Respiratory specimens of children with lower respiratory tract infections (LRTIs) hospitalized at the Children's Hospital of Chongqing Medical University from January 1, 2018 to December 31, 2021 were collected. Seven common pathogens, including respiratory syncytial virus (RSV), adenovirus (ADV), influenza virus A and B (Flu A, Flu B), and parainfluenza virus types 1-3 (PIV1-3), were detected by a multiplex direct immunofluorescence assay (DFA). Demographic data and laboratory test results were analyzed. Results: 1) A total of 31,113 children with LRTIs were enrolled, including 8141 in 2018, 8681 in 2019, 6252 in 2020, and 8059 in 2021. The overall detection rates decreased in 2020 and 2021 (P < 0.001). The detection rates of RSV, ADV, Flu A, PIV-1, and PIV-3 decreased when NPIs were active from February to August 2020, with Flu A decreasing most predominantly, from 2.7% to 0.3% (P < 0.05). The detection rates of RSV and PIV-1 resurged and even surpassed the historical level of 2018-2019, while Flu A continued decreasing when NPIs were lifted (P < 0.05). 2) Seasonal patterns of Flu A completely disappeared in 2020 and 2021. The Flu B epidemic was observed until October 2021 after a long period of low detection in 2020. RSV decreased sharply after January 2020 and stayed in a nearly dormant state during the next seven months. Nevertheless, the detection rates of RSV were abnormally higher than 10% in the summer of 2021. PIV-3 decreased significantly after the COVID-19 pandemic; however, it atypically surged from August to November 2020. Conclusion: The NPIs implemented during the COVID-19 pandemic affected the prevalence and seasonal patterns of certain viruses such as RSV, PIV-3, and influenza viruses. We recommend continuous surveillance of the epidemiological and evolutionary dynamics of multiple respiratory pathogens, especially when NPIs are no longer necessary. [ABSTRACT FROM AUTHOR]

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Recently, the outbreak of the coronavirus disease 2019 (COVID-19), caused by the SARS-CoV-2 virus, in China and its subsequent spread across the world has caused numerous infections and deaths and disrupted normal social activity. Presently, various techniques are used for the diagnosis of SARS-CoV-2 infection, with various advantages and weaknesses to each. In this paper, we summarize promising methods, such as reverse transcription-polymerase chain reaction (RT-PCR), serological testing, point-of-care testing, smartphone surveillance of infectious diseases, nanotechnology-based approaches, biosensors, amplicon-based metagenomic sequencing, smartphone, and wastewater-based epidemiology (WBE) that can also be utilized for the detection of SARS-CoV-2. In addition, we discuss principles, advantages, and disadvantages of these detection methods, and highlight the potential methods for the development of additional techniques and products for early and fast detection of SARS-CoV-2. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease (COVID-19) pandemic has had a significant impact on transportation. Understanding how the epidemic in China has affected people's travel mode choices can help city managers analyze the travel mode of different populations in the postepidemic phase. Based on a travel behavior questionnaire conducted in Guangzhou, China, during the COVID-19 epidemic and points-of-interest data, this study explored the impact of the built environment, travel characteristics, and socioeconomic factors on changes in commuters' travel modes during the postepidemic phase. We found that gender, age, occupation, the decline in the rate of travel frequency, and built environment characteristics significantly influenced the change in travel mode. When respondents had to give up public transport, those in different professions had different options for alternative means of transportation. The density of residential facilities, bus stations, and government institutions had a more significant impact on the change in the travel mode of commuters. The research results provide a theoretical basis for policy and practice. After the outbreak of a major public health event, urban transport managers and policymakers should consider individual heterogeneity and environmental factors when formulating strategies to address public travel in unconventional situations. [ABSTRACT FROM AUTHOR]

