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Infectious diseases are among the leading causes of mortality worldwide. A new coronavirus named severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) was identified in Wuhan, China in 2019, and the World Health Organization (WHO) declared its outbreak, coronavirus disease 2019 (COVID-19), as a global pandemic in 2020. COVID-19 can spread quickly from person to person. One of the most challenging issues is to identify the infected individuals and prevent potential spread of SARS-CoV-2. Recently, anti-SARS-CoV-2 immunoglobulin M (IgM) and immunoglobulin G (IgG) antibody tests using immunochromatographic methods have been used as a complement to current detection methods and have provided information of the approximate course of COVID-19 infection. However, blood sampling causes pain and poses risks of infection at the needle puncture site. In this study, a novel patch sensor integrating porous microneedles and an immunochromatographic assay (PMNIA) was developed for the rapid detection of anti-SARS-CoV-2 IgM/IgG in dermal interstitial fluid (ISF), which is a rich source of protein biomarkers, such as antibodies. Biodegradable porous microneedles (MNs) made of polylactic acid were fabricated to extract ISF from human skin by capillary effect. The extracted ISF was vertically transported and flowed into the affixed immunoassay biosensor, where specific antibodies could be detected colorimetrically on-site. Anti-SARS-CoV-2 IgM/IgG antibodies were simultaneously detected within 3 min in vitro. Moreover, the limit of detection of anti-SARS-CoV-2 IgM and IgG concentrations was as low as 3 and 7 ng/mL, respectively. The developed device integrating porous MNs and immunochromatographic biosensors is expected to enable minimally invasive, simple, and rapid anti-SARS-CoV-2 IgM/IgG antibody testing. Furthermore, the compact size of the MN and biosensor-integrated device is advantageous for its widespread use. The proposed device has great potential for rapid screening of various infectious diseases in addition to COVID-19 as an effective complementary method with other diagnostic tests. [ABSTRACT FROM AUTHOR]

The 2019 outbreak of coronavirus disease (COVID‐19) in Wuhan (Hubei province of China) has given rise to a pandemic spread of virus, more than 240 million incidences and a death toll larger than 5 million people. COVID‐19 has set off large efforts in research, therapy and patient care, as well as public and private debates in every imaginable form. A number of scientists used the publication platforms provided by the Federation of the European Biochemical Societies (FEBS) to present their research data, reviews, opinions and other contributions relating to COVID‐19 and severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). Here, I highlight the recent COVID‐19 papers which have been published and collected in a Virtual Issue in FEBS Letters, and discuss their implications towards understanding the molecular, biochemical and cellular mechanisms of SARS‐CoV‐2 infections, vaccine development and antiviral discovery strategies. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic originating in the Wuhan province of China in late 2019 has impacted global health, causing increased mortality among elderly patients and individuals with comorbid conditions. During the passage of the virus through affected populations, it has undergone mutations, some of which have recently been linked with increased viral load and prognostic complexities. Several of these variants are point mutations that are difficult to diagnose using the gold standard quantitative real-time PCR (qRT-PCR) method and necessitates widespread sequencing which is expensive, has long turn-around times, and requires high viral load for calling mutations accurately. Here, we repurpose the high specificity of Francisella novicida Cas9 (FnCas9) to identify mismatches in the target for developing a lateral flow assay that can be successfully adapted for the simultaneous detection of SARS-CoV-2 infection as well as for detecting point mutations in the sequence of the virus obtained from patient samples. We report the detection of the S gene mutation N501Y (present across multiple variant lineages of SARS-CoV-2) within an hour using lateral flow paper strip chemistry. The results were corroborated using deep sequencing on multiple wild-type (n = 37) and mutant (n = 22) virus infected patient samples with a sensitivity of 87% and specificity of 97%. The design principle can be rapidly adapted for other mutations (as shown also for E484K and T716I) highlighting the advantages of quick optimization and roll-out of CRISPR diagnostics (CRISPRDx) for disease surveillance even beyond COVID-19. This study was funded by Council for Scientific and Industrial Research, India. [ABSTRACT FROM AUTHOR]

