Worldwide Spread Of COVID-19 Research Articles

Examining the Psychological State Analysis Relationship Between Bitcoin Prices and COVID-19.

The rapid worldwide spread of COVID-19 forced many countries to enforce complete lockdown and strict quarantine policies. The strict lockdown and quarantine affect the psychological state of people toward cryptocurrency. The current research aims to examine the effect of COVID-19 on Bitcoin prices concerning cumulative deaths and confirmed cases. The research comprises daily data from January 20, 2020, to April 30, 2020, during the initial worldwide breakout of COVID-19. This research employed the augmented Dickey-Fuller test to check the stationarity of data, the co-integration test for the interdependency of variables, and the vector error correction model for identifying the direction and long or short-run relationship between Bitcoin prices and COVID-19. The research results show that Bitcoin prices are negatively significant and related to COVID-19 in the short-run. A unidirectional relationship between Bitcoin prices and cumulative deaths is also observed. Investors and the public’s psychological state were positively significant to Bitcoin prices in the long-term because of cashless transactions, unbanked, and less risky virus traveling. The second reason behind the positive psychological relation is un-centralization and easy-to-make payments by Bitcoin. This study’s finding provides timely evidence to decision-makers on Bitcoin price volatility and its impacts on the public’s psychological states regarding COVID-19.

A Novel Approach of Frequent Itemsets Mining for Coronavirus Disease (COVID-19)

The global pandemic of new coronaviruses (COVID-19) has infected many people around the world and became a worldwide concern since this disease caused illness and deaths. The vaccine and drugs are not scientifically established, but patients are recovering with antibiotic drugs, antiviral medicine, chloroquine, and vitamin C. Now it is obvious to the world that a quicker and faster solution is needed for monitoring and combating the further spread of COVID-19 worldwide, using non-clinical techniques, for example, data mining tools, enhanced intelligence, and other artificial intelligence technologies. In this paper, association rule mining is developing for the frequent itemsets discovery in COVID-19 datasets, and the extraction of effective association relations between them. This is done by demonstrates the analysis of the Coronavirus dataset by using the Apriori_Association_Rules algorithm. It involves a scheme for classification and prediction by recognizing the associated rules relating to Coronavirus. The major contribution of this study employment determines the effectiveness of the Apriori_Association_Rules algorithm towards a classification of medical reports. The experimental results provide evidence of the Apriori_Association_Rules algorithm regarding the execution time, memory consumption, and several associated rules that reflect its potential applications to different contexts. Therefore, the Apriori_Association_Rules algorithm will be very useful in healthcare fields to demonstrate the latest developments in medical studies fighting COVID-19.

Etoricoxib may inhibit cytokine storm to treat COVID-19

The worldwide spread of COVID-19 has caused an unprecedented disaster. The emergence of COVID-19-mediated cytokine storm is one of the most important contributors to the development of acute and severe illness in patients. At present, there is an urgent need for drugs that can inhibit cytokine storm to treat COVID-19. In the absence of specific drugs and vaccines, it is important to screen existing drugs as potential treatments. This article introduces a potential repositioning of the existing drug etoricoxib, which may inhibit cytokine storm to treat COVID-19 through reducing the activity of Cyclooxygenase-2 in the conversion of arachidonic acid to prostaglandin.

Machine learning discovery of distinguishing laboratory features for severity classification of COVID‐19 patients

The exponential spread of COVID‐19 worldwide is evident, with devastating outbreaks primarily in the United States, Spain, Italy, the United Kingdom, France, Germany, Turkey and Russia. As of 1 May 2020, a total of 3,308,386 confirmed cases have been reported worldwide, with an accumulative mortality of 233,093. Due to the complexity and uncertainty of the pathology of COVID‐19, it is not easy for front‐line doctors to categorise severity levels of clinical COVID‐19 that are general and severe/critical cases, with consistency. The more than 300 laboratory features, coupled with underlying disease, all combine to complicate proper and rapid patient diagnosis. However, such screening is necessary for early triage, diagnosis, assignment of appropriate level of care facility, and institution of timely intervention. A machine learning analysis was carried out with confirmed COVID‐19 patient data from 10 January to 18 February 2020, who were admitted to Tongji Hospital, in Wuhan, China. A softmax neural network‐based machine learning model was established to categorise patient severity levels. According to the analysis of 2662 cases using clinical and laboratory data, the present model can be used to reveal the top 30 of more than 300 laboratory features, yielding 86.30% blind test accuracy, 0.8195 F1‐score, and 100% consistency using a two‐way patient classification of severe/critical to general. For severe/critical cases, F1‐score is 0.9081 (i.e. recall is 0.9050, and precision is 0.9113). This model for classification can be accomplished at a mini‐second‐level computational cost (in contrast to minute‐level manual). Based on available COVID‐19 patient diagnosis and therapy, an artificial intelligence model paradigm can help doctors quickly classify patients with a high degree of accuracy and 100% consistency to significantly improve diagnostic and classification efficiency. The discovered top 30 laboratory features can be used for greater differentiation to serve as an essential supplement to current guidelines, thus creating a more comprehensive assessment of COVID‐19 cases during the early stages of infection. Such early differentiation will help the assignment of the appropriate level of care for individual patients.

