Fear of COVID-19 in dentistry students: an evaluation of their knowledge and practices

Background: This study's objective was to analyse the fear of COVID-19 among the dentistry student and the knowledge, attitude, and practices during the COVID-19 pandemic. Methods: It was a cross-sectional questionnaire-based study. An online survey form was designed and distributed to undergraduate dental students via google forms. A previously validated fear of coronavirus scale (FCV-19S) was used to analyse the fear. SPSS 21 was used for data entry and data analysis. Descriptive statistics were applied to calculate the frequencies of different variables. Independent t-test was executed to determine the difference of FCV-19S among gender and between public and private dental colleges. ANOVA was carried out to evaluate the difference in fear among different levels of BDS. Results: Data of 983 individuals from different dental colleges in Karachi, Pakistan, have been analysed. The majority of the students were females in 1st year and private sector dental colleges (P<0.001). The mean FCV-19S was 20.99 +or- 6.48, which is higher than the cut-off value 15. A highly significant difference in mean FCV-19S among the different variables has been observed (P<0.001). A significant difference has been observed among the gender (t (932) = -5.40, p<0.001) in all 4-years of BDS. Conclusion: Despite good knowledge and following the COVID-19 guidelines, fear is prevalent among the students.

Machine Learning-Based Hybrid Ensemble Model Achieving Precision Education for Online Education Amid the Lockdown Period of COVID-19 Pandemic in Pakistan

Institutions of higher learning have made persistent efforts to provide students with a high-quality education. Educational data mining (EDM) enables academic institutions to gain insight into student data in order to extract information for making predictions. COVID-19 represents the most catastrophic pandemic in human history. As a result of the global pandemic, all educational systems were shifted to online learning (OL). Due to issues with accessing the internet, disinterest, and a lack of available tools, online education has proven challenging for many students. Acquiring accurate education has emerged as a major goal for the future of this popular medium of education. Therefore, the focus of this research was to identifying attributes that could help in students' performance prediction through a generalizable model achieving precision education in online education. The dataset used in this research was compiled from a survey taken primarily during the academic year of COVID-19, which was taken from the perspective of Pakistani university students. Five machine learning (ML) regressors were used in order to train the model, and its results were then analyzed. Comparatively, SVM has outperformed the other methods, yielding 87.5% accuracy, which was the highest of all the models tested. After that, an efficient hybrid ensemble model of machine learning was used to predict student performance using NB, KNN, SVM, decision tree, and logical regression during the COVID-19 period, yielding outclass results. Finally, the accuracy obtained through the hybrid ensemble model was obtained as 98.6%, which demonstrated that the hybrid ensemble learning model has performed better than any other model for predicting the performance of students.

Does financial inclusion index and energy performance index co-move?

Financing strategies and energy performance have been extensively studied previously, and researchers frequently overlook the co-movements of integration of financial inclusion and energy performance index in the E7 Context. To address this gap, current research estimates the co-movement between the financial inclusion index and sustainable energy performance index to reflect the consequences of the COVID-19 crisis. Our findings show that in E7 economies, China exceeds the other nations in terms of energy performance. With a steady score, Russia is second in the group. Indonesia and Turkey are respectively fourth and fifth, and their total results show excellent prospective performances for sustainability. Mexico and Brazil follow this ranking with bad results and the lowest scores reported in the study results. The study findings are helpful for policy formulation and assessment. The study presented recommendations about financial inclusion and energy management practices in COVID-19 and delivered insights about the energy performance index in E7 economies. © 2023 Elsevier Ltd

Achieving Personalized Precision Education Using the Catboost Model during the COVID-19 Lockdown Period in Pakistan

With the emergence of the COVID-19 pandemic, access to physical education on campus became difficult for everyone. Therefore, students and universities have been compelled to transition from in-person to online education. During this pandemic, online education, the use of unfamiliar digital learning tools, the lack of internet access, and the communication barriers between teachers and students made precision education more difficult. Customizing models from previous studies that only consider a single course in order to make a prediction reduces the predictive power of the model because it only considers a small subset of the attributes of each possible course. Due to a lack of data for each course, overfitting often occurs. It is challenging to obtain a comprehensive understanding of the student's participation during the semester system or in a broader context. In this paper, a model that is flexible and more generalizable is developed to address these issues. This model resolves the problem of generalized models and overfitting by using a large number of responses from college and university students as a dataset that considered a broader range of attributes, regardless of course differences. CatBoost, an advanced type of gradient boosting algorithm, was used to conduct this research, and enabled the developed model to perform effectively and produce accurate results. The model achieved a 96.8% degree of accuracy. Finally, a comparison was made with other related work to demonstrate the concept, and the experimental results proved that the Catboost model is a viable, accurate predictor of students' performance. © 2023 by the authors.

