Search

COVID-19 virus is transmitted from human to human through splashes of saliva when the sufferer coughs, sneezes, or talks and is inhaled by the people around him. The spread of this virus started from the Chinese city of Wuhan in December 2019 which quickly spread to cities outside Wuhan including Jakarta, Indonesia. The same is in other countries, the local government in Jakarta taking some emergency decisions to minimize the virus spreading including the movement control order (MCO). This paper discusses the simulation of COVID-19 spreading in the early stage in Jakarta using a different model of disease spreading available on The Spatiotemporal Epidemiologic Modeler (STEM). The focus of this simulation is to see the effect of MCO on the COVID-19 spreading in the local area in Indonesia. The result shows that the MCO contribute to the decreasing of case number with some certain period of lag time. [ABSTRACT FROM AUTHOR]

This paper manages the Origin of Covid (SARS-CoV-2-novel Covid) and the Vaccination view. Within excess of 10 million tainted cases and more than 3 million setbacks at the hour of composing this piece, the original Covid actuated wellbeing disease has arisen as the most serious among every one of the worldwide pandemics. The novel (COVID-19 or 2019-nCoV) was found in Wuhan, Hubei Province, China. Bats were liable for the underlying spread, which was subsequently spread to people by the raccoon canine and palm civet. The normal COVID-19 manifestations are fever, dry hack, and sleepiness. The significant manifestations are trouble breathing, chest torment, and loss of speech. The indications and indications of SARS-CoV-2 prompting COVID-19 completely match those of the occasional or, dust sensitivities and flu as migraine, touchiness of the throat, dry hack, weariness, fever, and at times loss of sensation (Tu et al., year2020). An individual tainted with the diseases of sensitivity to pollen or, flu sensitivity is additionally liable to show temperature that can be recorded with thermo-scanners prompting the individual to turn into a suspect of the sickness. Along these lines, before immunizing, a fast and exact analytic meter or, the pack is the need of great importance to identify the SARS-CoV-2 inferable from the way that the testing in view of PCR is tedious and exorbitant (Sirkeci and Yucesahin, year2020). [ABSTRACT FROM AUTHOR]

As the first‐wave COVID‐19 has passed in 2020, people's awareness of self‐protection began to decline gradually. How to prevent and control the second‐wave COVID‐19 has become an important issue in many countries and regions. By analyzing the transmission of the second‐wave COVID‐19 caused by an imported case in Tonghua City, Jilin Province, China, in January 2021, we establish a new mathematical COVID‐19 model to simulate the transmission characteristics of the second‐wave COVID‐19. First, we analyze the basic properties of the model, prove the existence of the equilibrium point, and obtain the expression of the basic reproduction number with important biological significance. Secondly, we use the weighted nonlinear least square estimation method to fit the cases in Tonghua City of Jilin Province in January 2021, and get the estimated value of the parameters. The basic reproduction number of the second‐wave COVID‐19 in Tonghua City is R0=1.0695, which is much smaller than that of the first‐wave COVID‐19 in Wuhan in 2020. Finally, in the optimal control part, we consider two control methods (keeping social distance and nucleic acid detection of all people in the city) to simulate the control of the disease. The results show that the control intensity of the two control methods needs to be dynamically changed and adjusted, so that the cost can be minimized with the least infection. The results of this paper can not only provide suggestions for health management departments, but also provide a reference for the analysis of the second‐wave COVID‐19 in other countries or regions. [ABSTRACT FROM AUTHOR]

This study's main purpose is to provide helpful information using blood samples from COVID-19 patients as a non-medical approach for helping healthcare systems during the pandemic. Also, this paper aims to evaluate machine learning algorithms for predicting the survival or death of COVID-19 patients. We use a blood sample dataset of 306 infected patients in Wuhan, China, compiled by Tangji Hospital. The dataset consists of blood's clinical indicators and information about whether patients are recovering or not. The used methods include K-nearest neighbor (KNN), decision tree (DT), logistic regression (LR), support vector machine (SVM), random forest (RF), stochastic gradient descent (SGD), bagging classifier (BC), and adaptive boosting (AdaBoost). We compare the performance of machine learning algorithms using statistical hypothesis testing. The results show that the most critical feature is age, and there is a high correlation between LD and CRP, and leukocytes and CRP. Furthermore, RF, SVM, DT, AdaBoost, DT, and KNN outperform other machine learning algorithms in predicting the survival or death of COVID-19 patients. [ABSTRACT FROM AUTHOR]

The syndrome called COVID‐19 which was firstly spread in Wuhan, China has already been declared a globally "Pandemic." To stymie the further spread of the virus at an early stage, detection needs to be done. Artificial Intelligence‐based deep learning models have gained much popularity in the detection of many diseases within the confines of biomedical sciences. In this paper, a deep neural network‐based "LiteCovidNet" model is proposed that detects COVID‐19 cases as the binary class (COVID‐19, Normal) and the multi‐class (COVID‐19, Normal, Pneumonia) bifurcated based on chest X‐ray images of the infected persons. An accuracy of 100% and 98.82% is achieved for binary and multi‐class classification respectively which is competitive performance as compared to the other recent related studies. Hence, our methodology can be used by health professionals to validate the detection of COVID‐19 infected patients at an early stage with convenient cost and better accuracy. [ABSTRACT FROM AUTHOR]

After the outbreak of SARS and MERS, the world is now in the grip of another viral disease named COVID-19 caused by a beta Coronavirus – SARS COV-2 which appears to be the only one with a pandemic potential. The case of COVID-19 was reported in the Hubei province of Wuhan city in Central China at the end of December 2019 and it is suspected that the sea food market played a role in this outbreak which was closed abruptly. Subsequently, a Public Health Emergency of International Concern was declared on 30 January 2020 by the World Health Organization. Both SARS and MERS corona viruses had its reservoir in bats and were transferred to humans from palm civets and camels respectively. This virus can be transmitted through airborne droplets. Natural reservoir and intermediate host of COVID-19 is yet to be identified. This paper reviews the occurrences of viral diseases in the recent times including SARS and MERS. As an addition to this, the paper will contain a detailed examination of the COVID-19 Pandemic. [ABSTRACT FROM AUTHOR]

Objective: Coronavirus disease 19 (COVID-19) is a highly contagious disease characterized by severe respiratory infection by SARS-CoV-2 virus. COVID-19 was first appeared in Wuhan, China, in December 2019 and then rapidly became a global pandemic from a local outbreak. The present study aims to present the rapid increase of scientific productivity on COVID-19 by comparing publications of the first quarter with the first half of 2020. Materials and Methods: Web of Science (WoS) software was used for the search and the analysis. To compare scientific productivity of two periods as the first quarter and the first half of the pandemic era, all scientific papers published about COVID-19 included in Science Citation Index Expanded (SCI-E) from January 1st to April 5th and from January 1st to July 9th of 2020 were searched using the following terms: "COVID-19","2019-n-CoV","SARS-CoV-2","Coronavirus disease 19" and "2019 novel coronavirus" as nomenclatures of COVID-19. Results: Overall, 337 and 11.704 scientific papers related to COVID-19, indexed by SCI-E, were found in the first quarter and the first half of 2020, respectively. While the biggest contribution for publications was from People's Republic of China (PRC) in the first quarter and was from the USA in the first half of 2020 for COVID-19. Conclusion: We found a close correlation between the rapid acceleration of scientific papers and turning the disease from a local outbreak to a global pandemic. Since sharing experiences is as important as struggling with these kinds of novel diseases, we believe that encouraging researchers to make scientific publications for others is more important than ever in the circumstances like this. [ABSTRACT FROM AUTHOR]