FOCOMO: Forecasting and monitoring the worldwide spread of COVID-19 using machine learning methods

More than 185 countries get affected in the present SARS-COV-2 (aka COVID-19 or n-Corona virus) epidemic. Experts from different domains are contributing a lot to combat this pandemic effectively. Data analysts have made lots of efforts, IT experts also to forecast the severity of infected cases, the death rate, recovery rate, and other health indicators using various statistical and machine learning models. These forecasting models may motivate and help the policymakers to make the decisions based on these predicted results. Although any prediction on such a pandemic problem is non-monotonic and uncertain, these predictions may help us plan to take the needful actions while dealing with this tragic situation. Lots of symptomatic and asymptotic human behavior are observed while investigating the cases. Also, many countries are making dynamic changes in their policies to combat this pandemic. Therefore, such heterogeneous and dynamic changes in policies and human behavior make the problem of proposing an accurate predicting model more complex. We used machine learning methods to monitor the COVID-19 impact country-wise and developed a FOCOMO model to forecast the severity worldwide till the mid of Oct 2020. We referred to the COVID-19 dataset publicly shared by John Hopkins University. We proposed a purely predictive model to monitor the pandemic (without claiming it as perfect or accurate) into three Phases: i) categorization of countries based on the severity of COVID-19 cases, ii) prediction of daily case reporting, and iii) normalization of flattening the forecasting curve based on deaths and recovery rate. These forecasting models will help monitor and plan the human behaviors and severity of the pandemic country-wise and will provide an opportunity to take some corrective actions for shaping the future.

Новітня фразеологія періоду COVID-19

The spread of COVID-19 worldwide has caused certain processes in almost every language. In particular, in the Russian language, there is a phenomenon of adapting well-known fixed phrases to extra-lingual conditions (namely to the life of the country in quarantine) through their various transformations. It is a range of the transformed fixed phrases thematically related to the topic of coronavirus that have become the subject matter of this article. They have been systematized: a) considering the types of transformations applied to them (cases of structural and semantic transformations (substitution of components of phraseological units, extension of their components using everyday words related to COVID-19, phraseological allusion, contamination), and cases of semantic transformations (change of the meaning of the idiom, extension of its valence and dephraseologisation) have been identified); b) considering the influence of transformations on the semantics of fixed phrases (denotative meaning of occasional figurative units have been either concretized, have acquired new semes, or have been partially or completely deformed; non-figurative fixed phrases implement the meaning which constitutes the sum of the meanings of their components, the connotative meaning of units of any type of semantic connection between the components is enhanced by creating a comic effect, which is achieved by transforming fixed phrases); c) considering the subject: in analysing transformed idioms, a hierarchical classification structure has been used: phraseosemantic series — variant-synonymous group — phraseosemantic field — phraseothematic field — phraseothematic group (the phraseothematic group ‟COVID-19 Pandemic”, the phraseothematic fields “COVID-19 Disease” and “Human Activity during the COVID-19 Pandemic” have been singled out, which, in turn, have been formed using 11 and 4 phraseosemantic fields, respectively, which have been combined on the basis of integral or differential features into numerous variant-synonymous groups and phraseological series). These fixed phrases constitute the latest occasional phraseological picture of the Russian world. Their fixation and study are important for understanding speech processes in synchrony, in their relationship to the extra-lingual situation.

An Introduction to the Free COVID-19 Areas (FCAs)

This paper shows an elaborated model to try to stop the spread of COVID-19 cases everywhere and anytime. The main objective of this paper is to implement an alternative COVID-19 prevention model. This new model is entitled “the Free COVID-19 Areas (FCAs).” The FCA offers a serial of steps that can help to stop the fast spread of COVID-19 worldwide. Subsequently, the FCAs has five modules: Module-I: The COVID-19-GPS-Mapping; Module-II: The strategic planning by geographical area; Module-III: The national COVID-19 test; Module-IV: The green areas labeling (GAL); Module-V: The permanent cycle monitoring system (PCM-System).

