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In this paper, we present findings from four separate studies using different data sources and methods to examine Chinese attitudes toward the United States amid the COVID-19 pandemic. The empirical results consistently indicate a marked and significant decline in Chinese attitudes toward the US between late 2019 and the end of 2022. Using a quasi-experimental design and granular survey data that exploit daily variations in public opinion, we offer additional evidence that the decline in Chinese attitudes toward the United States followed a distinct pattern not true for Chinese attitudes toward other countries. Specifically, the rise in Chinese unfavorability toward the United States closely corresponded to the heightened Chinese attention to the pandemic's progression in the United States. These results collectively suggest a causal effect of COVID-19, shedding light on how public health crises, international relations, and media jointly shape the increasing enmity between the two great powers., Competing Interests: Competing interests statement:The authors declare no competing interest.

The novel coronavirus disease (COVID-19) pandemic has profoundly impacted the global economy and human health. The paper mainly proposed an improved susceptible-exposed-infected-recovered (SEIR) epidemic model with media coverage and limited medical resources to investigate the spread of COVID-19. We proved the positivity and boundedness of the solution. The existence and local asymptotically stability of equilibria were studied and a sufficient criterion was established for backward bifurcation. Further, we applied the proposed model to study the trend of COVID-19 in Shanghai, China, from March to April 2022. The results showed sensitivity analysis, bifurcation, and the effects of critical parameters in the COVID-19 model.

Background: Policy changes are often necessary to contain the detrimental impact of epidemics such as those brought about by coronavirus disease (COVID-19). In the earlier phases of the emergence of COVID-19, China was the first to impose strict restrictions on movement (lockdown) on January 23rd, 2020. A strategy whose effectiveness in curtailing COVID-19 was yet to be determined. We, therefore, sought to study the impact of the lockdown in reducing the incidence of COVID-19., Methods: Daily cases of COVID-19 that occurred in China which were registered between January 12th and March 30th, 2020, were extracted from the Johns Hopkins CSSE team COVID-19 ArcGIS® dashboards. Daily cases reported were used as data points in the series. Two interrupted series models were run: one with an interruption point of 23 January 2020 (model 1) and the other with a 14-day deferred interruption point of 6th February (model 2). For both models, the magnitude of change (before and after) and linear trend analyses were measured, and β -coefficients reported with 95% confidence interval (CI) for the precision., Results: Seventy-eight data points were used in the analysis. There was an 11% versus a 163% increase in daily cases in models 1 and 2, respectively, in the preintervention periods ( p ≤ 0.001). Comparing the period immediately following the intervention points to the counterfactual, there was a daily increase of 2,746% ( p < 0.001) versus a decline of 207% ( p = 0.802) in model 2. However, in both scenarios, there was a statistically significant drop in the daily cases predicted for this data and beyond when comparing the preintervention periods and postintervention periods ( p < 0.001)., Conclusion: There was a significant decrease the COVID-19 daily cases reported in China following the institution of a lockdown, and therefore, lockdown may be used to curtail the burden of COVID-19., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Mooketsi Molefi et al.)

Human coronaviruses (HCoVs) are associated with a range of respiratory complications. In the last two decades, three major outbreaks have been reported due to HCoVs including the current pandemic. In December 2019, a newly emerged virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first reported in Wuhan city, China. This paper presents a detailed review of the literature and discusses the uncertain spread of coronavirus disease 2019 (COVID-19) using fuzzy set as classical set theory logic to measure uncertainty and vagueness of COVID-19 in China. Our findings show that both infection and death rate touched the peak (normal fuzzy sets) and have shown a decline. The graphs are not convex, which shows that there remains much uncertainty in the spread of COVID-19. Effective vaccines are clearly needed to control and prevent the COVID-19 pandemic.