Background In present study, we aimed to identify and evaluate the trends of the 100 most-cited research articles on Covid-19 using bibliometric techniques. Methods Scopus online database hosted by Elsevier was used to extract relevant articles for this study. We have identified the top 100 research papers on Covid-19 published across the globe based on their citations data. Further analysis was made to find the countries of origin, journal wise distribution, author cluster network, keyword analysis and inter-citation map to understand and to establish the links among them. Results The T100 most cited articles were published between January-August, 2020 and their citations ranged from 304 to 5295. The T100 articles were contributed by 24 countries, with more than half is originated from China (n=63). Scientific publications originated from china received highest citations (55,688) followed by USA (13,996) and Hong Kong (9,501). Although 'New England Journal of Medicine' published the most papers (n=15) with the highest impact factor value of (74.699) but studies published in 'The Lancet' has received highest citations (n=18,431). The top five journals hold 44% of these influential studies. The Tongji medical college, Huazhong University of science & technology, Wuhan, China is the top institution with the most T100 articles in the field of Covid-19. Conclusions We analyzed the 100 most-cited articles in the field of Covid-19. China and USA are the dominant countries in terms of the number of T100 articles, scientists and institutions. Scientific publications originated from China also had the highest mean number of citations. The leading institutions with the most productive articles were Tongji medical college, Jin yin-tan hospital, Tsinghua university school of medicine (China) and University of North Carolina (US). The study authored by Huang C on "Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China" has received maximum citations (5295). [ABSTRACT FROM AUTHOR]

Around the end of 2019, in Wuhan, Hubei Province, China, the first confirmed cases of COVID-19 were identified, and from then on, the world we were used to knowing changed globally. The role of population density, in relation to the spread of the pandemic, has been widely scrutinised in urban studies, believed to be the triggering variable. However, the results so far are inconclusive. This paper suggests instead to shift the focus to socio-spatial vulnerabilities, as the effects of the pandemic's spread have been more severe in urban units which feature long-standing inequalities. The paper's aim is, therefore, twofold: on the one hand it aims at contributing to the debate on population density and COVID-19 in urban areas, and, on the other hand, to analyse the pandemic's spread in relation to socio-spatial vulnerabilities. Different cities across the globe are drawn into a comparative project, where the pandemic's spread is analysed in relation to variables of Population Density (PD) and a Social Vulnerability Index (SVI), by employing correlation matrices. The results suggest that there is no significant correlation between density and the spread of COVID-19. Instead, a positive correlation is in place when analysing the pandemic's diffusion with socio-spatial inequalities. • COVID-19 spread is not associated with population density while social vulnerability is found to be positively correlated. • The study invites to undertake a more critical understanding of how density and vulnerability are intertwined. • Measures based on physical concentration should be supplemented by topological density, made up of relational factors. [ABSTRACT FROM AUTHOR]

Travel restrictions have become an important epidemic preventive measure, but there are few relevant quantitative studies. In this paper, travel proportion is introduced into a four-compartment model to quantify the spread of COVID-19 in Wuhan. It is found that decreasing the travel proportion can reduce the peak of infections and delay the peak time. When the travel proportion is less than 35%, transmission can be prevented. This method provides reference for other places. [ABSTRACT FROM AUTHOR]

The increase in coronavirus disease 2019 (COVID-19) infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has placed pressure on healthcare services worldwide. Therefore, it is crucial to identify critical factors for the assessment of the severity of COVID-19 infection and the optimization of an individual treatment strategy. In this regard, the present study leverages a dataset of blood samples from 485 COVID-19 individuals in the region of Wuhan, China to identify essential blood biomarkers that predict the mortality of COVID-19 individuals. For this purpose, a hybrid of filter, statistical, and heuristic-based feature selection approach was used to select the best subset of informative features. As a result, minimum redundancy maximum relevance (mRMR), a two-tailed unpaired t-test, and whale optimization algorithm (WOA) were eventually selected as the three most informative blood biomarkers: International normalized ratio (INR), platelet large cell ratio (P-LCR), and D-dimer. In addition, various machine learning (ML) algorithms (random forest (RF), support vector machine (SVM), extreme gradient boosting (EGB), naïve Bayes (NB), logistic regression (LR), and k-nearest neighbor (KNN)) were trained. The performance of the trained models was compared to determine the model that assist in predicting the mortality of COVID-19 individuals with higher accuracy, F1 score, and area under the curve (AUC) values. In this paper, the best performing RF-based model built using the three most informative blood parameters predicts the mortality of COVID-19 individuals with an accuracy of 0.96 ± 0.062, F1 score of 0.96 ± 0.099, and AUC value of 0.98 ± 0.024, respectively on the independent test data. Furthermore, the performance of our proposed RF-based model in terms of accuracy, F1 score, and AUC was significantly better than the known blood biomarkers-based ML models built using the Pre_Surv_COVID_19 data. Therefore, the present study provides a novel hybrid approach to screen the most informative blood biomarkers to develop an RF-based model, which accurately and reliably predicts in-hospital mortality of confirmed COVID-19 individuals, during surge periods. An application based on our proposed model was implemented and deployed at Heroku. [ABSTRACT FROM AUTHOR]

CCOVID-19 was first reported in late 2019 in Wuhan, China, and spread throughout the world like a pandemic. The population all over the world used lots of medicinal plants for prevention purposes. India has been blessed with cultural wisdom. Social health practices across the country have been utilizing integrative treatments to fight against mild fever and cold flu-like infections. Herbal and Ayurveda medicines have shown their impact on treating such infections in the early stages. The impact of herbal medicines on Covid infection is quite evident and several drug tests and trials have proved its worth of it in treating mild to severe infections. The majority of these plants are used to treat respiratory diseases having the same signs and symptoms as coronavirus symptoms or other flu infections. Our body can fight infections only when immunity is strong and for that herbal drugs as immunomodulators work well. It has worked in boosting the immunity of the patients and normal people to fight against Corona. The paper analyzes such research studies on Covid-19 treatment by herbal medicines. It makes a point that herbal medicine-based treatments and social health practices will play a complementary role in managing Covid-19 and Post-Covid health emergencies. New-Normal social ethics will evolve through such an integrative social health culture. [ABSTRACT FROM AUTHOR]

Background: The emerging viral pandemic worldwide is associated with a novel coronavirus, SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2). This virus is said to emerge from its epidemic center in Wuhan, China, in 2019. Coronaviruses (CoVs) are single-stranded, giant, enveloped RNA viruses that come under the family of coronaviridae and order Nidovirales which are the crucial pathogens for humans and other vertebrates. Main body: Coronaviruses are divided into several subfamilies and genera based on the genomic structure and phylogenetic relationship. The name corona is raised due to the presence of spike protein on the envelope of the virus. The structural and genomic study revealed that the total genome size of SARS-CoV-2 is from 29.8 kb to 29.9 kb. The spike protein (S) is a glycoprotein that attaches to the receptor of host cells for entry into the host cell, followed by the attachment of virus RNA to the host ribosome for translation. The phylogenetic analysis of SARS-CoV-2 revealed the similarity (75–88%) with bat SARS-like coronavirus. Conclusion: The sign and symptoms of novel severe acute respiratory syndrome coronavirus 2 are also discussed in this paper. The worldwide outbreak and prevention from severe acute respiratory syndrome coronavirus 2 are overviewed in the present article. The latest variant of coronavirus and the status of vaccines are also overviewed in the present article. [ABSTRACT FROM AUTHOR]