What started as a cluster of patients with a mysterious respiratory illness in Wuhan, China, in December 2019, was later determined to be coronavirus disease 2019 (COVID-19). The pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel Betacoronavirus, was subsequently isolated as the causative agent. SARS-CoV-2 is transmitted by respiratory droplets and fomites and presents clinically with fever, fatigue, myalgias, conjunctivitis, anosmia, dysgeusia, sore throat, nasal congestion, cough, dyspnea, nausea, vomiting, and/or diarrhea. In most critical cases, symptoms can escalate into acute respiratory distress syndrome accompanied by a runaway inflammatory cytokine response and multiorgan failure. As of this article's publication date, COVID-19 has spread to approximately 200 countries and territories, with over 4.3 million infections and more than 290,000 deaths as it has escalated into a global pandemic. Public health concerns mount as the situation evolves with an increasing number of infection hotspots around the globe. New information about the virus is emerging just as rapidly. This has led to the prompt development of clinical patient risk stratification tools to aid in determining the need for testing, isolation, monitoring, ventilator support, and disposition. COVID-19 spread is rapid, including imported cases in travelers, cases among close contacts of known infected individuals, and community-acquired cases without a readily identifiable source of infection. Critical shortages of personal protective equipment and ventilators are compounding the stress on overburdened healthcare systems. The continued challenges of social distancing, containment, isolation, and surge capacity in already stressed hospitals, clinics, and emergency departments have led to a swell in technologically-assisted care delivery strategies, such as telemedicine and web-based triage. As the race to develop an effective vaccine intensifies, several clinical trials of antivirals and immune modulators are underway, though no reliable COVID-19-specific therapeutics (inclusive of some potentially effective single and multi-drug regimens) have been identified as of yet. With many nations and regions declaring a state of emergency, unprecedented quarantine, social distancing, and border closing efforts are underway. Implementation of social and physical isolation measures has caused sudden and profound economic hardship, with marked decreases in global trade and local small business activity alike, and full ramifications likely yet to be felt. Current state-of-science, mitigation strategies, possible therapies, ethical considerations for healthcare workers and policymakers, as well as lessons learned for this evolving global threat and the eventual return to a "new normal" are discussed in this article. [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]

First described in December 2019 in Wuhan (China), COVID-19 disease rapidly spread worldwide, constituting the biggest pandemic in the last 100 years. Even if SARS-CoV-2, the agent responsible for COVID-19, is mainly associated with pulmonary injury, evidence is growing that this virus can affect many organs, including the heart and vascular endothelial cells, and cause haemostasis, CNS, and kidney and gastrointestinal tract abnormalities that can impact in the disease course and prognosis. In fact, COVID-19 may affect almost all the organs. Hence, SARS-CoV-2 is essentially a systemic infection that can present a large number of clinical manifestations, and it is variable in distribution and severity, which means it is potentially life-threatening. The goal of this comprehensive review paper in the series is to give an overview of non-pulmonary involvement in COVID-19, with a special focus on underlying pathophysiological mechanisms and clinical presentation. [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]

Introduction: The world has been badly affected by the spread of a novel virus known as Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) that was first reported in Wuhan, China back in December 2019. As the virus is transmitted through aerosol, respiratory droplets and close contact, hand hygiene and wearing mask are the most important preventive measures for COVID-19. Studies showed hand hygiene intervention improved compliances. This paper discussed the development of a hand hygiene intervention module, validation, usability and effectiveness reports. Methods: This study involved 5 stages; need analysis, module development, validation, usability and effectiveness studies. Need analysis and module development were carried out through brainstorming and literature search. The content was evaluated by three experts, whereas the usability was screened by four preschool teachers. The effectiveness of knowledge transfer was evaluated via pre and post intervention surveys. Results: The hand hygiene educational module received valuable comments from the content expert and the end-users (teachers). Knowledge score related to COVID-19 transmission, prevention and hand hygiene moments among pre-school children were significantly increased after the intervention. Conclusion: This module can be used to improve teaching method related to COVID-19 and hand hygiene. The 'learn and play' approach may increase excitement, thus make learning process enjoyable and memorable. [ABSTRACT FROM AUTHOR]