Background: Since the start of the COVID-19 outbreak, a large number of COVID-19-related papers have been published. However, concerns about the risk of expedited science have been raised. We aimed at reviewing and categorizing COVID-19-related medical research and to critically appraise peer-reviewed original articles., Methods: The data sources were Pubmed, Cochrane COVID-19 register study, arXiv, medRxiv and bioRxiv, from 01/11/2019 to 01/05/2020. Peer-reviewed and preprints publications related to COVID-19 were included, written in English or Chinese. No limitations were placed on study design. Reviewers screened and categorized studies according to i) publication type, ii) country of publication, and iii) topics covered. Original articles were critically appraised using validated quality assessment tools., Results: Among the 11,452 publications identified, 10,516 met the inclusion criteria, among which 7468 (71.0%) were peer-reviewed articles. Among these, 4190 publications (56.1%) did not include any data or analytics (comprising expert opinion pieces). Overall, the most represented topics were infectious disease (n = 2326, 22.1%), epidemiology (n = 1802, 17.1%), and global health (n = 1602, 15.2%). The top five publishing countries were China (25.8%), United States (22.3%), United Kingdom (8.8%), Italy (8.1%) and India (3.4%). The dynamic of publication showed that the exponential growth of COVID-19 peer-reviewed articles was mainly driven by publications without original data (mean 261.5 articles ± 51.1 per week) as compared with original articles (mean of 69.3 ± 22.3 articles per week). Original articles including patient data accounted for 713 (9.5%) of peer-reviewed studies. A total of 576 original articles (80.8%) showed intermediate to high risk of bias. Last, except for simulation studies that mainly used large-scale open data, the median number of patients enrolled was of 102 (IQR = 37-337)., Conclusions: Since the beginning of the COVID-19 pandemic, the majority of research is composed by publications without original data. Peer-reviewed original articles with data showed a high risk of bias and included a limited number of patients. Together, these findings underscore the urgent need to strike a balance between the velocity and quality of research, and to cautiously consider medical information and clinical applicability in a pressing, pandemic context. SYSTEMATIC REVIEW REGISTRATION: https://osf.io/5zjyx/.

The current outbreak of coronavirus disease 2019 (COVID-19) has become a global crisis due to its quick and wide spread over the world. A good understanding of the dynamic of the disease would greatly enhance the control and prevention of COVID19. However, to the best of our knowledge, the unique features of the outbreak have limited the applications of all existing dynamic models. In this paper, a novel stochastic model was proposed aiming to account for the unique transmission dynamics of COVID-19 and capture the effects of intervention measures implemented in Mainland China. We found that: (1) instead of aberration, there was a remarkable amount of asymptomatic virus carriers, (2) a virus carrier with symptoms was approximately twice more likely to pass the disease to others than that of an asymptomatic virus carrier, (3) the transmission rate reduced significantly since the implementation of control measures in Mainland China, and (4) it was expected that the epidemic outbreak would be contained by early March in the selected provinces and cities in China.

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]

As a global pandemic, COVID-19 shows no sign of letting up. With the control of the epidemic in China, the proportion of patients with severe and critical diseases being cured and discharged from hospital has increased, and the recovery of COVID-19 patients has become an important issue that urgently needs attention and solutions. By summarizing the exercise rehabilitation strategies and progress of SARS in 2003, this paper analyzed the differences in clinical indicators and recovery characteristics of severe pneumonia caused by the two viruses, and provided comprehensive exercise guidance and intervention strategies for COVID-19 patients for rehabilitation and nursing by referring to the problems and treatment strategies in the rehabilitation and nursing work of SARS. In the post-epidemic period, China will build a multi-dimensional epidemic prevention system by improving the effectiveness of mass training and strengthening local risk prevention and control. This paper discusses the exercise rehabilitation strategy of SARS patients after recovery, which has guiding significance for exercise intervention and scientific fitness of COVID-19 patients after recovery during epidemic prevention period. [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]

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]

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]

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]

Released in China in the winter of 2019, COVID-19 (caused by SARS-CoV-2 virus) lead to a world pandemic and highly stressed all medical systems around the world. Although the knowledge of this new coronavirus is in development, the most common reason for hospitalization of patients with COVID-19 is a severe respiratory disorder. The thought that any nation can be an island in a coordinated worldwide economy is demonstrated off-base by the presence of the new COVID-19. Worldwide participation, in specific within the field of open wellbeing and financial advancement is basic. All major nations must take an interest effectively. It is as well late to act once the illness has taken hold in numerous other countries and attempt to shut borders once a widespread begun. The aim of this paper is to make an analysis as accurat as possible on the general picture of the pandemic with COVID-19, given that most studies have been conducted clinically and medically, and less in terms of economical effects of this pandemic, the end of which cannot yet be predicted. [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]

