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Background: Since the COVID-19 pandemic outbreak, diabetes mellitus (DM) has been at the core of the confirmed risk factors for fatal or critical care unittreated COVID-19 and COVID-19 related complications. Although relevant studies on DM have developed rapidly during the COVID-19 pandemic. However, the aforementioned research results have not been systematically quantified by means of bibliometric analysis. Purpose: The purpose of this study is to provide a comprehensive analysis of the current status and trends of publications related to DM research during the COVID19 epidemic. Methods: A bibliometric analysis was performed using the Web of Science database. In this study, we used citespace, R software and R-Bibliometrix to analyze keywords, most-cited authors, most-cited countries, most-cited global documents, and co-occurrence and co-citation networks. Results: A total of 1688 publications was included in this study. Investigators from the United States contributed the most publications. The United States, China and Europe have the most collaboration with the other countries/regions. A total of 3355 institutions made contributions to this study. Of the top 10 institutions with the most publications, N8 Research Partnership showed the most centrality. Among the top 10 journals, Diabetes Research and Clinical Practice published the most articles. Among authors included, Khunti Kamlesh is rated first with 27 papers and has the highest centrality. The most frequently co-cited article is entitled "Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study". The most popular keywords included diabetes, mortality, diabetes, outcome, occurrences, risk, and type 1 diabetes. Conclusion: This bibliometric study provides an overall picture of DM research and research trends during the COVID-19 pandemic and provides a basis for researchers to develop their next research strategies. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic was an unprecedented global event in recent history. Beginning with an initial outbreak in Wuhan, China, in December 2019, the virus spread rapidly across the globe, causing millions of deaths and triggering an unprecedented health, economic, and social crisis. The initial response to the outbreak in many countries was the implementation of social distancing measures, including the closure of schools and businesses, the cancellation of mass events, and the banning of travel. These measures were aimed at reducing the virus' spread and preventing health systems from being overwhelmed by the numerous severe COVID-19 cases. However, these measures also had a devastating economic impact, especially on precarious workers and freelancers, as well as those who were unable to work from home. As the pandemic (also considered a syndemic or synergistic epidemic) dragged on, countries adopted more flexible approaches to dealing with the virus, adopting mitigation measures rather than social distancing measures. These included the use of masks, testing and contact tracing, and the opening of businesses and schools with the implementation of additional safety measures. This paper highlights the social consequences of the pandemic, ultimately arguing that it is a total social fact (from the French fait social total), based on Marcel Mauss' categorization, since it encompassed and impacted all facets of human life. [ABSTRACT FROM AUTHOR]

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The COVID-19 pandemic has dramatically influenced urban mobility patterns. This paper explores the impact of the pandemic on the travel patterns and flow structure of bike-sharing, in Wuhan, China. We find that during the pandemic, bike-sharing became a major mode of transport and helped for the travel to hospitals. In post-pandemic Wuhan, more cycling neighbourhoods emerged in suburban areas, and the percentage of bike-sharing ridership in denser areas tends to decrease, especially in workplaces, indicating there is a decentralised trend of bike-sharing flow structure. These findings imply that bike-sharing plays a significant role in promoting urban resilience, and are helpful to build bike-friendly cities and communities in the post-pandemic era. [ABSTRACT FROM AUTHOR]

In December 2019, COVID-19 appeared for the first time in Wuhan (Hubei Province, China), after which it quickly spread over the entire earth. The World Health Organization quickly designated COVID-19 a pandemic because of the high number of deaths and rapid global spread of the disease. Because of this, many facets of society have been impacted, and those effects may last for years to come. Therefore, COVID-19 detection methods were the focus of numerous research projects in the past. This has led to the development of the COVID-19 AI Detector, a specialized area of artificial intelligence-based research. In this paper, we survey all significant current efforts that have taken advantage of machine learning to COVID-19 detection and prediction. We first surveyed all datasets used in relevant research, and then summarized them in a table containing the link to that data. Then, we mention all the methodologies employed to detect the presence of COVID-19 using such datasets. Later, the challenges and difficulties that facing the concerned researches are reported, while the results of all interesting related work that lay ahead in this field before concluding this paper were discussed fairly. [ABSTRACT FROM AUTHOR]

Since the outbreak of the global COVID-19 pandemic in Wuhan, China, in 2019, its impact has been seen worldwide. Early identification of COVID-19 is very crucial, as it keeps the infected people isolated from other people, thus minimizing the risk of further transmission. The standard diagnostic approach is based on RT-PCR. However, due to the scarcity of PCR kits in some regions and the costs associated with this technique, there is a growing demand for alternative solutions. Recently, diagnosis of COVID-19 by medical imaging has been recognized as a valid clinical practice. Meanwhile, the massive increase in COVID-19 cases has put considerable pressure on radiologists responsible for interpreting these scans. This paper introduces an automated detection approach as a rapid alternative for COVID-19 diagnosis. We present a deep CNN model to differentiate between normal and pneumonia cases, as well as patients with COVID-19. Our approach is based on EfficientNet-B7 architecture and improved with Squeeze and Excitation block as an attention mechanism. In addition, we propose an innovative architecture that combines CNN with SVM to achieve the best performance. Experimental results show that the proposed framework provides better performance than existing SOTA methods, with an average accuracy of 97.50%, while the precision and recall of COVID-19 are both 100%. [ABSTRACT FROM AUTHOR]