Thermal Sensors for Contactless Temperature Measurements, Occupancy Detection, and Automatic Operation of Appliances during the COVID-19 Pandemic: A Review.

The worldwide spread of COVID-19 has forced us to adapt to a new way of life made of social distancing, avoidance of physical contact and temperature checks before entering public places, in order to successfully limit the virus circulation. The role of technology has been fundamental in order to support the required changes to our lives: thermal sensors, in particular, are especially suited to address the needs arisen during the pandemic. They are, in fact, very versatile devices which allow performing contactless human body temperature measurements, presence detection and people counting, and automation of appliances and systems, thus avoiding the need to touch them. This paper reviews the theory behind thermal detectors, considering the different types of sensors proposed during the last ten years, while focusing on their possible employment for COVID-19 related applications.

Chatbot como herramienta comunicativa durante la crisis sanitaria COVID-19 en España

La expansión de la Covid-19 a nivel mundial desembocó en un colapso de las redes sanitarias en la mayoría de los países. En esta situación, las nuevas tecnologías adquirieron un papel relevante, especialmente en la gestión de la comunicación, gracias a su inmediatez y posibilidades de cobertura. El confinamiento establecido en España, como consecuencia del estado de alarma decretado en marzo de 2020, aceleró la adopción de diversos recursos tecnológicos en el área de la atención médica a distancia. En este contexto, las plataformas conversacionales se han ido convirtiendo en una de las herramientas más utilizadas por las instituciones para aliviar la falta de recursos en atención telefónica y presencial a la hora de atender las primeras consultas realizadas por los ciudadanos aquejados de síntomas, o por aquellos que solicitaban algún otro tipo de información urgente relacionada con la enfermedad. Esta demanda de agilidad vino a ser atendida por diferentes empresas que ofrecieron el desarrollo de servicios de bot en tiempo record. “Los chatbots son una solución que permite de forma rápida implementar un medio de respuesta inmediato a los usuarios” (Aguilar et al., 2020). El presente artículo analiza el grado de alcance que estas plataformas de respuesta en línea tuvieron –y aún tienen– en la descongestión de los servicios sanitarios en España y si su disponibilidad ha sido de algún modo útil para los ciudadanos. Para ello, se ha tomado una muestra de las cinco principales plataformas conversacionales y, utilizando un estudio de caso, se han revisado sus características particulares. Además se han aportado los datos obtenidos a través de un cuestionario realizado a usuarios y los extraídos de una serie de entrevistas a expertos. El resultado de esta investigación pone de manifiesto que el nivel de uso de los chatbots sanitarios específicos para la Covid-19 en la actualidad alcanza al 30% de la muestra analizada y que los que han empleado este tipo de asistentes lo han hecho fundamentalmente con fines informativos, siendo los meses de abril y octubre los que han reflejado mayor actividad en este sentido.

Female fertility under the impact of COVID-19 pandemic: a narrative review

AbstractCoronavirus disease 2019 (COVID-19) is a serious respiratory disease mediated by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The worldwide spread of COVID-19 has caused millions of confirmed cases and morbidity, and the crisis has greatly affected global economy and daily life and changed our attitudes towards life. The reproductive system, as a potential target, is at a high risk of SARS-CoV-2 infection, and females are more vulnerable to viral infection compared with males. Therefore, female fertility and associated reproductive health care in the COVID-19 era need more attention. This review summarises the mechanism of SARS-CoV-2 infection in the female reproductive system and discusses the impact of the COVID-19 crisis on female fertility. Studies have proven that COVID-19 might affect female fertility and interfere with assisted reproductive technology procedures. The side effects of vaccines against the virus on ovarian reserve and pregnancy have not yet been well investigated. In the future, the female fertility after SARS-CoV-2 infection and vaccination needs more attention because of the uncertainty of COVID-19.

A Novel Smart City-Based Framework on Perspectives for Application of Machine Learning in Combating COVID-19.

The spread of COVID-19 worldwide continues despite multidimensional efforts to curtail its spread and provide treatment. Efforts to contain the COVID-19 pandemic have triggered partial or full lockdowns across the globe. This paper presents a novel framework that intelligently combines machine learning models and the Internet of Things (IoT) technology specifically to combat COVID-19 in smart cities. The purpose of the study is to promote the interoperability of machine learning algorithms with IoT technology by interacting with a population and its environment to curtail the COVID-19 pandemic. Furthermore, the study also investigates and discusses some solution frameworks, which can generate, capture, store, and analyze data using machine learning algorithms. These algorithms can detect, prevent, and trace the spread of COVID-19 and provide a better understanding of the disease in smart cities. Similarly, the study outlined case studies on the application of machine learning to help fight against COVID-19 in hospitals worldwide. The framework proposed in the study is a comprehensive presentation on the major components needed to integrate the machine learning approach with other AI-based solutions. Finally, the machine learning framework presented in this study has the potential to help national healthcare systems in curtailing the COVID-19 pandemic in smart cities. In addition, the proposed framework is poised as a pointer for generating research interests that would yield outcomes capable of been integrated to form an improved framework.

