An SEQAIHR model to study COVID-19 transmission and optimal control strategies in Hong Kong, 2022.

During the COVID-19 pandemic, one of the major concerns was a medical emergency in human society. Therefore it was necessary to control or restrict the disease spreading among populations in any fruitful way at that time. To frame out a proper policy for controlling COVID-19 spreading with limited medical facilities, here we propose an SEQAIHR model having saturated treatment. We check biological feasibility of model solutions and compute the basic reproduction number ( R 0 ). Moreover, the model exhibits transcritical, backward bifurcation and forward bifurcation with hysteresis with respect to different parameters under some restrictions. Further to validate the model, we fit it with real COVID-19 infected data of Hong Kong from 19th December, 2021 to 3rd April, 2022 and estimate model parameters. Applying sensitivity analysis, we find out the most sensitive parameters that have an effect on R 0 . We estimate R 0 using actual initial growth data of COVID-19 and calculate effective reproduction number for same period. Finally, an optimal control problem has been proposed considering effective vaccination and saturated treatment for hospitalized class to decrease density of the infected class and to minimize implemented cost.

An SEQAIHR model to study COVID-19 transmission and optimal control strategies in Hong Kong, 2022

During the COVID-19 pandemic, one of the major concerns was a medical emergency in human society. Therefore it was necessary to control or restrict the disease spreading among populations in any fruitful way at that time. To frame out a proper policy for controlling COVID-19 spreading with limited medical facilities, here we propose an SEQAIHR model having saturated treatment. We check biological feasibility of model solutions and compute the basic reproduction number (

In-Silico Lead Druggable Compounds Identification against SARS COVID-19 Main Protease Target from In-House, Chembridge and Zinc Databases by Structure-Based Virtual Screening, Molecular Docking and Molecular Dynamics Simulations.

Pharmacological strategies to lower the viral load among patients suffering from severe diseases were researched in great detail during the SARS-CoV-2 outbreak. The viral protease Mpro (3CLpro) is necessary for viral replication and is among the main therapeutic targets proposed, thus far. To stop the pandemic from spreading, researchers are working to find more effective Mpro inhibitors against SARS-CoV-2. The 33.8 kDa Mpro protease of SARS-CoV-2, being a nonhuman homologue, has the possibility of being utilized as a therapeutic target against coronaviruses. To develop drug-like compounds capable of preventing the replication of SARS-main CoV-2's protease (Mpro), a computer-aided drug design (CADD) approach is extremely viable. Using MOE, structure-based virtual screening (SBVS) of in-house and commercial databases was carried out using SARS-CoV-2 proteins. The most promising hits obtained during virtual screening (VS) were put through molecular docking with the help of MOE. The virtual screening yielded 3/5 hits (in-house database) and 56/66 hits (commercial databases). Finally, 3/5 hits (in-house database), 3/5 hits (ZINC database), and 2/7 hits (ChemBridge database) were chosen as potent lead compounds using various scaffolds due to their considerable binding affinity with Mpro protein. The outcomes of SBVS were then validated using an analysis based on molecular dynamics simulation (MDS). The complexes' stability was tested using MDS and post-MDS. The most promising candidates were found to exhibit a high capacity for fitting into the protein-binding pocket and interacting with the catalytic dyad. At least one of the scaffolds selected will possibly prove useful for future research. However, further scientific confirmation in the form of preclinical and clinical research is required before implementation.