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]

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]

Questions persist as to the origin of the COVID-19 pandemic. Evidence is building that its origin as a zoonotic spillover occurred prior to the officially accepted timing of early December, 2019. Here we provide novel methods to date the origin of COVID-19 cases. We show that six countries had exceptionally early cases, unlikely to represent part of their main case series. The model suggests a likely timing of the first case of COVID-19 in China as November 17 (95% CI October 4). Origination dates are discussed for the first five countries outside China and each continent. Results infer that SARS-CoV-2 emerged in China in early October to mid-November, and by January, had spread globally. This suggests an earlier and more rapid timeline of spread. Our study provides new approaches for estimating dates of the arrival of infectious diseases based on small samples that can be applied to many epidemiological situations. Author summary: While the COVID-19 pandemic continues, questions still persist as to its origins. Evidence is building that its origin as a zoonotic spillover occurred before the officially accepted timing of early December, 2019. We date the origin of COVID-19 cases from 203 countries and territories using a model from conservation science. We use a method that was originally developed to date the timing of extinction, and turn it to date the timing of origination using case dates rather than sighting events. Our results suggest that the virus emerged in China in early October to mid-November, 2019 (the most likely date being November 17), and by January, 2020, had spread globally. This suggests a much earlier and more rapid spread than is evident from confirmed cases. In addition, our study provides a new approach for estimating dates of the arrival of infectious diseases in new areas that can be applied to many different situations in the future. [ABSTRACT FROM AUTHOR]

A coronavirus disease-19 (COVID-19) caused by a novel coronavirus has appeared in China by the end of 2019 and later recognized as a global pandemic. This pandemic has evolved as a global public health menace. It has affected every aspect of human life. In line with these concerns, governments and the scientific community react promptly to the outbreak of this pandemic. These efforts are devoted to develop vaccine and curative medicines. Further efforts are dedicated to assessing the impacts of the pandemic in relation to socioeconomic, psychological, and environmental dimensions. In this regard, it is important to follow up developments and research activities on this global issue. The present work intended to tracking the current hotspots and research trends on COVID-19 in environmental fields. Bibliometric analysis and visualization mapping were utilized with the objective of revealing and evaluating the developments in knowledge on COVID-2019 and its impacts based on a collection of environmental sources. A sum of 729 documents were collected from Scopus database limiting to environmental sources only. Of all these publications, 563 (77.2%) were articles, 56 (7.7%) were reviews, and 110 (15.0%) were others. China has the highest share of publications (163; 22.4%). It is followed by the USA (139; 19.15), and Italy (110; 15.1%). Most publications on COVID-19 were in prestigious journals. The most productive institution at global level was Chinese Academy of Sciences, China (24 documents; 3.3%). The most prevalent topics are in relation to impacts of the pandemic on air quality, mental health, psychological, and economic aspects. The development of these topics is based on cross-sectional studies, evidence-based tools, remote sensing, satellite mapping, geographic information systems, market analysis and sampling. The progress of environmental research on COVID-19 will guide the development of global environmental strategies to control future global environmental risks. [ABSTRACT FROM AUTHOR]

The novel coronavirus (COVID-19) pandemic is intensifying all over the world, but some countries, including China, have developed extensive and successful experience in controlling this pandemic. In this context, some questions arise naturally: What can countries caught up in the epidemic learn from China's experience? In regions where the outbreak is under control, what would lead to a resurgence of the epidemic? To address these issues, we investigate China's experience in anticontagion interventions and reopening process, focusing on the coevolution of epidemic and awareness during COVID-19 outbreak. Through an empirical analysis based on large-scale data and simulation based on a metapopulation and multilayer network model, we ascertain the impact of human movements and awareness diffusion on the epidemic, elucidate the inherent patterns and effective interventions of different epidemic prevention methods, and highlight the crunch time of each measure. The results are also employed to analyze COVID-19 evolution in other countries so as to find unified rules in complex situations around the world and provide advice on anticontagion and reopening policies. Our findings explain some key mechanisms of epidemic prevention and may help the epidemic analysis and decision-making in various countries suffering from COVID-19. [ABSTRACT FROM AUTHOR]