A COVID-19 time series forecasting model based on MLP ANN

With the accelerated spread of COVID-19 worldwide and its potentially fatal effects on human health, the development of a tool that effectively describes and predicts the number of infected cases and deaths over time becomes relevant. This makes it possible for administrative sectors and the population itself to become aware and act more precisely. In this work, a machine learning model based on the multilayer Perceptron artificial neural network structure was used, which effectively predicts the behavior of the series mentioned in up to six days. The model, which is trained with data from 30 countries together in a 20-day context, is assessed using global and local MSE and MAE measures. For the construction of training and test sets, four time series (number of: accumulated infected cases, new cases, accumulated deaths and new deaths) from each country are used, which are started on the day of the first confirmed infection case. In order to soften the sudden transitions between samples, a moving average filter with a window size 3 and a normalization by maximum value were used. It is intended to make the model’s predictions available online, collaborating with the fight against the pandemic.

Blockchain and AI-Based Solutions to Combat Coronavirus (COVID-19)-Like Epidemics: A Survey.

The beginning of 2020 has seen the emergence of coronavirus outbreak caused by a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems in timely handling public health emergencies. In such contexts, innovative technologies such as blockchain and Artificial Intelligence (AI) have emerged as promising solutions for fighting coronavirus epidemic. In particular, blockchain can combat pandemics by enabling early detection of outbreaks, ensuring the ordering of medical data, and ensuring reliable medical supply chain during the outbreak tracing. Moreover, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing. Therefore, we present an extensive survey on the use of blockchain and AI for combating COVID-19 epidemics. First, we introduce a new conceptual architecture which integrates blockchain and AI for fighting COVID-19. Then, we survey the latest research efforts on the use of blockchain and AI for fighting COVID-19 in various applications. The newly emerging projects and use cases enabled by these technologies to deal with coronavirus pandemic are also presented. A case study is also provided using federated AI for COVID-19 detection. Finally, we point out challenges and future directions that motivate more research efforts to deal with future coronavirus-like epidemics.

Current Trends in the Implementation of E-Governance Elements in the Field of Public Health under the Threat of a Global Pandemic

The worldwide spread of COVID-19 has led to the emergence of a number of useful electronic tools in the field of public healthcare. This aspect is relevant for study. Estonian experts are the first to rethink e-governance strategies in a pandemic. They offer to store public health data on remote corporate servers and to collect patient’s personal information using Blockchain technology. Modern technologies use drones, recordings of video cameras of street surveillance, connection to Wi-Fi networks to track citizens in quarantine conditions. One of the most effective tools for monitoring in the field of public health was electronic applications for smart phones. Electronic tools are relevant in the field of public health both at the regional level and in urbanized territories. Asian countries systematically implement such tools with the support and consent of the local population. And in Europe, governments are trying to protect the democratic freedoms of citizens and use the impersonal data of mobile operators to record mass gatherings of people on electronic maps. The Ministry of Digital Transformation of Ukraine has launched a mobile application ‘Diy Vdoma’ (‘Action at Home’) since the beginning of April 2020 to monitor the quarantine regime of observation and self-isolation. Consequently, the use of electronic tools in the field of public healthcare, especially during the global pandemic, gives state and regional authorities the opportunity to control the situation. However, the issue of the balance between the observance of public sanitary and epidemiological security and the preservation of democratic human rights and freedoms is remained. The practice of using e-governance tools is opens up an additional aspect for researchers to study There is an additional aspect to study, i.e. reliable and timely information about emergencies posted on government platforms on the Internet allows public and human rights organizations to control the activities of public authorities in crisis situations effectively and to prevent violations of individual rights and freedoms, to monitor the use of budget funds during a pandemic and to assess the quality of their functions by the state.