During the outbreak of a disease caused by a pathogen with unknown characteristics, the uncertainty of its progression parameters can be reduced by devising methods that, based on rational assumptions, exploit available information to provide actionable insights. In this study, performed a few (~6) weeks into the outbreak of COVID-19 (caused by SARS-CoV-2), one of the most important disease parameters, the average time-to-recovery, was calculated using data publicly available on the internet (daily reported cases of confirmed infections, deaths, and recoveries), and fed into an algorithm that matches confirmed cases with deaths and recoveries. Unmatched cases were adjusted based on the matched cases calculation. The mean time-to-recovery, calculated from all globally reported cases, was found to be 18.01 days (SD 3.31 days) for the matched cases and 18.29 days (SD 2.73 days) taking into consideration the adjusted unmatched cases as well. The proposed method used limited data and provided experimental results in the same region as clinical studies published several months later. This indicates that the proposed method, combined with expert knowledge and informed calculated assumptions, could provide a meaningful calculated average time-to-recovery figure, which can be used as an evidence-based estimation to support containment and mitigation policy decisions, even at the very early stages of an outbreak. [ABSTRACT FROM AUTHOR]

Role models are essential in medical education, yet empirical research is relatively insufficient on the influence of prosocial modelling on medical students' career commitment. The prosocial behaviour of medical staff involved in the fight against the novel coronavirus disease 2019 (COVID-19) at the beginning of 2020 presents an opportunity to fill the research gap. We explored and compared the different associations of the two most important role models for medical students – parents and faculty- with medical students' career commitment. The cross-sectional study was conducted with 99,559 undergraduate students majoring in clinical medicine in mainland China. Questions were asked to collect information about participants in the battle against COVID-19, medical students' determination to practice medicine after graduation, as well as students' socio-demographic characteristics. Chi-square tests and hierarchical regressions were performed to examine the associations between parent and faculty involvement and students' career commitment. The results showed statistically significant associations between prosocial modelling during the COVID-19 pandemic in China and students' intention to pursue medical careers. The association of faculty involvement (OR = 1.165, p <.001) with students' career commitment was greater than that of parents (OR = 0.970, p >.05). For faculty involvement, the association was stronger among male students (OR = 1.323, p <.001) and students who were already determined to be doctors (OR = 1.219, p <.001) before the pandemic. Our study provides new evidence on the potential roles of parents and faculty in shaping medical students' career commitment. Encouraging faculty to act as positive role models could help medical students increase their intention to become doctors. Prosocial modelling could enhance students' intention to pursue medical careers. The association of prosocial behaviour of faculty is larger than that of parents on medical students. Those who have prior medical career commitment are much more likely to persist in the medical profession, and prosocial modelling of faculty is positively associated with their medical career commitment. [ABSTRACT FROM AUTHOR]

Background: Clinical internship is an indispensable stage for nursing students to graduate successfully and become qualified nurses. However, COVID-19, a novel coronavirus disease with strong human-to-human transmission, hit China in late 2019 and forced the Chinese government to suspend classes and clinical internships. To cope with this situation, e-internship, which facilitate varied interactions without the need for direct contact, is used as an alternative strategy to help nursing students continue their internships. Objectives: To describe the perceptions and experiences of undergraduate nursing students in e-internships during the COVID-19 pandemic. Methods: A descriptive phenomenological design was adopted. Seventeen undergraduate nursing students in a major teaching hospital in Changsha, China, were recruited into the study. Data were collected through semi-structured, in-depth, face-to-face interviews. The interviews were transcribed verbatim and analyzed using Colaizzi's approach. Results: Four themes were captured from the data analysis: perceived images of clinical nurses in e-internships, psychological experience, perceived benefits of e-internships, and perceived limitations of e-internships. Conclusions: Our findings suggest that e-internship is a suitable method for training and cultivating undergraduate nursing students during a crisis. To enhance the efficiency of e-internships, guidelines and standards should be formulated, and effective measures should be taken to build better e-internship platforms. In the future, we suggest combine on-site internships with e-internships, thereby fully using their advantages, and improve the efficiency of internships as much as possible. [ABSTRACT FROM AUTHOR]