A study to evaluate the impact of COVID-19 on Lifestyle of Medical students

Background: Stressful situations are often linked with poor health and lifestyle behaviors (e.g., unhealthy diet, limited physical activity, poor sleep quality etc.). Subsequent to the nationwide lockdown in Pakistan due to COVID-19 pandemic, medical students experienced substantial lifestyle changes along with academic stresses. The goal of this study was to measure the impact that the COVID-19 lockdown had on lifestyle like sleep, physical activity and nutrition, substance abuse, dealing with finances, spirituality and family life, with respect to a sample of Pakistani medical students. Methods: This crosssectional online study involving 1100 medical students (68.7% females) from five medical colleges in Pakistan, used self-administered questionnaire to evaluate the impact of pandemic related restrictions on lifestyle of medical students in Pakistan from AugustSeptember 2020. Parameters such as physical activity, sleep, nutrition, smoking and substance abuse, family life, finances, internet use and spirituality were studied. Results: Fifty-nine percent of medical students reported a significant effect of lockdown on their physical activity. Only 5.8% people clearly followed or adhered to a routine during the pandemic. About 54.6% students have found that physical activity and exercise significantly reduced their anxiety. More than half of respondents reported increase in screen time, weight gain and poor sleep with 3.1% of students stating an increased use of sleeping pills to fall asleep Another 18.3% students had an increased worry regarding COVID19 based on the information they received on internet. No significant increase in substance uses and alcohol use or family conflicts among students post pandemic was noted. Conclusion: Our findings underscore that COVID-19 pandemic has led to undesired changes in health and lifestyle habits of medical students, which may, to some extent, be responsible for higher negative impact. Hence it is important for medical colleges to start awareness campaigns to tackle challenges for medical students physical and emotional well-being.

Impulsive and Compulsive Buying Tendencies and Consumer Resistance to Digital Innovations: The Moderating Role of Perceived Threat of COVID-19.

Based on the theory of reasoned action and innovation resistance theory, this study aims to explore the tendencies of consumer resistance to digital innovation and the moderating role of a perceived threat of coronavirus disease 2019 (COVID-19). Data were collected using a cross-sectional online survey of 1,000 consumers of fast-moving consumer goods (FMCGs) in Pakistan. The results revealed several significant relationships between tendencies (impulsive and compulsive) of consumer resistance to digital innovation and the perceived threat of COVID-19. This study brings several key insights for consumers of FMCG products from Pakistan, and many theoretical and practical implications and future research directions are suggested.

Application of Multifractal Analysis in Estimating the Reaction of Energy Markets to Geopolitical Acts and Threats

Since the industrial revolution, the geopolitics of energy has been a driver of global prosperity and security, and determines the survival of life on our planet. This study examines the nonlinear structure and multifractal behavior of the cross-correlation between geopolitical risk and energy markets (West Texas Intermediate (WTI), Brent, natural gas and heating oil), using the multifractal detrended cross-correlation analysis. Furthermore, an in-depth analysis reveals different associations of the indices of overall geopolitical risk, geopolitical acts, and geopolitical threats against the four energy products. Based on daily data ranging from 1 January 1985 to 30 August 2021, the findings confirm the presence of nonlinear dependencies, suggesting that geopolitical risk and energy markets are interlinked. Furthermore, significant multifractal characteristics are found and the degree of multifractality is stronger between the overall geopolitical risk and WTI while the lowest degree of multifractality is with Brent. Overall, for the WTI and heating-oil markets, the influence of geopolitical threats is more pronounced rather than their fulfilment. Contrarily, the Brent and natural gas are more correlated to geopolitical acts. Energy products exhibit heterogeneous persistence levels of cross-correlation with all the indicators of geopolitical risk, being more persistent in the case of small fluctuations compared to large fluctuations.

Harvesting Energy from Ocean: Technologies and Perspectives

The optimal utilization of renewable energies is a crucial factor toward the realization of sustainability and zero carbon in a future energy system. Tidal currents, waves, and thermal and salinity gradients in the ocean are excellent renewable energy sources. Ocean tidal, osmotic, wave, and thermal energy sources have yearly potentials that exceed the global power demand of 22,848 TWh/y. This paper extensively reviews the technologies related to energy harvesting from waves, tidal, ocean thermals, and the salinity gradient. Moreover, the socio-economic, social, and environmental aspects of the above technologies are also discussed. This paper provides a better picture of where to invest in the future energy market and highlights research gaps and recommendations for future research initiatives. It is expected that a better insight into ocean energy and a deep understanding of various potential devices can lead to a broader adoption of ocean energy. It is also clear that further research into control strategies is needed. Policy makers should provide financial support for technologies in the demonstration stage and employ road mapping to accelerate the cost and risk reductions to overcome economic hurdles. To identify traditional and online sources on the topic, the authors used electronic databases and keyword searching approaches. Among them, the International Renewable Energy Agency data were the primary database utilized to locate sources.

