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The impact of COVID‐19 has underlined the need for reliable information to guide clinical practice and policy. This urgency has to be balanced against disruption to journal handling capacity and the continued need to ensure scientific rigour. We examined the reporting quality of highly disseminated COVID‐19 research papers using a bibliometric analysis examining reporting quality and risk of bias (RoB) amongst 250 top scoring Altmetric Attention Score (AAS) COVID‐19 research papers between January and April 2020. Method‐specific RoB tools were used to assess quality. After exclusions, 84 studies from 44 journals were included. Forty‐three (51%) were case series/studies, and only one was an randomized controlled trial. Most authors were from institutions based in China (n = 44, 52%). The median AAS and impact factor was 2015 (interquartile range [IQR] 1,105–4,051.5) and 12.8 (IQR 5–44.2) respectively. Nine studies (11%) utilized a formal reporting framework, 62 (74%) included a funding statement, and 41 (49%) were at high RoB. This review of the most widely disseminated COVID‐19 studies highlights a preponderance of low‐quality case series with few research papers adhering to good standards of reporting. It emphasizes the need for cautious interpretation of research and the increasingly vital responsibility that journals have in ensuring high‐quality publications. [ABSTRACT FROM AUTHOR]

The emergence of COVID-19 has provided a new vigor to addressed global health issues. In this regard, global health diplomacy initiatives have seen a considerable surge. Countries around the world are using the opportunity to harness the potential of global health diplomacy in cultivating relations with several countries and also building a soft power projection. A case in point is how China has been spearheading global health efforts to improve relations while projecting a soft power about how China aims to act as global savior. Chinese efforts were able to gain significant traction owing to the fact that the United States under the administration of President Trump suspended its global leadership role, the first such instance in the post-World War-II era. This paper explains the concept of global health diplomacy by conducting a comparative analysis of the global health efforts and policies undertaken by both the United States and China by following qualitative research technique and secondary sources to explain the phenomenon. The study found that China led the response to COVID-19 not only domestically but globally as well. The U.S. lagged but gradually started to compete China in health diplomacy. There is an evident struggle for dominance in health sector between China and the U.S., however, it is not expected to lead into hard power struggle. The paper has concluded that it is important for both countries to work together in future in case of pandemic to spearhead an international effort against such crises. Moreover, it recommends Chinese authorities to include health corridor as a component with Belt and Road Initiative. On the other hand, the United States under the administration of President Joe Biden should invest in multilateral efforts so to empower global health institutions to better prepare for future health crises. [ABSTRACT FROM AUTHOR]

In the coronavirus epidemic, many Chinese hospitals have established buffer zones to prevent the spread and transmission of the virus. The buffer zone is a monitored and separate area where the patients who need hospitalizations after the quick treatments in the emergency department can temporarily wait for the Covid-19 test and receive some healthcare services to stabilize their conditions. Because the beds in the buffer zones are limited, the managers face the patient admission control problem for the buffer zone. This management and control problem is challenging since the patient arrivals are uncertain, and the patients’ conditions are different. In this paper, we build the infinite- and finite-horizon Markov decision process (MDP) models for this problem. We use the uniformization method to discretize the patient flow. We propose various iteration algorithms to solve the MDP models and obtain the optimal and threshold policies. Numerical experiments validate the advantages of the policies obtained by the algorithms in this paper over the current policies of hospitals. Note to Practitioners—The ongoing COVID-19 pandemic has been causing enormous damage to people’s health, jobs, and well-being. COVID-19 has affected almost all countries globally and has changed the operation mode of the healthcare system, especially the hospitals. The hospitals are the frontlines of healthcare service and the battle with the COVID-19 pandemic. This article is motivated by our collaborations with hospitals in Shanghai, China. In China, many hospitals establish buffer zones: a monitored area where the patients who need hospitalizations after the quick treatments in the emergency department can temporarily wait for the Covid-19 test and receive some healthcare services to stabilize their conditions. Because the zone’s capacity is limited, the managers must make dynamic patient admission control decisions according to multiple factors, such as patients’ health conditions and the usage of beds in the zone. We propose two MDP models to solve this complex problem. Several iteration algorithms are designed to solve the MDP models and obtain the optimal and threshold policies. Based on hospitals’ real-life data, we show the methods presented in this paper can help hospital managers make more reasonable decisions. Although we focus on the hospital’s buffer zone in China, the methodology and approach for this problem can be extended to other practical hospital management scenarios in the coronavirus pandemic. For example, For example, some hospitals have admission control problems for coronavirus patients due to hospital capacity limitations. The hospital has to decide if a patient is accepted as an inpatient or suggested to home quarantine. In such a case, the admission control problem can also be solved by the methodologies in the paper. [ABSTRACT FROM AUTHOR]

This paper studies the impact of urban density, city government efficiency, and medical resources on COVID‐19 infection and death outcomes in China. We adopt a simultaneous spatial dynamic panel data model to account for (i) the simultaneity of infection and death outcomes, (ii) the spatial pattern of the transmission, (iii) the intertemporal dynamics of the disease, and (iv) the unobserved city‐specific and time‐specific effects. We find that, while population density increases the level of infections, government efficiency significantly mitigates the negative impact of urban density. We also find that the availability of medical resources improves public health outcomes conditional on lagged infections. Moreover, there exists significant heterogeneity at different phases of the epidemiological cycle. [ABSTRACT FROM AUTHOR]