Background and Aims: The novel coronavirus disease 2019 (COVID‐19) causes severe respiratory illnesses, following exposure to air‐borne droplets or direct contact, posing a great threat to human life. This study aimed to investigate perceived stress and its correlation with the health behaviors of Chinese residents during the COVID‐19 epidemic. Methods: An Internet survey was conducted among 2449 residents in 20 provinces of China on residents' perceived stress, perception of COVID‐19, and health behaviors. SAS 9.4 was used to analyze the relationship between health behaviors and perceived stress, and logistic regression was used to explore the factors influencing health risk stress. Results: The participants' perceived stress score was 22.25 ± 7.2 (total 56), and the incidence of health risk stress was 39.89% (977/2449). Females, students, and medical staff were at high risk. Health risk stress refers to a level of stress that is hazardous to health (score over 25). Perceived stress increased, while the frequency of health behaviors decreased. Age, perception of susceptibility to COVID‐19, life‐threatening level of COVID‐19, perception of the importance of home isolation, and perception of the difference between a common cold and COVID‐19 were positively related to the occurrence of health risk stress. Conclusions: A negative correlation was found between health behaviors and perceived stress. Therefore, it is of great significance to provide psychological interventions for those who are experiencing health risk stress and to promote their health behaviors. [ABSTRACT FROM AUTHOR]

Background and objectives: The COVID-19 pandemic continues worldwide, and there is no effective treatment to treat it. Chinese medicine is considered the recommended treatment for COVID-19 in China. This study aimed to examine the effectiveness of tetrandrine in treating COVID-19, which is originally derived from Chinese medicine. Materials and Methods: A total of 60 patients, categorized into three types (mild, moderate, severe), from Daye Hospital of Chinese Medicine with a diagnosis of COVID-19 were included in this study. Demographics, medical history, treatment, and results were collected. We defined two main groups according to the clinical outcome between improvement and recovery. All underlying factors including clinical outcomes were assessed in the total number of COVID-19 patients and moderate-type patients. Results: In a total of 60 patients, there were significant differences in the clinical outcome underlying treatment with antibiotics, tetrandrine, and arbidol (p < 0.05). When the comparison was limited to the moderate type, treatment with tetrandrine further increased recovery rate (p = 0.007). However, the difference disappeared, and no association was indicated between the clinical outcome and the treatment with and without antibiotic (p = 0.224) and arbidol (p = 0.318) in the moderate-type patients. In all-type and moderate-type patients, tetrandrine improved the rate of improvement in cough and fatigue on day 7 (p < 0.05). Conclusions: Tetrandrine may improve clinical outcome in COVID-19 patientsand could be a promising potential natural antiviral agent for the prevention and treatment of COVID-19. [ABSTRACT FROM AUTHOR]

Polymeric materials are used for personal protective equipment (PPE), which is mandatory for clinicians to use when handling coronavirus disease 2019 (COVID-19) patients. The development of diagnostic tools and vaccines for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is also dependent on polymer technology. This current report aims to provide readers with the trend of polymer research regarding the COVID-19 pandemic, by employing bibliometric analysis. A literature search on the Scopus database (31 January 2022) was carried out using predetermined terms. Using Scopus database features, the publications were filtered based on the year of publication (2020–2022), types of articles (original research and review), and language (English). The metadata were extracted in a CSV (.csv) file, to be later used in VOSviewer software. The data were presented in a table, graph, and network visualization. As many as 512 publications were included, consisting of 66.4% original research articles and 33.6% review articles. Most of the publications were written by authors whose affiliation was in the United States (n = 118, 23%) and covering the Materials Science subject area (n = 142, 27.7%). The Ministry of Education of China was the most productive organization, publishing 11 articles. The National Science Foundation of China was the top funding source, supporting 45 publications. Heinz C. Schröder was the most prolific author, publishing nine articles. Science of the Total Environment was the leading journal publishing the included studies. The trend of polymer technology related to COVID-19 mostly covers PPE and waste-management themes. The use of polymer technology as a delivery system for the anti-SARS-CoV-2 and COVID-19 vaccine is also among the frequently researched areas. We encourage more research in the field of polymer technology be carried out, to overcome the global pandemic. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has driven public health intervention strategies, including keeping social distance, wearing masks in crowded places, and having good health habits, to prevent the transmission of the novel coronavirus (SARS-CoV-2). However, it is unknown whether the use of these intervention strategies influences morbidity in other human infectious diseases, such as tuberculosis.Methods: In this study, three prediction models were constructed to compare variations in PTB incidences after January 2020 without or with intervention includes strict and regular interventions, when the COVID-19 outbreak began in China. The non-interventional model was developed with an autoregressive integrated moving average (ARIMA) model that was trained with the monthly incidence of PTB in China from January 2005 to December 2019. The interventional model was established using an ARIMA model with a continuing intervention function that was trained with the monthly PTB incidence in China from January 2020 to December 2020.Results: Starting with the assumption that no COVID-19 outbreak had occurred in China, PTB incidence was predicted, and then the actual incidence was compared with the predicted incidence. A remarkable overall decline in PTB incidence from January 2020 to December 2020 was observed, which was likely due to the potential influence of intervention policies for COVID-19. If the same intervention strategy is applied for the next 2 years, the monthly PTB incidence would reduce on average by about 1.03 per 100,000 people each month compared with the incidence predicted by the non-interventional model. The annual incidence estimated 59.15 under regular intervention per 100,000 in 2021, and the value would decline to 50.65 with strict interventions.Conclusions: Our models quantified the potential knock-on effect on PTB incidence of the intervention strategy used to control the transmission of COVID-19 in China. Combined with the feasibility of the strategies, these results suggested that continuous regular interventions would play important roles in the future prevention and control of PTB. [ABSTRACT FROM AUTHOR]