COVID-19 is a contagious disease caused by a virus, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The first known case was identified in Wuhan, China, in December 2019. The disease quickly spread worldwide, resulting in the COVID-19 pandemic. The outburst of COVID-19 pandemic had tremendous effect on the whole world and analysis of the data can be meaningful in many ways to better understand the effect it had in our society. This paper aims in the direction of analysis of COVID-19 daily information based on country and continent level in terms of understanding the number of cases and deaths and their relationship, besides this is aims to better understand the vaccination number by country and effect of cases/death how they have affected these numbers. The solution was created on analysis of a dataset that contains daily information on each country, and using MySQL, SQL and PowerBi to generate the results for this work in way of query results which have been transformed to visuals using PowerBi for better understanding for further research work on this topic. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19 in 2020 caused extensive impact on household income and foods consumption. However, little attention has been paid to the immediate impact of income loss on animal-source foods consumption in the early stage of the COVID-19 pandemic. This paper aims to narrow this gap, and a total of 1301 valid samples of household food consumption surveys in Wuhan and Nanjing were obtained through specially designed online questionnaires. The surveys show that there were 69.6% (Wuhan) and 42.2% (Nanjing) of surveyed households whose animal-source foods consumption were affected, and there were 47.4% (Wuhan) and 18.9% (Nanjing) of surveyed households who suffered income loss. Furthermore, this paper makes an empirical study on the linkage between income loss and animal-source foods consumption. The results show that the pandemic affected household income, resulting in an immediate impact on animal-source foods consumption. This immediate impact may have been due to the combination of price increases, income loss and insufficient savings, which led to a "perfect storm" for animal-source foods consumption. Moreover, household income loss affected various animal-source foods consumption differently. For households suffering income losses, the odds of pork, beef and mutton, poultry, aquatic products, eggs and dairy products consumption being affected were increased by a factor of 1.894, 2.140, 2.773, 2.345, 1.802, 2.835, respectively, holding other variables constant. The results may be related to residents' consumption habits and food prices. During the COVID-19 pandemic, the reduction of animal-source foods consumption may have led to a state of tension concerning an increase in the development of nutrition intake and health, which may have led to increased food security risks. [ABSTRACT FROM AUTHOR]

Introduction: In December 2019, a cluster of atypical cases of pneumonia was reported in Wuhan, China, which was later designated as Coronavirus disease 2019 (COVID-19) by the World Health Organization (WHO) on Feb 11, 2020. We all are facing a global pandemic, and it is very important to be clear that there is no correct roadmap to navigate this difficult situation. It is imperative to state that this global pandemic impacted the spine care services of our institute. In the present study, we have assessed the spine surgeries performed by orthopedic surgeons in terms of volume and etiologies during the COVID-19 pandemic and compared the data with a pre-COVID period. Materials and Methods: We retrospectively collected data from all patients who underwent spinal surgeries at our institute under the department of orthopedics from August 20, 2019 to August 20, 2020 (a total of 12 months duration). The data was then divided into two groups--pre-COVID period (August 20, 2019-February 19, 2020-6 months) and during the COVID pandemic (February 20, 2020-August 20, 2021-6 months). Results: A total of 140 patients underwent surgery at our institute from August 20, 2019 to August 20, 2020. Of these, 91 patients underwent surgery during the pre-COVID period, and 49 patients underwent surgery during the COVID pandemic. In this devastating phase of the pandemic, our department's total number of surgeries significantly declined to 46.15%. The routine surgeries performed during the pandemic phase show a steep fall from 59.34% in the pre-COVID period to 10.20%. Conclusion: This paper is meant to focus attention on the exorbitant reduction in the operative workflow of the spine patients during the COVID-19 pandemic at a tertiary healthcare institute. It is the need of the hour that orthopedic surgeons maintain equilibrium while providing the best possible treatment to their patients and limiting the spread of the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The recently discovered coronavirus, SARS-CoV-2, which was detected in Wuhan, China, has spread worldwide and is still being studied at the end of 2019. Detection of COVID-19 at an early stage is essential to provide adequate healthcare to affected patients and protect the uninfected community. This paper aims to design and develop a novel ensemble-based classifier to predict COVID-19 cases at a very early stage so that appropriate action can be taken by patients, doctors, health organizations, and the government. In this paper, a synthetic dataset of COVID-19 is generated by a dataset generation algorithm. A novel ensemble-based classifier of machine learning is employed on the COVID-19 dataset to predict the disease. A convex hull-based approach is also applied to the data to improve the proposed novel, ensemble-based classifier's accuracy and speed. The model is designed and developed through the python programming language and compares with the most popular classifier, i.e., Decision Tree, ID3, and support vector machine. The results indicate that the proposed novel classifier provides a more significant precision, kappa static, root means a square error, recall, F-measure, and accuracy. [ABSTRACT FROM AUTHOR]

The search-engine-based surveillance methods for the early warning and prediction of infectious diseases cannot achieve search engine keywords automatic filtering and real-time updating, lead to powerless for the early warning of emerging infectious diseases. The aim of this study is to develop an artificial intelligence (AI) method for search-engine-based surveillance to improve the early warning ability for emerging infectious diseases. The 32 keywords (444 million search queries) that may be related to the coronavirus disease (COVID-19) outbreak was collected from December 18, 2019 to February 11, 2020 from Baidu's search engine database. The graph convolution network (GCN) model was used to select search engine keywords automatically, and then, multiple linear regression was performed to explore the relationship between the daily query frequencies of keywords and daily new cases. The GCN model was used to automatically select keywords. The prediction trend of the GCN model was highly consistent with the true curve with a mean absolute error of 81.65. Three keywords including "epidemic", "mask" and "coronavirus" were selected. The selection keywords in the search queries were highly correlated with the daily number of confirmed cases (r = 0.96, 0.94, and 0.89; P < 0.01). An abnormal initial peak (3.05 times the normal volume) in queries appeared on December 31, 2019, which could have served as an early warning signal for an outbreak. Of particular concern, 17.5% of query volume originated from the Hubei Province, 51.15% of which was from Wuhan City. The coefficients of determination (R2) of our constructed model were 0.88, 0.88, 0.84, 0.77, 0.77, 0.75, 0.73, and 0.73 for a time lag of 0–7 days, respectively, using selection keywords. The model we constructed was used in the Beijing Xinfadi outbreak as an independent test dataset, which successfully predicted the daily numbers of cases for the following days and detected an early signal during the Beijing Xinfadi outbreak (R2 = 0.79). In this paper search-engine-based surveillance based on the AI method was established for the early detection of the COVID-19 epidemic for the first time. The model achieves automatic filtering and real-time updating of search engine keywords and can effectively detect the early signals of emerging infectious diseases. [ABSTRACT FROM AUTHOR]