Additive manufacturing is sustainable technology: citespace based bibliometric investigations of fused deposition modeling approach

Purpose>Additive manufacturing (AM) technology has a huge influence on the real world because of its ability to manufacture massively complicated geometrics. The purpose of this study is to use CiteSpace (CS) visual analysis to identify fused deposition modeling (FDM) research and development patterns to guide researchers to decide future research and provide a framework for corporations and organizations to prepare for the development in the rapid prototyping industry. Three-dimensional printing (3DP) is defined to budget minimize manufactured input and output for aviation and the medical product industrial sectors. 3DP has implemented its potential in the Coronavirus Disease of 2019 (COVID-19) reaction.Design/methodology/approach>First, 396 original publications were extracted from the web of science (WOS) with the comprehensive list and did scientometrics analysis in CS software. The parameters are specified in CS including the span (from 2011 to 2019, one year slice for the co-authorship and the co-accordance analysis), visualization (show the merged networks), specific criteria for selection (top 20%), node form (author, organization, region, reference cited;cited author, journal and keywords) and pruning (pathfinder and slicing network). Finally, correlating data was studied and showed the results of the visualization study of FDM research were shown.Findings>The framework of FDM information is beginning to take shape. About hot research topics, there are “Morphology,” “Tensile Property by making Blends,” “Use of Carbon nanotube in 3DP” and “Topology optimization.” Regarding the latest research frontiers of FDM printing, there are “Fused Filament Fabrication,” “AM,” in FDM printing. Where “Post-processing” and “environmental impact” are the research hotspots in FDM printing. These research results can provide insight into FDM printing and useful information to consider the existing studies and developments in FDM researchers’ analysis.Research limitations/implications>Despite some important obtained results through FDM-related publications’ visualization, some deficiencies remain in this research. With >99% of articles written in English, the input data for CS was all downloaded from WOS databases, resulting in a language bias of papers in other languages and neglecting other data sources. Although, there are several challenges being faced by the FDM that limit its wide variety of applications. However, the significance of the current work concerning the technical and engineering prospects is discussed herein.Originality/value>First, the novelty of this work lies in describing the FDM approach in a Scientometric way. In Scientometric investigation, leading writers, organizations, keywords, hot research and emerging knowledge points were explained. Second, this research has thoroughly and comprehensively examined the useful sustainability effects, i.e. economic sustainability, energy-based sustainability, environmental sustainability, of 3DP in industrial development in qualitative and quantitative aspects by 2025 from a global viewpoint. Third, this work also described the practical significance of FDM based on 3DP since COVID-19. 3DP has stepped up as a vital technology to support improved healthcare and other general response to emergency situations.

New Horizons: COVID-19 and the Burden of Neuropsychiatric Illness in Pakistan.

This manuscript reviews the current state of knowledge about the burden of mental illness and assesses the impact of COVID-19 illness on mental health in Pakistan. For this we analyzed secondary data obtained from the Institute of Health Metrics and Evaluation. The Global Burden of Disease (GBD) study draws from a wide range of data sources to quantify global and regional effects of a disease. We also did a literature search on the effects of COVID-19 illness on mental health and the psychosocial effects of COVID-19 and other Corona virus related illnesses such as SARS-CoV and MERS-CoV. Data from the studies obtained was utilized to extrapolate the anticipated effects of COVID-19 illness on healthcare workers, COVID-19 patients and the general public in Pakistan. Mental illness poses a significant challenge to Pakistan's under resourced health care system. COVID-19 has the potential to strain Pakistan's healthcare system to the breaking point. So far, the general morbidity from COVID-19 illness in Pakistan has been low compared to other countries but this could change in the coming weeks and months. Hidden within this crisis are also some opportunities for both healthcare and education.

The Impact of Job Stress and State Anger on Turnover Intention Among Nurses During COVID-19: The Mediating Role of Emotional Exhaustion

Based on the social exchange theory, the aim of this study is to identify the association between job stress state anger, emotional exhaustion and job turnover intention. This study postulates that job related stress and state anger among nurses during COVID-19 subsequently leads to their job turnover intentions. In addition, the study also aims to see the mediating role of emotional exhaustion between COVID-19-related job stress, state anger, and turnover intentions. The sample of this study is gathered from 335 registered nurses working in Pakistani hospitals dealing with COVID-19-related patients. The interrelationships between variables are checked by using structural equation modeling through AMOS. Key findings confirm that COVID-19-related job stress and state anger had a significant effect on nurses’ turnover intentions. Furthermore, emotional exhaustion mediated the relationship between COVID-19-related job stress, state anger, and turnover intentions. There is a lack of research which has assessed the impact of Novel COVID-19-related job stress and state anger on nurses’ turnover intentions in hospitals, providing empirical evidence from a developing country-Pakistan. This study offers managerial implications for hospital management and health policymakers. Moreover, nursing managers need to pay attention to nurses’ turnover intentions who are facing the issue at the front line as patients receive their initial treatment from nurses in the COVID-19 outbreak.

