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During the last three years, numerous research papers have been reported which use social media data to explore several issues related to the COVID-19 pandemic. Bibliometric methods in this work are used to analyze 1427 peer-reviewed documents from the last three years extracted from the Web of Science database. The results of this study show that there was high growth in publications in open access journals with an annual rate reaching 19.3% and they also identify the top cited journals and research papers. The thematic analysis of papers shows that research topics related to social media for surveillance and monitoring of public attitudes and perceptions, mental health, misinformation, and fake news are important and well-developed, whereas topics related to distance-learning education with social media are emerging. The results also show that the USA, China, and the UK have published many papers and received a high number of citations because of their strong international collaboration. [ABSTRACT FROM AUTHOR]

Novel coronavirus (COVID-19) is started from Wuhan (City in China), and is rapidly spreading among people living in other countries. Today, around 215 countries are affected by COVID-19 disease. WHO announced approximately number of cases 11,274,600 worldwide. Due to rapidly rising cases daily in the hospitals, there are a limited number of resources available to control COVID-19 disease. Therefore, it is essential to develop an accurate diagnosis of COVID-19 disease. Early diagnosis of COVID-19 patients is important for preventing the disease from spreading to others. In this paper, we proposed a deep learning based approach that can differentiate COVID- 19 disease patients from viral pneumonia, bacterial pneumonia, and healthy (normal) cases. In this approach, deep transfer learning is adopted. We used binary and multi-class dataset which is categorized in four types for experimentation: (i) Collection of 728 X-ray images including 224 images with confirmed COVID-19 disease and 504 normal condition images (ii) Collection of 1428 X-ray images including 224 images with confirmed COVID-19 disease, 700 images with confirmed common bacterial pneumonia, and 504 normal condition images. (iii) Collections of 1442 X- ray images including 224 images with confirmed COVID-19 disease, 714 images with confirmed bacterial and viral pneumonia, and 504 images of normal conditions (iv) Collections of 5232 X- ray images including 2358 images with confirmed bacterial and 1345 with viral pneumonia, and 1346 images of normal conditions. In this paper, we have used nine convolutional neural network based architecture (AlexNet, GoogleNet, ResNet-50, Se-ResNet-50, DenseNet121, Inception V4, Inception ResNet V2, ResNeXt-50, and Se-ResNeXt-50). Experimental results indicate that the pre trained model Se-ResNeXt-50 achieves the highest classification accuracy of 99.32% for binary class and 97.55% for multi-class among all pre-trained models. [ABSTRACT FROM AUTHOR]

When labeling an infectious disease, officially sanctioned scientific names, e.g., "H1N1 virus," are recommended over place-specific names, e.g., "Spanish flu." This is due to concerns from policymakers and the WHO that the latter might lead to unintended stigmatization. However, with little empirical support for such negative consequences, authorities might be focusing on limited resources on an overstated issue. This paper empirically investigates the impact of naming against the current backdrop of the 2019–2020 pandemic. The first hypothesis posited that using place-specific names associated with China (e.g., Wuhan Virus or China Virus) leads to greater levels of sinophobia, the negative stigmatization of Chinese individuals. The second hypothesis posited that using a scientific name (e.g., Coronavirus or COVID-19) leads to increased anxiety, risk aversion, beliefs about contagiousness of the virus, and beliefs about mortality rate. Results from two preregistered studies [ N(Study 1) = 504; N(Study 2) = 412], conducted across three countries with the first study during the early outbreak (April 2020) and the second study at a later stage of the pandemic (August 2020), found no evidence of any adverse effects of naming on sinophobia and strong support for the null hypothesis using Bayesian analyses. Moreover, analyses found no impact of naming on anxiety, risk aversion, beliefs about contagiousness of the virus, or beliefs about mortality rate, with mild to strong support for the null hypothesis across outcomes. Exploratory analyses also found no evidence for the effect of naming being moderated by political affiliation. In conclusion, results provide no evidence that virus naming impacted individual's attitudes toward Chinese individuals or perceptions of the virus, with the majority of analyses finding strong support for the null hypothesis. Therefore, based on the current evidence, it appears that the importance given to naming infectious diseases might be inflated. [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 coronavirus disease (COVID-19) has become a global public health threaten. A series of strict prevention and control measures were implemented in China, contributing to the improvement of air quality. In this study, we described the trend of air pollutant concentrations and the incidence of COVID-19 during the epidemic and applied generalized additive models (GAMs) to assess the association between short-term exposure to air pollution and daily confirmed cases of COVID-19 in 235 Chinese cities. Disease progression based on both onset and report dates as well as control measures as potential confounding were considered in the analyses. We found that stringent prevention and control measures intending to mitigate the spread of COVID-19, contributed to a significant decline in the concentrations of air pollutants except ozone (O 3 ). Significant positive associations of short-term exposure to air pollutants, including particulate matter with diameters ≤2.5 μm (PM 2.5 ), particulate matter with diameters ≤10 μm (PM 10 ), and nitrogen dioxide (NO 2 ) with daily new confirmed cases were observed during the epidemic. Per interquartile range (IQR) increase in PM 2.5 (lag0-15), PM 10 (lag0-15), and NO 2 (lag0-20) were associated with a 7% [95% confidence interval (CI): (4-9)], 6% [95% CI: (3-8)], and 19% [95% CI: (13-24)] increase in the counts of daily onset cases, respectively. Our results suggest that there is a statistically significant association between ambient air pollution and the spread of COVID-19. Thus, the quarantine measures can not only cut off the transmission of virus, but also retard the spread by improving ambient air quality, which might provide implications for the prevention and control of COVID-19., Competing Interests: Declaration of competing interest 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 © 2020 Elsevier Ltd. All rights reserved.)

Although anthropogenic emissions decreased, polluted days still occurred in the Beijing-Tianjin-Hebei (BTH) region during the initial outbreak of the coronavirus disease (COVID-19). Analysis of the characteristics and source distribution of large-scale air pollution episodes during the COVID-19 outbreak (from 23 January to April 8, 2020) in the BTH region is helpful for exploring the efficacy of control measures and policy making. The results indicated that the BTH region suffered two large-scale air pollution episodes (23-28 January and 8-13 February), which were characterized by elevated PM 2.5 , SO 2 , NO 2 , and CO concentrations, while the O 3 concentration decreased by 1.5%-33.9% (except in Shijiazhuang, where it increased by 16.6% during the second episode). These large-scale air pollution episodes were dominated by unfavorable meteorological conditions comprising a low wind speed and increased relative humidity. The transport pathways and source distribution were explored using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT), potential source contribution function (PSCF), and concentration weighted trajectory (CWT) models. The air pollution in the BTH region was mainly affected by local emission sources during the first episode, which contributed 51.6%-60.6% of the total trajectories in the BTH region with a PM 2.5 concentration ranging from 146.2 μg/m 3 to 196.7 μg/m 3 . The short-distance air masses from the southern and southwestern areas of the BTH region were the main transport pathways of airflow arriving in the BTH region during the second episode. These contributed 51.9%-57.9% of the total trajectories and originated in Hebei, Henan, central Shanxi, and Shaanxi provinces, which were the areas contributing the most to the PM 2.5 level and exhibited the highest PSCF and CWT values. Therefore, on the basis of local emission reduction, enhancing regional environmental cooperation and implementing a united prevention and control of air pollution are effective mitigation measures for the BTH region., Competing Interests: Declaration of competing interest 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 © 2020 Elsevier Ltd. All rights reserved.)