Corona virus disease (Covid-19) has out broken in Wuhan, China in Dec-2019 latter was declared Pandemic by WHO on 11 March 2020. The pandemic has caused distressing and devastating economic, health and social impact worldwide. The pandemic has caused one of the most serious Economic and public health crises faced by South Asian Countries. The present paper aims to analyse the impact of Covid-19 on India's trade with South Asian Countries based on the reports and data collected from Ministry of Industries and Trade Govt. of India. Out of the total exports of India to all the countries of the world the share of export to South Asian countries were 7.681% in 2018-19 just before Covid-19 which has fallen to 7.0036% in 2019-20 which gradually rises to 7.5495% in 2020-21 and 8.1451% in 2021-22. Similarly imports has also been affected by the pandemic out of the total imports of India to all the countries of the world the share of import to South Asian countries were 0.8502% in 2018-19 and deteriorated to .8094% in 2019-20 which gradually improves and started to rise in 2020-21 it was 0.8555% which has further increased to 0.9174% in 2021-22. [ABSTRACT FROM AUTHOR]

The new emerged infectious disease that is known the coronavirus disease (COVID-19), which is a high contagious viral infection that started in December 2019 in China city Wuhan and spread very fast to the rest of the world. This infection caused millions of infected cases globally and still poses an alarming situation for human lives. Pakistan in Asian countries is considered the third country with higher number of cases of coronavirus with more than 649824. Recently, some mathematical models have been constructed for better understanding the coronavirus infection. Mostly, these models are based on classical integer-order derivative using real numbers which cannot capture the fading memory. So at the current position it is a challenge for the world to understand and control the spreading of COVID-19. Therefore, the aim of our paper is to develop some novel techniques, namely complex Pythagorean fuzzy weighted averaging (abbreviated as CPFWA) operator, complex Pythagorean fuzzy ordered weighted averaging (abbreviated as CPFOWA) operator, complex Pythagorean fuzzy hybrid averaging (abbreviated as CPFHA) operator, induced complex Pythagorean fuzzy ordered weighted averaging (abbreviated as I-CPFOWA) operator and induced complex Pythagorean fuzzy hybrid averaging (abbreviated as I-CPFHA) operator to analysis the spreading of COVID-19. At the end of the paper, an illustrative the emergency situation of COVID-19 is given for demonstrating the effectiveness of the suggested approach along with a sensitivity analysis, showing the feasibility and reliability of its results. [ABSTRACT FROM AUTHOR]

In December 2019, a group of people in Wuhan city of Hubei province of China were found to be affected by an infection called dark etiology pneumonia. The outbreak of this pneumonia infection was declared a deadly disease by the China Center for Disease Control and Prevention on January 9, 2020, named Novel Coronavirus 2019 (nCoV-2019). This nCoV- 2019 is now known as COVID-19. There is a big list of infections of this coronavirus which is present in the form of a big family. This virus can cause several diseases that usually develop with a serious problem. According to the World Health Organization (WHO), 2019-nCoV has been placed as the modern generation of Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS) coronaviruses, so COVID-19 can repeatedly change its internal genome structure to extend its existence. Understanding and accurately predicting the mutational properties of the genome structure of COVID-19 can forma good leadership role in preventing and fighting against coronavirus. In this research paper, an analytical approach has been presented which is based on the k-means cluster technique of machine learning to find the clusters over the mutational properties of the COVID-19 viruses' complete genome. This method would be able to act as a promising tool to monitor and track pathogenic infections in their stable and local genetics/hereditary varieties. This paper identifies five main clusters of mutations with k = 5 as best in most cases in the coronavirus that could help scientists and researchers develop disease control vaccines for the transformation of coronaviruses. [ABSTRACT FROM AUTHOR]

Simple Summary: This paper proposes a modified SEIR model to study COVID-19 in Wuhan. The modified model is calibrated by the public number of COVID-19 hospitalization cases in Wuhan. The paper further uses this model to estimate the earliest date of COVID-19 infection in Wuhan, which is in agreement with some existing results. Based on SEIR (susceptible–exposed–infectious–removed) epidemic model, we propose a modified epidemic mathematical model to describe the spread of the coronavirus disease 2019 (COVID-19) epidemic in Wuhan, China. Using public data, the uncertainty parameters of the proposed model for COVID-19 in Wuhan were calibrated. The uncertainty of the control basic reproduction number was studied with the posterior probability density function of the uncertainty model parameters. The mathematical model was used to inverse deduce the earliest start date of COVID-19 infection in Wuhan with consideration of the lack of information for the initial conditions of the model. The result of the uncertainty analysis of the model is in line with the observed data for COVID-19 in Wuhan, China. The numerical results show that the modified mathematical model could model the spread of COVID-19 epidemics. [ABSTRACT FROM AUTHOR]

