Determining clinical biomarkers to predict long-term SARS-CoV-2 antibody response among COVID-19 patients in Bangladesh.

Background: Information on antibody responses following SARS-CoV-2 infection, including the magnitude and duration of responses, is limited. In this analysis, we aimed to identify clinical biomarkers that can predict long-term antibody responses following natural SARS-CoV-2 infection. Methodology: In this prospective study, we enrolled 100 COVID-19 patients between November 2020 and February 2021 and followed them for 6 months. The association of clinical laboratory parameters on enrollment, including lactate dehydrogenase (LDH), neutrophil-lymphocyte ratio (NLR), C-reactive protein (CRP), ferritin, procalcitonin (PCT), and D-dimer, with predicting the geometric mean (GM) concentration of SARS-CoV-2 receptor-binding domain (RBD)-specific IgG antibody at 3 and 6 months post-infection was assessed in multivariable linear regression models. Result: The mean ± SD age of patients in the cohort was 46.8 ± 14 years, and 58.8% were male. Data from 68 patients at 3 months follow-up and 55 patients at 6 months follow-up were analyzed. Over 90% of patients were seropositive against RBD-specific IgG till 6 months post-infection. At 3 months, for any 10% increase in absolute lymphocyte count and NLR, there was a 6.28% (95% CI: 9.68, -2.77) decrease and 4.93% (95% CI: 2.43, 7.50) increase, respectively, in GM of IgG concentration, while any 10% increase for LDH, CRP, ferritin, and procalcitonin was associated with a 10.63, 2.87, 2.54, and 3.11% increase in the GM of IgG concentration, respectively. Any 10% increase in LDH, CRP, and ferritin was similarly associated with an 11.28, 2.48, and 3.0% increase in GM of IgG concentration at 6 months post-infection. Conclusion: Several clinical biomarkers in the acute phase of SARS-CoV-2 infection are associated with enhanced IgG antibody response detected after 6 months of disease onset. The measurement of SARS-CoV-2 specific antibody responses requires improved techniques and is not feasible in all settings. Baseline clinical biomarkers can be a useful alternative as they can predict antibody response during the convalescence period. Individuals with an increased level of NLR, CRP, LDH, ferritin, and procalcitonin may benefit from the boosting effect of vaccines. Further analyses will determine whether biochemical parameters can predict RBD-specific IgG antibody responses at later time points and the association of neutralizing antibody responses.

Correction to: Comment on: 'Should We Interfere with the Interleukin-6 Receptor During COVID-19: What Do We Know?' (Drugs, (2023), 10.1007/s40265-023-01869-6)

The title that reads Interleukin-6 Receptor Inhibitors and long COVID Should read Comment on: 'Should We Interfere with the Interleukin-6 Receptor During COVID-19: What Do We Know?' The original article has been corrected.Copyright © 2023 Springer Nature Switzerland AG.

Analysis and simulation of modified susceptible-infected-recovered model with vaccination for COVID-19 outbreak

In this paper, we develop and analyse a modified susceptible-infected-recovered (SIR) compartment model by integrating the vaccination factor as a model parameter to investigate the effect of vaccination parameter on the long-term outcomes of the COVID-19 pandemic. Mathematical analysis is used to determine the disease-free equilibrium, the endemic equilibrium, and the basic reproduction number of the developed model. The stability of the model is studied using the Routh-Hurwitz criterion, and numerical simulations are conducted to assess the impact of vaccination on the disease at different rates. The findings suggest that vaccination rate influences the transmission dynamics, and the vaccine can speed up the COVID-19 recovery and contain the outbreak. © 2023 Inderscience Enterprises Ltd.

Smart Methods to Deal with COVID-19 at University-Level Institutions Using Social Network Analysis Techniques

The current global health crisis is a consequence of the pandemic caused by COVID-19. It has impacted the lives of people from all factions of society. The re-emergence of new variants is threatening the world, which urges the development of new methods to prevent rapid spread. Places with more extensive social dealings, such as offices, organizations, and educational institutes, have a greater tendency to escalate the viral spread. This research focuses on developing a strategy to find out the key transmitters of the virus, particularly at educational institutes. The reason for considering educational institutions is the severity of the educational needs and the high risk of rapid spread. Educational institutions offer an environment where students come from different regions and communicate with each other at close distances. To slow down the virus's spread rate, a method is proposed in this paper that differs from vaccinating the entire population or complete lockdown. In the present research, we identified a few key spreaders, which can be isolated and can slow down the transmission rate of the contagion. The present study creates a student communication network, and virus transmission is modeled over the predicted network. Using student-to-student communication data, three distinct networks are generated to analyze the roles of nodes responsible for the spread of this contagion. Intra-class and inter-class networks are generated, and the contagion spread was observed on them. Using social network strategies, we can decrease the maximum number of infections from 200 to 70 individuals, with contagion lasting in the network for 60 days.