Due to continuous spread of coronavirus disease 2019 (COVID-19) worldwide, long-term effective prevention and control measures should be adopted for public transport facilities, as they are increasing in popularity and serve as the principal modes for travel of many people. The human infection risk could be extremely high due to length of exposure time window, transmission routes and structural characteristics during travel or work. This can result in the rapid spread of the infection. Based on the transmission characteristics of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and the nature of public transport sites, we identified comprehensive countermeasures toward the prevention and control of COVID-19, including the strengthening of personnel management, personal protection, environmental cleaning and disinfection, and health education. Multi-pronged strategies can enhance safety of public transportation. The prevention and control of the disease during the use of public transportation will be particularly important when all countries in the world resume production. The aim of this study is to introduce experience of the prevention and control measures for public transportation in China to promote the global response to COVID-19., Competing Interests: Declaration of competing interest 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 © 2020 Elsevier Ltd. All rights reserved.)

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.

Social and economic factors relate to the prevention and control of infectious diseases. The purpose of this paper was to assess the distribution of COVID-19 morbidity rate in association with social and economic factors and discuss the implications for urban development that help to control infectious diseases. This study was a cross-sectional study. In this study, social and economic factors were classified into three dimensions: built environment, economic activities, and public service status. The method applied in this study was the spatial regression analysis. In the 13 districts in Wuhan, the spatial regression analysis was applied. The results showed that: 1) increasing population density, construction land area proportion, value-added of tertiary industry per unit of land area, total retail sales of consumer goods per unit of land area, public green space density, aged population density were associated with an increased COVID-19 morbidity rate due to the positive characteristics of estimated coefficients of these variables. 2) increasing average building scale, GDP per unit of land area, and hospital density were associated with a decreased COVID-19 morbidity rate due to the negative characteristics of estimated coefficients of these variables. It was concluded that it is possible to control infectious diseases, such as COVID-19, by adjusting social and economic factors. We should guide urban development to improve human health.

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a novel coronavirus first identified in Wuhan, China, and the etiological agent of Coronavirus Disease-2019 (COVID-19). This infection spreads mainly through direct contact with Flügge micro droplets or core droplets that remain suspended as aerosol. Moreover, it has been reported that infected subjects, both with and without clinical signs of COVID-19, can transmit the virus. Since the infection typically enters through mouth, nose, and eyes, dentistry is one of the medical practices at highest risk of infection due to the frequent production of aerosol and the constant presence of saliva. The World Health Organization (WHO) has suggested that only emergency/urgent procedures should be performed during the coronavirus outbreak. Considering the virus' route of transmission, a specific protocol should be applied to reduce the risk of infection in addition to measures that prevent the spread of infection from a patient to another person or medical tools and equipment (cross-infection). This protocol should be implemented by modifying both patient management and clinical practice, introducing particular devices and organizational practices. This paper aims to discuss and suggest the most appropriate procedures in every aspect of dental practice to reduce infection risk.

To investigate the effect of villagers' moral obligation and village cadres' public leadership on villagers' collective action for epidemic prevention and control, against the background of the corona virus disease 2019 (COVID-19) emergency in China, we constructed models based on the institutional analysis and development (IAD) framework and employed principal component analysis (PCA) and ordered probit regression, drawing on survey data from 533 villagers in Henan province adjacent to the COVID-19 origin province, Hubei, China. The results indicate that: (1) generally, both moral obligation and public leadership as well as their constituent indicators contributed positively to collective action for COVID-19 prevention and control; (2) moreover, moral obligation and public leadership can strengthen each other's positive role in collective action for COVID-19 prevention and control. Based on the above findings, this paper suggests that villagers' moral obligation can be perfected through internalizing epidemic prevention and control norms into the villagers' moral norms by the way of villagers mastering the rural public health governance scheme. In addition, public leadership can be improved through professional training of village cadres and by motivating village elites to run for village cadres. With improved villagers' moral obligation and village cadres' public leadership, collective action for epidemic prevention and control could be more likely to be realized.

Background: As part of the response plans for the current outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), authorities are drafting and implementing containment measures across jurisdictions worldwide in the effort to slow down transmission and reduce the infection rate. A solid communication strategy is needed to increase the reach of valid information to health professionals, reduce misinformation, and efficiently implement recommended measures., Objective: The aim of this paper is to describe the utilization of a dedicated mobile health (mHealth) platform to disseminate up-to-date and validated information about SARS-CoV-2 to all medical staff of the Children's Hospital at the University Hospitals of Geneva., Methods: Three documents containing institutional information concerning screening, local containment procedures, and frequently asked questions and answers for parents were made available to the staff through a mobile app developed in the University of Geneva, Switzerland. Using a third-party statistics tool, we anonymously monitored user activity as well as content utilization patterns since the diagnosis of the first case of SARS-CoV-2 in Switzerland on February 25, 2020., Results: From February 25, 2020, to March 13, 2020 (18 days), information documents on SARS-CoV-2 were viewed 859 times, which accounted for 35.6% of the total content views (total views=332). User activity increased significantly with 50.8 (SD 14.4) users per day in this period as compared to the previous weeks (mean 26.4, SD 9.8; P<.001). In addition, session numbers per day more than doubled during the aforementioned period (P<.001). In a survey, medical staff found the information easy to find within the app. On a 10-point Likert scale, the ability of the app to reassure staff in clinical practice was rated as 7.6 (SD 2.1), time-saving ability was rated as 8.5 (SD 2.1), and the need to look for information from other sources was rated as 5.9 (SD 3.3)., Conclusions: The use of an mHealth solution to disseminate novel coronavirus-related information seemed to be an effective and time-saving communication channel within our institution during the SARS-CoV-2 outbreak. Medical staff felt reassured and informed in daily practice. More research should be done on the clinical impact and outcomes of the integration of mHealth solutions as a communication channel of validated information within health institutions., (©Ido Zamberg, Sergio Manzano, Klara Posfay-Barbe, Olivier Windisch, Thomas Agoritsas, Eduardo Schiffer. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 14.04.2020.)