The objective of this paper is to investigate three new methods to regulate and analyze the spreading of coronavirus disease (COVID-19). COVID-19 started in the city of Wuhan, China at the end of 2019 and spread to the whole world in a short time. This infection caused millions of infected cases globally and still poses a disturbing situation for the people. Recently, some mathematical models have been constructed for better understanding of the coronavirus infection. Mostly, these models are based on classical integer-order derivative using real numbers which cannot capture the fading memory. So, at the current position, it is a challenge for the world to control the spreading of COVID-19. Therefore, the aim of this paper is to utilize fuzzy logic to control the transmission and spreading of COVID-19. Here, we develop three new methods such as the generalized interval-valued intuitionistic fuzzy Einstein weighted geometric (GIVIFEWG) operator, the generalized interval-valued intuitionistic fuzzy Einstein ordered weighted geometric (GIVIFEOWG) operator, and the generalized interval-valued intuitionistic fuzzy Einstein hybrid geometric (GIVIFEHG) operator. Intuitionistic fuzzy information for interval-valued is the moral and decent method to precise the fuzzy information for judgment or decision and Einstein operations are the best approximations, and the generalized aggregation operators are a generalization of most aggregation operators so, in these notes, we can spread the Einstein operations to aggregate the interval-valued intuitionistic fuzzy information based on the generalized aggregation operators. At the end of the paper, an illustration of the emergency situation of COVID-19 is given for demonstrating the effectiveness of the suggested approach, showing the feasibility and reliability of the new methods. [ABSTRACT FROM AUTHOR]

Purpose: Taking a global perspective, this paper aims to examine the impact of COVID-19 on Airbnb booking activities through three critical perspectives – the initial Wuhan lockdown, local COVID-19 cases and local lockdowns. Design/methodology/approach: Using Airbnb reviews and cancellations as proxies for Airbnb bookings on a global scale, econometrics was used to examine the impacts of the initial Wuhan lockdown, local COVID-19 cases and local lockdowns on Airbnb bookings. Findings: The authors find that local lockdowns result in a 57.8% fall in global booking activities. Every doubling of newly infected cases is associated with a 4.16% fall in bookings. The sensitivity of bookings to COVID-19 decreases with geographic distance to Wuhan and increases with government stringency of lockdown policies and human mobility within a market. Practical implications: The empirical evidence from this research can provide governments with insights into more accurate assessment of the financial loss of Airbnb hosts so that proper support can be offered based on the financial needs because of due to sudden lockdown. Originality/value: This research contributes to new knowledge on peer-to-peer accommodation during a time of crisis and provides much needed global evidence to understand the impacts of COVID-19 on the accommodation industry. [ABSTRACT FROM AUTHOR]

Mortality is one of the most important epidemiological measures and a key indicator of the effectiveness of potential treatments or interventions. In this paper, a permutation test method of variance analysis is proposed to test the null hypothesis that the real-time fatality rates of multiple groups were equal during the epidemic period. In light of large-scale simulation studies, the proposed test method can accurately identify the differences between different groups and display satisfactory performance. We apply the proposed method to the real dataset of the COVID-19 epidemic in mainland China (excluding Hubei), Hubei Province (excluding Wuhan), and Wuhan from 31 January 2020 to 30 March 2020. By comparing the differences in the disease severity for differential cities, we show that the severity of the early disease of COVID-19 may be related to the effectiveness of interventions and the improvement in medical resources. [ABSTRACT FROM AUTHOR]

In this paper, we propose a fractional order Coronavirus (COVID-19) model incorporating non-pharmaceutical interventions and vaccine hesitancy. The proposed model was calibrated with data from literature and validated with reported daily cases of COVID-19 from Wuhan, China. We derived the reproduction number and demonstrated that it is an important threshold parameter for disease persistence and extinction. We examined the relationship between the reproduction number and model parameters. Our findings underscore the importance of awareness and vaccine uptake on mitigating the spread of COVID-19. [ABSTRACT FROM AUTHOR]

The recent pandemic caused by the Coronavirus Disease (COVID-19) first surfaced in Wuhan, China in December 2019. This paper presents an expert system for the diagnosis of COVID-19 based on its symptoms to aid people in taking precautionary measures. When experts are not available, an expert system that can effectively diagnose the disease is crucial. It takes the place of one or more experts in decision-making and problem-solving. An expert system for diagnosis of COVID-19 is a system developed to recognize early COVID-19 symptoms that individuals may experience by allowing users to directly check the disease with results that can serve as a foundation for additional testing. This study's primary goal is to identify useful COVID-19 detection patterns or knowledge by examining the historical data we have obtained from the Kaggle dataset. The patterns are presented as rules, which are given to the expert system after consultation with a domain expert. A total of 1,048,575 pieces of data were used for model training and testing. To detect COVID-19 disease, we employ a PART rule-based algorithm, which performed 92.47% accurately in a 10-fold cross-validation test. We can therefore draw the conclusion that the algorithm produces a promising result and that the expert system aids in the diagnosis of the disease. The system offers a suggestion in line with the identified symptom. [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]