Background: Since the outbreak of the respiratory infectious disease caused by the novel coronavirus in 2019, the COVID-19 epidemic has posed a serious threat to the life and safety of the public, and has also seriously affected the normal study and life of college students in China. Although a series of studies have been conducted on college students' knowledge, attitudes and practices of COVID-19, the results vary widely. This study aimed to evaluate the pooled estimated level of knowledge, attitudes, and practices (KAP) about COVID-19 among college students in China. Methods: We conducted a comprehensive search on Scopus, ProQuest, PubMed, EMbase, Web of Science, the Cochrane Library, Chinese Biomedical Literature Database (CBM), China National Knowledge Infrastructure (CNKI), VIP Database and Wanfang Database up to 13 February 2022. We then assessed the quality of included studies using a checklist developed by the Joanna Briggs Institute (JBI) for cross-sectional studies and analyzed using STATA.15 after two researchers independently extracted relevant data and entered them into Microsoft Excel. Funnel plots and Egger's regression tests were used to check for publication bias, and sensitivity analysis was performed to assess the robustness of the results. A random-effects model was used for the meta-analysis, on the basis of which subgroup analyses were performed by time of investigation (study period) and by gender and major of the subjects. Result: A total of 42 studies including 174,440 subjects were included in this review, and the quality of the included studies was mainly intermediate and advanced. The findings of the meta-analysis showed that the overall levels of Chinese college students' knowledge, positive and negative attitudes, and practice of preventive measures towards COVID-19 were 74% (95%CI: 69%-79%), 84% (95%CI: 80%-88%), 31% (95%CI: 23%-38%) and 82% (95%CI: 77%-86%), respectively. The results of subgroup analysis showed that female and medical college students had higher levels of knowledge and practice on COVID-19. Conclusion: The study findings showed that the overall KAP level of college students in China included in the study was relatively optimistic. Influenced by gender, major and time, there were differences in the KAP level of college students. In order to promote the effective prophylaxis and control of pandemic, we recommend implementing targeted measures to improve the awareness rate of COVID-19-related knowledge among this group and the implementation rate of COVID-19 preventive measures among male and non-medical college students. [ABSTRACT FROM AUTHOR]