In recent years, with the in-depth development of information technology, its role in people's production and life has become increasingly prominent, and it has played a significant role in improving work efficiency. At the beginning of the new year in 2020, the novel coronavirus pneumonia (COVID-19) outbreak broke out in Wuhan City, Hubei Province and spread rapidly across the country. The country attaches great importance to it, and the medical staff acted quickly and actively participated in this war against the virus without the smoke of gunpowder. After arduous efforts from all over the country, our country's COVID-19 epidemic prevention and control situation has been further consolidated, and the prevention and control work has changed from an emergency state to a normal state of prevention and control. Hospital informatization construction is an objective requirement for hospital modernization and the prevention and control of the COVID-19, and it is of great significance in the context of the normalization of the COVID-19 epidemic prevention and control. This paper elaborates on the four aspects of hospital information construction in the background of COVID-19, the existing problems in hospital information construction, the practice of hospital information construction and the exploration of hospital informatization construction, and is committed to exploring the overall thinking of hospital information construction in special period. [ABSTRACT FROM AUTHOR]

COVID-19 is an infectious disease caused by the virus SARS-CoV-2, a coronavirus variant, which emerged on December 2019 in Wuhan, China. This paper presents an analysis and reflection of emerging research on the efficacy of Chinese medicine (CM) and acupuncture as a treatment for COVID-19. In CM, COVID-19 falls under the category of an epidemic disease. CM places COVID-19 into four stages: early, advanced, critical, and recovery. This research analysis presents the application of immunomodulatory acupuncture points and Chinese herbal prescriptions that may assist the body to defend against epidemic pathogens such as COVID-19 and future epidemic threats. Acupuncture has proven to be able to activate immune cells, at specific entry viral points, to prepare the body's immune system to fend off the viral invasion. The most common Chinese herbs to appear in research are Toujie Quwen granules, Lianhua Qingwen Capsules, Keguan-1, Xuebijing injection, and Chinese syndrome differentiation decoctions. The summary of evidence showed a benefit in the clinical cure, chest image improvement, and a reduction in clinical deterioration, acute respiratory distress syndrome, mechanical ventilation, and mortality with CM plus routine Western treatment compared to routine Western treatment alone. This critical analysis may prepare future generations for prevention, treatment, and integration of varying medical paradigms to effectively respond to a virus with global impact. Traditional Chinese medicine (TCM) is a viable adjunct in supportive care for patients with COVID-19. [ABSTRACT FROM AUTHOR]

This paper treats the outbreak of coronavirus disease 2019 (COVID-19) as a natural experiment that can provide insights into the effects of investor sentiment on stock market reactions. Employing the event study methodology (ESM) and taking the date of the Wuhan lockdown as the event date, we find that average abnormal return (AAR) and cumulative abnormal return (CAR) are significantly negative, and average trading volume excesses far more than before within two days of the outbreak. Further, we establish a difference-in-differences (DID) model to investigate the differences between Hubei and non-Hubei listed companies. The results show that for Hubei listed companies, the change of excessive trading volume (ETV) between pre-event and post-event period is significantly higher than that of non-Hubei listed companies, while there exhibits no relationship between the change of AAR and registration place. Overall, our findings provide new evidence for the interaction of local bias and investor sentiment affecting stock market reactions. [ABSTRACT FROM AUTHOR]

COVID‐19 has brought the human world to crisis. This work examines how nonhuman animals are also connected to the pandemic in three ways that emerged early in the crisis. First, COVID‐19 is a zoonotic disease that reached humans via nonhuman animal hosts. Animal geographers and others cast light on the spread and control of animal diseases and on the "almost animal," including the microorganism. Second, the outbreak is understood to have transferred to humans in a market selling live animals in Wuhan, China. Disputes over the source of the outbreak signal the complexity that characterises the commodification of animals. Third, social restrictions have seen city streets empty of people. In the ensuing quiet, unexpected animals have entered cities or begun to behave differently. The arrivals, and the wonder they have sparked among people, raise questions about who and what is deemed to belong in the city. This commentary aims to unpack how geographers and those in related fields might turn to the animals to analyse and respond to the pandemic and other disasters. Through these three case studies, I find that the lives of human and nonhuman animals are deeply connected, and yet relations are characterised by separation and objectification. I argue for further relational and political work to more deeply understand connections and their implications. COVID‐19 has shaken the human world. This paper traces how nonhuman animals are also enrolled in the pandemic. It unpacks how the discipline of geography might turn to the animals to analyse and respond to the unfolding pandemic. [ABSTRACT FROM AUTHOR]

After the outbreak of SARS and MERS, the world is now in the grip of another viral disease named COVID-19 caused by a beta Coronavirus – SARS COV-2 which appears to be the only one with a pandemic potential. The case of COVID-19 was reported in the Hubei province of Wuhan city in Central China at the end of December 2019 and it is suspected that the sea food market played a role in this outbreak which was closed abruptly. Subsequently, a Public Health Emergency of International Concern was declared on 30 January 2020 by the World Health Organization. Both SARS and MERS corona viruses had its reservoir in bats and were transferred to humans from palm civets and camels respectively. This virus can be transmitted through airborne droplets. Natural reservoir and intermediate host of COVID-19 is yet to be identified. This paper reviews the occurrences of viral diseases in the recent times including SARS and MERS. As an addition to this, the paper will contain a detailed examination of the COVID-19 Pandemic. [ABSTRACT FROM AUTHOR]

The Covid-19 first occurs in Wuhan, China in December 2019. After that, the virus has spread all over the world and at the time of writing this paper the total number of confirmed cases are above 11 million with over 600,000 deaths. The pattern recognition of complex environment can be used to determine if a COVID-19 breath pattern can be established with accuracy. The traditional decorative pattern detection method has a high degree of recognition in simple scene. However, the efficiency of decorative pattern detection in complex scenes is low and the recognition accuracy is not high. Firstly, the evaluation index of target detection method is designed. Through this paper, it is found that the success rate of some targets is naturally better than other targets, and easy to distinguish from the background. In order to improve the recognition success rate of the object in the complex environment and determine the position and attitude of the object, the pattern as the artificial identification in the environment is proposed. The interior art decoration pattern is selected as the experimental pattern and the pattern classification evaluation index is designed. The experimental results show that the method proposed in this paper can optimize the pattern subsets which are confused with each other and easy to distinguish from the background. It has a certain reference value for decorative pattern recognition in complex environment for COVID-19 epidemic. [ABSTRACT FROM AUTHOR]