Modeling Dynamic Multifractal Efficiency of US Electricity Market

The dramatic deregulatory reforms in US electricity markets increased competition, resulting in more complex prices compared to other commodities. This paper aims to investigate and compare the overall and time-varying multifractality and efficiency of four major US electricity regions: Mass Hub, Mid C, Palo Verde, and PJM West. Multifractal detrended fluctuation analysis (MFDFA) is employed to better quantify the intensity of self-similarity. Large daily data from 2001 to 2021 are taken in order to make a more conclusive analysis. The four electricity market returns showed strong multifractal features with PJM West having the highest multifractality (corresponding to lowest efficiency) and Mass Hub having the lowest multifractality (i.e., highest efficiency). Moreover, all series exhibited mean reverting (anti-persistent) behavior in the overall time period. The findings of MFDFA rolling window suggest Palo Verde as the most volatile index, while a significant upward trend in the efficiency of Mass Hub and PJM West is observed after the first quarter of 2014. The novel findings have important implications for policymakers, regulatory authorities, and decision makers to forecast electricity prices better and control efficiency.

Mathematical modeling of reservoir-mediator-human dynamical transmission of covid-19 outbreak

Coronaviruses are highly transmissible and are pathogenic viruses of the 21st century worldwide. The outbreak of Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome (SARS) coronavirus 2 (SARS-CoV-2). In general, SARS-CoV-2 viruses are originated in bats or other wild animals and outbreak was originated from the Wuhan city of China at the end of December 2019. At the same time, the transmission of the infection to the human is caused by domestic animals that represent in the habitat the mediator. We especially emphasized the interaction of reservoir, mediator, and human the spread of coronavirus through our proposed model. In this paper, we reviewed currently information and established a model of differential equations, which has twelve ODEs with effective parameters to discuss the spread of the infection from the natural reservoir to the mediator, and from them to the human. We show analytical results as boundedness, non-negativity of solution, disease-free equilibrium point, endemic equilibrium point, basic reproduction number, steady state and numerical simulation. We aimed to estimate the numerical result of our model showed that the new incidence of symptomatic and asymptomatic infections, deaths, and isolations under quarantine scenarios. COVID-19 transmission rate among natural reservoir-mediator-human is the main theme of this study. © IEOM Society International.

Acute motor-sensory axonal polyneuropathy variant of Guillain-Barre syndrome complicating the recovery phase of coronavirus disease 2019 infection: a case report.

INTRODUCTION: The novel coronavirus, since its first identification in China, in December 2019, has shown remarkable heterogeneity in its clinical behavior. It has affected humans on every continent. Clinically, it has affected every organ system. The outcome has also been variable, with most of the older patients showing grave outcomes as compared with the younger individuals. Here we present a rare and severe variant of Guillain-Barre syndrome that complicated the disease in recovery phase. CASE PRESENTATION: A 60-year-old Afghan man, who had been recovering from symptoms related to novel coronavirus associated disease, presented with sudden onset of progressive muscle weakness and oxygen desaturation. Electrophysiological workup confirmed the diagnosis of Guillain-Barre syndrome, and early institution of intravenous immunoglobulin resulted in complete resolution. CONCLUSION: Guillain-Barre syndrome has recently been reported in many patients diagnosed with novel coronavirus associated disease. While clinical suspicion is mandatory to guide towards an effective diagnostic workup, early diagnosis of this complication and timely institution of therapeutic interventions are indispensable and lifesaving.

Immunoinformatics and Molecular Docking Studies Predicted Potential Multiepitope-Based Peptide Vaccine and Novel Compounds against Novel SARS-CoV-2 through Virtual Screening.