Previous epidemic management research proves the importance of city-level information, but also highlights limited expertise in urban data applications during a pandemic outbreak. In this paper, we provide an overview of city-level information, in combination with analytical and operational capacity, that define urban intelligence for supporting response to disease outbreaks. We present five components (movement, facilities, people, information, and engagement) that have been previously investigated but remain siloed to successfully orchestrate an integrated pandemic response. Reflecting on the coronavirus disease (COVID-19) outbreak that was first identified in Wuhan, China, we discuss the opportunities, technical challenges, and foreseeable controversies for deploying urban intelligence during a pandemic. Finally, we emphasize the urgency of building urban intelligence through cross-disciplinary research and collaborative practice on a global scale., (©Yuan Lai, Wesley Yeung, Leo Anthony Celi. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 14.04.2020.)

Coronavirus (COVID-19) is a humanitarian emergency, which started in Wuhan in China in early December 2019, brought into the notice of the authorities in late December, early January 2020, and, after investigation, was declared as an emergency in the third week of January 2020. The WHO declared this as Public Health Emergency of International Concern (PHEIC) on 31th of January 2020, and finally a pandemic on 11th March 2020. As of March 24th, 2020, the virus has caused a casualty of over 16,600 people worldwide with more than 380,000 people confirmed as infected by it, of which more than 10,000 cases are serious. Mainly based on Chinese newspapers, social media and other digital platform data, this paper analyzes the timeline of the key actions taken by the government and people over three months in five different phases. It found that although there was an initial delay in responding, a unique combination of strong governance, strict regulation, strong community vigilance and citizen participation, and wise use of big data and digital technologies, were some of the key factors in China's efforts to combat this virus. Being inviable and non-measurable (unlike radioactive exposure), appropriate and timely information is very important to form the basic foundation of mitigation and curative measures. Infodemic, as it is termed by WHO, is a key word, where different stakeholder's participation, along with stricter regulation, is required to reduce the impact of fake news in this information age and social media. Although different countries will need different approaches, focusing on its humanitarian nature and addressing infodemic issues are the two critical factors for future global mitigation efforts.

The recent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, previously known as 2019-nCoV) outbreak has engulfed an unprepared world amidst a festive season. The zoonotic SARS-CoV-2, believed to have originated from infected bats, is the seventh member of enveloped RNA coronavirus. Specifically, the overall genome sequence of the SARS-CoV-2 is 96.2% identical to that of bat coronavirus termed BatCoV RaTG13. Although the current mortality rate of 2% is significantly lower than that of SARS (9.6%) and Middle East respiratory syndrome (MERS) (35%), SARS-CoV-2 is highly contagious and transmissible from human to human with an incubation period of up to 24 days. Some statistical studies have shown that, on average, one infected patient may lead to a subsequent 5.7 confirmed cases. Since the first reported case of coronavirus disease 2019 (COVID-19) caused by the SARS-CoV-2 on December 1, 2019, in Wuhan, China, there has been a total of 60,412 confirmed cases with 1370 fatalities reported in 25 different countries as of February 13, 2020. The outbreak has led to severe impacts on social health and the economy at various levels. This paper is a review of the significant, continuous global effort that was made to respond to the outbreak in the first 75 days. Although no vaccines have been discovered yet, a series of containment measures have been implemented by various governments, especially in China, in the effort to prevent further outbreak, whilst various medical treatment approaches have been used to successfully treat infected patients. On the basis of current studies, it would appear that the combined antiviral treatment has shown the highest success rate. This review aims to critically summarize the most recent advances in understanding the coronavirus, as well as the strategies in prevention and treatment.

In a large-scale epidemic outbreak, there can be many high-risk individuals to be transferred for medical isolation in epidemic areas. Typically, the individuals are scattered across different locations, and available quarantine vehicles are limited. Therefore, it is challenging to efficiently schedule the vehicles to transfer the individuals to isolated regions to control the spread of the epidemic. In this paper, we formulate such a quarantine vehicle scheduling problem for high-risk individual transfer, which is more difficult than most well-known vehicle routing problems. To efficiently solve this problem, we propose a hybrid algorithm based on the water wave optimization (WWO) metaheuristic and neighborhood search. The metaheuristic uses a small population to rapidly explore the solution space, and the neighborhood search uses a gradual strategy to improve the solution accuracy. Computational results demonstrate that the proposed algorithm significantly outperforms several existing algorithms and obtains high-quality solutions on real-world problem instances for high-risk individual transfer in Hangzhou, China, during the peak period of the novel coronavirus pneumonia (COVID-19).

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

Background: Severe Acute Respiratory Syndrome coronavirus-2 (SARS-CoV-2) has challenged public health agencies globally. In order to effectively target government responses, it is critical to identify the individuals most at risk of coronavirus disease-19 (COVID-19), developing severe clinical signs, and mortality. We undertook a systematic review of the literature to present the current status of scientific knowledge in these areas and describe the need for unified global approaches, moving forwards, as well as lessons learnt for future pandemics.Methods: Medline, Embase and Global Health were searched to the end of April 2020, as well as the Web of Science. Search terms were specific to the SARS-CoV-2 virus and COVID-19. Comparative studies of risk factors from any setting, population group and in any language were included. Titles, abstracts and full texts were screened by two reviewers and extracted in duplicate into a standardised form. Data were extracted on risk factors for COVID-19 disease, severe disease, or death and were narratively and descriptively synthesised.Results: One thousand two hundred and thirty-eight papers were identified post-deduplication. Thirty-three met our inclusion criteria, of which 26 were from China. Six assessed the risk of contracting the disease, 20 the risk of having severe disease and ten the risk of dying. Age, gender and co-morbidities were commonly assessed as risk factors. The weight of evidence showed increasing age to be associated with severe disease and mortality, and general comorbidities with mortality. Only seven studies presented multivariable analyses and power was generally limited. A wide range of definitions were used for disease severity.Conclusions: The volume of literature generated in the short time since the appearance of SARS-CoV-2 has been considerable. Many studies have sought to document the risk factors for COVID-19 disease, disease severity and mortality; age was the only risk factor based on robust studies and with a consistent body of evidence. Mechanistic studies are required to understand why age is such an important risk factor. At the start of pandemics, large, standardised, studies that use multivariable analyses are urgently needed so that the populations most at risk can be rapidly protected.Registration: This review was registered on PROSPERO as CRD42020177714 . [ABSTRACT FROM AUTHOR]