The Outbreak of Coronavirus (COVID-19) came to the world in early December 2019. The early cases of coronavirus were reported in Wuhan City, Hubei Province, China. Till May 18, 2020, 198 countries have been affected by this life-threatening disease. The most common and known traits of COVID-19 are tiredness, fever, and dry cough. In this paper, we have discussed the Predictive data mining approach for COVID-19 predictions. In Predictive data mining, a model is developed and trained using supervised learning and then it predicts the behavior of provided data. Predictive data mining is a renowned technique known to many health organizations for the classification and prediction of diseases such as Heart disease and various types of cancers etc. There are several factors for comparing the model's accuracy, scalability, and interpretability. This predictive model is compared to the basics of its accuracy. In this proposed approach, we have used WEKA as it provides a vast collection of many machine learning algorithms. The main objective of this paper is to forecast the possible future incidence of corona cases in Pakistan. This study concludes that the number of corona cases will increase swiftly. If the government take proactive steps and strictly implement precautionary measures, then Pakistan may be able to overcome this pandemic. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19 was first experienced in Wuhan City, China, during December 2019 before it rapidly spread over globally. This paper has proposed a mathematical model for studying its transmission dynamics in the presence of face mask wearing and hospitalization services of human population in Tanzania. Disease-free and endemic equilibria were determined and subsequently their local and global stabilities were carried out. The trace-determinant approach was used in the local stability of disease-free equilibrium point while Lyapunov function technique was used to determine the global stability of both disease-free and endemic equilibrium points. Basic reproduction number, R 0 , was determined in which its numerical results revealed that, in the presence of face masks wearing and medication services or hospitalization as preventive measure for its transmission, R 0 = 0.698 while in their absence R 0 = 3.8. This supports its analytical solution that the disease-free equilibrium point E 0 is asymptotically stable whenever R 0 < 1 , while endemic equilibrium point E ∗ is globally asymptotically stable for R 0 > 1. Therefore, this paper proves the necessity of face masks wearing and hospitalization services to COVID-19 patients to contain the disease spread to the population. [ABSTRACT FROM AUTHOR]

Drawing on a recent online survey combined with city-level data, this paper examines the impact of the COVID-19 on consumers' online food purchase behavior in the short term. To address the potential endogeneity issues, we adopt an instrumental variable (IV) strategy, using the distance from the surveyed city to Wuhan as the instrumental variable. We show that our IV method is effective in minimizing potential bias. It is found that the share of confirmed COVID-19 cases increases the possibility of consumers purchasing food online. This is more likely to be the case for young people having a lower perceived risk of online purchases and living in large cities. Despite some limitations, this paper has policy implications for China and other countries that have been influenced by the COVID-19 epidemic. Specifically, government support and regulation should focus on (i) ensuring the safety of food sold on the internet, (ii) protecting the carrier from becoming infected, and (iii) providing financial support to the poor since they may have difficulties in obtaining access to food living in small cities. Moreover, how to help those who are unable to purchase food online because of their technical skills (e.g., the elderly who are not familiar with smart phones or the internet) also deserves more attention for the government and the public. [ABSTRACT FROM AUTHOR]

To understand the roles of different transport modes in the spread of COVID-19 pandemic across Chinese cities, this paper looks at the factors influencing the number of imported cases from Wuhan and the spread speed and pattern of the pandemic. We find that frequencies of air flights and high-speed train (HST) services out of Wuhan are significantly associated with the number of COVID-19 cases in the destination cities. The presence of an airport or HST station at a city is significantly related to the speed of the pandemic spread, but its link with the total number of confirmed cases is weak. The farther the distance from Wuhan, the lower number of cases in a city and the slower the dissemination of the pandemic. The longitude and latitude coordinates do not have a significant relationship with the number of total cases but can increase the speed of the COVID-19 spread. Specifically, cities in the higher longitudinal region tended to record a COVID-19 case earlier than their counterparties in the west. Cities in the north were more likely to report the first case later than those in the south. The pandemic may emerge in large cities earlier than in small cities as GDP is a factor positively associated with the spread speed. • This paper explores the factors influencing the COVID-19 spread speed and spatial pattern. • Frequencies of flights and HSTs are significantly associated with the number of cases. • The farther the distance from Wuhan, the lower the number of cases and dissemination speed. [ABSTRACT FROM AUTHOR]

In the late December of 2019, a novel coronavirus was discovered in Wuhan, China. In March 2020, WHO announced this epidemic had become a global pandemic and that the novel coronavirus may be mild to most people. However, some people may experience a severe illness that results in hospitalization or maybe death. COVID-19 classification remains challenging due to the ambiguity and similarity with other known respiratory diseases such as SARS, MERS, and other viral pneumonia. The typical symptoms of COVID-19 are fever, cough, chills, shortness of breath, loss of smell and taste, headache, sore throat, chest pains, confusion, and diarrhoea. This research paper suggests the concept of transfer learning using the deterministic algorithm in all binary classification models and evaluates the performance of various CNN architectures. The datasets of 746 CT images of COVID-19 and non-COVID-19 were divided for training, validation, and testing. Various augmentation techniques were applied to increase the number of datasets except for testing images. The images were then pretrained using CNN to obtain a binary class. ResNeXt101 and ResNet152 have the best F1 score of 0.978 and 0.938, whereas GoogleNet has an F1 score of 0.762. ResNeXt101 and ResNet152 have an accuracy of 97.81% and 93.80%. ResNeXt101, DenseNet201, and ResNet152 have 95.71%, 93.81%, and 90% sensitivity, whereas ResNeXt101, ResNet101, and ResNet152 have 100%, 99.58%, and 98.33 specificity, respectively. [ABSTRACT FROM AUTHOR]

Wuhan Province in China reported the first case of novel corona virus as pneumonia outbreak during December 2019. The novel coronavirus was soon declared a pandemic by the World Health Organization. On 16th of July 2021, the number of COVID-19 confirmed cases was 188,128,952 globally, out of which 4,059,339 individuals succumbed to this deadly virus. In a short span of time, eight vaccines were approval for emergency use in different nations. The selection of vaccine depends upon many criteria. Concepts from multi-criteria decision making (MCDM) are appropriate to compare and rank them. The paper proposes analytical network processing (ANP) method to rank the eight vaccines according to seven criteria. The study proposes a decision tool to select the best vaccine among the candidate vaccines. A mathematical model based on ANP approach with three clusters having interrelationships within and among the clusters is proposed. [ABSTRACT FROM AUTHOR]

In December 2019, human cases of novel coronavirus infection were detected in Wuhan, China which have been named as COVID-19 by the World Health Organization (WHO). Since COVID-19 was first detected in China, the virus has reached more than 120 countries and was declared a global pandemic on March 11, 2020 by the WHO. In this paper, we have highlighted the influence of temperature on the spread of COVID-19. For this, the dynamic transmission of COVID-19 is modeled taking into account the influence of the temperature on the persistence of coronavirus in the environment. We also took into account the impact of proportion of people who respect the barrier measures published by the WHO on the scale of the COVID-19 pandemic. Taking into account the influence of the temperature on the persistence of the virus in the environment, the dynamic transmission has been described by a system of ordinary differential equations (ODEs). First, we analyzed the solutions of system in the case where the impact of the temperature on the virus is neglected. Second, we carried out the mathematical analysis of the solutions of the system in the non-autonomous case. Mathematical analyzes have enabled us to establish that the temperature and proportion of persons who respect the barrier rules can affect the spread of COVID-19. Some numerical simulations have been proposed to illustrate the behavior of the pandemic in some countries. [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]