COVID-19 is a Severe Acute Respiratory Syndrome (SARS), caused by SARS-CoV-2, a novel virus which belongs to the family Coronaviridae. It was first reported in December 2019 in the Wuhan city of China and soon after, the virus and hence the disease got spread to the entire world. As of February 26, 2021, SARS-CoV-2 has infected ~112.20 million people and caused ~2.49 million deaths across the globe. Although the case fatality rate among SARS-CoV-2 patient is lower (~2.15%) than its earlier relatives, SARS-CoV (~9.5%) and MERS-CoV (~34.4%), the SARS-CoV-2 has been observed to be more infectious and caused higher morbidity and mortality worldwide. As of now, only the knowledge regarding potential transmission routes and the rapidly developed diagnostics has been guiding the world for managing the disease indicating an immediate need for a detailed understanding of the pathogen and the disease-biology. Over a very short period of time, researchers have generated a lot of information in unprecedented ways in the key areas, including viral entry into the host, dominant mutation, potential transmission routes, diagnostic targets and their detection assays, potential therapeutic targets and drug molecules for inhibiting viral entry and/or its replication in the host including cross-neutralizing antibodies and vaccine candidates that could help us to combat the ongoing COVID-19 pandemic. In the current review, we have summarized the available knowledge about the pathogen and the disease, COVID-19. We believe that this readily available knowledge base would serve as a valuable resource to the scientific and clinical community and may help in faster development of the solution to combat the disease. [ABSTRACT FROM AUTHOR]

Since the emergence of the new severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) at the end of December 2019 in China, and with the urge of the coronavirus disease 2019 (COVID-19) pandemic, there have been huge efforts of many research teams and governmental institutions worldwide to mitigate the current scenario. Reaching more than 1,377,000 deaths in the world and still with a growing number of infections, SARS-CoV-2 remains a critical issue for global health and economic systems, with an urgency for available therapeutic options. In this scenario, as drug repurposing and discovery remains a challenge, computer-aided drug design (CADD) approaches, including machine learning (ML) techniques, can be useful tools to the design and discovery of novel potential antiviral inhibitors against SARS-CoV-2. In this work, we describe and review the current knowledge on this virus and the pandemic, the latest strategies and computational approaches applied to search for treatment options, as well as the challenges to overcome COVID-19. [ABSTRACT FROM AUTHOR]

COVID-19 emerged from China in December 2019 and during 2020 spread to every continent including Antarctica. The coronavirus, SARS-CoV-2, has been identified as the causative pathogen, and its spread has stretched the capacities of healthcare systems and negatively affected the global economy. This review provides an update on the virus, including the genome, the risks associated with the emergence of variants, mode of transmission, immune response, COVID-19 in children and the elderly, and advances made to contain, prevent and manage the disease. Although our knowledge of the mechanics of virus transmission and the immune response has been substantially demystified, concerns over reinfection, susceptibility of the elderly and whether asymptomatic children promote transmission remain unanswered. There are also uncertainties about the pathophysiology of COVID-19 and why there are variations in clinical presentations and why some patients suffer from long lasting symptoms—" the long haulers." To date, there are no significantly effective curative drugs for COVID-19, especially after failure of hydroxychloroquine trials to produce positive results. The RNA polymerase inhibitor, remdesivir, facilitates recovery of severely infected cases but, unlike the anti-inflammatory drug, dexamethasone, does not reduce mortality. However, vaccine development witnessed substantial progress with several being approved in countries around the globe. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has instigated immediate and massive worldwide research efforts. Rapid publication of research data may be desirable but also carries the risk of quality loss.Objective: This analysis aimed to correlate the severity of the COVID-19 outbreak with its related scientific output per country.Methods: All articles related to the COVID-19 pandemic were retrieved from Web of Science and analyzed using the web application SciPE (science performance evaluation), allowing for large data scientometric analyses of the global geographical distribution of scientific output.Results: A total of 7185 publications, including 2592 articles, 2091 editorial materials, 2528 early access papers, 1479 letters, 633 reviews, and other contributions were extracted. The top 3 countries involved in COVID-19 research were the United States, China, and Italy. The confirmed COVID-19 cases or deaths per region correlated with scientific research output. The United States was most active in terms of collaborative efforts, sharing a significant amount of manuscript authorships with the United Kingdom, China, and Italy. The United States was China's most frequent collaborative partner, followed by the United Kingdom.Conclusions: The COVID-19 research landscape is rapidly developing and is driven by countries with a generally strong prepandemic research output but is also significantly affected by countries with a high prevalence of COVID-19 cases. Our findings indicate that the United States is leading international collaborative efforts. [ABSTRACT FROM AUTHOR]