Monte Carlo Dropout for Uncertainty Analysis and ECG Trace Image Classification

Cardiovascular diseases (CVDs), such as arrhythmias (abnormal heartbeats) are the prime cause of mortality across the world. ECG graphs are utilized by cardiologists to indicate any unexpected cardiac activity. Deep Neural Networks (DNN) serve as a highly successful method for classifying ECG images for the purpose of computer-aided diagnosis. However, DNNs can not quantify uncertainty in predictions, as they are incapable of discriminating between anomalous data and training data. Hence, a lack of reliability in automated diagnosis and the potential to cause severe decision-making issues is created, particularly in medical practises. In this paper, we propose an uncertainty-aware ECG classification model where Convolutional Neural Networks (CNN), combined with Monte Carlo Dropout (MCD) is employed to evaluate the uncertainty of the model, providing a more trustworthy process for real-world scenarios. We use ECG images dataset of cardiac and covid-19 patients containing five categories of data, which includes COVID-19 ECG records as well as data from other cardiovascular disorders. Our proposed model achieves 93.90% accuracy using this dataset. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A crisis within a crisis: Role of COVID-19 pandemic in poor compliance of Community-based Management of Severe Acute Malnutrition among children younger than five years in Pakistan

Introduction In order to attain Sustainable Development Goal 2 (SDG-II) of eradicating malnutrition among children by 2030, Pakistan has initiated a Community-based Management of Severely Acute Malnutrition (CMAM) program. This program has been established at the public level to detect and treat uncomplicated Severely Acute Malnourished (SAM) children at an early stage. However, during the outbreak of COVID-19, very poor compliance with the CMAM program was observed. Consequently, the nutritional quality of children's diets has deteriorated, with malnutrition rates expected to rise. Therefore, this study has been set up to evaluate the effect of the COVID-19 lockdown on the health of SAM children and compliance with the CMAM program. Methodology This study used a multicenter cross-sectional design in District Dera Ghazi Khan's rural areas located in the Southern Province of Punjab. Data were collected from the parents/guardians of SAM children through the researcher-administrated questionnaire. The sample size was 196, and data were analyzed through SPSS version 25. Results The majority of the children enrolled were males (52.5%), had fathers aged between 41 and 50 years (52.0%), mothers aged between 21 and 30 years (52.5%), had illiterate fathers (40.1%), illiterate mothers (73.8%) and had a monthly household income of PKR <15,000 (91.1%). All of the respondents mentioned that COVID-19 affected them in one way or the other (100.0%), with a majority of them did not visit the hospital during COVID-19 for their SAM child (52.5%) as they were afraid of COVID-19 (63.2%) and/or they lacked access to transport for visiting a hospital (93.4%). Bivariate analysis revealed that the father's age (P = 0.02) and income (P = 0.00) is associated with the perceived effect of COVID-19 on income. In contrast, only the gender of the child (P = 0.00) is related to the visit to the hospital, and the gender of the child (P = 0.01) and mother's literacy (P = 0.00) is associated with the choice of treatment from any other setup, including Hakeem and Peer. Conclusion This study concludes that health emergencies like the COVID-19 pandemic pose a significant barrier to access to healthcare services and subject a more vulnerable state to already vulnerable groups like SAM children. To lessen their vulnerability, initiatives like mobile health care services should be introduced, especially for socially disadvantaged communities, localities, and groups on regular basis and for future emergencies.

Evaluation of Liver Function Tests (LFT) and C-reactive protein in COVID-19 (SARS Cov-2) positive patients diagnosed by Real-time PCR

Background/Aim: Unpredicted social and economic consequences have resulted from the SARS-CoV-2 pandemic worldwide. Limited information is currently available addressing the COVID-19 infection impact on the RT-PCR Cycle threshold value trend, infection risk factors, impact on liver enzymes, etc. Method(s): From November 2020 to March 2021, a cross-sectional study was carried out in the Chemical Pathology also Molecular Biology divisions of the Pathology Department of Shalamar Hospital, Lahore. Result(s): Males had a higher risk of contracting SARS-CoV-2 infection than females about 51%. 36.5 percent of total infected people fell within the 20-40 age range. Significant factors that affect the severity of COVID-19 include age and underlying comorbidities. The majority of the patients (78.3%) reported fever, 50.4% had a cough, and 50.1% had myalgias. Low Ct value of RT-PCR may be asignificant predictor of illness severity and risk of mortality, with p values of 0.001 and 0.003, correspondingly. Disease severity was substantially associated with CRP, AST, ALT, and bilirubin indirect. It was observed that the Deritis ratio and CRP were highly associated with the risk of mortality. These markers can therefore be used to evaluate a patient's status as it progresses toward a severe disease, liver damage from treatment, and death risk. Conclusion(s): For doctors making patient management decisions, real-time PCR data and Ct values for SARS-CoV-2 may be useful. Age and comorbidities, among other risk variables, have been recognized as being associated with the likelihood of having a fatal illness. ALT, AST, Deritis ratio, and CRP are biochemical laboratory indicators that can be considered prognostic biomarkers for the development of severe disease and risk of mortality. Copyright © 2022 Authors. All rights reserved.