Objective: This paper used meta-regression to analyze the heterogenous factors contributing to the prevalence rate of mental health symptoms of the general and frontline healthcare workers (HCWs) in China under the COVID-19 crisis. Method: We systematically searched PubMed, Embase, Web of Science, and Medrxiv and pooled data using random-effects meta-analyses to estimate the prevalence rates, and ran meta-regression to tease out the key sources of the heterogeneity. Results: The meta-regression results uncovered several predictors of the heterogeneity in prevalence rates among published studies, including severity (e.g., above severe vs. above moderate, p < 0.01; above moderate vs. above mild, p < 0.01), type of mental symptoms (PTSD vs. anxiety, p = 0.04), population (frontline vs. general HCWs, p < 0.01), sampling location (Wuhan vs. Non-Wuhan, p = 0.04), and study quality (p = 0.04). Conclusion: The meta-regression findings provide evidence on the factors contributing to the prevalence rate of mental health symptoms of the general and frontline healthcare workers (HCWs) to guide future research and evidence-based medicine in several specific directions. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display%5frecord.php?RecordID=220592, identifier: CRD42020220592. [ABSTRACT FROM AUTHOR]

Since the initial reports of the Coronavirus surfacing in Wuhan, China, the novel virus currently without a cure has spread like wildfire across the globe, the virus spread exponentially across all inhabited continent, catching local governments by surprise in many cases and bringing the world economy to a standstill. As local authorities work on a response to deal with the virus, the scientific community has stepped in to help analyze and predict the pattern and conditions that would influence the spread of this unforgiving virus. Using existing statistical modeling tools to the latest artificial intelligence technology, the scientific community has used public and privately available data to help with predictions. A lot of this data research has enabled local authorities to plan their response—whether that is to deploy tightly available medical resources like ventilators or how and when to enforce policies to social distance, including lockdowns. On the one hand, this paper shows what accuracy of research brings to enable fighting this disease; while on the other hand, it also shows what lack of response from local authorities can do in spreading this virus. This is our attempt to compile different research methods and comparing their accuracy in predicting the spread of COVID-19. [ABSTRACT FROM AUTHOR]

Many researchers began doing studies about pandemic COVID-19 which began to spread from Wuhan, China in 2019 to all around the world and so far, numerous researches have been done around the world to control this contagious disease. In this paper, we proposed a MSIlIhR-V mathematical model to study the spreading of pandemic COVID-19. This paper is aimed to study the vaccination effect in the control of the disease propagation rate. Another goal of this paper is to find the maximum number of susceptible people, minimum number of infected people, and the best value for number of vaccination people. The Jacobian matrix was obtained in the virus absenteeism equilibrium point for the proposed dynamical system. The spectral radius method was applied to find the analytical formula for the reproductive number. Reproductive number is one of the most benefit and important tools to study of epidemic model's stability and instability. In the following, by adding a controller to the model and also using the optimal control strategy, model performance was improved. To validate of the proposed models with controller and without controller we use the real data of COVID-19 from 4 January, 2021 up to 14 June, 2021 in Iran. Maple and MATLAB software's will be used for programming. We will use Maple software for analytical parts and MATLAB software for numerical and simulation parts. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to ignite a conversation on the challenges of the ongoing 'novel' surrounding the coronavirus (SARS-CoV-2) pandemic in the Southern Africa region. The global COVID-19 pandemic started with the outbreak in Wuhan, China, in December 2019. It is understood that different countries, regions and territories will have different, context-specific challenges in fighting the pandemic. These will differ from country to country and therefore necessitates contextual research. By sharing African perspectives on swift action against this coronavirus, this paper is intended to appraise the nexus between the coronavirus and tourism in order to prepare tourism practitioners for the recovery strategies that will be needed to address the damages caused by the pandemic. This paper points to tourism researchers focusing on the Southern African region and possible key areas for further research as the coronavirus situation unfolds. It draws from published material and document analysis, including from academic research, media reports and social media platforms. [ABSTRACT FROM AUTHOR]

Different industries throughout the world face a tough time confronting the pandemic of novel coronavirus (COVID-19), so does the hotel industry. Hence, this research attempts to determine the impact of the COVID-19 crisis on the hotel industry in terms of guest retention. The crisis which started in Wuhan, China (December 2019) has spread to the whole world and continues to spread. Although in newspapers, journals, and online media there are few published articles, they are mostly based on secondary data. This study is an attempt to examine the perceptions of hotel employees regarding the effect of COVID-19 on the hotel industry through qualitative analysis. Further, the study attempts to evaluate the crisis preparedness and steps taken to combat COVID-19, and eventually determine the positive outcome of the crisis. This paper presents an overview of the advent of COVID-19 in New Delhi, India, and more importantly, reports on the approaches that hotels employ to sustain during this diffcult time. Through the in-depth interviews with senior employees in six hotels in the national capital city New Delhi, India (four interviews before the lockdown and two interviews during the lockdown). Empirical findings showed that at various phases of the crisis, hotels were taking different approaches to survive. The hotels were introducing strategies for guest retention to create stronger guest relationships. This paper presents the impact of this crisis on hotel employees and how the hotels are reacting to this crisis. The practical and theoretical implications of the study are discussed. [ABSTRACT FROM AUTHOR]

This paper evaluated whether the citizens are satisfied with outcomes and performance of lockdown initiatives in defending against COVID-19. We statistical analyzed participants' characteristics and used a logit regression to analyze the factors associated with their experience and satisfaction with the lockdown initiative. According to our results, the issues of information disclosure, reasonable resource arrangements, and timely governmental responses significantly influenced the experiences of citizens during the lockdown period. The findings can guide policy makers regarding implementing more progressive initiatives to limit the spread of COVID-19 and facilitate the use of emergency measures for the sake of public health. [ABSTRACT FROM AUTHOR]