With the gradual normalization of the COVID-19, unmanned delivery has gradually become an important contactless distribution method around China. In this paper, we study the routing problem of unmanned vehicles considering path flexibility and the number of traffic lights in the road network to reduce the complexity of road conditions faced by unmanned vehicles as much as possible. We use Monte Carlo Tree Search algorithm to improve the Genetic Algorithm to solve this problem, first use Monte Carlo Tree Search Algorithm to compute the time-saving path between two nodes among multiple feasible paths and then transfer the paths results to Genetic Algorithm to obtain the final sequence of the unmanned vehicles fleet. And the hybrid algorithm was tested on the actual road network data around four hospitals in Beijing. The results showed that compared with normal vehicle routing problem, considering path flexibility can save the delivery time, the more complex the road network composition, the better results could be obtained by the algorithm. [ABSTRACT FROM AUTHOR]

Among modern cities developing in a large-scale, extensive and unbalanced manner, smaller cities are relatively lagged behind due to relatively underdeveloped infrastructure, inadequate capital and technology talents, and insufficient attention from the national government, and thus they are more vulnerable when hit by unexpected disasters. The rampant pandemic of coronavirus disease 2019 (COVID-19) has made it even clearer that small cities must be equipped with stronger abilities to timely identify and prevent potential disease outbreaks. This paper takes Zhaodong City as an example to study how to better locate spaces with cluster infection risks in small cities. It combines spatial syntax, points of interest (POI), and geographical information system (GIS), and adopts hotspot analysis, average nearest neighbour analysis, kernel density estimation and other methods, to identify and locate potentially vulnerable spaces in neighbourhoods with relatively frequent people-to-people contact and thus higher disease transmission risks. Results show that there are three point-space, four line-space, and one plane-space with high risk of outbreaks in Zhaodong City, verifying the efficacy of the identification method for small cities. [ABSTRACT FROM AUTHOR]

The impacts of COVID-19 on travel demand, traffic congestion, and traffic safety are attracting heated attention. However, the influence of the pandemic on electric bike (e-bike) safety has not been investigated. This paper fills the research gap by analyzing how COVID-19 affects China's e-bike safety based on a province-level dataset containing e-bike safety metrics, socioeconomic information, and COVID-19 cases from 2017 to 2020. Multi-output regression models are adopted to investigate the overall impact of COVID-19 on e-bike safety in China. Clustering-based regression models are used to examine the heterogeneous effects of COVID-19 and the other explanatory variables in different provinces/municipalities. This paper confirms the high relevance between COVID-19 and the e-bike safety condition in China. The number of COVID-19 cases has a significant negative effect on the number of e-bike fatalities/injuries at the country level. Moreover, two clusters of provinces/municipalities are identified: one (cluster 1) with lower and the other (cluster 2 that includes Hubei province) higher number of e-bike fatalities/injuries. In the clustering-based regressions, the absolute coefficients of the COVID-19 feature for cluster 2 are much larger than those for cluster 1, indicating that the pandemic could significantly reduce e-bike safety issues in provinces with more e-bike fatalities/injuries. [ABSTRACT FROM AUTHOR]

COVID-19 onset has questioned the proclaimed notions of Globalization such as shared prosperity and security across the board. The ongoing pandemic once again calls for collective action in men and material terms rather than erecting technology barriers and doubting the intentions of each other. This paper aims to address the following inter-related queries: What is the nature of China's health-based assistance to the South Asian states for mitigating the COVID-19 risks? How and why do the United States and India remain skeptical of Chinese anti COVID- 19 Vaccine efforts? And how do other South Asian states perceive Chinese contributions to deal with the ongoing Pandemic? And, finally what lessons can be learned from this divergent debate centered on: Chinese Vaccine Diplomacy? Comparison and contrast of the available information is employed as an analytical technique to determine the nature of skepticism surrounding the Chinese vaccination campaign in South Asia. Consulting data on three various accounts assisted to conclude the findings of this research paper. The findings of this study establish that Western and Indian skepticism, directed against Chinese health diplomacy in South Asia is motivated by their geopolitical contestations with China. In nutshell, the paper's main thrust is to stress the essential value of mutual cooperation in dealing with the Pandemic like COVID-19. There is a need to embrace the concept of 'holistic security' in the highly interconnected world of today and tomorrow. [ABSTRACT FROM AUTHOR]