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The emergence of the novel coronavirus disease 2019 (COVID-19) is a serious global public health problem. The mystery around the causative agent was revealed on 7 January 2020 when the pathogen was isolated by Chinese scientists and named as novel coronavirus-2019 (2019-nCoV). With quicker reaction to the contagion, the number of scholarly research publications have increased exponentially, which calls for bibliometric analysis and review of the incipient studies. Therefore on 7 Feb 2020, the English language and Chinese publications on COVID-19 were retrieved from PubMed and CNKI (China National Knowledge Infrastructure). A retrospective bibliometric analysis was performed to gauge the output, language, document type, journal, authorship, geographical distribution, research focus, resulting in a total of 154 papers comprising 100 papers in English and another 54 in Chinese. There were 30 journals published at least 2 papers, and the Journal of Medical Virology published the most papers (11). China and USA were the most productive countries, and there were 24 international collaborative papers. Fudan University contributed the most papers. Isaac I. Bogoch, Kunling Shen, Xingguang LI, Hongzhou Lu were the most active authors as first authors, with 2 papers respectively. Among the 154 publications, 35 were opinions papers, and 30 were research articles. Papers were categorized by research areas, and five research foci were identified. Our study offers an overview of the incipient publications and quantitative information for future research on coronavirus. Besides, Chinese publications were also retrieved for an integrated data analysis and diversity comparison. [ABSTRACT FROM AUTHOR]

The recent coronavirus disease (COVID‐19) is impacting the research community worldwide with unforeseen long‐term consequences for research, doctoral training, and international collaboration. It is already clear that the immediate effects of the crisis resulting from disrupted research stays and reduced career development opportunities are being most detrimental to early‐career researchers. Based on a Sino‐German international research training group dedicated to doctoral training and biodiversity‐ecosystem functioning research, we show how resilience of large collaborative research programs can be promoted in times of global crisis. We outline possible adaptations in the areas of funding, research, teaching and learning, supervision and mentoring, and international collaboration helping to reduce detrimental impact for early‐career researchers and to permanently strengthen the performance of large collaborative research groups in the postpandemic era. [ABSTRACT FROM AUTHOR]

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

• The latest summary of the COVID-19 outbreak in China; • There might be an oral-fecal transmission of the virus; • Aggregates and consolidates the epidemiology, clinical manifestations, diagnosis, treatments and preventions of this new type of coronavirus. There is a current worldwide outbreak of the novel coronavirus Covid-19 (coronavirus disease 2019; the pathogen called SARS-CoV-2; previously 2019-nCoV), which originated from Wuhan in China and has now spread to 6 continents including 66 countries, as of 24:00 on March 2, 2020. Governments are under increased pressure to stop the outbreak from spiraling into a global health emergency. At this stage, preparedness, transparency, and sharing of information are crucial to risk assessments and beginning outbreak control activities. This information should include reports from outbreak site and from laboratories supporting the investigation. This paper aggregates and consolidates the epidemiology, clinical manifestations, diagnosis, treatments and preventions of this new type of coronavirus. [ABSTRACT FROM AUTHOR]

The recent emergence of a new coronavirus (COVID-19) has gained a high cover in public media and worldwide news. The virus has caused a viral pneumonia in tens of thousands of people in Wuhan, a central city of China. This short paper gives a brief introduction on how the demand for information on this new epidemic is reported through Google Trends. The reported period is 31 December 2020 to 20March 2020. The authors draw conclusions on current infodemiological data on COVID-19 using three main search keywords: coronavirus, SARS and MERS. Two approaches are set. First is the worldwide perspective, second - the Chinese one, which reveals that in China this disease in the first days was more often referred to SARS then to general coronaviruses, whereas worldwide, since the beginning, it is more often referred to coronaviruses. [ABSTRACT FROM AUTHOR]

This manuscript presents a geospatial and temporal analysis of the COVID'19 along with its mortality rate worldwide and an empirical evaluation of social distance policies on economic activities. Stock Market Indices, Purchasing Manager Index (PMI), and Stringency Index values are evaluated with respect to rising COVID-19 cases based on the collected data from Jan 2020 to June 2021. The findings for the stock market index reveal the highest negative correlation coefficient value, i.e., −0.2, for the Shanghai index, representing a negative relation on stock markets, whereas the value of the correlation coefficient is minimum for Indian markets, i.e., 0.3, indicating the most impact by COVID-19 spread. Further, the results concerning PMI show that the highest value of the correlation coefficient is for the China i.e., −0.52, points to the sharpest pace of contraction. This reflects the lower value of the correlation indicating that the economy is on the way of growth, which can be seen from the PMI value of the various countries. The manuscript presents a novel geospatial model by empirically evaluating the correlation coefficient of COVID-19 with stock market index, PMI, and stringency index to understand the effect of COVID-19 on the global economy. [ABSTRACT FROM AUTHOR]

Introduction: COVID-19 (coronavirus disease 2019) was first identified in China in December 2019 and is rapidly spreading worldwide as a pandemic. Since COVID-19 causes mild to severe acute respiratory syndrome, most studies in this context have focused on pathogenesis primarily in the respiratory system. However, evidence shows that the central nervous system (CNS) may also be affected by COVID-19. Since COVID-19 is spreading, it is necessary to study its possible cognitive effects on COVID-19 patients and their recovery. Methods: The articles used in this study were searched by keywords, such as cytokine storm and COVID-19, COVID-19 and executive dysfunction, cognitive disorder, and COVID-19, central nervous system (CNS) and COVID-19, coronavirus, neuroinvasion in Science Direct, Scopus, PubMed, Embase, and Web of Science databases based on preferred reporting items for systematic reviews and meta-analysis (PRISMA) checklist. The study evaluates all observational studies published between December 2019 and April 2021 in peer-reviewed journals, including cross-sectional, cohort, case-control studies, case reports, and case series. The search result was 106 articles, of which 73 articles related to COVID-19, the stages of infection by this virus, its effect on the nervous system and neurological symptoms, the cytokine storm caused by this infection, and the possible cognitive consequences caused by this virus in patients, has been reviewed. Other articles were not checked due to their limited relevance to the topic under discussion. Results: Studies showed that neurons may be directly affected by severe acute respiratory syndrome coronavirus (SARS-CoV)-1 and SARS-CoV-2. Furthermore, various studies indicated that systemic inflammation (so-called “cytokine storm”) is also responsible for brain damage induced by infection with SARS-CoV-1 and SARS-CoV-2. In such a way that these patients showed elevated levels of interleukin (IL-), 6, 8, and 10 and of tumor necrosis factor- alpha (TNF-α) in their blood. Conclusion: Various cognitive defects have been observed following an increased level of cytokines, such as tumor necrosis factor-alpha (TNF-α) and interleukin (IL)-6, 8. Therefore, due to the increased level of these pro-inflammatory factors in the brains of these patients, cognitive deficits can be expected, which need further investigation. [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]