E-commerce and cybersecurity challenges: Recent advances and future trends

COVID-I9 has accelerated the digital transformation in the business sector as many business organizations adopted electronic commerce to keep their operations running. Business organizations have also increased their participation on social networking applications to attract customers. Due to huge presence of users, social networking sites have also evolved into an emerging marketplace, which is referred as social commerce. There are many security issues involved in technological adoption in different business processes. On the other hand, social media is extensively used for product marketing, so fake information and fake product reviews can also influence consumers purchasing decision, so providing accurate marketing information is also a challenge for business organizations. In this chapter, the authors conduct a systematic literature review to understand the cybersecurity issues faced by business organizations and customers and how recent advances such as fintech, etc. provide additional cybersecurity challenges for business organization to protect themselves and their customers. © 2023, IGI Global.

Detection of Omicron Caused Pneumonia from Radiology Images Using Convolution Neural Network (CNN)

COVID-19 disease caused by the SARS-CoV-2 virus has created social and economic disruption across the world. The ability of the COVID-19 virus to quickly mutate and transfer has created serious concerns across the world. It is essential to detect COVID-19 infection caused by different variants to take preventive measures accordingly. The existing method of detection of infections caused by COVID-19 and its variants is costly and time-consuming. The impacts of the COVID-19 pandemic in developing countries are very drastic due to the unavailability of medical facilities and infrastructure to handle the pandemic. Pneumonia is the major symptom of COVID-19 infection. The radiology of the lungs in varies in the case of bacterial pneumonia as compared to COVID-19-caused pneumonia. The pattern of pneumonia in lungs in radiology images can also be used to identify the cause associated with pneumonia. In this paper, we propose the methodology of identifying the cause (either due to COVID-19 or other types of infections) of pneumonia from radiology images. Furthermore, because different variants of COVID-19 lead to different patterns of pneumonia, the proposed methodology identifies pneumonia, the COVID-19 caused pneumonia, and Omicron caused pneumonia from the radiology images. To fulfill the above-mentioned tasks, we have used three Convolution Neural Networks (CNNs) at each stage of the proposed methodology. The results unveil that the proposed step-by-step solution enhances the accuracy of pneumonia detection along with finding its cause, despite having a limited dataset. © 2023 Tech Science Press. All rights reserved.

COVID-19 Classification from X-Ray Images using 2D CNN

The coronavirus (COVID-19) detection has been a crucial task for researchers, scientists, health experts all across the world and everyone is trying together to find a solution to it. The X-rays images of lungs have become one of the most prevalent and effective procedures used by researchers to monitor COVID-19. Unfortunately, inspecting each case involves multiple radiology experts and time, which is one of the critical tasks in such an outbreak. In this paper, a deep learning approach, 2D convolutional neural networks (CNN) has been used to classify healthy and COVID-19 chest X-ray images. 'Curated Dataset for COVID-19 Posterior-Anterior Chest Radiography Images (X-Rays)' dataset has been used in this study. The major indicator of this study is the accuracy of the proposed model. The classification model, 2D CNN has achieved accuracy and f1-score of 0.96 and 0.95 respectively. © 2022 IEEE.

Prevalence of SARS-COV-2 Antibodies in Dental Healthcare Providers

Background: Occupational hazards and risks are a common public health issue, especially when healthcare workers safety is concerned;they are on high risk of catching infections such like COVID-19. The possibility of cross-infection between dental practitioners and patients is significantly higher due to the close exposure of dental staff to patient oral environment. Aim(s): To assess the prevalence of SARS-COV-2 antibodies in dental workers working in the Peshawar Dental College and Hospital, Peshawar. Study Design: Cross sectional study Place and Duration of Study: Department of Orthodontics, Peshawar Dental College & Hospital, Peshawar from 1st January 2020 to 31st December 2020. Methodology: One hundred and thirty three dental workers were enrolled. The investigation was run to detect immunoglobulin G and M antibodies against the SARS-CoV-2-2. The aspirated aerosol and air was evacuated and dissipated into the atmosphere. Result(s): Mean age was 29.4+/-1.4 years and males were dominant 74 (55.6%) and male workers found greater with positive antibodies. The prevalence of SARS-CoV-2 antibodies was 33.0%. Proportionately dental assistants (20.5% vs 16.9%) and ancillary staff (20.5% vs 10.1%) had higher prevalence. Sore throat and body aches were more common in positive antibodies cases while travel history was found significantly associated with it (40.9% vs 25.0%, p-value, 0.05). Conclusion(s): High frequency of SARS-COV-2 antibodies was found in dental workers showing a high infection rate of COVID-19 in healthcare workers in local settings. Copyright © 2022 Lahore Medical And Dental College. All rights reserved.