BACKGROUND: Coronaviruses (CoVs) belong to the RNA viruses family. The viruses in this family are known to cause mild respiratory disease in humans. The origin of the novel SARSCOV2 virus that caused the coronavirus-19 disease (COVID-19) is the Wuhan city in China from where it disseminated to cause a global pandemic. Although lungs are the predominant target organ for Coronavirus Disease-19 (COVID-19), since its outbreak, the disease is known to affect heart, blood vessels, kidney, intestine, liver and brain. This review aimed to summarize the catastrophic impacts of Coronavirus disease-19 on heart and liver along with its mechanisms of pathogenesis. METHODS: The information used in this review was obtained from relevant articles published on PubMed, Google Scholar, Google, WHO website, CDC and other sources. Key searching statements and phrases related to COVID-19 were used to retrieve information. Original research articles, review papers, research letters and case reports were used as a source of information. RESULTS: Besides causing severe lung injury, COVID-19 has also been reported to affect and cause dysfunction of many other organs. COVID-19 infection can affect people by downregulating membrane-bound active angiotensin-converting enzyme (ACE). People who have deficient ACE2 expression are more vulnerable to COVID-19 infection. The patients' pre-existing co-morbidities are major risk factors that predispose individuals to severe COVID-19. CONCLUSION: The disease severity and its broad spectrum phenotype is a result of combined direct and indirect pathogenic factors. Therefore, protocols that harmonize many therapeutic preferences should be the best alternatives to de-escalate the disease and obviate deaths caused as a result of multiple organ damage and dysfunction induced by the disease. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has been a global public health concern since March 11, 2020. Healthcare systems struggled to meet patients' growing needs for diagnosis, treatment, and care. As healthcare industries struggled to cope with the overwhelming demands, advanced intelligence and computing technologies have become essential. Artificial intelligence techniques have become essential for identifying and triaging patients, predicting disease severity, and detecting outcomes. The aim of the paper is to propose a gradient boosting-based model to predict the mortality of COVID-19 patients and to improve the prediction accuracy by incorporating resampling strategies. A real COVID-19 data that includes patients' travel, health, geographical, and demographic information is obtained from a public repository. The dataset used in the study has the class imbalance problem, and several approaches are applied to solve the problem. In this study, a gradient boosting-based model for predicting the mortality of COVID-19 patients is proposed. This approach incorporates resampling strategies, such as synthetic minority oversampling technique (SMOTE), random under-sampling, and clustering-based under-sampling, to address the imbalanced class distribution problem in the dataset. Then, gradient boosting machines (GBM) such as extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost) are analyzed in terms of accuracy and computational time. Random search method is used to find the optimal hyper-parameters for the algorithms. A stacking-based hybrid model that combines the XGBoost, LightGBM, and CatBoost algorithms was used for comparison in the experiments. In the experiments, the factors that can influence the mortality of COVID-19 patients are investigated. And, it is found that the age of the patient, whether the patient belonged to Wuhan, the difference between when they first noticed symptoms and when they visited the hospital (in days) affect the mortality. By utilizing over/under-sampling approaches, we ameliorated the concern of class imbalance. XGBoost, LightGBM, and CatBoost are effectively analyzed in terms of various performance metrics to determine the suitable GBM for the proposed system. The experimental results revealed that the stacking-based hybrid model performs well with the balanced dataset provided by SMOTE. CatBoost produces superior results for a balanced dataset with random under-sampling and clustering-based under-sampling. The main focus of the study is to propose a gradient boosting-based model for predicting the mortality of COVID-19 patients. This study also emphasizes the importance of addressing the imbalanced class distribution problem in the dataset and incorporates resampling strategies to improve the prediction accuracy. Our promising result confirms the success of the proposed system in predicting mortality of COVID-19 disease. [ABSTRACT FROM AUTHOR]