Background: Telehealth systems can significantly improve the triage, treatment and care of COVID-19 patients. Both patients and health providers are strongly motivated to use telehealth services, due to the quarantine and social distancing policies. Objective: This study aimed to systematically review the characteristics of telehealth-based services developed to support COVID-19 patients and/or healthcare providers. Material and Methods: A comprehensive search for the English language articles had been conducted up to 13 May 2020 using PubMed and Scopus electronic databases. In this review paper, only studies have been included that focus on a telehealth-based service to support COVID-19 patients and/or healthcare providers. Later, the general and detailed characteristics of the included studies were extracted. Results: In this review, fifteen out of the 1,291 retrieved articles met the study eligibility criteria. The studies came from 7 countries, where most of them (9, 56.25%) were conducted in the United States, and 3 (18.75%) of them implemented in China. The majority of studies (n=14, 93.4%) addressed patients as the telehealth recipients. Seven studies (46.6%) introduced a mobile application as the main means of the needed communication, followed by videoconferencing or video calls (6, 40%), and phone calls (5, 33.3%) as the media. 10 studies used a synchronous communication, four used an asynchronous communication, and one addressed both communications. Four studies (26.6%) developed a guideline-based telehealth tool. Of 15 included studies, seven (46.6%) addressed diagnosis, three (20%) prevention, three (20%) treatment, and three (20%) follow-up aspects of COVID-19. Totally, 10 group barriers were identified in literature. Conclusion: This review reveals the usefulness of the telehealth-based services during the COVID-19 outbreak. The existing challenges identified through the literature pointed out the need for clear guidelines, scientific evidence, and innovative policies to implement successful telehealth projects. [ABSTRACT FROM AUTHOR]

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

Currently, the "2019-CoV-2" has been raging across the world for months, causing massive death, huge panic, chaos, and immeasurable economic loss. Such emerging epidemic viruses come again and again over years, leading to similar destructive consequences. Air-borne transmission among humans is the main reason for the rapid spreading of the virus. Blocking the air-borne transmission should be a significant measure to suppress the spreading of the pandemic. Considering the hospital is the most probable place to occur massive cross-infection among patients as emerging virus usually comes in a disguised way, an air distribution optimization of a general three-bed hospital ward in China is carried out in this paper. Using the Eulerian-Lagrangian method, sneeze process from patients who are assumed to be the virus carrier, which is responsible for a common event to trigger cross-infection, is simulated. The trajectory of the released toxic particle and the probability of approaching others in the same ward are calculated. Two evaluation parameter, total maximum time (TMT) and overall particle concentration (OPC) to reflect the particle mobility and probability to cause cross-infection respectively, are developed to evaluate the proposed ten air distributions in this paper. A relatively optimized air distribution proposal with the lowest TMT and OPC is distinguished through a three-stage analysis. Results show that a bottom-in and top-out air distribution proposal is recommended to minimize cross-infections. • A ward-based sneeze process from a patient with epidemic disease is modelled. • Air distribution (AD) affects the flow pattern of the sneeze-induced virus particles. • Ten AD proposals are developed to evaluate the their probability of cross-infection. • Two parameters are proposed to judge which proposals is better quantitatively. • The direct probability of cross-infection can be decreased by 95% via optimization. [ABSTRACT FROM AUTHOR]

As the Coronavirus (COVID-19) expands its impact from China, expanding its catchment into surrounding regions and other countries, increased national and international measures are being taken to contain the outbreak. The placing of entire cities in 'lockdown' directly affects urban economies on a multi-lateral level, including from social and economic standpoints. This is being emphasised as the outbreak gains ground in other countries, leading towards a global health emergency, and as global collaboration is sought in numerous quarters. However, while effective protocols in regard to the sharing of health data is emphasised, urban data, on the other hand, specifically relating to urban health and safe city concepts, is still viewed from a nationalist perspective as solely benefiting a nation's economy and its economic and political influence. This perspective paper, written one month after detection and during the outbreak, surveys the virus outbreak from an urban standpoint and advances how smart city networks should work towards enhancing standardization protocols for increased data sharing in the event of outbreaks or disasters, leading to better global understanding and management of the same. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) has been recently identified as a novel member of beta coronaviruses (CoVs) and the cause of coronavirus disease 2019 (COVID-19). It has been first discovered in China and soon has spread across continents with an escalating number of mortalities. There is an urgent need for developing a COVID-19 vaccine to control the rapid transmission and the deleterious impact of the virus. The potent vaccine should have a good tolerable and efficacious profile to induce target-specific humoral and cellular immune responses. It should also exhibit no or minimal detrimental effects in children, young adults, and elderly people with or without co-morbidities from different racial backgrounds. Previously published findings of SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV) played vital role in the characterization of surface spike proteins as the tool of entry of the SARS-CoV-2 into host cells. It has become evident that SARS-CoVs have high genetic similarity and this implies antecedent vaccination strategies could be implicated in the production of COVID-19 vaccines. Although several vaccines have been approved and rolled out, only a handful of them have passed the three phases of clinical studies. This review highlights the completed, and ongoing clinical trials of COVID-19 vaccines and efforts are being made globally to avert the pandemic. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has underscored the importance of behaviours such as social distancing in controlling pandemics. Currently, the epidemic is under control in China and production has resumed in various industries. This study investigates the behavioural compliance and related factors for COVID-19 prevention among employees returning to the workplace and provide strategic recommendations for improving individual-level preventive behaviour to prevent a new outbreak.Methods: A cross-sectional study design was used. Data were gathered from returning employees in China using an online questionnaire survey, from March to May, 2020. The questionnaire covered participants' COVID-19-related knowledge, compliance with recommended preventive behaviours, and levels of depression and anxiety. Univariate and multi-factor methods were used to analyse the data and identify factors influencing behaviour compliance.Results: Of the 1300 participants completing the full survey, more than half were male (71.92%) and 61% were aged between 31 and 50 years. Six hundred and ninety-eight (53.7%) participants showed high compliance, while 602 (46.3%) showed low compliance. In models adjusted for demographic and socio-economic factors, high education level (odds ratio [OR] = 0.23, 95% confidence interval [CI]: 0.07-0.70), office staff (OR = 0.51, 95% CI: 0.33-0.78), higher knowledge of COVID-19 (OR = 0.74, 95% CI: 0.67-0.81), and quarantining (OR = 0.74, 95% CI: 0.57-0.96) predicted better compliance with preventive behaviours (P <  0.05), while high anxiety levels (OR = 1.55, 95% CI: 1.10-2.18) predicted lower compliance with preventive behaviours (P <  0.05).Conclusion: For employees returning to work during the post-COVID-19-epidemic period, compliance with recommended preventive behaviours requires improvement. Consequently, comprehensive intervention measures, including the provision of health education and psychological counselling, as well as the continuance of a strict isolation policy, could enhance such compliance. [ABSTRACT FROM AUTHOR]