In this paper, I explore the mental consequences of Covid‐19. Starting with the images of the pandemic – the intubated patient, the anonymous mass grave, and the death on FaceTime – images that express the horrendous threat of the coronavirus death, I emphasize that the pandemic reminds us of our vulnerability and existential dependence on the other. Based on examples, I discuss different reactions to Covid‐19 from a psychoanalytic perspective, specifically highlighting the mechanisms of denial, splitting and projection – the latter is illustrated in the scapegoating of the pandemic, for example, blaming China, the immigrants or youth parties. I argue that a common characteristic of those different defense mechanisms may be a contempt for weakness – as demonstrated when coronavirus deniers and Proud Boys are marching together. The paper also examines the importance of structure and the consequences of the loss of everyday structure – be it job, school, peer groups, training studios, senior centers, or culture. I conclude by emphasizing that the pandemic has taught us how dependent we are on each other – and that it should inspire us to greater tolerance for vulnerability, and less contempt for weakness. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has given rise to a major impact on traffic mobility. To implement preventive measures and manage transportation, understanding the transformation of private driving behavior during the pandemic is critical. A data-driven forecasting model is proposed to estimate daily charging demand in the absence of the COVID-19 pandemic by leveraging electric vehicle (EV) charging data from four cities in China. It serves as a benchmark for quantifying the impact of the COVID-19 pandemic on EV charging demand. A vector autoregressive (VAR) model is then used to investigate the dynamic relationship between the changes in charging demand and potential influencing factors. Potential influencing factors are selected from three aspects: public health data, public concern, and the level of industrial activity. The results show that the magnitude of the decline in EV charging demand varied by city during the pandemic. Furthermore, COVID-19 related factors such as daily hospitalizations and national confirmed cases are the primary causes of the decline in charging demand. The research framework of this paper can be generalized to analyze the changes in other driving behaviors during the pandemic. Finally, three policy implications are proposed to assist other countries in dealing with similar events and to stimulate the recovery of the transport system during the post-pandemic period. • A data-driven forecasting model is proposed to quantify the impact of the COVID-19 pandemic on EV charging demand. • This study investigates the dynamic relationship between the changes in charging demand and potential influencing factors. • The magnitude of the decline in EV charging demand varied by city during the pandemic. • COVID-19 related factors are the primary causes of the decline in EV charging demand. • Policy implications are recommended based on the findings of this study. [ABSTRACT FROM AUTHOR]

Introduction: The epidemic of viral respiratory diseases in the last 20 years has affected many people around the world. In these situations, telemedicine may reduce unnecessary contacts and the risk of exposure to infection. This study aimed to review the papers performed to manage viral respiratory disease epidemics using telemedicine. Material and Methods: The PubMed and Scopus databases were searched in May 2020 for this systematic review study. Data were extracted from the final included papers based on the author, country, type of epidemic, telemedicine modality, telecommunication method, objective, participants, clinical outcome, cost, and satisfaction. Descriptive statistics were used to analyze data. Results: From 365 retrieved papers, 18 papers were included. Most of the papers were done in the US and China (67%). Half of the papers were done during the COVID-19 pandemic. Real-time modality was used in 78% of the papers. The telecommunication method in half of the papers was internetbased. Patients’ management and treatment was the main objective of the six papers. In 81% of the teleconsultation papers, the consultation was performed between patients and healthcare providers. The clinical outcome of all papers showed that telemedicine was successful in the management of viral respiratory disease epidemics. Cost and satisfaction outcomes were considered in a few papers. Conclusion: There is considerable evidence to show that telemedicine is a useful and convenient method to manage and control viral respiratory disease epidemics. Therefore, countries should pay special attention to telemedicine to control the current pandemic and future epidemics and use it extensively. [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]

This paper investigates a new multi-objective order assignment and scheduling problem for personal protective equipment (PPE) production and distribution during the outbreak of epidemics like COVID-19. The objective is to simultaneously minimize the total cost and maximize the PPE supply timeliness. For the problem, we first develop a bi-objective mixed-integer linear program (MILP). Then an $\epsilon $ -constraint combined with logic-based Benders decomposition method is proposed based on some explored properties. We then extend the proposed model to handle dynamics and randomness. In particular, we design a predictive reactive rescheduling approach to address random order arrivals and manufacturer disruptions. Computational experiments on a real case from China and 100 randomly generated instances are conducted. Results show that the proposed algorithm significantly outperforms an adapted $\epsilon $ -constraint method combined with the proposed MILP and the widely used non-dominated sorting genetic (NSGA-II) in obtaining high-quality Pareto solutions. Note to Practitioners—The unprecedented outbreak of COVID-19 and its rapid spread caught numerous national and local governments unprepared. Healthcare systems faced a vital scarcity of PPEs. The urgency of producing and delivering PPEs increases as the number of infected cases rapidly increases. A key challenge in response to the epidemic is effectively and efficiently matching the demands and needs. Performing practical and efficient order assignment and scheduling for PPE production during the COVID-19 outbreak is critical to curbing the COVID-19 pandemic. This work first proposes a bi-objective mixed-integer linear program for optimal order assignment and scheduling for PPE production. The aim is to achieve an economical and timely PPE production and supply. A novel method that combines the $\epsilon $ -constraint framework and the logic-based Benders decomposition is proposed to yield high-quality Pareto solutions for practical-sized problems. Computational results indicate that the proposed approaches are practical and feasible, which can help decision-makers to perform acceptable order assignment and scheduling decisions. [ABSTRACT FROM AUTHOR]

This paper argues that the COVID-19 outbreak is a "nodal point" that will lead to certain irreversible changes in the world economic and political order in the near future. These changes, however, will not only be an outcome of the current health pandemic, but also of the economic and political pandemics that have grown worse over a period of time. This paper identifies two far-reaching consequences of the accumulating pandemics that have the potential to lead the world towards a qualitatively better future. The first is the possible death of neoliberal capitalism that will lead to higher state intervention. The second is the end of the unipolar imperialist world order and subsequent emergence of a bipolar world with a rising China as a probable occupant of the non-imperialist pole. Both these outcomes are subject to the people's resistance for a better future, and China's alacrity in undermining the hegemony of imperialist-led globalization. [ABSTRACT FROM AUTHOR]