Coronavirus is considered the first virus to sweep the world in the twenty-first century, it appeared by the end of 2019. It started in the Chinese city of Wuhan and began to spread in different regions around the world too quickly and uncontrollable due to the lack of medical examinations and their inefficiency. So, the process of detecting the disease needs an accurate and quickly detection techniques and tools. The X-Ray images are good and quick in diagnosing the disease, but an automatic and accurate diagnosis is needed. Therefore, this paper presents an automated methodology based on deep learning in diagnosing COVID-19. In this paper, the proposed system is using a convolutional neural network, which is considered one of the mostly prominent techniques used today for its reliability and ability to generate rapid results. The system was trained on a set of X-Ray images taken of the chest area of infected and uninfected people. The CNN structure gave accuracy, Precision, Recall and F-Measure 98%. This model is characterized by its ability to distinguish efficiently and adapt to different cases. [ABSTRACT FROM AUTHOR]

Considering the high variability of Covid-19 and unknown sequelae of rebound infections. We may be caught in a long-term struggle against the virus. The evaluation and summary of past anti-epidemic policies highlight profound practical significance for either decision makers or individuals. This paper applies both theoretical and empirical approaches to study the impact of city lockdown and air pollution on Covid-19. First, we apply an extended Susceptible-Infected-Removed (SIR) model to identify the relationship between lockdown, air pollution, and Covid-19 cases. Second, we apply the Differences-in-Differences (DID) model and Poisson Pseudo-Maximum Likelihood (PPML) model to test the impact of lockdown on Covid-19, by sorting out lockdown policies implemented by cities during the epidemic. Third, we empirically analyze the impact of air pollution on Covid-19, based on daily data from 257 cities in China. Finally, we examine the mechanism by which lockdown impacts Covid-19. Newly confirmed Covid-19 cases are reduced by 31.1% nationwide (excluding Wuhan) during the lockdown period. Regions with lower air pollution experience fewer Covid-19 cases. Air pollution aggravates Covid-19, with the pollutants PM2.5, PM10, NO2, CO, and O3 having the greatest impact. The results indicate that an increase in one unit of air quality index (AQI) concentrations is associated with 1.723 more newly confirmed cases. The effects of air pollution on the spread of Covid-19 diminish as the population size increases. Lockdown can inhibit the spread of Covid-19 by reducing air pollution and population movement. [ABSTRACT FROM AUTHOR]

In this paper, the mathematical modeling of the novel corona virus (COVID-19) is considered. A brief relationship between the unknown hosts and bats is described. Then the interaction among the seafood market and peoples is studied. After that, the proposed model is reduced by assuming that the seafood market has an adequate source of infection that is capable of spreading infection among the people. The reproductive number is calculated and it is proved that the proposed model is locally asymptotically stable when the reproductive number is less than unity. Then, the stability results of the endemic equilibria are also discussed. To understand the complex dynamical behavior, fractal-fractional derivative is used. Therefore, the proposed model is then converted to fractal-fractional order model in Atangana-Baleanu (AB) derivative and solved numerically by using two different techniques. For numerical simulation Adam-Bash Forth method based on piece-wise Lagrangian interpolation is used. The infection cases for Jan-21, 2020, till Jan-28, 2020 are considered. Then graphical consequences are compared with real reported data of Wuhan city to demonstrate the efficiency of the method proposed by us. [ABSTRACT FROM AUTHOR]

A new mathematical model of Coronavirus (2019-nCov) using piecewise hybrid fractional order derivatives is given in this paper. Moreover, in order to be consistent with the physical model problem, a new parameter μ is presented. The boundedness, existence, and positivity of the solutions for the proposed model are discussed. Two improved numerical methods are presented in this paper. The Caputo proportional constant nonstandard modified Euler–Maruyama method is introduced to study the fractional stochastic model, and the Grünwald–Letnikov nonstandard finite difference method is presented to study the hybrid fractional order deterministic model. Comparative studies with real data from Spain and Wuhan are presented. [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]