Big Data Analysis of Korean Travelers’ Behavior in the Post-COVID-19 Era

Due to the spread of COVID-19 worldwide in 2020, the tourism industry is facing a calamitous crisis as migration and travel between the countries has been restricted. The purpose of this study is to explore changes in Korean tourism behavior after COVID-19. Korean social media data was collected from the World Health Organization’s declaration of a public health emergency of international concern, in January 2020, to August 2020, the summer vacation season. Keywords were extracted via text mining, and the current perspectives of Koreans concerning tourism that manifest content analysis were investigated. In addition, UCINET 6 was used to visualize related words by conducting centrality analysis, CONCOR (CONvergent CORrelation) analysis, and semantic system network analysis. The analysis showed that Korean tourists have increased their preference for domestic travel, and showed interest in government subsidies. Korean tourists also showed greater interest in eco-trips and gourmet trips. Although this study is limited to Korea, it can be used as a regional strategy.

코로나19에 따른 해양계 학교의 승선실습 법제도 개선 연구

Under the recent worldwide spread of COVID-19, the maritime schools need to review the mandatory minimum requirements for issuing a certification of officers through the suspension and resumption of on-board training. The domestic law requires at least one year of sea-going services for graduates of maritime schools to issue a certification of officers, and requires at least six months of on-board training during their sea-going services. However, STCW allows online education methods such as e-learning by approval of the competent authority for one year

Life as a Professor Amid COVID-19

Since March, we have experienced difficult times with the spread of COVID-19 worldwide. Classes went online; unknowns remain for the fall semester and beyond. This article highlights transformations needed to be a successful professor under the “new normal.”

A logistic model and predictions for the spread of the COVID-19 pandemic

The rapid spread of COVID-19 worldwide presents a great challenge to epidemic modelers. Model outcomes vary widely depending on the characteristics of a pathogen and the models. Here, we present a logistic model for the epidemic spread and divide the spread of the novel coronavirus into two phases: the first phase is a natural exponential growth phase that occurs in the absence of intervention and the second phase is a regulated growth phase that is affected by enforcing social distancing and isolation. We apply the model to a number of pandemic centers. Our results are in good agreement with the data to date and show that social distancing significantly reduces the epidemic spread and flattens the curve. Predictions on the spreading trajectory including the total infections and peak time of new infections for a community of any size are made weeks ahead, providing the vital information and lead time needed to prepare for and mitigate the epidemic. The methodology presented here has immediate and far-reaching applications for ongoing outbreaks or similar future outbreaks of other emergent infectious diseases.

Radiology indispensable for tracking COVID-19

With the rapid spread of COVID-19 worldwide, early detection and efficient isolation of suspected patients are especially important to prevent the transmission. Although nucleic acid testing of SARS-CoV-2 is still the gold standard for diagnosis, there are well-recognized early-detection problems including time-consuming in the diagnosis process, noticeable false-negative rate in the early stage and lacking nucleic acid testing kits in some areas. Therefore, effective and rational applications of imaging technologies are critical in aiding the screen and helping the diagnosis of suspected patients. Currently, chest computed tomography is recommended as the first-line imaging test for detecting COVID-19 pneumonia, which could allow not only early detection of the typical chest manifestations, but also timely estimation of the disease severity and therapeutic effects. In addition, other radiological methods including chest X-ray, magnetic resonance imaging, and positron emission computed tomography also show significant advantages in the detection of COVID-19 pneumonia. This review summarizes the applications of radiology and nuclear medicine in detecting and diagnosing COVID-19. It highlights the importance for these technologies to curb the rapid transmission during the pandemic, considering findings from special groups such as children and pregnant women.

Validity and Reliability of the Obsession with the COVID-19 Scale

Aims: The spread of Covid-19 worldwide has challenged public health and has had various psychological consequences. The present study aimed to investigate the psychometric properties of the Obsession with the COVID-19 scale. Instrument & Methods: As a descriptive-correlational study and to validate the Obsession with the COVID-19 scale, 392 people over 18 participated online, using the available sampling method from May to August 2020. A demographic questionnaire, Obsession with the COVID-19 Scale and Fear of Covid-19 Scale, was used to collect data. To evaluate the instrument's reliability, Cronbach's alpha internal consistency method and halving in the form of SPSS 25 statistical software and to evaluate the construct validity, confirmatory and exploratory factor analysis with the orthogonal rotation of Varimax type in Lisrel 8.8 software were used. Findings: Exploratory factor analysis identified all four items as one factor. Also, the results of the confirmatory factor analysis confirmed the construction. The convergent validity of the scale with fear of Covid-19 showed a correlation value of 0.25 (p<0.01). In the scale's validity, Cronbach's alpha value was estimated to be 0.797, and the split-half reliability coefficient was estimated to be 0.771. Conclusion: Obsession with the COVID-19 Scale can be used as a suitable measurement tool in research.