This paper presents the main macroeconomic policy measures adopted by China in response to the economic crisis caused by the COVID-19 pandemic. The main argument of the paper is that it is not possible to understand the process of combating the pandemic and the measures for the recovery of the Chinese economy, without considering the institutional framework of State action and the conduct of macroeconomic policy. We conclude that the Chinese government's strategic performance presents good results in the recovery of economic activity, but it also paves the way for a favorable long-term development path. [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]

This paper considers the pattens of international collaboration by analysing publications on COVID‐19 published in the first 6 months of the pandemic. The data set comprised articles on COVID‐19 indexed in the Web of Science Core Collection (WoS CC) downloaded four times between 1 April 2020 and 1 June 2020. The analysis of 5,827 documents revealed that 128 countries, 23,127 authors, and 6,349 institutes published on the pandemic. The data reveal that the three main publishing countries were the USA, China, and England with Italy closely following. Although publication was widely spread, most of the institutions with the highest volume of output were in China. Network analysis showed growth in international cooperation with an average degree of country/region cooperation rising to 23.06 by 1 June. There was also a clear core‐periphery structure to international collaboration. Institutional collaboration was shown to be highly regionalized. The data reveal a high and growing incidence of international collaboration on the pandemic. [ABSTRACT FROM AUTHOR]

By calculating the mutual information of stock indexes of 10 primary industry sectors in China, this paper analyzes the dependence relationship among Chinese stock sectors during the COVID-19 and the dynamic evolution of the relationship by using the sliding window method. According to the actual situation of the development of COVID-19 in China, the samples were divided into three stages, namely, calm period, pandemic period, and post-pandemic period. The results show that the dependence relationship among Chinese stock sectors is significantly enhanced in the pandemic period, but it decreases in the post-pandemic period and the dependence structure is similar to that in the calm period. The industrials sector is most closely connected with other sectors in the pandemic period. The information technology sector and telecommunication services sector maintain strong dependence in the three periods and share little contact with other sectors. In the pandemic period, the dependence between the consumer staples sector and other sectors is significantly enhanced, and consumer staples sector and health care sector maintain a strong dependence. From the results of the sliding window, the Chinese stock market is sensitive to the impact of COVID-19, but the duration of the impact on the dependence among the stock sectors is not long. [ABSTRACT FROM AUTHOR]

COVID-19 is now becoming a global issue and declared as pandemic by world health organization. This virus spread out from China to entire world. This paper performed a descriptive analysis of COVID-19 in special reference of India. Different attributes such as age, gender, travel history, communication type and current status are analyzed. Till now it can be stated from the study that age is not a significant factor that affect a person to be captured by this disease, also age attribute is normally distributed in current dataset. A significant relationship is found between gender (male and female) and Transmission Type (imported from other country or communicated from local) of the patients. [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]

Severe Acute Respiratory Syndrome Corona Virus 2 or SARS-CoV-2 (COVID-19) has affected 21 million people worldwide and is responsible for 0.75 million deaths (as of August 2020). Declared as a pandemic by the WHO, the virus has affected almost every country after originating in China articulating how contagious the virus is. With reasonable social measures, countries have already shown depreciation in coronavirus cases. This paper deals with detecting and distinguishing the COVID-19 disease from normal patients through frontal chest X-ray scans using Convolutional Neural Networks. Different combinations of parameters were used to train multiple CNNs and their behavior was noted. After thorough experimentation of different models, the best model which achieved an accuracy of 0.98 on the test set with a loss of 0.036 was nominated. The selected model's more intuitive accuracy metrics were calculated and its intermediate convolutional neural activations and receiver-operating characteristics curve is shown. [ABSTRACT FROM AUTHOR]

Due to the COVID-19 pandemic, education abruptly adhered to Digital Information and Communication Technologies (DICT) as a way to return to classes. This paper aims to analyze how DICT during the pandemic. The methodology was a Systematic Literature Review to identify articles on the theme and were selected 36 articles related to the research objective. The results show a predominance of qualitative and empirical studies on DICT and address the impacts of the transition from classroom teaching to remote teaching, the challenges, and the possibilities regarding their use in education. On the origin, most studies are from China and Indonesia. About the public, studies with university students stand out. Thus, the use of DICT in education was a major challenge. Teachers and students did not use DICT more intensively for teaching and learning before the pandemic. [ABSTRACT FROM AUTHOR]

CoronaVirus Disease-2019 (COVID-19) is a pandemic that started in China and influenced the whole world, causing more than 2.5 million people to die. There is not enough evidence to date that any diet, food, or nutrient prevents or treats COVID-19. However, it is thought that optimal nutrition can be a preventive form of "pre-rehabilitation" against deadly viruses. This paper aims to review the latest scientific data that examine the relationship between COVID-19 and nutrition. In this context, this study focuses on the relationship of COVID-19 with dietary protein, bioactive compounds, zinc, selenium, vitamins C and D, omega-3, probiotics, and the Mediterranean diet, which are particularly emphasized in the literature. [ABSTRACT FROM AUTHOR]