An outbreak of coronavirus disease 2019 (COVID-19) occurred in Wuhan and it has rapidly spread to almost all parts of the world. In the field of Medical Science, concerning the definition of the topological index on the molecular structure and corresponding medical, biological, chemical, pharmaceutical properties of drugs can be studied by the topological index calculation. In this paper, we compute some of the general topological properties of chloroquine and hydroxychloroquine used to inhibit the outbreak of coronavirus disease-19. The results in this paper may be useful in finding new drug and vaccine for the treatment and prevention of COVID-19. [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]

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]

A bacterial infection in the lungs can cause viral pneumonia, a disease. Later the middle of December 2019, there have been multiple episodes of pneumonia in Wuhan City, China, with no known cause; it has since been discovered that this pneumonia is actually a new respiratory condition brought on by coronavirus infection. Humans who have lung abnormalities are more likely to develop high-risk conditions; this risk can be decreased with much quicker and more effective therapy. The symptoms of Covid-19 pneumonia are similar to those of viral pneumonia; they are not distinctive. X-ray or Computed Tomography (CT) scan images are used to identify lung abnormalities. Even for a skilled radiologist, it might be challenging to identify Covid-19/Viral pneumonia by looking at the X-ray images. For prompt and effective treatment, accurate diagnosis is essential. In this epidemic condition, delayed diagnosis can cause the number of cases to double, hence a suitable tool is required is necessary for the early identification of Covid-19. This paper highlights various AI techniques as a part of our contribution to swift identification and curie Covid-19 to front-line corona. The safety of Covid-19 people who have viral pneumonia is a concern. Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), two AI technologies from Deep Learning (DL), were utilized to identify Covid-19/Viral pneumonia. The Algorithm is taught utilizing non-public local hospitals or Covid-19 wards, as well as X-ray images of healthy lungs, fake lungs from viral pneumonia, and ostentatious lungs from Covid-19 that are all publicly available. The model is also validated over a lengthy period of time using the transfer learning technique. The results correspond with clinically tested positive Covid-19 patients who underwent Swap testing conducted by medical professionals, giving us an accuracy of 78 to 82 percent. We discovered that each DL model has a unique expertise after testing the various models. [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]

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]

The deadly coronavirus disease 2019 (COVID‐19) has recently affected each corner of the world. Many governments of different countries have imposed strict measures in order to reduce the severity of the infection. In this present paper, we will study a mathematical model describing COVID‐19 dynamics taking into account the government action and the individuals reaction. To this end, we will suggest a system of seven fractional deferential equations (FDEs) that describe the interaction between the classical susceptible, exposed, infectious, and removed (SEIR) individuals along with the government action and individual reaction involvement. Both human‐to‐human and zoonotic transmissions are considered in the model. The well‐posedness of the FDEs model is established in terms of existence, positivity, and boundedness. The basic reproduction number (BRN) is found via the new generation matrix method. Different numerical simulations were carried out by taking into account real reported data from Wuhan, China. It was shown that the governmental action and the individuals' risk awareness reduce effectively the infection spread. Moreover, it was established that with the fractional derivative, the infection converges more quickly to its steady state. [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]

COVID-19, a viral respiratory illness, is caused by Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2), which was first identified in Wuhan, China, in 2019 and rapidly spread worldwide. Testing and isolation were essential to control the virus's transmission due to the severity of the disease. In this context, there is a global interest in the feasibility of employing nano-biosensors, especially those using graphene as a key material, for the real-time detection of the virus. The exceptional properties of graphene and the outstanding performance of nano-biosensors in identifying various viruses prompted a feasibility check on this technology. This paper focuses on the recent advances in using graphene-based electrochemical biosensors for sensing the SARS-CoV-2 virus. Specifically, it reviews various types of electrochemical biosensors, including amperometric, potentiometric, and impedimetric biosensors, and discusses the current challenges associated with biosensors for SARS-CoV-2 detection. The conclusion of this review discusses future directions in the field of electrochemical biosensors for SARS-CoV-2 detection, underscoring the importance of continued research and development in this domain. [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]