The pandemic caused by coronavirus disease 2019(COVID-19) continues to disrupt the global supply chain system, bringing new risks and challenges. The uncertainty created by COVID-19 makes it is difficult for various industries to deal with the pandemic. Since the pandemic, the supply chain's resilience has been discussed and examined in some studies. However, most existing works start from a single industry perspective or pay more attention to the disturbance caused by changes in the production side. Supply chain networks of different industries, mainly transport networks, are relatively limited under the epidemic's impact. In this paper, from the perspective of highway freight transport, a comprehensive competitiveness evaluation framework was proposed to reveal and the disruption and resilience of the supply chain under the outbreak based on nine indexes with five dimensions, including efficiency, capacity, activity, connectivity, and negotiability. Based on the availability of the data(Large-scale truck trajectory), we sorted out seven categories of Chinese industries(related to highway transport) and divided them into four categories respectively: (a) Slight disruption and worse resilience; (b) Slight disruption and remarkable resilience; (c) Serious disruption and worse resilience; (d) Serious disruption and remarkable resilience. The measurement results of supply chain network performance show that the industries (cold-chain, general products, and other industries) dominated by "Efficiency - Negotiability - Connectivity" are slightly disrupted (about 33%), forming a spatial diffusion with Wuhan(the city where the pandemic first broke out) as the disrupted center, spreading outward in a circle structure. Simultaneously, five urban agglomerations surrounding it have been impacted. By contrast, due to the strict isolation measures, the industries (building materials, construction, engineering, and high-value products industry) more vulnerable to be disrupted seriously (about 82%) tend to be the pattern of "Capacity - Activity". However, a large-scale centralized disruption was observed in the Triangle of Central China urban agglomeration was presented, resulting in almost stagnation of industry development. Meanwhile, as the future of the pandemic remains uncertain, the supply chain represented by the engineering industry, construction industry, etc are deserved to be paid more attention in line with they are prone to large-scale centralized damage due to the disruption of a single city node. • Disruption and resilience of multi-industry supply chain under COVID-19 epidemic. • Multi-dimensional comprehensive competitiveness evaluation framework for supply chain performance. • Supply chain destruction and repair performance of seven industries in China under COVID-19 epidemic. • Characteristic differences of supply chain between different industries from the perspective of highway freight transport. [ABSTRACT FROM AUTHOR]

Covid 19 infection first emerged in Wuhan, China in December 2019 and spread rapidly and became a pandemic that affected the whole world. Almost 94 % of learners worldwide were affected by the pandemic. In many countries, schools were closed for a period of time. Education has changed dramatically, with the distinctive rise of e-learning, whereby teaching was undertaken remotely and on digital platforms. In this paper the effect of online education during covid -19 pandemic has been discussed with under the headlines of advantages or disadvantages of technology, how effective is distance learning and the problems of children having to stay home all the time due to lockdown. [ABSTRACT FROM AUTHOR]

The mortality risk factors for coronavirus disease (COVID‐19) must be early predicted, especially for severe cases, to provide intensive care before they develop to critically ill immediately. This paper aims to develop an optimized convolution neural network (CNN) for predicting mortality risk factors for COVID‐19 patients. The proposed model supports two types of input data clinical variables and the computed tomography (CT) scans. The features are extracted from the optimized CNN phase and then applied to the classification phase. The CNN model's hyperparameters were optimized using a proposed genetic‐based adaptive momentum estimation (GB‐ADAM) algorithm. The GB‐ADAM algorithm employs the genetic algorithm (GA) to optimize Adam optimizer's configuration parameters, consequently improving the classification accuracy. The model is validated using three recent cohorts from New York, Mexico, and Wuhan, consisting of 3055, 7497,504 patients, respectively. The results indicated that the most significant mortality risk factors are: CD 8+ T Lymphocyte (Count), D‐dimer greater than 1 Ug/ml, high values of lactate dehydrogenase (LDH), C‐reactive protein (CRP), hypertension, and diabetes. Early identification of these factors would help the clinicians in providing immediate care. The results also show that the most frequent COVID‐19 signs in CT scans included ground‐glass opacity (GGO), followed by crazy‐paving pattern, consolidations, and the number of lobes. Moreover, the experimental results show encouraging performance for the proposed model compared with different predicting models. [ABSTRACT FROM AUTHOR]

Pandemic novel Coronavirus (Covid‐19) is an infectious disease that primarily spreads by droplets of nose discharge when sneezing and saliva from the mouth when coughing, that had first been reported in Wuhan, China in December 2019. Covid‐19 became a global pandemic, which led to a harmful impact on the world. Many predictive models of Covid‐19 are being proposed by academic researchers around the world to take the foremost decisions and enforce the appropriate control measures. Due to the lack of accurate Covid‐19 records and uncertainty, the standard techniques are being failed to correctly predict the epidemic global effects. To address this issue, we present an Artificial Intelligence (AI)‐based meta‐analysis to predict the trend of epidemic Covid‐19 over the world. The powerful machine learning algorithms namely Naïve Bayes, Support Vector Machine (SVM) and Linear Regression were applied on real time‐series dataset, which holds the global record of confirmed, recovered, deaths and active cases of Covid‐19 outbreak. Statistical analysis has also been conducted to present various facts regarding Covid‐19 observed symptoms, a list of Top‐20 Coronavirus affected countries and a number of coactive cases over the world. Among the three machine learning techniques investigated, Naïve Bayes produced promising results to predict Covid‐19 future trends with less Mean Absolute Error (MAE) and Mean Squared Error (MSE). The less value of MAE and MSE strongly represent the effectiveness of the Naïve Bayes regression technique. Although, the global footprint of this pandemic is still uncertain. This study demonstrates the various trends and future growth of the global pandemic for a proactive response from the citizens and governments of countries. This paper sets the initial benchmark to demonstrate the capability of machine learning for outbreak prediction. [ABSTRACT FROM AUTHOR]

The global outbreak of COVID-19 has further exposed deficiencies in city logistics based on human and ground roads, such as poor emergency response capacity and high risk of infection during transportation. Metro-based underground logistics system (M-ULS) may be an innovative approach to deal with this city-level disaster due to its efficient operation, contactless and driverless characteristics. However, the market evolution process and the quantitative calculation framework of comprehensive benefits after the application of M-ULS are still unclear, which has become a problem of mutual restriction with the extensive application of M-ULS. This paper attempts to use the system dynamics method, based on the real-world simulation, to analyze the quantitative relationship between the M-ULS implementation and the city logistics performance under epidemic outbreaks. Wuhan city in China was selected as the empirical background, and five simulation scenarios were set under different implementation strategies of M-ULS in response to the epidemic. Six variables were selected to measure city logistics performance and M-ULS operation status, including demand fill-rate, unit delivery time, total deprivation cost, total transportation cost, total number of susceptible people, and utilization rate of M-ULS. The results show that M-ULS is effective in improving the performance of city logistics and responding to the epidemic. The delivery time and transportation cost have a strong impact on the market share of M-ULS. Finally, a set of incentive policies was designed to promote the adoption of M-ULS. The findings not only provide a method for evaluating the overall performance of M-ULS, but also provide a unique perspective for promoting the implementation of M-ULS and responding to the transportation challenges brought by the epidemic. • M-ULS is introduced to break through the plight of city logistics. • A set of effective macro-micro incentive policies are established to promote the implementation of M-ULS. • The delivery time, transportation cost and government subsidies play different roles in the market penetration of M-ULS. • The benefits of M-ULS have been analyzed. [ABSTRACT FROM AUTHOR]