Early in the COVID-19 pandemic, predictions of international outbreaks were largely based on imported cases from Wuhan, China, potentially missing imports from other cities. We provide a method, combining daily COVID-19 prevalence and flight passenger volume, to estimate importations from 18 Chinese cities to 43 international destinations, including 26 in Africa. Global case importations from China in early January came primarily from Wuhan, but the inferred source shifted to other cities in mid-February, especially for importations to African destinations. We estimate that 10.4 (6.2 – 27.1) COVID-19 cases were imported to these African destinations, which exhibited marked variation in their magnitude and main sources of importation. We estimate that 90% of imported cases arrived between 17 January and 7 February, prior to the first case detections. Our results highlight the dynamic role of source locations, which can help focus surveillance and response efforts. Sparse testing early in the SARS-CoV-2 pandemic hinders estimation of the dates and origins of initial case importations. Here, the authors show that the main source of cases imported from China shifted from Wuhan to other Chinese cities by mid-February, especially for African locations. [ABSTRACT FROM AUTHOR]

Background/aim: Sars-CoV-2 virus infection (COVID-19) was observed in China in the last months of 2019. In the period following, this infection spread all over the world. In March 2020 the World Health Organization announced the existence of a pandemic. The aim of this manuscript is to investigate skin diseases associated with COVID-19 under three main headings: skin problems related to personal protective equipment and personal hygiene measures, skin findings observed in SARS-CoV-2 virus infections, and skin findings due to COVID-19 treatment agents. Materials and methods: In PubMed, Google Scholar databases, skin lesions related to personal protective equipment and personal hygiene measures, skin findings observed in SARS-CoV-2 virus infections and skin findings due to COVID-19 treatment agents subjects are searched in detail. Results: Pressure injury, contact dermatitis, itching, pressure urticaria, exacerbation of preexisting skin diseases, and new skin lesion occurrence/new skin disease occurrence may be due to personal protective equipment. Skin problems related to personal hygiene measures could include itching, dryness, and contact dermatitis. Skin findings may also be observed in SARS-CoV-2 virus infections. The incidence of skin lesions due to COVID-19 was reported to be between 0.2% and 29%. Many skin lesions including maculopapular, urticarial, vesicular, chilblain-like, thrombotic/ischemic, etc. are observed in COVID-19 patients. Some authors have stated that there is an absence of SARS-CoV-2 virus infection-specific skin findings. However, in asymptomatic or presymptomatic COVID-19 patients in particular, skin lesions can lead to the diagnosis of COVID-19. In addition, skin lesions may occur due to COVID-19 treatment agents. Conclusion: Many skin lesions may appear as a result of COVID-19. Even in the absence of a COVID-19 diagnosis, skin findings should be evaluated carefully in this pandemic period. [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]

The recent outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or coronavirus disease 2019 (COVID-19) in China, which spread to the rest of the world, led the World Health Organization to classify it as a global pandemic. COVID-19 belongs to the Bettacoronavirus genus of the Coronaviridae family, and it mainly spreads through the respiratory tract. Studies have now confirmed a human-to-human transmission as the primary pathway of spread. COVID-19 patients with a history of diseases such as respiratory system diseases, immune deficiency, diabetes, cardiovascular disease, and cancer are prone to adverse events (admission to the intensive care unit requiring invasive ventilation or even death). The current focus has been on the development of novel therapeutics, including antivirals, monoclonal antibodies, and vaccines. However, although there is undoubtedly an urgent need to identify effective treatment options against infection with COVID-19, it is equally important to clarify management protocols for the other significant diseases from which these patients may suffer, including cancer. This review summarizes the current evidence regarding the epidemiology, pathogenesis, and management of patients with COVID-19. It also aims to provide the reader with insights into COVID-19 in pregnant patients and those with cancer, outlining necessary precautions relevant to cancer patients. Finally, we provide the available evidence on the latest potent antiviral drugs and vaccines of COVID-19 and the ongoing drug trials. [ABSTRACT FROM AUTHOR]