Background: In December of 2019, a new disease which is caused by SARS-CoV-2, as an epidemic disease out of Wuhan, China, began to circulate. On March 11, 2020, the Republic of Turkey Ministry of Health had announced the first case from Turkey. The aim of this study is to analyze the scientific publications in the field of COVID-19 included in Science Citation Index Expanded (SCIE) from Turkey and to establish a theoretical background for future studies in the health literature with obtained valuable information about the publications. We searched all papers published in the field of COVID-19 by using the terms of "COVID-19," "2019-n-CoV," "SARS-CoV-2," "Coronavirus disease 19," and "2019 novel coronavirus" as scientific nomenclatures of COVID-19 in the topic search section of the software. Results: Overall, 47,368 papers, indexed by SCI-E, were found related to COVID-19 between January 1, 2020, and December 13, 2020. Of these, 931 were from Turkey. In terms of specialities, the most contribution was from the Medicine General Internal followed by Dermatology. Most of the publications were article. English was the most preferred language in papers. Dermatological Theraphy published the most paper. Conclusions: Applying this kind of analysis on an intermittent basis gives a general perspective for contribution of a countries to scientific publications and useful for the further studies. [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]

Introduction: The world has been badly affected by the spread of a novel virus known as Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) that was first reported in Wuhan, China back in December 2019. As the virus is transmitted through aerosol, respiratory droplets and close contact, hand hygiene and wearing mask are the most important preventive measures for COVID-19. Studies showed hand hygiene intervention improved compliances. This paper discussed the development of a hand hygiene intervention module, validation, usability and effectiveness reports. Methods: This study involved 5 stages; need analysis, module development, validation, usability and effectiveness studies. Need analysis and module development were carried out through brainstorming and literature search. The content was evaluated by three experts, whereas the usability was screened by four preschool teachers. The effectiveness of knowledge transfer was evaluated via pre and post intervention surveys. Results: The hand hygiene educational module received valuable comments from the content expert and the end-users (teachers). Knowledge score related to COVID-19 transmission, prevention and hand hygiene moments among pre-school children were significantly increased after the intervention. Conclusion: This module can be used to improve teaching method related to COVID-19 and hand hygiene. The 'learn and play' approach may increase excitement, thus make learning process enjoyable and memorable. [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 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]

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]

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]

In December 2019, Coronavirus Disease 2019 (COVID-19) was reported in Wuhan, China. Comprehensive strategies for quick identification, prevention, control, and remedy of COVID-19 have been implemented until today. Advances in various nanoparticle-based technologies, including organic and inorganic nanoparticles, have created new perspectives in this field. These materials were extensively used to control COVID-19 because of their specific attribution to preparing antiviral face masks, various safety sensors, etc. In this review, the most current nanoparticle-based technologies, applications, and achievements against the coronavirus were summarized and highlighted. This paper also offers nanoparticle preventive, diagnostic, and treatment options to combat this pandemic. [ABSTRACT FROM AUTHOR]

Corona disease outbreak occurred in 2019 from Wuhan, China and the virus was identified to be a member of the Coronaviridae family. the disease was named as COVID-19 by WHO. It started as an epidemic and later turned into pandemic affecting world wide. the present study was aimed to conduct a systematic review on most prevalent symptoms associated with COVID-19 and to perform a Meta analysis on Comorbidities associated with COVID-19 and assess its severity in underlying conditions. A literature search was conducted using search engines like Pubmed, Web of Science, elsevier COVID-19 and litCovid databases between January 2020 and August 2020. Forest plot analysis was done using Random effects model to estimate odds ratio and 95% confidence interval estimate. 83 papers with a sample size of 19,454 were analysed statistically after adopting inclusion and exclusion criteria. Our results showed the most prevalent symptoms to be Cough, fever, dyspnoea and myalgia in interval plot analysis. When assessed for comorbidities, Hypertension (27.02%), Diabetes (16.61%), Cardiovascular disease (12.75%) and Chronic pulmonary obstructive diseases(19.9%) are found to be the most risk factors in comparison with others. However a finer experimental research needs to be confirmed with the findings. [ABSTRACT FROM AUTHOR]