Purpose: This study is an attempt to evaluating the growth of scientific literature in the domain of coronavirus and Covid-19 pandemic research based on scientometric indicators: prolific countries and relative citation impact (RCI); influential institutions; author analysis and network, h-index and citation; DC (degree of collaboration), CC (collaboration coefficient), MCI (modified collaboration index) in the subject domain of coronavirus and Covid-19 research. Design/methodology/approach: The authors adopted approaches to obtain the literature data from Scopus database from 2000 to 2020 by conducting a systematic search using keywords related to the studied subject domain. In total, 15,297 numbers of records were considered for the literature analysis considering the real significant growth of this subject domain. This study presented the scientometric analysis of these publications. Furthermore, statistical correlations have been used to understand the collaboration pattern. Visualization tool VOSviewer is used to construct the co-author network. Findings: The present study found that 53.57% (8,195) of the research documents published on the open-access platform. Journal of Virology was found to be most preferred journal by the researcher producing around 839(5.48%) articles. USA and China dominate in the research output, and the University of Hong Kong has produced the highest number of research paper 547(3.58%). A significant portion of the research documents are published in the subject domain of medicine (49.70%), followed by immunology and microbiology (35.72%), and biochemistry, genetics and molecular biology subject domains (22.32%). There has been an unparalleled proliferation of publications on COVID-19 since January 2020 and also a significant distribution of research funds across the globe. Research limitations/implications: The study exclusively examines 15,297 research outputs which have been indexed in the Scopus database from 2000 to 2020 (till 01 April 2020). Thus, documents published in any other different channels and sources which are not covered in Scopus are excluded from the purview of research. Practical implications: It will be beneficial for researchers and practitioners worldwide for understanding the growth of scientific literature in the coronavirus and COVID-19 and identifying potential collaborator. Originality/value: Considering the global impact and social distress due to the outbreak of COVID-19 pandemic, this study is significant in the present scenario for identifying the growth of scientific literature in this field and evolving of this domain of research around the globe. The research results are useful to identify valuable research patterns from publications and of developments in the field of coronavirus and COVID-19. [ABSTRACT FROM AUTHOR]

COVID‐19 diseases have been a nationwide pandemic condition. However, cutaneous, skin histopathological manifestations of COVID‐19 infection are not well described. Our study aims are to present heterogeneous cutaneous, histopathological manifestations in COVID‐19 patients, to investigate the possible relationship between cutaneous manifestations and histopathological features in COVID‐19 infection. We performed a systemic review in PubMed database and Chinese medical journal search engines which were wangfang.data (http://www.wanfangdata.com.cn/), Science China (http://www.cnki.net/) until June 17th, 2020. Search terms "COVID‐19," "SARS‐Coronavirus‐2" and "Coronavirus" were used in combination with "cutaneous," "rash," "skin," "dermatology." Seventy‐five papers were included with confirmed COVID‐19 infection. The most frequent cutaneous manifestation of COVID‐19 present was erythema, nearly 38.4%. Trunk was the most affected location, presenting in 51.4% patients. Rash occurred before onset of other symptoms was in 5.3% patients. Seventy‐seven patients were received treatments. Rash was dismissed in 49% patients, improved in 21.2% patients ranged from 0 to 17 days. The histopathological examination present in 39 patients. Skin is one of target organs affected by COVID‐19 infection. Cutaneous manifestations should be paid more attention. It can help doctors diagnose COVID‐19 infection in prodromal stage, understand progression, and determine prognosis of COVID‐19 infection. [ABSTRACT FROM AUTHOR]

Three months into the Covid-19 crisis, lockdown has become a global response to the pandemic. Why have so many countries resorted to lockdown? How is it being implemented in different places? Why have some places had more success with lockdowns and others not? What does the effectiveness of lockdowns tell us about the local institutions entrusted with enforcing them? This paper compares how lockdown orders have been implemented in China, Italy, and the U.S. The analysis points to two major factors that have shaped the enforcement: tensions between national and local governments, and the strength of local territorial institutions. [ABSTRACT FROM AUTHOR]

This paper seeks to understand whether a catastrophic and urgent event, such as the first months of the COVID-19 pandemic, accelerates or reverses trends in international collaboration, especially in and between China and the United States. A review of research articles produced in the first months of the COVID-19 pandemic shows that COVID-19 research had smaller teams and involved fewer nations than pre-COVID-19 coronavirus research. The United States and China were, and continue to be in the pandemic era, at the center of the global network in coronavirus related research, while developing countries are relatively absent from early research activities in the COVID-19 period. Not only are China and the United States at the center of the global network of coronavirus research, but they strengthen their bilateral research relationship during COVID-19, producing more than 4.9% of all global articles together, in contrast to 3.6% before the pandemic. In addition, in the COVID-19 period, joined by the United Kingdom, China and the United States continued their roles as the largest contributors to, and home to the main funders of, coronavirus related research. These findings suggest that the global COVID-19 pandemic shifted the geographic loci of coronavirus research, as well as the structure of scientific teams, narrowing team membership and favoring elite structures. These findings raise further questions over the decisions that scientists face in the formation of teams to maximize a speed, skill trade-off. Policy implications are discussed. [ABSTRACT FROM AUTHOR]

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

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

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

Coronavirus disease 2019 (COVID-19), first reported in December 2019 in Wuhan, China, has progressed to a pandemic associated with substantial morbidity and mortality. Little is known about the healthcare workers who died fighting the disease in China. This paper analyzed the data of 78 Chinese healthcare workers who died in the fight against COVID-19 between 23 January and 2 June, 2020, and revealed the following characteristics. First, compared to the number of deaths directly attributable to COVID-19, more healthcare workers died from pre-existing disease attack induced by excessive fatigue or died from accidents. Second, the median age of the healthcare workers who died directly from COVID-19 was younger than that of the Wuhan non- healthcare workers who died of COVID 19. Third, although more women than men were involved in fighting the pandemic, more men died. Fourth, more healthcare workers died in Hubei than in other provinces. Fifth, most of the healthcare workers who died directly from COVID-19 were non-professionals. [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]