In this paper, the mathematical modeling of five different classes for coronavirus disease-19 (COVID-19) is considered using the fractional arbitrary order derivative in Atangana–Baleanu sense. We use nonlinear analysis for the existence theory of the solution for the suggested model. Additionally, the modified Adam–Bashforth method is used for the numerical approximation of the assumed model. Finally, we simulate the results for 100 days with the help of data from the literature to display the excellency of the suggested model. [ABSTRACT FROM AUTHOR]

The coronavirus outbreak in Wuhan, China has sparked a global epidemic, which the World Health Organization declared a public health emergency of international concern on 31st January 2020 (Beijing time). This crisis has attracted intense media attention. Recently, some media outlets inappropriately labelled the coronavirus by race, using such headlines as "Chinese virus pandemonium" and even suggesting "China kids stay home." The biased and misleading coverage presented via Western media channels has incited anger throughout the Chinese community and has placed undue stress upon Chinese individuals living outside China. This post-published review takes a tourism-focused perspective to examine findings from a quantitative study (Rodriguez-Seijas, Stohl, Hasin, & Eaton, 2015) published in 2015 in JAMA Psychiatry. The current paper highlights the potential impacts of misleading and biased media coverage on Chinese individuals' mental health. Specifically, this work considers perceived racial discrimination stemming from coronavirus as a public health crisis and the effects of such discrimination on individuals of Chinese heritage. Similarly imperative are pertinent effects on country image and destination image with respect to tourism marketing and tourist behaviour during times of crisis. By considering racism in the context of the coronavirus outbreak, this paper identifies potential avenues for relevant research in tourism and hospitality. [ABSTRACT FROM AUTHOR]

Current epidemic models mainly estimate the number of confirmed patients by fitting statistical data. Few studies consider the direct effect of fine-grained spatial crowd mobile interaction on the spatial-temporal diffusion features. A new method for estimating the spatial-temporal spread process of coronavirus disease 2019 (COVID-19) is proposed, incorporating spatial interaction features into epidemiological models. This paper also estimates the number of confirmed patients and spatial-temporal spread process of COVID-19 in Wuhan from December 2019 to March 2020. The results show that the method proposed in this paper can effectively estimate the daily traffic analysis zones (TAZs) where new confirmed patients appear, completely covering the TAZs with the epidemic announcements. And the TAZs with the epidemic announcements account for 72.7% of the estimated TAZs. The cumulative number of estimated confirmed patients agrees very well with the total number of officially announced confirmed patients after February 18, 2020, with a gap of approximately 5.6%, indirectly verifying the rationality of the previous estimation. The method proposed in this paper can effectively estimate the spread of infectious diseases under fine-grained spaces. It also has scientific significance in understanding the influence mechanism of the crowd interaction under finegrained spaces on the spatial-temporal spread of infectious diseases, and enhancing the macroscopically spatial interpretability of epidemiological models macroscopic. [ABSTRACT FROM AUTHOR]

Background and Objective. The new coronavirus disease (known as COVID-19) was first identified in Wuhan and quickly spread worldwide, wreaking havoc on the economy and people's everyday lives. As the number of COVID-19 cases is rapidly increasing, a reliable detection technique is needed to identify affected individuals and care for them in the early stages of COVID-19 and reduce the virus's transmission. The most accessible method for COVID-19 identification is Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR); however, it is time-consuming and has false-negative results. These limitations encouraged us to propose a novel framework based on deep learning that can aid radiologists in diagnosing COVID-19 cases from chest X-ray images. Methods. In this paper, a pretrained network, DenseNet169, was employed to extract features from X-ray images. Features were chosen by a feature selection method, i.e., analysis of variance (ANOVA), to reduce computations and time complexity while overcoming the curse of dimensionality to improve accuracy. Finally, selected features were classified by the eXtreme Gradient Boosting (XGBoost). The ChestX-ray8 dataset was employed to train and evaluate the proposed method. Results and Conclusion. The proposed method reached 98.72% accuracy for two-class classification (COVID-19, No-findings) and 92% accuracy for multiclass classification (COVID-19, No-findings, and Pneumonia). The proposed method's precision, recall, and specificity rates on two-class classification were 99.21%, 93.33%, and 100%, respectively. Also, the proposed method achieved 94.07% precision, 88.46% recall, and 100% specificity for multiclass classification. The experimental results show that the proposed framework outperforms other methods and can be helpful for radiologists in the diagnosis of COVID-19 cases. [ABSTRACT FROM AUTHOR]

This paper presents the findings of a study on the impact of COVID-19 misinformation and the role of information professionals in fighting this scourge. A quantitative research approach was deployed to collect data from Google scholar database and analysed via Statistical Package for Social Sciences. The study established that with regard to misinformation on the origins of COVID-19, 21 (44%) sources claimed that the disease originated from Wuhan city market. Results further revealed that concerning the impact of misinformation on the individual, social withdrawal, vulnerability and death (15, 48.4%) were the main impacts. As regards the role of information professionals, findings show that 18(58.1%) of the sources reported information packaging and repackaging as the main strategy for information dissemination. Findings suggest several ways for information professionals to minimise the spread and impact of COVID-19 misinformation. The study recommends a paradigm shift in information service delivery among information professionals while viewing the COVID-19 outbreak as an opportunity to reassert their roles in the changing information landscape. [ABSTRACT FROM AUTHOR]

Medical laboratory personnel encounter diverse health and workplace-related hazards leading to severe health challenges including the ravaging SARS-CoV-2 infection, which is the causal agent of COVID-19. It was first announced in Wuhan, China in December 2019 but started to spread globally by late January 2020. COVID-19 pandemic and subsequent global spreading poses additional danger to healthcare personnel particularly the laboratorians. Other health practitioners may engage patients by observing social / physical distancing, but how laboratory staff observe or apply same rule to infectious samples remain a notable question. Activities of laboratorians result in repeated exposure at close interactions to patient's samples including SARS-CoV-2 infected specimens, which make them susceptible to COVID-19. Therefore, it is imperative to review mitigating measures in restraining possible exposure and spreading of SARS-CoV-2 in the best interest of laboratory staff and pathologists. It is against this backdrop that this paper intends to update readers on measures to restrain SARS-CoV-2 invasion in histopathology laboratory and deduce precautionary measures for observation by healthcare providers in COVID-19 era. Also discussed, is health hazards associated with histopathology laboratory with the objective of encouraging safety consciousness and safe laboratory practices in the face of COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) remains a global pandemic at present. Although the human-to-human transmission route for this disease has been well established, its transmission mechanism is not fully understood. In this paper, we propose a mathematical model for COVID-19 which incorporates multiple transmission pathways and which employs time-dependent transmission rates reflecting the impact of disease prevalence and outbreak control. Applying this model to a retrospective study based on publicly reported data in China, we argue that the environmental reservoirs play an important role in the transmission and spread of the coronavirus. This argument is supported by our data fitting and numerical simulation results for the city of Wuhan, for the provinces of Hubei and Guangdong, and for the entire country of China. [ABSTRACT FROM AUTHOR]