The whole world is suffering from a pandemic virus known as COVID-19 nowadays. This virus is also known as novel corona virus. There are various statements coming out regarding its generation but till now one thing is coming is that its origin place seems to be a lab somewhere in Wuhan city of China. It is also said off and on the record that covid-19 virus came into existence near November 2019 but on record, the WHO office (China) reported on 31st December 2019 that an unknown virus is detected in Wuhan city. On 7th January 2020, Chinese authorities announced about the existence of this virus. At the initial stage, it was supposed that this virus might be bat-origin but after that several other statements are coming day by day. Authors are not going to discuss about the causes of its origin. But since this virus has become the pandemic around the world so it is necessary to study this virus and its nature to predict the future outbreaks and also control strategies by using mathematical modeling. In this paper, we are going to study the model by using Atangana-Baleanu fractional operator. Also we calculated the numeric results with graphical representation. [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]

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]

The novel coronavirus disease 2019 (COVID-19) pandemic which emanated from Wuhan city, China and its successive spread across the globe has made a tremendous impact on virtually all spheres of human endeavors; ranging from economy, health, livelihood, and education amongst others. The widespread restrictions currently placed on movement and mass gatherings have made educational and academic activities be restructured to a pedagogical order. In recent times, the concept of learning has gradually transitioned from traditional classroom methods to online learning where students acquire knowledge through "hybrid". Most institutions of learning in Africa may encounter difficulties in migrating to this new form of learning due to the lack of information and communications technology (ICT) facilities of international standards. As of today, many students possess internet-enabled devices, yet cannot access the internet as a result of unaffordable data subscriptions, which has made most learners result in using their devices for non-educational purposes. As the COVID-19 pandemic continues to ravage the African continent returning to its former modus operandi by African schools is a herculean task. Thus, governments, authorities, and regulatory bodies in individual countries must improve their existing ICT and infrastructures for a better online learning experience. This paper serves as a wake-up call for Africa and its educational sector to establish online learning. [ABSTRACT FROM AUTHOR]

COVID-19, the disease caused by the novel coronavirus (SARS-CoV-2), first emerged in Wuhan, China late in December 2019. Not long after, the virus spread worldwide and was declared a pandemic by the World Health Organization in March 2020. This caused many changes around the world and in the United States, including an educational shift towards online learning. In this paper, we seek to understand how the COVID-19 pandemic and the increase in online learning impact college students' emotional wellbeing. We use several machine learning and statistical models to analyze data collected by the Faculty of Public Administration at the University of Ljubljana, Slovenia in conjunction with an international consortium of universities, other higher education institutions, and students' associations. Our results indicate that features related to students' academic life have the largest impact on their emotional wellbeing. Other important factors include students' satisfaction with their university's and government's handling of the pandemic as well as students' financial security. [ABSTRACT FROM AUTHOR]

The corona virus disease 2019 (COVID-19) pandemic, which originated in the city of Wuhan, China, has rapidly spread to various countries, with many cases having been reported. Some existing antiviral agents showed positive results to inhibit the infection and transmission of the novel corona virus 2019 (2019-nCoV) in vitro. Covaxin is India's first indigenous COVID-19 vaccine. In this paper, we introduce the Gourava and hyper-Gourava polynomials of a graph. We compute the Gourava and hyper-Gourava polynomials of some specific class of graphs. Further, we obtain the Gourava and hyper-Gourava polynomials of some chemical structures viz., chloroquine, hydroxychloroquine, remdesivir and theaflavin which were used in the treatment of COVID-19. The Gourava and hyper-Gourava indices are investigated for the aforesaid antiviral drugs using polynomial approach. The Indian Council of Medical Research and Bharat Biotech announced that Phase 3 results of covaxin, developed by them, have shown an interim vaccine efficacy of 81 percent. So, the results obtained may be useful for finding the design of new medicine for the treatment of COVID-19. [ABSTRACT FROM AUTHOR]