Skill and knowledge requirements of entry-level logistics professionals in the apparel industry of Bangladesh: an importance-expertise matrix analysis

This paper provides an assessment of the skills needed for entry-level logistics professionals in Bangladesh's apparel industry and suggests the critical skill areas that require improvement. Two studies were conducted to get the responses from supply chain and logistics professionals who have direct interactions with entry-level logistics professionals in the workplace. In study 1, an Importance-Expertise Matrix (IEM) analysis was conducted to provide an assessment of the relative importance and expertise of 40 skill items and investigate the skill gaps. The results reveal that 27.5% of the skill items have a noticeable gap between their importance and expertise level, indicating further improvement is needed. In Study 2, a qualitative approach was used, and the findings reinforced those of Study 1 and offered new and important information about skill and knowledge requirements amid the COVID-19 pandemic. This research offers implications for the apparel industry, academia, policymakers, and training agencies in Bangladesh.

Oral manifestations in COVID-19 patients among the Bangladeshi population: a cross-sectional study

Background: COVID-19 is a multi-system all-pervasive disease with protean manifestations, and its major signs and symptoms, such as incessant dry cough, fever, and pneumonia, are well known. Yet, its mucocutaneous manifestations, particularly those of the oral cavity, appear to be little recognized. This may be due either to the rarity of oral manifestations of COVID-19, or poor detection of such symptoms by attending physicians who may do only a cursory examination of the oral mucosa because of the overwhelming gravity of the other major systemic presentations. Nevertheless, there are now a considerable number of reports, including systematic reviews, on oral manifestations of COVID-19 in the literature. This observational study was performed to determine the oral manifestations among COVID-19 patients. Material & Methods: A cross-sectional study was carried out among COVID-19 recovered patients. 120 Covid 19 recovered patients were purposively selected as study samples. All the samples diagnosed as mild and moderate cases of COVID-19 disease were selected based on inclusion and exclusion criteria. Results: The study comprised the majority of males (68%) where females represent (32%) of the study population and the mean age was 39.3+or-12.4. Oral manifestations among study subjects during and after the disease illness including loss of taste being the commonest symptom (40%), followed by erythema and coated tongue (7.5%), mouth ulcerations (6.7%) and dry mouth (1.7%). The study revealed that the 41-60 age group subjects represented the highest (43%) oral manifestations. Conclusions: Early identification of oral symptoms in COVID-19 recovered or suspected cases can help a dentist or a general physician to diagnose high-risk groups, mitigate transmission, and promote overall health.

Bereavement in the Time of Covid-19: Learning from Experiences of the Bereaved

Introduction The COVID-19 pandemic has resulted in many people experiencing bereavement in challenging circumstances. In April 2020 at a large London Trust, a "Bereavement Welfare Hub" (BWH) was established to offer support and advice by telephone to relatives and carers of all adults who died as inpatients. Data from these calls has been used to examine and learn from experiences of the bereaved at this time. Methods Data from BWH call records regarding 809 adults who died at the Trust in March - May 2020 were collated and analysed quantitatively. A random selection of 149 call records were examined using thematic analysis. Results 809 adults died at the Trust between March and May 2020. The mean age at death was 76 (SD=14) and 86% of deaths occurred on medical wards (outside intensive care). Bereavement calls were completed in 663 (82%) of cases. From analysis of call records, several themes that influenced the bereavement experience were identified. These included support from family and community, communication and contact with the dying person, support from bereavement services and ability to carry out usual rituals associated with dying. Conclusions Age is a significant risk factor for death from COVID-19 and the majority of deaths have occurred on medical wards. Improving hospital care of dying patients during the pandemic or at any time is relevant to geriatricians and other healthcare professionals working with older people. Our analysis identifies several factors which positively or negatively influenced the experiences of people bereaved during the first wave of COVID-19. From these findings, recommendations have been made which have the potential to improve the bereavement experience, particularly during the pandemic era.