After the outbreak of COVID-19, the freight demand fell briefly, and as production resumed, the trucking share rate increased again, further increasing energy consumption and environmental pollution. To optimize the sudden changing freight structure, the study aims on developing an evolution model based on Markov's theory to estimate the freight structure post-COVID-19. The current study applies economic cybernetics to establish a freight structural adjustment path optimization model and solve the problem of how much freight transportation should increase each year under the premise that the total turnover of the freight industry continues to grow, and how many years it will take at least to reach a reasonable freight structure. The freight transport structure of China is used to examine the feasibility of the proposed model. The finding indicates that the development of China's freight transport structure is at an adjustment period and should enter a stable period by 2035 and the COVID-19 makes it harder to adjust the freight structure. Increasing the growth rate of the freight volume of railway and waterway transportation is the key to realizing the optimization of the freight structure, and the freight structure path optimization method can realize the rationalization of the freight structure in advance. • Markov theory is used to predict and optimize the freight transport structure after the COVID-19. • The prediction results of the Markov chain are optimized by data smoothing. • From the perspective of route optimization, this paper realizes the method innovation of optimal control theory. [ABSTRACT FROM AUTHOR]

The complex dynamics of human mobility, combined with sporadic cases of local outbreaks, make assessing the impact of large-scale social distancing on COVID-19 propagation in China a challenge. In this paper, with the travel big dataset supported by Baidu migration platform, we develop a reactive–diffusion epidemic model on human mobility networks to characterize the spatio-temporal propagation of COVID-19, and a novel time-dependent function is incorporated into the model to describe the effects of human intervention. By applying the system control theory, we discuss both constant and time-varying threshold behavior of proposed model. In the context of population mobility-mediated epidemics in China, we explore the transmission patterns of COVID-19 in city clusters. The results suggest that human intervention significantly inhibits the high correlation between population mobility and infection cases. Furthermore, by simulating different population flow scenarios, we reveal spatial diffusion phenomenon of cases from cities with high infection density to cities with low infection density. Finally, our model exhibits acceptable prediction performance using actual case data. The localized analytical results verify the ability of the PDE model to correctly describe the epidemic propagation and provide new insights for controlling the spread of COVID-19. • A reaction–diffusion epidemic model on human mobility networks is studied. • The constant and time-varying threshold behavior of the model is discussed. • Human intervention inhibits the high correlation between migration and pandemics. • The spatial diffusion of cases among cities is numerically simulated. • The predictive performance of the model is validated by using real case data. [ABSTRACT FROM AUTHOR]

The rapid spread of coronavirus (COVID-19) has a significant impact on the world economy, especially on the financial market. Investors are panicking about the future. This paper considers industry data and aims to investigate the impact of the pandemic on China's stock market. The Asymmetric-GARCH-BEKK model and complex network theory were combined to construct the interaction networks. From the perspective of spillover effect, we investigated the time varying co-movement during the pandemic. The results indicate that the outbreak of COVID-19 weakens the mean spillover, but enhances the volatility spillover among China's stock market. However, both mean spillover and volatility spillover decreased rapidly during the period of regular epidemic prevention and control. We also found that different industries have various sensitivity to the COVID-19 pandemic. • The outbreak of COVID-19 has enhanced volatility spillover among China's stock market. • The spillover effect among China's stock market decreased rapidly in middle and late stages of the pandemic. • Ten industries shown different sensitivity to the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

• A deep learning COVID-19 diagnostic system is developed using state-of-the-art deep learning architectures. • A detailed analysis of different methods used for detecting COVID-19 is presented in this paper. • Chest X-rays from different sources are collected to build a robust classification model. • The developed diagnostic model provided efficient results, even with a limited number of input images. • The results produced in this work are validated by an expert radiologist. In recent months, a novel virus named Coronavirus has emerged to become a pandemic. The virus is spreading not only humans, but it is also affecting animals. First ever case of Coronavirus was registered in city of Wuhan, Hubei province of China on 31st of December in 2019. Coronavirus infected patients display very similar symptoms like pneumonia, and it attacks the respiratory organs of the body, causing difficulty in breathing. The disease is diagnosed using a Real-Time Reverse Transcriptase Polymerase Chain reaction (RT-PCR) kit and requires time in the laboratory to confirm the presence of the virus. Due to insufficient availability of the kits, the suspected patients cannot be treated in time, which in turn increases the chance of spreading the disease. To overcome this solution, radiologists observed the changes appearing in the radiological images such as X-ray and CT scans. Using deep learning algorithms, the suspected patients' X-ray or Computed Tomography (CT) scan can differentiate between the healthy person and the patient affected by Coronavirus. In this paper, popular deep learning architectures are used to develop a Coronavirus diagnostic systems. The architectures used in this paper are VGG16, DenseNet121, Xception, NASNet, and EfficientNet. Multiclass classification is performed in this paper. The classes considered are COVID-19 positive patients, normal patients, and other class. In other class, chest X-ray images of pneumonia, influenza, and other illnesses related to the chest region are included. The accuracies obtained for VGG16, DenseNet121, Xception, NASNet, and EfficientNet are 79.01%, 89.96%, 88.03%, 85.03% and 93.48% respectively. The need for deep learning with radiologic images is necessary for this critical condition as this will provide a second opinion to the radiologists fast and accurately. These deep learning Coronavirus detection systems can also be useful in the regions where expert physicians and well-equipped clinics are not easily accessible. [ABSTRACT FROM AUTHOR]