Background: The 2019 novel coronavirus (COVID-19)-related depression symptoms of healthcare workers have received worldwide recognition. Although many studies identified risk exposures associated with depression symptoms among healthcare workers, few have focused on a predictive model using machine learning methods. As a society, governments, and organizations are concerned about the need for immediate interventions and alert systems for healthcare workers who are mentally at-risk. This study aims to develop and validate machine learning-based models for predicting depression symptoms using survey data collected during the COVID-19 outbreak in China. Method: Surveys were conducted of 2,574 healthcare workers in hospitals designated to care for COVID-19 patients between 20 January and 11 February 2020. The patient health questionnaire (PHQ)-9 was used to measure the depression symptoms and quantify the severity, a score of ≥5 on the PHQ-9 represented depression symptoms positive, respectively. Four machine learning approaches were trained (75% of data) and tested (25% of data). Cross-validation with 100 repetitions was applied to the training dataset for hyperparameter tuning. Finally, all models were compared to evaluate their predictive performances and screening utility: decision tree, logistics regression with least absolute shrinkage and selection operator (LASSO), random forest, and gradient-boosting tree. Results: Important risk predictors identified and ranked by the machine learning models were highly consistent: self-perceived health status factors always occupied the top five most important predictors, followed by worried about infection, working on the frontline, a very high level of uncertainty, having received any form of psychological support material and having COVID-19-like symptoms. The area under the curve [95% CI] of machine learning models were as follows: LASSO model, 0.824 [0.792–0.856]; random forest, 0.828 [0.797–0.859]; gradient-boosting tree, 0.829 [0.798–0.861]; and decision tree, 0.785 [0.752–0.819]. The calibration plot indicated that the LASSO model, random forest, and gradient-boosting tree fit the data well. Decision curve analysis showed that all models obtained net benefits for predicting depression symptoms. Conclusions: This study shows that machine learning prediction models are suitable for making predictions about mentally at-risk healthcare workers predictions in a public health emergency setting. The application of multidimensional machine learning models could support hospitals' and healthcare workers' decision-making on possible psychological interventions and proper mental health management. [ABSTRACT FROM AUTHOR]

SARS-CoV-2 pandemic first emerged in late 2019 in China. It has since infected more than 298 million individuals and caused over 5 million deaths globally. The identification of essential proteins in a protein–protein interaction network (PPIN) is not only crucial in understanding the process of cellular life but also useful in drug discovery. There are many centrality measures to detect influential nodes in complex networks. Since SARS-CoV-2 and (H1N1) influenza PPINs pose 553 common human proteins. Analyzing influential proteins and comparing these networks together can be an effective step in helping biologists for drug-target prediction. We used 21 centrality measures on SARS-CoV-2 and (H1N1) influenza PPINs to identify essential proteins. We applied principal component analysis and unsupervised machine learning methods to reveal the most informative measures. Appealingly, some measures had a high level of contribution in comparison to others in both PPINs, namely Decay, Residual closeness, Markov, Degree, closeness (Latora), Barycenter, Closeness (Freeman), and Lin centralities. We also investigated some graph theory-based properties like the power law, exponential distribution, and robustness. Both PPINs tended to properties of scale-free networks that expose their nature of heterogeneity. Dimensionality reduction and unsupervised learning methods were so effective to uncover appropriate centrality measures. [ABSTRACT FROM AUTHOR]

Background: Ozone (O3) and nitrogen dioxide (NO2) are substances with oxidizing ability in the atmosphere. Only considering the impact of a single substance is not comprehensive. However, people's understanding of "total oxidation capacity" (Ox) and "weighted average oxidation" (Oxwt) is limited. Objectives: This investigation aims to assess the impact of Ox and Oxwt on the novel coronavirus disease (COVID-19). We also compared the relationship between the different calculation methods of Ox and Oxwt and the COVID-19 infection rate. Method: We recorded confirmed COVID-19 cases and daily pollutant concentrations (O3 and NO2) in 34 provincial capital cities in China. The generalized additive model (GAM) was used to analyze the nonlinear relationship between confirmed COVID-19 cases and Ox and Oxwt. Result: Our results indicated that the correlation between Ox and COVID-19 was more sensitive than Oxwt. The hysteresis effect of Ox and Oxwt decreased with time. The most obvious statistical data was observed in Central China and South China. A 10 µg m−3 increase in mean Ox concentrations were related to a 23.1% (95%CI: 11.4%, 36.2%) increase, and a 10 µg m−3 increase in average Oxwt concentration was related to 10.7% (95%CI: 5.2%, 16.8%) increase in COVID-19. In conclusion, our research results show that Ox and Oxwt can better replace the single pollutant research on O3 and NO2, which is used as a new idea for future epidemiological research. [ABSTRACT FROM AUTHOR]