Coronavirus disease (COVID-19), also known as Severe acute respiratory syndrome (SARS-COV2) and it has imposed deep concern on public health globally. Based on its fast-spreading breakout among the people exposed to the wet animal market in Wuhan city of China, the city was indicated as its origin. The symptoms, reactions, and the rate of recovery shown in the coronavirus cases worldwide have been varied . The number of patients is still rising exponentially, and some countries are now battling the third wave. Since the most effective treatment of this disease has not been discovered so far, early detection of potential COVID-19 patients can help isolate them socially to decrease the spread and flatten the curve. In this study, we explore state-of-the-art research on coronavirus disease to determine the impact of this illness among various age groups. Moreover, we analyze the performance of the Decision tree (DT), K-nearest neighbors (KNN), Naïve bayes (NB), Support vector machine (SVM), and Logistic regression (LR) to determine COVID-19 in the patients based on their symptoms. A dataset obtained from a public repository was collected and pre-processed, before applying the selected Machine learning (ML) algorithms on them. The results demonstrate that all the ML algorithms incorporated perform well in determining COVID-19 in potential patients. NB and DT classifiers show the best performance with an accuracy of 93.70%, whereas other algorithms, such as SVM, KNN, and LR, demonstrate an accuracy of 93.60%, 93.50%, and 92.80% respectively. Hence, we determine that ML models have a significant role in detecting COVID-19 in patients based on their symptoms. [ABSTRACT FROM AUTHOR]

Objectives: Thousands of designated COVID-19 hospitals have been set up in China to fight the ongoing COVID-19 pandemic. Anecdotal reports indicate a falling rate of acute stroke diagnoses in these hospitals during the COVID-19 period. We conducted an exploratory single-center analysis to estimate the change in acute stroke presentation at the designated COVID-19 hospitals. Methods: This retrospective observational study included all patients admitted to Yongchuan Hospital Affiliated to Chongqing Medical University with acute stroke between January 24 and March 10, 2020. Patient demographics, characteristics of the stroke, treatment details, and clinical outcomes were compared with those of patients admitted in the corresponding period in the year before (2019, "the pre-COVID-19 period"). Subgroup analysis was performed in the ischemic and hemorrhagic stroke groups. Results: A total of 110 patients presented with acute stroke symptoms during the COVID-19 pandemic, compared with 173 patients in the pre-COVID-19 period. A higher proportion of stroke patients presented to the hospital via emergency medical services during the pandemic (48.2 vs. 31.8%, p = 0.006). There was a lower proportion of ischemic stroke patients (50.9 vs. 65.3%, p = 0.016) than in the preceding year. There were significantly fewer patients with 90-day modified Rankin Scale score ≥3 in the COVID-19 period compared with the pre-COVID-19 period (17.3 vs. 30.6%, p = 0.012). Among patients with ischemic stroke, the mean time from patient arrival to vessel puncture for emergency endovascular therapy in the COVID-19 period was shorter than that in the pre-COVID-19 period (109.18 ± 71.39 vs. 270.50 ± 161.51 min, p = 0.002). Among patients with hemorrhagic stroke, the rate of emergency surgical operation in the COVID-19 period was higher than that in the pre-COVID-19 period (48.1 vs. 30.0%, p = 0.047). The mean time from patient arrival to emergency surgical operation (15.31 ± 22.89 vs. 51.72 ± 40.47 min, p = 0.002) was shorter in the COVID-19 period than in the pre-COVID-19 period. Conclusions: Although fewer acute stroke patients sought medical care in this designated COVID-19 hospital during the COVID-19 pandemic, this type of hospital was more efficient for timely treatment of acute stroke. Recognizing how acute strokes presented in designated COVID-19 hospitals will contribute to appropriate adjustments in strategy for dealing with acute stroke during COVID-19 and future pandemics. [ABSTRACT FROM AUTHOR]

Severe acute respiratory syndrome (SARS) once caused great harm in China, but now it is the coronavirus disease 2019 (COVID-19) pandemic that has become a huge threat to global health, which raises urgent demand for developing effective treatment strategies to avoid the recurrence of tragedies. Yinqiao powder, combined with modified Sangju decoction (YPCMSD), has been clinically proven to have a good therapeutic effect on COVID-19 in China. This study aimed to analyze the common mechanism of YPCMSD in the treatment of SARS and COVID-19 through network pharmacology and molecular docking and further explore the potential application value of YPCMSD in the treatment of coronavirus infections. Firstly, the active components were collected from the literature and Traditional Chinese Medicine Systems Pharmacology database platform. The COVID-19 and SARS associated targets of the active components were forecasted by the SwissTargetPrediction database and GeneCards. A protein–protein-interaction network was drawn and the core targets were obtained by selecting the targets larger than the average degree. By importing the core targets into database for annotation, visualization, and integrated discovery, enrichment analysis of gene ontology, and construction of a Kyoto Encyclopedia of genes and genomes pathway was conducted. Cytoscape 3.6.1 software was used to construct a “components–targets–pathways” network. Active components were selected to dock with acute respiratory syndrome coronavirus type 2 (SARS-COV-2) 3CL and angiotensin-converting enzyme 2 (ACE2) through Discovery Studio 2016 software. A network of “components–targets–pathways” was successfully constructed, with key targets involving mitogen-activated protein kinase 1, caspase-3 (CASP3), tumor necrosis factor (TNF), and interleukin 6. Major metabolic pathways affected were those in cancer, the hypoxia-inducible factor 1 signaling pathway, the TNF signaling pathway, the Toll-like receptor signaling pathway, and the PI3KAkt signaling pathway. The core components, such as arctiin, scopolin, linarin, and isovitexin, showed a strong binding ability with SARS-COV-2 3CL and ACE2. We predicted that the mechanism of action of this prescription in the treatment of COVID19 and SARS might be associated with multicomponents that bind to SARS-COV-2 3CL and ACE2, thereby regulating targets that coexpressed with them and pathways related to inflammation and the immune system. [ABSTRACT FROM AUTHOR]