A novel coronavirus (SARS-CoV-2), causing an emerging coronavirus disease (COVID-19), first detected in Wuhan City, Hubei Province, China, which has taken a catastrophic turn with high toll rates in China and subsequently spreading across the globe. The rapid spread of this virus to more than 210 countries while affecting more than 25 million people and causing more than 843,000 human deaths, it has resulted in a pandemic situation in the world. The SARS-CoV-2 virus belongs to the genus Betacoronavirus, like MERS-CoV and SARS-CoV, all of which originated in bats. It is highly contagious, causing symptoms like fever, dyspnea, asthenia and pneumonia, thrombocytopenia, and the severely infected patients succumb to the disease. Coronaviruses (CoVs) among all known RNA viruses have the largest genomes ranging from 26 to 32 kb in length. Extensive research has been conducted to understand the molecular basis of the SARS-CoV-2 infection and evolution, develop effective therapeutics, antiviral drugs, and vaccines, and to design rapid and confirmatory viral diagnostics as well as adopt appropriate prevention and control strategies. To date, August 30, 2020, no effective, proven therapeutic antibodies or specific drugs, and vaccines have turned up. In this review article, we describe the underlying molecular organization and phylogenetic analysis of the coronaviruses, including the SARS-CoV-2, and recent advances in diagnosis and vaccine development in brief and focusing mainly on developing potential therapeutic options that can be explored to manage this pandemic virus infection, which would help in valid countering of COVID-19. [ABSTRACT FROM AUTHOR]

Since its emergence in China in December 2019, COVID-19 has quickly spread around the globe causing a pandemic. Vaccination or the development of herd immunity seems the only way to slow down the spread of the virus; however, both are not achievable in the near future. Therefore, effective treatments to mitigate the burden of this pandemic and reduce mortality rates are urgently needed. Preclinical and clinical studies of potential antiviral and immunomodulatory compounds and molecules to identify safe and efficacious therapeutics for COVID-19 are ongoing. Two compounds, remdesivir, and dexamethasone have been so far shown to reduce COVID-19–associated death. Here, we provide a review of the potential therapeutic agents being considered for the treatment and management of COVID-19 patients. [ABSTRACT FROM AUTHOR]

Since the coronavirus disease 2019 (COVID-19) identified in Wuhan, Hubei, China in December 2019, it has been characterized as a pandemic by World Health Organization (WHO). It was reported that asymptomatic persons are potential sources of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We present an outbreak among health-care workers incited by a doctor who cared a patient with COVID-19 in a Hospital in Wuhan, Hubei, China, which indicates existence of super-spreader even during incubation period. [ABSTRACT FROM AUTHOR]

COVID-19 is caused by a novel coronavirus (2019-nCoV), which was declared as a pandemic after it emerged in China 2019. A vast international effort has been conducted to prevent and treat COVID-19 due to its high transmissibility and severe morbidity and mortality rates, particularly in individuals with chronic co-morbidities. In addition, polymorphic variants increased the need for proper vaccination to overcome the infectivity of new variants that are emerging across the globe. Many treatment options have been proposed and more than 25 vaccines are in various stages of development; however, the infection peaks are oscillating periodically, which raises a significant question about the effectiveness of the prevention measures and the persistence of this pandemic disease. In this review, we are exploring the most recent knowledge and advances in the treatment and vaccination options as well as the new emerging variants of 2019-nCoV and the possible mitigation of one of the most aggressive pandemics in the last centuries. [ABSTRACT FROM AUTHOR]