Novel Coronavirus (Covid-2019), which first appeared in December 2019 in the Chinese city of Wuhan. It is spreading rapidly in most parts of the world and becoming a global epidemic. It is devastating, affecting public health, daily life, and the global economy. According to the statistics of the World Health Organization on August 11, the number of cases of coronavirus (Covid-2019) reached nearly 17 million, and the number of infections globally distributed among most European countries and most countries of the Asian continent, and the number of deaths from the Corona virus reached 700 thousand people around the world. . It is necessary to detect positive cases as soon as possible in order to prevent the spread of this epidemic and quickly treat infected patients. In this paper, the current literature on the methods used to detect Covid is presented. In these studies, the research that used different techniques of artificial intelligence to detect COVID-19 was reviewed as the convolutionary neural network (ResNet50, ResNet101, ResNet152, InceptionV3 and Inception-ResNetV2) were proposed for the identification of patients infected with coronavirus pneumonia using chest X-ray radiographs By using 5-fold cross validation, three separate binary classifications of four grades (COVID-19, normal (healthy), viral pneumonia and bacterial pneumonia) were introduced. It has been shown that the pre-trained ResNet50 model offers the highest classification performance (96.1 percent accuracy for Dataset-1, 99.5 percent accuracy for Dataset-2 and 99.7 percent accuracy for Dataset-2) based on the performance results obtained. [ABSTRACT FROM AUTHOR]

Difficulties to use convenient data during the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) pandemic outbreak and complexities of the problem attitude crucial challenges in infectious disease modelling studies. Motivated by the on-going reach to predict a potential reactivated SARS-CoV-2 (COVID-19), we suggest a prediction model that beyond the clinical characteristics based evaluation approaches. In particular, we developed a possibly available and more efficient prediction model to predict a potential reactivated SARS-CoV-2 (COVID-19) patient. Our paper aims to explore the applicability of a modified Technique for Order Preference by Similarity to Ideal Solutions (MTOPSIS) integrated Design of Experiment (DoE) method to predict a potential reactivated COVID-19 patient in real-time clinical or laboratory applications. The presented novel model may be of interest to the readers studying similar research areas. We illustrate MTOPSIS integrated DoE method by applying it to the COVID-19 pandemic real clinical cases from Wuhan/China-based data. Despite the small sample size, our study provides an encouraging preliminary model framework. Finally, a step by step algorithm is suggested in the study for future research perspectives. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19 in 2020 has had drastic impacts on urban economies and activities, with transit systems around the world witnessing an unprecedented decline in ridership. This paper attempts to estimate the effect of COVID-19 on the daily ridership of urban rail transit (URT) using the Synthetic Control Method (SCM). Six variables are selected as the predictors, among which four variables unaffected by the pandemic are employed. A total of 22 cities from Asia, Europe, and the US with varying timelines of the pandemic outbreak are selected in this study. The effect of COVID-19 on the URT ridership in 11 cities in Asia is investigated using the difference between their observed ridership reduction and the potential ridership generated by the other 11 cities. Additionally, the effect of the system closure in Wuhan on ridership recovery is analyzed. A series of placebo tests are rolled out to confirm the significance of these analyses. Two traditional methods (causal impact analysis and straightforward analysis) are employed to illustrate the usefulness of the SCM. Most Chinese cities experienced about a 90% reduction in ridership with some variation among different cities. Seoul and Singapore experienced a minor decrease compared to Chinese cities. The results suggest that URT ridership reductions are associated with the severity and duration of restrictions and lockdowns. Full system closure can have severe impacts on the speed of ridership recovery following resumption of service, as demonstrated in the case of Wuhan with about 22% slower recovery. The results of this study can provide support for policymakers to monitor the URT ridership during the recovery period and understand the likely effects of system closure if considered in future emergency events. • The Synthetic Control Method helps evaluate COVID-19's impact on public transit. • A reduction effect of COVID-19 on ridership is indicated in most Chinese cities. • Ridership reduction varies between cities and is not guided by the infection rate. • Ridership reduction is influenced by the severity and duration of lockdowns. • Full system closure delayed the ridership recovery following service resumption. [ABSTRACT FROM AUTHOR]

The accurate knowledge of demographic, signs and symptoms, imaging characteristics of coronavirus disease 2019 (COVID-19) is essential for the accurate management of these patients. However, the claims between the previous papers are not always consistent and may even contradict each other, for example, some claims the virus infects more men than women in Wuhan. In this large-scale cohort study, we aimed to update the demographic, signs and symptoms, imaging characteristics of patients with COVID-19 in the whole quarantine of Wuhan, China. A cohort of 2126 patients with a diagnosis of COVID-19 pneumonia (confirmed by real-time reverse transcriptase-polymerase chain reaction, RT-PCR) who were admitted to one hospital in Wuhan were retrospectively enrolled. Data were collected between January 13, 2020, and April 8, 2020, the end of Wuhan quarantine. Demographic, signs and symptoms, imaging characteristics were analyzed. CT imaging characteristics associated with respiratory failure or death were identified. Of the 2126 patients with COVID-19, 1051 (49.44%) were men and 1075 (50.56%) were women, 1933 (90.92%) have fever and 1328 (62.46%) have dry cough. The mean age was 57.43 years of age (range 1–95). The CT imaging findings were bilateral pneumonia (1883[88.57%]), unilateral pneumonia (243[11.43%]), ground-glass opacity (GGO) or consolidation (1175[55.27%]), pleural effusion (69[3.25%]). Patients with respiratory failure or death were more likely to have pleural effusion on CT than patients without respiratory failure or death (p < 0.05). Men and women have been infected by SARS-CoV-2 in roughly equal numbers. Fever and cough are the most prevalent symptoms at disease onset in patients. Other prevalent symptoms include fatigue, and sputum production. COVID-19 patients with bilateral pneumonia and pleural effusion are more likely to develop respiratory failure or death. • Men and women have been infected by SARS-CoV-2 in roughly equal numbers. • Fever and cough are the most prevalent symptoms at disease onset in patients. Other prevalent symptoms include fatigue, and sputum production. • COVID-19 patients with bilateral pneumonia and pleural effusion are more likely to develop respiratory failure or death. [ABSTRACT FROM AUTHOR]

Background: Since the first reports of coronavirus disease 2019 (COVID-19) in Wuhan, China, in December 2019, there have been 198 million confirmed cases worldwide as of August 2021. The scientific community has joined efforts to gain knowledge of the newly emerged virus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the immunopathological mechanisms leading to COVID-19, and its significance for patients with allergies and asthma. Methods: Based on the current literature, recent advances and developments in COVID-19 in the context of allergic diseases were reviewed. Results and Conclusions: In this review, we discuss the prevalence of COVID-19 in subjects with asthma, attacks of hereditary angioedema, and other allergic diseases during COVID-19. Underlying mechanisms suggest a protective role of allergy in COVID-19, involving eosinophilia, SARS-CoV-2 receptors expression, interferon responses, and other immunological events, but further studies are needed to fully understand those associations. There has been significant progress in disease evaluation and management of COVID-19, and allergy care should continue during the COVID-19 pandemic. The European Academy of Allergy & Clinical Immunology (EAACI) launched a series of statements and position papers providing recommendations on the organization of the allergy clinic, handling of allergen immunotherapy, asthma, drug hypersensitivity, allergic rhinitis, and other allergic diseases. Treatment of allergies using biologics during the COVID-19 pandemic has also been discussed. Allergic reactions to the COVID-19 vaccines, including severe anaphylaxis, have been reported. Vaccination is a prophylactic strategy that can lead to a significant reduction in the mortality and morbidity associated with SARS-CoV-2 infection, and in this review, we discuss the proposed culprit components causing rare adverse reactions and recommendations to mitigate the risk of anaphylactic events during the administration of the vaccines. [ABSTRACT FROM AUTHOR]