The coronavirus disease (COVID-19) outbreak reported in China in December 2019 has spread throughout the world. The WHO declared it as a pandemic in March 2020. The pandemic severely affected public health and the global economy. Many studies conducted on the coronavirus have helped us to elucidate its pathogenicity and pathophysiology. However, it is important to study the behavior of the pathogen in the environment to develop effective control measures. While studying the persistence and transmission of viruses in drinking water and wastewater systems, a low concentration of coronavirus and its nucleic acids have been detected in municipal wastewaters. This could be due to their high susceptibilities to degradation in aqueous environments. Epidemiological study on coronaviruses in wastewater will serve two purposes, i.e., in early detection of outbreak and in identifying asymptomatic carriers. In such cases, the epidemiological study will help in early detection of the presence of the virus in the community. Secondly, it will help in knowing if there are asymptomatic carriers, as such people do not show any signs of symptoms but shed the viruses in feces. The present review focuses on the epidemiological surveillance of wastewater for coronaviruses, as in recent years these are increasingly causing global pandemics. In this review we have discussed, the four pertinent areas of coronavirus study: (1) occurrence of coronavirus in wastewater, (2) wastewater based epidemiological surveillance of coronaviruses, (3) epidemiological surveillance tools used for detection of coronaviruses in sewage, and (4) persistence and sustainability of coronaviruses in wastewater. [ABSTRACT FROM AUTHOR]

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The COVID‐19 pandemic is expanding worldwide. This pandemic associated with COVID‐19 placed the spotlight on how bacterial (e.g., methicillin‐resistant Staphylococcus aureus) co‐infections may impact responses to coronavirus. In this review the ways in which nanoparticles can contain and rapidly diagnose COVID‐19 under the umbrella of nanotheranostics (i.e., smart, single agents combining nanodiagnostics and nanotherapeutics) are elaborated. The present work provides new insights into the promising incorporation of antiviral nanotheranostics into nanostructured materials, including electrospun fibers with tailored pore sizes and hydrophobicity, namely "superhydrophobic self‐disinfecting electrospun facemasks/fabrics (SSEF)." SSEFs are proposed as smart alternatives to address the drawbacks of N95 respirators. The challenges of coronavirus containment are underscored, literature is reviewed, and "top‐five suggestions" for containing COVID‐19 are offered, including: i) preventive appraisals—avoiding needless hospital admission and practicing frequent hand washing (from 20 to 60 s). ii) Diagnostics—highly recommending nanodiagnostics, detecting COVID‐19 within 10 min. iii) Therapeutics—expanding nanotherapeutics to treat COVID‐19 and bacterial co‐infections after safety assessments and clinical trials. iv) Multipronged and multinational, including China, collaborative appraisals. v) Humanitarian compassion to traverse this pandemic in a united way. [ABSTRACT FROM AUTHOR]

The novel coronavirus (2019-nCoV) originated in China has now covered around 213 countries globally. It has posed health calamities which have threatened the world with the emergence. Owing to the number of confirmed cases still rising every day, it has now become a phase of an international health emergency. Sudden outbreak of coronavirus disease 2019 (COVID-19) has brought global declines in the commodity process. This has majorly affected the demand as well as supply of the commodities. The oil market has been severely affected due to the outrageous collapse in the demand majorly due to travel restrictions which has also caused the steepest decline in oil prices. The prices of both precious and industrial metals have also fallen, although the price drop is less than that of oil prices. The agriculture industry is one of the least affected so far by this pandemic due to its indirect relation with economic activities. However, the ultimate impact of COVID-19 pandemic will greatly depend on the severity and duration of its outspread, but it is expected to have long-lasting implications. [ABSTRACT FROM AUTHOR]

Background: The aim of our study was to determine through a systematic review and meta-analysis the incubation period of COVID-19. It was conducted based on the preferred reporting items for systematic reviews and meta-analyses (PRISMA). Criteria for eligibility were all published population-based primary literature in PubMed interface and the Science Direct, dealing with incubation period of COVID-19, written in English, since December 2019 to December 2020. We estimated the mean of the incubation period using meta-analysis, taking into account between-study heterogeneity, and the analysis with moderator variables. Results: This review included 42 studies done predominantly in China. The mean and median incubation period were of maximum 8 days and 12 days respectively. In various parametric models, the 95th percentiles were in the range 10.3–16 days. The highest 99th percentile would be as long as 20.4 days. Out of the 10 included studies in the meta-analysis, 8 were conducted in China, 1 in Singapore, and 1 in Argentina. The pooled mean incubation period was 6.2 (95% CI 5.4, 7.0) days. The heterogeneity (I2 77.1%; p < 0.001) was decreased when we included the study quality and the method of calculation used as moderator variables (I2 0%). The mean incubation period ranged from 5.2 (95% CI 4.4 to 5.9) to 6.65 days (95% CI 6.0 to 7.2). Conclusions: This work provides additional evidence of incubation period for COVID-19 and showed that it is prudent not to dismiss the possibility of incubation periods up to 14 days at this stage of the epidemic. [ABSTRACT FROM AUTHOR]

Background: Coronavirus disease 2019 (COVID-19) pandemic has become the most severe global health issue. Abnormal liver functions are frequently reported in these patients. However, liver function abnormality was often overlooked during COVID-19 treatment, and data regarding liver functions after cure of COVID-19 is limited. This study aimed to reveal the changes of liver function tests (LFTs) during hospitalization, and its clinical significance in patients with COVID-19. Methods: In this retrospective, bi-center study, a total of 158 hospitalized patients diagnosed with COVID-19 in China were included from January 22nd, 2020 to February 20th, 2020. Clinical features, laboratory parameters including LFTs, and treatment data were collected and analyzed. LFTs included alanine transaminase, aspartate aminotransferase, alkaline phosphatase, gamma-glutamyl transferase, and total bilirubin. Patients were considered with abnormal LFTs when any value of these tests was higher than upper limit of normal. Results: Of 158 patients with COVID-19, 67 (42.41%) patients had abnormal LFTs on admission and another 50 (31.65%) patients developed abnormal LFTs during hospitalization. The incidence of LFTs abnormality in severe COVID-19 cases was significantly higher than non-severe cases. All LFTs in COVID-19 patients were correlated with oxygenation index. There was no statistical difference in treatment between the patients with or without liver test abnormalities. By the time of discharge, there were still 64 (40.50%) patients with abnormal LFTs. Logistic regression analysis identified younger age, hypertension and low lymphocyte counts as independent risk factors for persistent abnormal LFTs during hospitalization. Conclusion: Liver function tests abnormality was common in COVID-19 patients and was more prevalent in severe cases than in non-severe cases. A substantial percentage of patients still had abnormal LFTs by the time of discharge. [ABSTRACT FROM AUTHOR]