Background: The novel coronavirus disease 2019 (COVID-19) outbreak has seriously threatened the mental health of college students. This study intended to invest whether perceived threat avoidability of COVID-19 relates to psychic anxiety among college students during the early stage of the COVID-19 pandemic, as well as the mediating roles of COVID-19-specific wishful thinking and COVID-19-specific protective behaviors in this relationship. Methods: A cross-sectional study was conducted in China, using a random sampling method (February 6–25, 2020). Self-reported questionnaires were conducted online included the Perceived Threat Avoidability of COVID-19 Scale, COVID-19-specific Wishful Thinking Scale, COVID-19-specific Protective Behaviors Scale, and the Hamilton Psychogenic Anxiety Scale. The data were analyzed using Structural equation modeling and Bootstrapping procedure. Results: A total of 2922 samples were collected in this study. Perceived threat avoidability of COVID-19 is negatively related to psychic anxiety (β = −0.158, p < 0.001), and both COVID-19-specific wishful thinking (β = −0.006, p = 0.029, 95% CI: [−0.012, −0.001]) and protective behaviors (β = −0.029, p < 0.001, 95% CI: [−0.043, −0.018]) mediate this relationship. Also, COVID-19-specific wishful thinking is found to correlate with COVID-19-specific protective behaviors negatively (β = −0.112, p < 0.001). Conclusion: Perceived threat avoidability of COVID-19 contributes to psychic anxiety among college students. COVID-19-specific wishful thinking strategy plays a negative mediating role and increases the level of anxiety; COVID-19-specific protective behaviors strategy plays a positive mediating role and reduces the level of anxiety; meanwhile, wishful thinking also suppresses college students from adopting protective behaviors. [ABSTRACT FROM AUTHOR]

Background: At the end of 2019, news circulated the world that a new, highly contagious coronavirus had emerged in China that caused severe disease in the respiratory tract. Subsequently, SARS-CoV-2 spread rapidly around the world. Healthcare professionals, including midwives, have been on the front lines of the fight against this disease, and their working conditions changed overnight, causing tremendous stress. Aim of the study: The purpose of this study was to analyze stress among midwives in relation to the SARSCoV- 2 pandemic. Material and methods: This study was conducted in 2020/2021 among 500 midwives. The testing method used was a diagnostic survey consisting of a self-compiled questionnaire. 444 midwives properly completed the survey and their results were analyzed. Results: The study confirmed that the work of a midwife is stressful. More than half of those surveyed changed their attitude towards work when SARS-CoV-2 emerged. Midwives were also more likely to experience stress before going to work. Only a small percentage of the respondents had the opportunity to receive psychological help in the workplace. Conclusions: The stress caused by the SARS-CoV-2 pandemic in the midwifery profession has had a negative impact on the psychophysical health of this occupational group. Thus, there is a dire need for workplace interventions for midwives to minimize the accompanying stress. [ABSTRACT FROM AUTHOR]