In December 2019, the novel Coronavirus, also known as 2019-nCoV or SARS-CoV-2 or COVID-19, was first recognized as a deadly disease in Wuhan, China. In this paper, we analyze two different nonclassical Coronavirus models to observe the outbreaks of this disease. Caputo and Caputo–Fabrizio (C–F) fractional derivatives are considered to simulate the given epidemic models by using two separate methods. We perform all required graphical simulations with the help of real data to demonstrate the behavior of the proposed systems. We observe that the given schemes are highly effective and suitable to analyze the dynamics of Coronavirus. We find different natures of the given model classes for both Caputo and C-F derivative sense. The main contribution of this study is to propose a novel framework of modeling to show how the fractional-order solutions can describe disease dynamics much more clearly as compared to integer-order operators. The motivation to use two different fractional derivatives, Caputo (singular-type kernel) and Caputo–Fabrizio (exponential decay-type kernel) is to explore the model dynamics under different kernels. The applications of two various kernel properties on the same model make this study more effective for scientific observations. [ABSTRACT FROM AUTHOR]

The first case of COVID‐19 in India detected on January 30, 2020, after its emergence in Wuhan, China, in December 2019. The lockdown was imposed as anemergency measure by the Indian government to prevent the spread of COVID‐19 but gradually eased out due to its vast economic consequences. Just 15 days after the relaxation of lockdown restrictions, Delhi became India's worst city in terms of COVID‐19 cases. In this paper, we propose a variable‐order fractional SIR (susceptible, infected, removed) model at state‐level scale. We introduce a algorithm that uses the differential evolution algorithm in combination with Adam–Bashforth–Moulton method to learn the parameters in a system of variable‐order fractional SIR model. The model can predict the confirm COVID‐19 cases in India considering the effects of nationwide lockdown and the possible estimate of the number of infliction inactive cases after the removal of lockdown on June 1, 2020. A new parameter p is introduced in the classical SIR model representing the fraction of infected people that get tested and are thereby quarantined. The COVID‐19 trajectory in Delhi, as per our model, predicts the slowing down of the spread between January and February 2021, touching a peak of around 5 lakh confirmed cases. [ABSTRACT FROM AUTHOR]

Novel coronavirus (COVID‐19), a global threat whose source is not correctly yet known, was firstly recognised in the city of Wuhan, China, in December 2019. Now, this disease has been spread out to many countries in all over the world. In this paper, we solved a time delay fractional COVID‐19 SEIR epidemic model via Caputo fractional derivatives using a predictor–corrector method. We provided numerical simulations to show the nature of the diseases for different classes. We derived existence of unique global solutions to the given time delay fractional differential equations (DFDEs) under a mild Lipschitz condition using properties of a weighted norm, Mittag–Leffler functions and the Banach fixed point theorem. For the graphical simulations, we used real numerical data based on a case study of Wuhan, China, to show the nature of the projected model with respect to time variable. We performed various plots for different values of time delay and fractional order. We observed that the proposed scheme is highly emphatic and easy to implementation for the system of DFDEs. [ABSTRACT FROM AUTHOR]

The novel coronavirus 2019 (COVID-19) emerges from the Chinese city Wuhan and its spread to the rest of the world, primarily affected economies and their businesses, leading to a global depression. The explanatory and cross-sectional regression approach assesses the impact of COVID-19 cases on healthcare expenditures, logistics performance index, carbon damages, and corporate social responsibility in a panel of 77 countries. The results show that COVID-19 cases substantially increase healthcare expenditures and decrease corporate social responsibility. On the other hand, an increase in the coronavirus testing capacity brings positive change in reducing healthcare expenditures, increased logistics activities, and corporate social responsibility. The cost of carbon emissions increases when corporate activities begin to resume. The economic affluence supports logistics activities and improves healthcare infrastructure. It linked to international cooperation and their assistance to supply healthcare logistics traded equipment through mutual trade agreements. The greater need to enhance global trade and healthcare logistics supply helps minimize the sensitive coronavirus cases that are likely to provide a safe and healthy environment for living. [ABSTRACT FROM AUTHOR]

During the ongoing Covid-19 pandemic, telemedicine has provided people with a technical solution to alleviate chronic disease management without the fear of cross-contamination of Covid-19. This research adopted semi-structured interviews to understand Wuhan senior citizens' intentions to use digital devices for chronic disease management during and after the lockdown. The research illustrated how citizens in Wuhan managed their medical requirements by the 'grid-managed' online community. This paper also revealed different emphases on telemedicine between middle-aged and senior citizens. In addition, the findings suggested that cultural background, medical literacy, and socio-economic infrastructures play a pivotal role in influencing senior citizens' perceptions and use of telemedicine and telecare services. The findings also identified the key role of social networks which we term as a new form of acquaintance society. [ABSTRACT FROM AUTHOR]

The outbreak of Novel Coronavirus infection (COVID-19) has enormous economic consequences. It happened in the Wuhan city of China and rapidly spread across the world. Malaysia has suffered enormous economic losses. Extended lockdowns, flight disruptions and other measures taken to contain the infection, and releasing stimulus packages have caused considerable downturn in the economic growth. A genesis of the pandemic origin and transmission is necessary not only to mitigate the prevailing situation but also preventing a repeat of such an event in the future. The coronavirus outbreak is a wakeup call for insulating the society and economy, especially the most vulnerable sectors. Although the outbreak happened in December 2019, it continues to inflict health hazards and economic meltdown. The local bourse has plunged and the losses in the capital markets will have a significant impact on the psychographics of the general populace. As this phenomenon escalates, the financial structure of banks and firms would be rendered unstable, and the impact of this was a severe contraction in the economy and a high unemployment rate, also vulnerability of the tourism sector to pandemics is due to obvious reasons; people avoid visiting places in the grip of pandemic and tourism destinations close their doors to arrivals from the virus-affected countries. There is a need for international mechanisms for regulating the factors that lead to origin of the pandemic and applying available means for containing it should that happen. This paper highlights the causes of the pandemic to effectively link its consequences for Malaysia economic while also discussing the need to support its revival. An important measure emphasized in in this paper as a revival strategy is building resilience in the natural capital. This systems approach is necessary in dealing with the post-COVID-19 scenario and in better preparedness for any future economic hazards. [ABSTRACT FROM AUTHOR]