This study aimed to determine the relationship between acute stress and quality of life and explore their influencing factors on health care workers. A descriptive cross-sectional study was conducted, and a sample of 525 health care workers was recruited from 15 hospitals through a convenient sampling method. Participants completed an online self-report questionnaire to assess their acute stress and quality of life. Descriptive and multiple linear regression statistics were used for this analysis. The results regarding acute stress responses varied significantly among the differences in marital status, physical activity, work status, perceived risk of contracting COVID-19, and the expected duration of the pandemic. Moreover, a younger age, lack of physical activity, being a front-line medical staff, and higher acute stress scores indicated a worse quality of life. Healthcare workers' acute stress was negatively correlated with their quality of life. Therefore, the authorities should pay special attention to health care workers' mental health and provide them with timely protection during the pandemic. [ABSTRACT FROM AUTHOR]

Objectives: During the 2019 Coronavirus disease (COVID-19) outbreak, malnutrition may contribute to COVID-19 adverse outcomes. We conducted a clinical epidemiological analysis to investigate the association of malnutrition with hospitalized duration in patients with COVID-19. Design: Retrospective survey study. Setting: Taikang Tongji (Wuhan) hospital in Wuhan, China. Participants: 139 patients with COVID-19. Methods: In total, 139 patients with COVID-19 from patients in the Infection Department of Taikang Tongji (Wuhan) hospital from February 2020 to April 2020 were analyzed retrospectively. We used the "Global leadership Initiative on Malnutrition(GLIM)" assessment standard published in 2019 to assess nutritional status. Prolonged hospitalization was lasting more than the median value of the hospitalized days (17 days) in this population. Results: According to the assessment results of GLIM nutrition assessment, the patients were divided into malnutrition group and normal nutrition group. Compared with the patients in the normal nutrition group, the hospitalization time was longer(15.67±6.26 days versus 27.48±5.04 days, P = 0.001). Kaplan-Meier analysis showed patients with malnutrition were more likely to be hospitalized longer compared with those normal nutrition (mean with 95% confidence interval [CI]: 28.91[27.52–30.30] versus 22.78[21.76–23.79], P = 0.001). COX regression analysis showed that malnutrition (hazard ratio [HR] = 3.773, P for trend = 0.001) was proportional associated with being discharged from hospital delayed. Conclusion and implications: Present findings suggested that malnutrition contributed to predicting a probability of prolonged hospitalization in patients with COVID-19 infection, to whom extra attentions and precautions should be paid during clinical treatments. Based on the existing results, it is recommended that inpatients with nutritional risk or malnutrition start nutritional support treatment as soon as possible. [ABSTRACT FROM AUTHOR]

The results of a clinical trial comparing hydroxychloroquine with or without azithromycin to the standard of care for the treatment of COVID-19 were recently published by Philippe Gautret et al. This study provides outstanding results for the combination of hydroxychloroquine and azithromycin over the standard of care, but the evidence was deemed insufficiently robust to warrant a public health decision to widen the use of hydroxychloroquine for the treatment of COVID-19. We provide a scientific critical review of the Gautret et al. publication, put the results in the context of the current knowledge, provide an evaluation of the validity of the results (from a methodologic perspective), and discuss public health implications. The study has a number of limitations, including small sample size, lack of comparability between patients in active treatment and control arms, lack of blinding, use of interim analyses without controlling for the risk of type 1 error, use of analysis in the per-protocol population instead of the intention-to-treat population, and inconsistencies between the study protocol and article. However, none of these observations is of a nature to reverse the conclusions. The study brings useful knowledge consistent with available evidence and clinical practice from China and South Korea, which could have prompted quicker policy decision-making. [ABSTRACT FROM AUTHOR]

Background: The twenty-first century viral respiratory epidemics have taught us valuable lessons. Our systematic review examined the impact of these epidemics, including coronavirus disease 2019 (COVID-19), on mental health among different population groups, drawing on their insights for recommendations for the current COVID-19 pandemic. Methods: Searches were performed on PubMed, Embase, PsycINFO, Web of Science, Scopus, CINAHL, and Cochrane on April 4, 2020. Studies that had undefined mental health outcomes or did not use a validated scale for measure were excluded. Quality assessment was carried out via the Newcastle–Ottawa Scale. Results: We included 95 studies, most of which were conducted in Hong Kong (31.6%) and China (21.4%). A total of 30 (30.9%) studies are on the general public, 41 (42.2%) on healthcare workers, and 26 (26.6%) on patients and quarantined individuals. Furthermore, 36 (37.1%) of the studies are of high quality, 48 (49.5%) are of moderate quality, and 13 (13.4%) are of low quality. The most significant mental health outcomes reported include anxiety, depression, and post-traumatic stress disorder symptoms. The subgroups identified to have a higher risk of psychiatric symptoms among the general public include females, the elderly, individuals with chronic illness, migrant workers, and students. Long-term mental health impact was reported in some healthcare workers and epidemic patients, even up to 3 years in the former. Interestingly, when compared to non-quarantined groups, quarantine was not significantly associated with worse mental health outcomes. Conclusion: Important implications for the COVID-19 pandemic were highlighted. Respiratory epidemics pose a significant psychological morbidity onto many population groups. Psychological support for vulnerable groups, including healthcare workers and patients, should be implemented to prevent them from spiraling into clinical psychiatric conditions. [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]

Zoonotic coronaviruses represent an ongoing threat, yet the myriads of circulating animal viruses complicate the identification of higher-risk isolates that threaten human health. Swine acute diarrhea syndrome coronavirus (SADS-CoV) is a newly discovered, highly pathogenic virus that likely evolved from closely related HKU2 bat coronaviruses, circulating in Rhinolophus spp. bats in China and elsewhere. As coronaviruses cause severe economic losses in the pork industry and swine are key intermediate hosts of human disease outbreaks, we synthetically resurrected a recombinant virus (rSADS-CoV) as well as a derivative encoding tomato red fluorescent protein (tRFP) in place of ORF3. rSADS-CoV replicated efficiently in a variety of continuous animal and primate cell lines, including human liver and rectal carcinoma cell lines. Of concern, rSADS-CoV also replicated efficiently in several different primary human lung cell types, as well as primary human intestinal cells. rSADS-CoV did not use human coronavirus ACE-2, DPP4, or CD13 receptors for docking and entry. Contemporary human donor sera neutralized the group I human coronavirus NL63, but not rSADS-CoV, suggesting limited human group I coronavirus cross protective herd immunity. Importantly, remdesivir, a broad-spectrum nucleoside analog that is effective against other group 1 and 2 coronaviruses, efficiently blocked rSADS-CoV replication in vitro. rSADS-CoV demonstrated little, if any, replicative capacity in either immune-competent or immunodeficient mice, indicating a critical need for improved animal models. Efficient growth in primary human lung and intestinal cells implicate SADS-CoV as a potential higher-risk emerging coronavirus pathogen that could negatively impact the global economy and human health. [ABSTRACT FROM AUTHOR]