Background: The outbreak of coronavirus disease 2019 (COVID-19) has become a global pandemic acute infectious disease, especially with the features of possible asymptomatic carriers and high contagiousness. Currently, it is difficult to quickly identify asymptomatic cases or COVID-19 patients with pneumonia due to limited access to reverse transcription-polymerase chain reaction (RT-PCR) nucleic acid tests and CT scans. Goal: This study aimed to develop a scientific and rigorous clinical diagnostic tool for the rapid prediction of COVID-19 cases based on a COVID-19 clinical case database in China, and to assist doctors to efficiently and precisely diagnose asymptomatic COVID-19 patients and cases who had a false-negative RT-PCR test result. Methods: With online consent, and the approval of the ethics committee of Zhongshan Hospital Fudan University (NCT04275947, B2020-032R) to ensure that patient privacy is protected, clinical information has been uploaded in real-time through the New Coronavirus Intelligent Auto-diagnostic Assistant Application of cloud plus terminal (nCapp) by doctors from different cities (Wuhan, Shanghai, Harbin, Dalian, Wuxi, Qingdao, Rizhao, and Bengbu) during the COVID-19 outbreak in China. By quality control and data anonymization on the platform, a total of 3,249 cases from COVID-19 high-risk groups were collected. The effects of different diagnostic factors were ranked based on the results from a single factor analysis, with 0.05 as the significance level for factor inclusion and 0.1 as the significance level for factor exclusion. Independent variables were selected by the step-forward multivariate logistic regression analysis to obtain the probability model. Findings: We applied the statistical method of a multivariate regression model to the training dataset (1,624 cases) and developed a prediction model for COVID-19 with 9 clinical indicators that are accessible. The area under the receiver operating characteristic (ROC) curve (AUC) for the model was 0.88 (95% CI: 0.86, 0.89) in the training dataset and 0.84 (95% CI: 0.82, 0.86) in the validation dataset (1,625 cases). Discussion: With the assistance of nCapp, a mobile-based diagnostic tool developed from a large database that we collected from COVID-19 high-risk groups in China, frontline doctors can rapidly identify asymptomatic patients and avoid misdiagnoses of cases with false-negative RT-PCR results. [ABSTRACT FROM AUTHOR]

The Coronavirus Disease 2019 (COVID-19) is a pulmonary disease produced by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and has become a global pandemic March 2019. The aim of this research is to study a modelling approach on the spread of the COVID-19 pandemic utilizing analysis that is based on the well-known susceptible-infectious-removed (SIR) model. Since the declaration of the epidemic in Wuhan-city in China and other places around the world, several studies have addressed the development and evolution of the disease in different countries and have shown good stability of the use of such model. The United Arab Emirates is among the countries affected by the spread of the virus. Using collected data from 1 March 2020 to 31 August 2020 collected by John Hopkins University and the Federal Competitiveness and Statistics Authority (FCSA) in the UAE, we apply the SIR model to simulate the evolution of the virus in the United Arab Emirates. Our model predicts a peak in the daily infected cases on 19 May 2020, and the cumulative number of infected individuals to reach 100,000 cases by the end of October 2020. [ABSTRACT FROM AUTHOR]

The pandemic of the novel coronavirus disease 2019 (COVID-19) has caused severe harm to the health of people all around the world. Molecular detection of the pathogen, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), played a crucial role in the control of the disease. Reverse transcription digital PCR (RT-dPCR) has been developed and used in the detection of SARS-CoV-2 RNA as an absolute quantification method. Here, an interlaboratory assessment of quantification of SARS-CoV-2 RNA was organized by the National Institute of Metrology, China (NIMC), using in vitro transcribed RNA samples, among ten laboratories on six different dPCR platforms. Copy number concentrations of three genes of SARS-CoV-2 were measured by all participants. Consistent results were obtained with dispersion within 2.2-fold and CV% below 23% among different dPCR platforms and laboratories, and Z′ scores of all the reported results being satisfactory. Possible reasons for the dispersion included PCR assays, partition volume, and reverse transcription conditions. This study demonstrated the comparability and applicability of RT-dPCR method for quantification of SARS-CoV-2 RNA and showed the capability of the participating laboratories at SARS-CoV-2 test by RT-dPCR platform. [ABSTRACT FROM AUTHOR]