In the latter part of 2019, an unknown virus had started to spread, which was first reported from Wuhan city, China. After a few months, it was declared a deadly virus by the WHO and named ‘Coronavirus’ or ‘Covid-19’. Coronavirus is still deadly and hundreds of thousands of human lives are being lost. Since the outbreak became a pandemic, people started to share valuable information. Most people are using the social media platform to share their views, updates, etc. Twitter is one of the most used social media networks in the world. People and other reliable sources are constantly updating every possible information related to coronavirus spread, variants and deaths. One of the possible ways is extracting the information and creating a location-based hotspot to identify the areas of coronavirus spread and coronavirus deaths. This will be more helpful for the government and other authorities to identify potential coronavirus outbreak areas. In this paper, a method is proposed to identify and track the areas of coronavirus spread and deaths using Long Short-Term Memory (LSTM) technique. We have compared the proposed LSTM-based approach with classical machine learning approaches such as Support Vector Machine (SVM), and the proposed approach outperforms the classical methods. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) was first reported in December 2019 in Wuhan, China, and then moved to almost every country showing an unprecedented outbreak. The world health organization declared COVID-19 a pandemic. Since then, millions of people were infected, and millions have lost their lives all around the globe. By the end of 2020, effective vaccines that could prevent the fast spread of the disease started to loom on the horizon. Nevertheless, isolation, social distancing, face masks, and quarantine are the best-known measures, in the time being, to fight the pandemic. On the other hand, contact tracing is an effective procedure in tracking infections and saving others' lives. In this paper, we devise a new approach using a hybrid harmony search (HHS) algorithm that casts the problem of finding strongly connected components (SCCs) to contact tracing. This new approach is named as hybrid harmony search contact tracing (HHS-CT) algorithm. The hybridization is achieved by integrating the stochastic hill climbing into the operators' design of the harmony search algorithm. The HHS-CT algorithm is compared to other existing algorithms of finding SCCs in directed graphs, where it showed its superiority over these algorithms. The devised approach provides a 77.18% enhancement in terms of run time and an exceptional average error rate of 1.7% compared to the other existing algorithms of finding SCCs. [ABSTRACT FROM AUTHOR]

Background: In December 2019, the first case of COVID-19 was described in Wuhan, China, and by July 2022, there were already 540 million confirmed cases. Due to the rapid spread of the virus, the scientific community has made efforts to develop techniques for the viral classification of SARS-CoV-2. Results: In this context, we developed a new proposal for gene sequence representation with Genomic Signal Processing techniques for the work presented in this paper. First, we applied the mapping approach to samples of six viral species of the Coronaviridae family, which belongs SARS-CoV-2 Virus. We then used the sequence downsized obtained by the method proposed in a deep learning architecture for viral classification, achieving an accuracy of 98.35%, 99.08%, and 99.69% for the 64, 128, and 256 sizes of the viral signatures, respectively, and obtaining 99.95% precision for the vectors with size 256. Conclusions: The classification results obtained, in comparison to the results produced using other state-of-the-art representation techniques, demonstrate that the proposed mapping can provide a satisfactory performance result with low computational memory and processing time costs. [ABSTRACT FROM AUTHOR]

This paper aims to examine the Covid-19 phenomenon and its multidimensional impacts on the form of disruption of social life and efforts to overcome it. At the end of 2019, the city of Wuhan had a new pneumonia outbreak and spread to 190 countries. The outbreak was named Corona Virus Disease 2019 (Covid-19). The Covid-19 pandemic has spread throughout the world and has claimed millions of victims, which until now have not been found a vaccine to treat it. The development of Covid-19 has had a multidimensional impact causing a decline in health, welfare, economic growth, anomies and disruption in various lives. Various strategies and policies have been taken by governments around the world in the form of the use of personal protective equipment (PPE), social distancing, and reducing crowds involving large crowds, new normal and various life revolutions so that people are able to sustain their lives and reduce the new Covid-19 cluster. Until now, the efforts taken by various countries including in the medical field, namely finding a new vaccine, but it have not been successful to be found. So the Covid-19 pandemic has caused a revolution and disruption in various aspects of the social-cultural, health and economy. [ABSTRACT FROM AUTHOR]

Introduction: Coronavirus disease 2019 (COVID-19) is a novel coronavirus that was frequently isolated from Wuhan, China in patients with respiratory tract infections. The most prevalent symptoms are fever and respiratory tract involvement. In this paper, seizures, an uncommon manifestation of the condition, is described. Case Discussion: A previously healthy 34-year-old female was taken to the emergency room with a widespread tonic-clonic seizure. Five days prior to admission, the patient complained of fever, cough, headache, widespread weakness, and myalgias. She experienced two episodes of convulsions around one hour ago. The MRI of the brain was normal, whereas the CT of the chest indicated localised ground-glass opacities. Using real-time PCR, the respiratory material was positive for COVID-19. The patient's symptoms improved after using anticonvulsive and antiviral drugs. Conclusions: To our knowledge, this is one of the rare case studies reporting a link between frequent seizures and COVID - 19. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19 from Wuhan, China in 2019 has spread across the world, with more than 687 million confirmed coronavirus cases still increasing. Vaccines and medicines are one aspect of fighting the pandemic outbreak, but modern Information Technologies, web/ mobile-based technologies, blockchain, and Internet of Things (IoT) devices are being used to control the pandemic outbreak at an earlier/later stage. The IoT devices with sensors, and mobile apps/ websites having self-assessment tests can help in tracking COVID-19 infections. These people's tracking information and self-assessment tests are generating huge amounts of data. This white paper introduces an IoT and cloud-based architectural framework in the COVID-19 pandemic to predict infection risk based on citizens' self-assessments and storage of data in the cloud and the intelligent technique of hesitant Intuitionistic Fuzzy Set (IFS) has been used on the self-assessment dataset to predict the risk. [ABSTRACT FROM AUTHOR]