• This paper firstly shares the ambulance situation in China. • Number of Ambulances in China increased greatly, especially for the negative pressure ambulance. • Negative pressure ambulances had positive influence on the contain of COVID-19. • Influence of negative pressure ambulance on containment of COVID-19 is discussed. • This article offers a valuable reference on ambulance now and in the future. New coronavirus (COVID-19) is a newly emerging and highly infectious form of typical pneumonia with a high rate of transmission. The COVID-19 pneumonia has spread to 147 countries and areas as of Mar.16th 2020, which has tremendous impact on the global public health. It is well known that, in China, the pandemic has been contained effectively with a series of emergency measures. It is necessary to share the existing experience of China in the fight against the pandemic to the world. Especially, during the process of prevention and containment of the pandemic, ambulances play an important role in transporting infectious patients. In this paper, focusing on the safety and quantity of negative pressure ambulances, the influence of ambulance on newly confirmed cases is given. Analysis of negative pressure ambulance on the containment of COVID-19 is shown. The potential development of ambulance is discussed. While the pandemic still continues spreading across the world, we hope to share our experience in the implementation of these strategies by China to save more life. [ABSTRACT FROM AUTHOR]

Socio-economic development is undergoing changes in China, such as the recently proposed carbon peak and carbon neutral targets, new infrastructure development strategy and the Coronavirus disease 2019 (COVID-19) pandemic. Meanwhile, the new-round marketization reform of the electricity industry has been ongoing in China since 2015. Therefore, it is urgent to evaluate the risk of electric power grid investment in China under new socio-economic development situation, which can help the investors manage risk and reduce risk loss. In this paper, a hybrid novel multi-criteria decision making (MCDM) method combining the latest group MCDM method, namely, Bayesian best–worst method (BBWM) and improved matter-element extension model (IMEEM) is proposed for risk evaluation of electric power grid investment in China under new socio-economic development situation. The BBWM is used for the weights' determination of electric power grid investment risk criteria, and the IMEEM is employed to rank risk grade of electric power grid investment. The risk evaluation index system of electric power grid investment is built, including economic, social, environmental, technical and marketable risks. The risk of electric power grid investment under new socio-economic development situation in Inner Mongolia Autonomous Region of China is empirically evaluated by using the proposed MCDM method, and the results indicate that it belongs to "Medium" grade, but closer to "High" grade. The main contributions of this paper include: (1) it proposes a hybrid novel MCDM method combining the BBWM and IMEEM for risk evaluation of electric power grid investment; and (2) it provides a new view for risk evaluation of electric power grid investment including economic, social, environmental, technical and marketable risks. The proposed hybrid novel MCDM method for the risk evaluation of electric power grid investment is effective and practical. [ABSTRACT FROM AUTHOR]

Infectious diseases, such as the current COVID-19, have a huge economic and societal impact. The ability to model its transmission characteristics is critical to minimize its impact. In fact, predicting how fast an infection is spreading could be a major factor in deciding on the severity, extent and strictness of the applied mitigation measures, such as the recent lockdowns. Even though modelling epidemics is a well studied subject, usually models do not include quarantine or other social measures, such as those imposed in the recent pandemic. The current work builds upon a recent paper by Maier and Brockmann (2020), where a compartmental SIRX model was implemented. That model included social or individual behavioural changes during quarantine, by introducing state X , in which symptomatic quarantined individuals are not transmitting the infection anymore, and described well the transmission in the initial stages of the infection. The results of the model were applied to real data from several provinces in China, quite successfully. In our approach we use a Monte-Carlo simulation model on networks. Individuals are network nodes and the links are their contacts. We use a spreading mechanism from the initially infected nodes to their nearest neighbours, as has been done previously. Initially, we find the values of the rate constants (parameters) the same way as in Maier and Brockmann (2020) for the confirmed cases of a country, on a daily basis, as given by the Johns Hopkins University. We then use different types of networks (random Erdős–Rényi, Small World, and Barabási–Albert Scale-Free) with various characteristics in an effort to find the best fit with the real data for the same geographical regions as reported in Maier and Brockmann (2020). Our simulations show that the best fit comes with the Erdős–Rényi random networks. We then apply this method to several other countries, both for large-size countries, and small size ones. In all cases investigated we find the same result, i.e. best agreement for the evolution of the pandemic with time is for the Erdős–Rényi networks. Furthermore, our results indicate that the best fit occurs for a random network with an average degree of the order of 〈 k 〉 ≈ 10–25, for all countries tested. Scale Free and Small World networks fail to fit the real data convincingly. • Monte-Carlo simulation model for the spread of COVID-19 in the early days of the pandemic. • Spreading of the virus is done on networks. Several different types of networks are tested. • The best fit with the real-world data is with Erdős–Rényi type of network. • We find that the average degree for several different countries in the world is in the range ∼ 10–25. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic, an exceptional crisis, sparked the introduction of new digital infrastructure to halt the novel coronavirus's spread. This paper explores how such digital infrastructure's impact might reverberate over the long term, by comparing Singapore, Hong Kong, and mainland China's utilization of digital technology in response to the 2003 SARS outbreak, and their responses to the 2020 COVID-19 pandemic. We find that advancements in digital technology since 2003 have boosted governments' surveillance and segregation abilities substantially—most dramatically so in China. Even though some of these new digital interventions are ostensibly designed to be temporary ones to address the needs of the immediate crisis, we argue that the resultant extensions of state power experienced during COVID-19 are likely to have profound long-term effects because they fundamentally affect sociopolitical contexts, institutional capabilities, and digital cultures. We also find that the extent to which governments can extend digital surveillance and segregation abilities during the pandemic is contingent on their respective sociopolitical, institutional, and digital cultural contexts. • COVID-19 pandemic sparked calls for a new digital infrastructure. • We compare 2003 SARS outbreak to COVID-19 pandemic. • New digital technologies increased governments' ability to surveil and segregate. • Their impact on surveillance and segregation will persist post COVID-19. [ABSTRACT FROM AUTHOR]