The new coronavirus that was first identified in December 2019 in Wuhan China, now called SARS-CoV-2, which causes the disease called COVID-19, has spread from China to the entire world in a few months. Due to its contagious potential (R0: 5.7) and because there is still no effective treatment to stop the infection, and a vaccine for prevention it is not yet available to the general population, COVID-19 is currently considered a global health problem. The need to implement sensitive methods for the identification of individuals with COVID-19 has led to the development of different molecular and immunological tests. The importance of a timely and accurate diagnosis is essential to determine the course of the pandemic. The interpretation of the results obtained by each test as well as the factors that affect these results have not been fully described. In this review, we describe and analyze the different SARS-CoV-2 detection methods that have been performed in Mexico and are available worldwide, outlining their strengths and weaknesses. Further, a broader perspective of the correct use and interpretation of the results obtained with these diagnostic tools is proposed to improve the containment strategy and identify the true impact of the pandemic. [ABSTRACT FROM AUTHOR]

Introduction: In late 2019, an epidemic outbreak emerges in China caused by a new coronavirus with high transmission and human infection potential which in March 2020, was characterized by WHO as a pandemic. The lockdown has repercussions on the population's well-being, reflected in their food choices. There is a tendency to increase the consumption of energy dense food, rich in fat and carbohydrates, which are related to an increased risk of depression. Objectives: The main goal of this non-systematic literature review was to understand the impact of the Mediterranean Diet on Mental Health promotion in SARSCoV-2 pandemic. Methods: Literature from Pubmed database were searched, with the following keywords: COVID-19, Depression, Anxiety, Mental Health and Mediterranean Diet. Results: Studies indicate that a diet based on the Mediterranean Diet is associated with a decreased risk of developing depressive symptoms, especially when there is moderate to high adherence to this dietary pattern. High consumption of plant and fish foods, reduced consumption of sugary products, processed and red meats and the use of olive oil as a fat source, are principles of the Mediterranean diet, associated with an improvement in endothelial function, increased levels of eicosanoids and serotonin synthesis and regulation of serotonin which seem to explain this protective effect. Conclusions: In addition to decreasing the risk of obesity, diabetes, and hypertension, comorbidities associated with the most serious disease of COVID-19, the Mediterranean Diet seems to play an important role in promoting mental health, with a decreased risk of developing depressive symptoms. [ABSTRACT FROM AUTHOR]

The Coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 has been rapidly spreading globally and has caused worldwide social and economic disruption. Currently, no specific antiviral drugs or clinically effective vaccines are available to prevent and treat COVID-19. Traditional Chinese medicine (TCM) can facilitate syndrome differentiation and treatment according to the clinical manifestations of patients and has demonstrated effectiveness in epidemic prevention and control. In China, TCM intervention has helped to control the epidemic; however, TCM has not been fully recognized worldwide. In this review, we summarize the epidemiology and etiological characteristics of severe acute respiratory syndrome coronavirus 2 and the prevention and treatment measures of COVID-19. Additionally, we describe the application of TCM in the treatment of COVID-19 and the identification of small molecules of TCM that demonstrate anti-coronavirus activity. We also analyze the current problems associated with the recognition of TCM. We hope that, through the contribution of TCM, combined with modern technological research and the support of our international counterparts, COVID-19 can be effectively controlled and treated. [ABSTRACT FROM AUTHOR]

A pneumonia outbreak with unknown etiology was reported in Wuhan, Hubei province, China, in December 2019, associated with the Huanan Seafood Wholesale Market. The causative agent of the outbreak was identified by the WHO as the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), producing the disease named coronavirus disease-2019 (COVID-19). The virus is closely related (96.3%) to bat coronavirus RaTG13, based on phylogenetic analysis. Human-to-human transmission has been confirmed even from asymptomatic carriers. The virus has spread to at least 200 countries, and more than 1,700,000 confirmed cases and 111,600 deaths have been recorded, with massive global increases in the number of cases daily. Therefore, the WHO has declared COVID-19 a pandemic. The disease is characterized by fever, dry cough, and chest pain with pneumonia in severe cases. In the beginning, the world public health authorities tried to eradicate the disease in China through quarantine but are now transitioning to prevention strategies worldwide to delay its spread. To date, there are no available vaccines or specific therapeutic drugs to treat the virus. There are many knowledge gaps about the newly emerged SARS-CoV-2, leading to misinformation. Therefore, in this review, we provide recent information about the COVID-19 pandemic. This review also provides insights for the control of pathogenic infections in humans such as SARS-CoV-2 infection and future spillovers. [ABSTRACT FROM AUTHOR]