Covid-19 Vaccine safety and adverse event analysis from Pakistan

Covid immunization commenced on 2nd Feb 2021 in Pakistan and as of 7th Sep 2021, over 84 million vaccine doses were administered in Pakistan, of which 72% procured by the government, 22% received through Covax and 6% were donated. The vaccines rolled out nationally included: Sinopharm, Sinovac and CanSinoBIO (China), AstraZeneca (UK), Moderna and Pfizer (USA), Sputnik (Russia), and PakVac (China/Pakistan). About half of the eligible population in Pakistan (63 m) had received at least one dose of Covid vaccine as of Sep 2021. Pakistan National Pharmacovigilance Centre (PNPC) in coordination with WHO, MHRA and Uppsala Monitoring Centre (UMC) established pharmacovigilance centers across Pakistan. The Covid vaccine AEFIs in Pakistan were mainly reported via NIMS (National Immunization Management System), COVIM (Covid-19 Vaccine Inventory Management System), 1166 freephone helpline and MedSafety. There have been 39,291 ADRs reported as of 30th Sept 2021, where most reported after the first dose (n = 27,108) and within 24-72 h of immunization (n = 27,591). Fever or shivering accounted for most AEFI (35%) followed by injection-site pain or redness (28%), headache (26%), nausea/vomiting (4%), and diarrhoea (3%). 24 serious AEFIs were also reported and investigated in detail by the National AEFI review committee. The rate of AEFIs reports ranged from 0.27 to 0.79 per 1000 for various Covid vaccines in Pakistan that was significantly lower than the rates in UK (~4 per 1000), primarily atrributed to underreporting of cases in Pakistan. Finally, Covid vaccines were well tolerated and no significant cause for concern was flagged up in Pakistan's Covid vaccine surveillance system concluding overall benefits outweighed risks.Copyright © 2022

From eating out to online food ordering amid COVID-19: a case of food delivery apps

In Bangladesh, app-based online food delivery services are gaining popularity. The increased use of mobile phones and the affordability of the internet aided businesses in meeting new customer demands such as food delivery to the customers' homes. Thus, the primary goal of this research is to identify the factors influencing customer intention to use food delivery apps (FDAs) during COVID-19. Data are collected using a structured questionnaire and analysed quantitatively using partial least square structured equation modelling (PLS-SEM). Results show that price value and convenience are the most influential factors affecting the intention to use FDAs during COVID-19. There is, however, insufficient evidence to demonstrate the influence of the other three factors (e.g., service quality, delivery experience, ease of use) on FDA use intention. A better understanding of emerging student consumers will assist management in leveraging the factors that must be considered when providing services to this market.

Post-Pandemic Sentiment Analysis Based on Twitter Data Using Deep Learning

Since January 2020, COVID-19 has been spreading over the world and has been declared a pandemic. Nation and society are growing scared of it as fresh instances and mass deaths increase daily. India was one of the major countries to suffer the consequences of COVID-19 during that phase as multiple waves hit India. Many social media channels were being used by people from all over the country to discuss this pandemic and its aftereffects. One of the most popular ways to share opinions or judgments today is through social media. Therefore, machines are continuously being developed to analyze what people post on social networking sites like Twitter, Facebook, Instagram, and other platforms thanks to advancements in current computing technology. Based on their mood, these ideas or points of view can be grouped and examined. In this paper, we used tweets collected from Twitter to analyze the sentiment that people conveyed on social media after the second wave of Corona Virus. The sentiment of the tweets has been divided into five categories: "Strongly Negative", "Negative", "Neutral", "Positive"and "Strongly Positive". First, we classify data using Python's Vader. We have trained a model using our own labeled dataset and evaluated its performance using Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN). © 2022 IEEE.

Mathematical Model of COVID-19 Pandemic with Double Dose Vaccination.