With the coronavirus pandemic wreathing havoc around the world, power industry has been hit hard due to the proposal of lockdown policies. However, the impact of lockdowns and shutdowns on the power system in different regions as well as periods of the pandemic can hardly be reflected on the foundation of current studies. In this paper, a prediction-based analysis method is developed to point out the electricity consumption gap resulted from the pandemic situation. The core of this method is a novel optimized grey prediction model, namely Rolling IMSGM(1,1) (Rolling Mechanism combined with grey model with initial condition as Maclaurin series), which achieves better prediction results in the face of long-term emergencies. A novel initial condition is adopted to track data with various characteristics in the form of higher-order polynomials, which are then determined by intelligent algorithms to realize accurate fitting. Historical power consumption data in China are utilized to carry out the monthly forecasts during COVID-19. Compared with other competitive models' prediction results, the superiority of IMSGM(1,1) are demonstrated. Through analyzing the gap between predicted consumption values and the actual data, it can be found that the impact of the pandemic on electricity varies in different periods, which is related to its severity and the local lockdown policies. This study helps to understand the impact on power industry in the face of such an emergency intuitively so as to respond to possible future events. [ABSTRACT FROM AUTHOR]

• A Maximum-Entropy Ecological Niche Model is used to estimate a global-scale probability distribution of COVID-19 high infection rate. • Environmental parameters (surface air temperature, precipitation, and elevation) and humanrelated parameters (CO 2 emission and population density) are used in the model. • The model is trained only with data of Italian provinces with high infection rate, but predicts known actual infection focuses, e.g. the Hubei province in China. • A risk index is proposed, which correctly classifies most World countries, which have reported high COVID-19 spread rate, as zones with high-risk of infection rate increase. • The methodology follows an Open-science approach where the model is published as a standardized Web service that maximises re-usability on new data and new diseases, and guarantees the transparency of the approach and the results. COVID-19 pandemic is a global threat to human health and economy that requires urgent prevention and monitoring strategies. Several models are under study to control the disease spread and infection rate and to detect possible factors that might favour them, with a focus on understanding the correlation between the disease and specific geophysical parameters. However, the pandemic does not present evident environmental hindrances in the infected countries. Nevertheless, a lower rate of infections has been observed in some countries, which might be related to particular population and climatic conditions. In this paper, infection rate of COVID-19 is modelled globally at a 0.5∘ resolution, using a Maximum Entropy-based Ecological Niche Model that identifies geographical areas potentially subject to a high infection rate. The model identifies locations that could favour infection rate due to their particular geophysical (surface air temperature, precipitation, and elevation) and human-related characteristics (CO 2 and population density). It was trained by facilitating data from Italian provinces that have reported a high infection rate and subsequently tested using datasets from World countries' reports. Based on this model, a risk index was calculated to identify the potential World countries and regions that have a high risk of disease increment. The distribution outputs foresee a high infection rate in many locations where real-world disease outbreaks have occurred, e.g. the Hubei province in China, and reports a high risk of disease increment in most World countries which have reported significant outbreaks (e.g. Western U.S.A.). Overall, the results suggest that a complex combination of the selected parameters might be of integral importance to understand the propagation of COVID-19 among human populations, particularly in Europe. The model and the data were distributed through Open-science Web services to maximise opportunities for re-usability regarding new data and new diseases, and also to enhance the transparency of the approach and results. [ABSTRACT FROM AUTHOR]

COVID-19 (SARS-CoV-2) is currently a global pandemic. This paper will attempt to estimate global infection rates and potential resultant mortality in the absence of effective treatment and/or vaccination. Calculations are based on World Health Organisation data from Wuhan in China: 14% of infected cases are severe, 5% require intensive care and 4% die. Estimated infection rates and mortality rates at the level of continents and some individual countries (when these are of sufficient size) are tabulated. This pandemic may cause close to half a billion deaths, i.e. 6% of the global population - and potentially more. At the risk of sounding sensational, but with a sober sense of realism, healthcare risks being plunged into the Middle-Ages if the public do not do their part. Infection cannot occur in the absence of contact. The only way to mitigate these numbers is to apply social distancing and take the standard precautions so frequently reiterated by Public Health: hand washing, avoid touching the face and so on. These measures are crucial as the human cost is going to be unthinkable even in the best-case scenarios that epidemiologists are modelling. [ABSTRACT FROM AUTHOR]