BACKGROUND: Coronaviruses (CoVs) are enveloped positive-strand RNA viruses which have club-like spikes at the surface with a unique replication process. Coronaviruses are categorized as major pathogenic viruses causing a variety of diseases in birds and mammals including humans (lethal respiratory dysfunctions). Nowadays, a new strain of coronaviruses is identified and named as SARS-CoV-2. Multiple cases of SARS-CoV-2 attacks are being reported all over the world. SARS-CoV-2 showed high death rate; however, no specific treatment is available against SARS-CoV-2. METHODS: In the current study, immunoinformatics approaches were employed to predict the antigenic epitopes against SARS-CoV-2 for the development of the coronavirus vaccine. Cytotoxic T-lymphocyte and B-cell epitopes were predicted for SARS-CoV-2 coronavirus protein. Multiple sequence alignment of three genomes (SARS-CoV, MERS-CoV, and SARS-CoV-2) was used to conserved binding domain analysis. RESULTS: The docking complexes of 4 CTL epitopes with antigenic sites were analyzed followed by binding affinity and binding interaction analyses of top-ranked predicted peptides with MHC-I HLA molecule. The molecular docking (Food and Drug Regulatory Authority library) was performed, and four compounds exhibiting least binding energy were identified. The designed epitopes lead to the molecular docking against MHC-I, and interactional analyses of the selected docked complexes were investigated. In conclusion, four CTL epitopes (GTDLEGNFY, TVNVLAWLY, GSVGFNIDY, and QTFSVLACY) and four FDA-scrutinized compounds exhibited potential targets as peptide vaccines and potential biomolecules against deadly SARS-CoV-2, respectively. A multiepitope vaccine was also designed from different epitopes of coronavirus proteins joined by linkers and led by an adjuvant. CONCLUSION: Our investigations predicted epitopes and the reported molecules that may have the potential to inhibit the SARS-CoV-2 virus. These findings can be a step towards the development of a peptide-based vaccine or natural compound drug target against SARS-CoV-2.

Determine the Potential Epitope Based Peptide Vaccine Against Novel SARS-CoV-2 Targeting Structural Proteins Using Immunoinformatics Approaches.

Coronaviruses (CoVs) belong to the Coronaviridae-family. The genus Beta-coronaviruses, are enveloped positive strand RNA viruses with club-like spikes at the surface with a unique replication process and a large RNA genome (∼25 kb). CoVs are known as one of the major pathogenic viruses causing a variety of diseases in birds and mammals including humans (lethal respiratory dysfunctions). Recently, a new strain of coronavirus has been identified and named as SARS-CoV-2. A large number of COVID-19 (disease caused by SARS-CoV-2) cases are being diagnosed all over the World especially in China (Wuhan). COVID-19 showed high mortality rate exponentially, however, not even a single effective cure is being introduced yet against COVID-19. In the current study, immunoinformatics approaches were employed to predict the antigenic epitopes against COVID-19 for the development of a coronavirus peptide vaccine. Cytotoxic T-lymphocyte (CTL) and B-cell epitopes were predicted for SARS-CoV-2 coronavirus structural proteins (Spikes, Membrane, Envelope, and Nucleocapsid). The docking complexes of the top 10 epitopes having antigenic sites were analyzed led by binding affinity and binding interactional analyses of top ranked predicted peptides with the MHC-I HLA molecule. The predicted peptides may have potential to be used as peptide vaccine against COVID-19.

Modeling the Effective Control Strategy for Transmission Dynamics of Global Pandemic COVID-19 (preprint)

The novel coronavirus disease (namely COVID-19) has taken attention because of its deadliness across the globe, causing a massive death as well as critical situation around the world. It is an infectious disease which is caused by newly discovered coronavirus. Our study demonstrates with a nonlinear model of this devastating COVID-19 which narrates transmission from human-to-human in the society. Pontryagin Maximum principle has also been applied in order to obtain optimal control strategies where the maintenance of social distancing is the major control. The target of this study is to find out the most fruitful control measures of averting coronavirus infection and eventually, curtailed of the COVID-19 transmission among people. The model is investigated analytically by using most familiar necessary conditions of Pontryagin maximum principle. Furthermore, numerical simulations have been performed to illustrate the analytical results. The analysis reveals that implementation of educational campaign, social distancing and developing human immune system are the major factors which can be able to plunge the scenario of becoming infected.