The outbreak of the COVID-19 pandemic has a lasting and unprecedented negative impact on the global aviation industry. While countries such as China have successfully curbed the domestic outbreak of the virus with various restrictive and preventive measures, the challenge of avoiding imported cases remains. More importantly, it is still unclear to what extent these implemented aviation emergency responses have effectively mitigated the transmission risk of the virus. This paper provides an empirical assessment of aviation responses to the control of imported COVID-19 cases, with a focus on the following three strategies: the "circuit breaker" policy, the "negative Nucleic Acid testing (NAT)", and the "double negative tests" requirement. Non-recursive structural equation models (SEM) with latent variables were applied to detailed international flight data and individual epidemic survey data of Guangzhou, China, between May 1 and November 30, 2020. The results show that the "double negative tests" measure has a positive effect on eliminating the number of SARS-CoV-2 carriers, while the effects of single "circuit breaker" and its co-intervention with "negative NAT" are conterproductive. This study provides important implications to civil aviation agencies in regard to medium and long-term risk control of imported cases. Specifically, although the circuit breaker mechanism was designed to target on the risk control of imported COVID-19 cases, it may be more effective to carefully maintain a timely and reliable pre-boarding screening and testing to curb the number of imported cases. • We evaluated the aviation responses to the control of imported COVID-19 cases in China. • Detailed international flight data and individual epidemic survey data were used. • SEM method was used to evaluate the effects of three aviation response strategies. • The "double negative tests" measure has a positive effect on eliminating the imported cases. • The effects of "circuit breaker" with the"Negative Nucleic Acid testing" requirement are minimal. [ABSTRACT FROM AUTHOR]

Objective: The novel coronavirus disease 2019 (COVID-19) is a global public health emergency that has caused worldwide concern. The mental health of medical students under the COVID-19 epidemic has attracted much attention. This study aims to identify subgroups of medical students based on depression and anxiety and explore the influencing factors during the COVID-19 epidemic in China. Methods: A total of 29,663 medical students were recruited during the epidemic of COVID-19 in China. Depression and anxiety symptoms were assessed using Patient Health Questionnaire 9 (PHQ9) and Generalized Anxiety Disorder 7 (GAD7) respectively. Latent class analysis was performed based on depression and anxiety symptoms in medical students. The latent class subtypes were compared using the chi-square test. Multinomial logistic regression was used to examine associations between identified classes and related factors. Results: In this study, three distinct subgroups were identified, namely, the poor mental health group, the mild mental health group and the low symptoms group. The number of medical students in each class is 4325, 9321 and 16,017 respectively. The multinomial logistic regression results showed that compared with the low symptoms group, the factors influencing depression and anxiety in the poor mental health group and mild mental health group were sex, educational level, drinking, individual psychiatric disorders, family psychiatric disorders, knowledge of COVID-19, fear of being infected, and participate in mental health education on COVID-19. Conclusions: Our findings suggested that latent class analysis can be used to categorize different medical students according to their depression and anxiety symptoms during the outbreak of COVID-19. The main factors influencing the poor mental health group and the mild mental health group are basic demographic characteristics, disease history, COVID-19 related factors and behavioural lifestyle. School administrative departments can carry out targeted psychological counseling according to different subgroups to promote the physical and mental health of medical students. [ABSTRACT FROM AUTHOR]

The entire world has suffered a lot since the outbreak of the novel coronavirus (COVID-19) in 2019, so simulation models of COVID-19 dynamics are urgently needed to understand and control the pandemic better. Meanwhile, emotional contagion, the spread of vigilance or panic, serves as a negative feedback to the epidemic, but few existing models take it into consideration. In this study, we proposed an innovative multi-layer hybrid modelling and simulation approach to simulate disease transmission and emotional contagion together. In each layer, we used a hybrid simulation method combining agent-based modelling (ABM) with system dynamics modelling (SDM), keeping spatial heterogeneity while reducing computation costs. We designed a new emotion dynamics model IWAN (indifferent, worried, afraid and numb) to simulate emotional contagion inside a community during an epidemic. Our model was well fit to the data of China, the UK and the US during the COVID-19 pandemic. If there weren't emotional contagion, our experiments showed that the confirmed cases would increase rapidly, for instance, the total confirmed cases during simulation in Guangzhou, China would grow from 334 to 2096, which increased by 528%. We compared the calibrated emotional contagion parameters of different countries and found that the suppression effect of emotional contagion in China is relatively more visible than that in the US and the UK. Due to the experiment results, the proposed multi-layer network model with hybrid simulation is valid and can be applied to the quantitative analysis of the epidemic trends and the suppression effect of emotional contagion in different countries. Our model can be modified for further research to study other social factors and intervention policies in the COVID-19 pandemic or future epidemics. [ABSTRACT FROM AUTHOR]