The world is currently facing an unprecedented pandemic crisis. The highly contagious coronavirus, or COVID-19, first occurred in Wuhan (China) in December 2019. The outbreak of the virus quickly spread all over the world, reaching Europe in January 2020. The first case in Germany was reported to be diagnosed on January 27. This study focuses on assessing the mental health consequences of the German public during COVID-19 outbreak. Psychological discomfort, generalized anxiety disorder, sleep disturbances, depressive symptoms and threat perception are especially investigated with respect to demographics, security importance and negative affectivity. The psychological vulnerabilities that go along with the pandemic are evaluated in detail. The cross-sectional online survey conducted in Germany reveals a prevalence of depressive symptoms, psychological discomfort, threat perception, generalized anxiety disorder and sleep disturbances associated with the pandemic crisis. The results also indicate a relationship between mental health issues and negative affectivity as well as the perception of threat. This paper gives an outlook on long-term consequences and what could be the strategies to mitigate the negative mental health outcomes of the crisis. [ABSTRACT FROM AUTHOR]

At the end of 2019, in Wuhan, China, a coronavirus pandemic started, later called SARS CoV 2 and identified as the cause of CO VID 19 disease. This virus is a public health concern due to its ease of transmission. This virus is a public health concern due to its ease of transmission. One way to reduce this spread is by reinforcing biosafety care, therefore, this research aimed to describe the biosafety measures that should be adopted by laboratories to contain the new coronavirus in the workplace and also present the effective ways of inactivating the virus in clinical samples. For this, a bibliographic review was carried out in the online database of the Virtual Health Library (VHL), using the descriptors "Coronavirus Infections" or "Coronavirus Infections" and "Biosafety". After applying the inclusion and exclusion criteria, thirteen papers were used to write this material. Subsequently, biosafety measures against SARS CoV 2 were described, sectioned by: work team, personal protective equipment, laboratory environment, sample handling, sample processing and analysis, accidents and virus inactivation in samples. It can be concluded that it is essential for the laboratory team to know the principles of biosafety, in order to try to reduce the transmission of the new coronavirus, in addition to continuous training for professionals and the adoption of strict protocols and guidelines. [ABSTRACT FROM AUTHOR]

The spatial diffusion of epidemic disease follows distance decay law in geography and social physics, but the mathematical models of distance decay depend on concrete spatio-temporal conditions. This paper is devoted to modeling spatial diffusion patterns of COVID-19 stemming from Wuhan city to Hubei province, China. The modeling approach is to integrate analytical method and experimental method. The local gravity model is derived from allometric scaling and global gravity model, and then the parameters of the local gravity model are estimated by observational data and least squares calculation. The main results are as below. The local gravity model based on power law decay can effectively describe the diffusion patterns and process of COVID-19 in Hubei Province, and the goodness of fit of the gravity model based on negative exponential decay to the observational data is not satisfactory. Further, the goodness of fit of the model to data entirely became better and better over time, the size elasticity coefficient increases first and then decreases, and the distance attenuation exponent decreases first and then increases. Moreover, the significance of spatial autoregressive coefficient in the model is low, and the confidence level is less than 80%. The conclusions can be reached as follows. (1) The spatial diffusion of COVID-19 of Hubei bears long range effect, and the size of a city and the distance of the city to Wuhan affect the total number of confirmed cases. (2) Wuhan direct transmission is the main process in the spatial diffusion of COVID-19 in Hubei at the early stage, and the horizontal transmission between regions is not significant. (3) The effect of spatial lockdown and isolation measures taken by Chinese government against the transmission of COVID-19 is obvious. This study suggests that the role of urban gravity (size and distance) should be taken into account to prevent and control epidemic disease. [ABSTRACT FROM AUTHOR]

The outbreak of the COVID-19 disease was first reported in Wuhan, China, in December 2019. Cases in the United States began appearing in late January. On March 11, the World Health Organization (WHO) declared a pandemic. By mid-March COVID-19 cases were spreading across the US with several hotspots appearing by April. Health officials point to the importance of surveillance of COVID-19 to better inform decision makers at various levels and efficiently manage distribution of human and technical resources to areas of need. The prospective space-time scan statistic has been used to help identify emerging COVID-19 disease clusters, but results from this approach can encounter strategic limitations imposed by constraints of the scanning window. This paper presents a different approach to COVID-19 surveillance based on a spatiotemporal event sequence (STES) similarity. In this STES based approach, adapted for this pandemic context we compute the similarity of evolving daily COVID-19 incidence rates by county and then cluster these sequences to identify counties with similarly trending COVID-19 case loads. We analyze four study periods and compare the sequence similarity-based clusters to prospective space-time scan statistic-based clusters. The sequence similarity-based clusters provide an alternate surveillance perspective by identifying locations that may not be spatially proximate but share a similar disease progression pattern. Results of the two approaches taken together can aid in tracking the progression of the pandemic to aid local or regional public health responses and policy actions taken to control or moderate the disease spread. [ABSTRACT FROM AUTHOR]

Coronavirus Disease 2019 (COVID-19), first reported in December 2019 from Wuhan in China, had reached the stage of a pandemic. To date (28.06.2021), there are more than 181.9 million confirmed cases of the disease in the world. Even though vaccines have been developed, antiviral drugs are yet to be developed for management and treatment of SARS-CoV-2. Since several medicinal plants are used in traditional medicines, though with controversial efficacy claims, they can be considered as sources of new antiviral drug compounds against emerging viruses. In this context, spices are noteworthy as their anti-oxidant, anti-viral, anti-inflammatory and immunostimulatory properties are well studied and documented. The emerging scientific literature includes a number of papers on the anti-SARS-CoV-2 activity of spice extracts and specific compounds proven through different types of laboratory experiments. Here, the information pertaining to anti-viral properties of spice-based phytochemicals or natural compounds (not crude extracts) is summarized in this review. Spice-based compounds discussed here are an option for testing in COVID-19 patients though we don't have strong data to support their active recommendation. Because of their natural origin, safety, and low cost, they can be a viable option in our fight against viruses and this compilation may be useful for planning and designing more robust experiments in future. [ABSTRACT FROM AUTHOR]

This paper demonstrates that transportation networks may be used to assess and predict the regional risk of COVID-19 infection from the outbreak. We use China's high-speed rail (HSR) network at the scale of prefecture level to assess, based on a probabilistic risk model, the risk of COVID-19 infection from Wuhan to the country's 31 province-level regions at the early stage of domestic spread. We find that the high-risk regions are mainly distributed along the southern half of Beijing-Hong Kong HSR line, where a large number of infection cases have been confirmed at the early stage. Furthermore, the two components of the infection risk, namely, the probability (proxied by the region's correlation with Wuhan through HSR) and the impact (proxied by the region's population with mobility), can play different roles in the risk ranking for different regions. For public health administrators, these findings may be used for better decision making, including the preparation of emergency plans and supplies, and the allocation of limited resources, before the extensive spread of the epidemic. Moreover, the administrators should adopt different intervention measures for different regions, so as to better mitigate the epidemic spread according to their own risk scenarios with respect to the probability of occurring and, once occurred, the impact. [ABSTRACT FROM AUTHOR]