The onset of the COVID-19 pandemic in late 2019 triggered an unprecedented global health crisis. Originating in Wuhan, China, the spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to a worldwide upheaval, affecting every aspect of human life. As countries grappled with rising case counts, hospitalizations, and fatalities, the role of geospatial information and visualization in managing and understanding the pandemic became increasingly vital. This special issue of the Cartography and Geographic Information Science (CaGIS) Journal sought to explore the innovative and impactful ways in which geovisualization techniques have been employed to confront and elucidate the complexities of COVID-19. We describe significance and contribution of each paper that is part of the special issue, and avenues for new, exciting research around infectious diseases, keeping in mind that the research needs to be reproducible. [ABSTRACT FROM AUTHOR]

An outbreak of a novel form of pneumonia due to a coronavirus occurred in Wuhan City, Hubei Province, China, in December 2019, and the disease was officially named coronavirus 2019 (COVID-19). COVID-19 is an infectious disease caused by the SARS-CoV-2 virus which attacks the human respiratory system. In critical cases of COVID-19, special treatment is administered in the ICU using a mechanical ventilator. However, since the results are less than optimal, additional interventions such as orienting the patient in the prone position can be employed, as this can reduce the pressure on the dorsal lung area so that alveolar recruitment can be improved. This study examines the effect of the prone position on increasing pulmonary ventilation. This study is a literature review study employing a narrative method and was conducted by tracing and analyzing articles relevant to the topic. This literature review searched three databases, namely, PubMed, Google Scholar, and ProQuest using the keywords "COVID-19 OR intubated COVID-19" AND "prone position OR mechanical ventilator" AND "Lung ventilation". From these databases, 1062 journals were found and filtered based on inclusion and exclusion criteria, resulting in the inclusion of 10 journals. Then, they were filtered again using a critical appraisal instrument called "JBI Critical Appraisal", which was conducted by two people, N.P. and A.S.; consequently, the results of 5 journals were obtained for further analysis; cumulatively, these papers represented a total population of 1789 patients. It was concluded that the prone position can improve pulmonary ventilation by up to 50% and reduce the total length of stay by up to 5 days in the hospital, as long as the patients do not possess any comorbid diseases. [ABSTRACT FROM AUTHOR]

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COVID-19 is a disease caused by infection with the virus 2019-nCoV, a single-stranded RNA virus. During the infection and transmission processes, the virus evolves and mutates rapidly, though the disease has been quickly controlled in Wuhan by 'Fangcang' hospitals. To model the virulence evolution, in this paper, we formulate a new age structured epidemic model. Under the tradeoff hypothesis, two special scenarios are used to study the virulence evolution by theoretical analysis and numerical simulations. Results show that, before 'Fangcang' hospitals, two scenarios are both consistent with the data. After 'Fangcang' hospitals, Scenario I rather than Scenario II is consistent with the data. It is concluded that the transmission pattern of COVID-19 in Wuhan obey Scenario I rather than Scenario II. Theoretical analysis show that, in Scenario I, shortening the value of L (diagnosis period) can result in an enormous selective pressure on the evolution of 2019-nCoV. [ABSTRACT FROM AUTHOR]

Towards the end of 2019 in Wuhan, suspicions of a new dangerous virus circulating in the air began to arise. It was the start of the world pandemic coronavirus disease 2019 (COVID-19). Since then, considerable research data and review papers about this virus have been published. Hundreds of researchers have shared their work in order to achieve a better comprehension of this disease, all with the common goal of overcoming this pandemic. The coronavirus is structurally similar to influenza A. Both are RNA viruses and normally associated with comparable infection symptoms. In this review, different case studies targeting polymeric materials were appraised to highlight them as an indispensable tool to fight these RNA viruses. In particular, the main focus was how polymeric materials, and their versatile features could be applied in different stages of viral disease, i.e., in protection, detection and treatment. [ABSTRACT FROM AUTHOR]

The coronavirus epidemic (COVID-19) is growing quickly around the globe. The first acute atypical respiratory illness was reported in December 2019, in Wuhan, China. This quickly spread from Wuhan city to other locations. Deep learning (DL) algorithms are one of the greatest solutions for consistently and readily recognizing COVID-19. Previously, many researchers used state-of-the-art approaches for the classification of COVID-19. In this paper, we present a deep learning approach with the EfficientnetB4 model, centered on transfer learning, for the classification of COVID-19. Transfer learning is a popular technique that uses pre-trained models that have been trained on the ImageNet database and employed on a new problem to increase generalization. We presented an in-depth training approach to extract the visual properties of COVID-19 in exchange for providing a medical assessment before infection testing. The proposed methodology is assessed on a publicly accessible X-ray imaging dataset. The proposed framework achieves an accuracy of 97%. Our model's experimental findings demonstrate that it is extremely successful at identifying COVID-19 and that it may be supplied to health organizations as a precise, quick, and successful decision support system for COVID-19 identification. [ABSTRACT FROM AUTHOR]