This paper is concerned with the formulation and analysis of an epidemic model of COVID-19 governed by an eight-dimensional system of ordinary differential equations, by taking into account the first dose and the second dose of vaccinated individuals in the population. The developed model is analyzed and the threshold quantity known as the control reproduction number [Formula: see text] is obtained. We investigate the equilibrium stability of the system, and the COVID-free equilibrium is said to be locally asymptotically stable when the control reproduction number is less than unity, and unstable otherwise. Using the least-squares method, the model is calibrated based on the cumulative number of COVID-19 reported cases and available information about the mass vaccine administration in Malaysia between the 24th of February 2021 and February 2022. Following the model fitting and estimation of the parameter values, a global sensitivity analysis was performed by using the Partial Rank Correlation Coefficient (PRCC) to determine the most influential parameters on the threshold quantities. The result shows that the effective transmission rate [Formula: see text], the rate of first vaccine dose [Formula: see text], the second dose vaccination rate [Formula: see text] and the recovery rate due to the second dose of vaccination [Formula: see text] are the most influential of all the model parameters. We further investigate the impact of these parameters by performing a numerical simulation on the developed COVID-19 model. The result of the study shows that adhering to the preventive measures has a huge impact on reducing the spread of the disease in the population. Particularly, an increase in both the first and second dose vaccination rates reduces the number of infected individuals, thus reducing the disease burden in the population.

Landscape Determinants of Infectivity and Insights into Vaccine Development and Effectiveness-Novel Coronavirus

Novel technology has led to advanced approaches and understandings of viral biology, and the advent in previous years has raised the possibility of determination of mechanisms of viral replication and infection, trans-species adaption, and disease. The outbreak of Coronavirus 2019 (COVID-19) has become a global life-threatening concern recently. The war against COVID19 has now reached the most critical point, whereby it has caused worldwide social and economic disruption. Unfortunately, limited knowledge persists among the community regarding the biology of SARS-CoV-2 infection. The present review will summarize the basic life cycle and replication of the well-studied coronaviruses, identifying the unique characteristics of coronavirus biology and highlighting critical points where research has made significant advances that might represent targets for antivirals or vaccines. Areas where rapid progress has been made in SARS-CoV research have been highlighted. Additionally, an overview of the efforts dedicated to an effective vaccine for this novel coronavirus, particularly different generations of vaccines, which has crippled the world, has also been discussed. Areas of concern for research in coronavirus replication, genetics, and pathogenesis have been explained as well. Speedy evaluation of multiple approaches to elicit protective immunity and safety is essential to curtail unwanted immune potentiation, which plays an important role in the pathogenesis of this virus. Hope is to provide a glimpse into the current efforts, and the progress is made with reference to Coronaviruses and how the community can work together to prevent and control coronavirus infection now and in the future. Copyright © 2023 Bentham Science Publishers.

Contactless Surveillance for Preventing Wind-Borne Disease using Deep Learning Approach

Covid-19 has been marked as a pandemic worldwide caused by the SARS-CoV-2 virus. Different studies are being conducted with a view to preventing and lessening the infections caused by covid-19. In future, many other wind-borne diseases may also appear and even emerge as "pandemic”. To prevent this, various measures should be an integral part of our daily life such as wearing face masks. It is tough to manually ensure individuals safety. The goal of this paper is to automate the process of contactless surveillance so that substantial prevention can be ensured against all kinds of wind-borne diseases. For automating the process, real time analysis and object detection is a must for which deep learning is the most efficient approach. In this paper, a deep learning model is used to check if a person takes any preventive measures. In our experimental analysis, we considered real time face mask detection as a preventive measure. We proposed a new face mask detection dataset. The accuracy of detecting a face mask along with the identity of a person achieved accuracy of 99.5%. The proposed model decreases time consumption as no human intervention is needed to check an individual person. This model helps to decrease infection risk by using a contactless automation system. © 2022,International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Systematic Analysis of Several Deep Learning Approaches for COVID-19 Detection Using X-ray Images

COVID-19 is a virus-borne malady. A clinical study of infected COVID-19 patients found that most COVID-19 patients suffered lung infection after contracting the disease. Consequently, chest X-rays are a more effective and lower-cost imaging technique for diagnosing lung-related problems. This study used deep learning models, including MobileNetV2,DenseNet201, ResNet50, and VGG19, for COVID-19 prediction. For the study, we used chest X-ray image data for binary classification of COVID-19. 7207 chest X-ray image data were obtained from the Kaggle repository, with 5761 being utilized for training and 1446 being used for validation. A comparative analysis was conducted among the models and examined their accuracy. It has been determined that the DenseNet201 models achieved the highest accuracy of 93.02% for detecting COVID-19 in the lowest compilation time of 27secs. The models, MobileNetV2, ResNet50, and VGG19 had the accuracy rate of 77.28%, 65.86% and 74.92%, respectively. The research indicates that the DenseNet201 model is the most effective in detecting COVID-19 using x-ray imaging. © 2022 IEEE.