IMPACT OF THE ONLINE LEARNING TECHNIQUES ON STUDENTS' CUMULATIVE GRADE POINTS

When COVID-19 prevailed, the educational system was shifted to online rather than traditional to facilitate the learning process. This study aimed at exploring the impacts of online learning techniques on the students' Cumulative Grade Point Average (CGPA). A total of 155 randomly selected students currently studying M. Phill education at the University of Agriculture Faisalabad, Pakistan participated in this study. Data were collected through validated, pre-tested and reliable questionnaires. Collected data were analyzed using Statistical Package for Social Sciences (SPSS). Findings unveiled that online learning techniques improved the learning abilities, personality traits and teaching styles as perceived by the respondents which further improved the CGPA of students. Within the effects on learning abilities, enabling students to judicious use of technology, multimedia, observation and clearing the concepts were major improvements which helped students to attain an increase in CGPA. As for as effects on personality traits were concerned, social interaction enhanced communication skills and improvement in understanding, social skills and confidence led the students to get high CGPA. Moreover, online learning improved the teaching styles by integrating video lectures, immediate results assessment and easy access to the technology were key drivers of the increase in CGPA. This study suggested a hybrid educational system at the University of Agriculture Faisalabad for effective learning in students.

Impact of COVID-19 Pandemic on Antimicrobial Resistance Pattern;Transition from Resistivity to Susceptibility

Objective: The study aimed to assess the antibiotic resistance pattern before and after the pandemic to provide the physicians with general guidance for antibiotic prescribing. Material and Methods: The yearly antibiograms of different tertiary care hospitals were extracted from Pakistan Antimicrobial Resistance (AMR) Network. The data timeline observed was from January 2016 to December 2020. The data were scrutinized to the most common organism studied with the most recurring antimicrobial used. Results: Among the Gram-positive organisms, increased resistivity against penicillin was observed against both the organism, while a good susceptibility was observed against vancomycin. Among the Gram-negative organisms, the highest resistance was observed in Ceftriaxone, Ciprofloxacin, and Cotrimoxazole. Staphylococcus aureus and Escherichia coli are the most prevalent organisms in tertiary care hospitals. Conclusion: While satisfactory susceptibility was observed in Amikacin and Piperacillin/Tazobactam. The post-pandemic era resulted in a decrease in AMR due to significant changes in antibiotic prescribing patterns. This report may guide future antibiotic prescribing.

COVID-19 Prediction, Diagnosis and Prevention Through Computer Vision

Recent Corona Virus Disease (COVID) outbreak, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV2), has been posing a big threat to global health since December 2019. In response, research community from all over the world has shifted all their efforts to contribute in this global war by providing crucial solutions. Various computer vision (CV) technologies along with other artificial intelligence (AI) subsets have significant potential to fight in frontline of this turbulent war. Normally radiologists and other clinicians are using reverse transcript polymerase chain reaction (RT-PCR) for diagnosing COVID-19, which requires strict examination environment and a set of resources. Further, this method is also prone to false negative errors. One of the potential solutions for effective and fast screening of doubtful cases is the intervention of computer vision-based support decision systems in healthcare. CT-scans, X-rays and ultrasound images are being widely used for detection, segmentation and classification of COVID-1. Computer vision is using these modalities and is providing the fast, optimal diagnosis at the early-stage controlling mortality rate. Computer vision-based surveillance technologies are also being used for monitoring physical distance, detecting people with or without face masks, screening infected persons, measuring their temperature, tracing body movements and detecting hand washing. In addition to these, it is also assisting in production of vaccine and contributing in administrative tasks and clinical management. This chapter presents an extensive study of some computer vision-based technologies for detection, diagnosis, prediction and prevention of COVID. Our main goal here is to draw a bigger picture and provide the role of computer vision in fight against COVID-19 pandemic. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

KEEPING TIME: WARD-BASED INTERVENTIONS IMPROVE LEVODOPA ADMINISTRATION AT THE NATIONAL HOSPITAL OF NEUROLOGY AND NEUROSURGERY

Background The National Hospital for Neurology and Neurosurgery admits Parkinson's disease (PD) patients for medical management and deep brain stimulation, as well as non-PD inpatients on levodopa with dystonia or atypical Parkinsonism. Previous work showed 24.5% of administrations were outside of the recommended 30-minute time window. Methods We introduced interventions based on the Leeds Quality Improvement Project 'Get it Right on Time', adapted for local protocols and focused questionnaires. Due to cancellation of elective PD admissions during the SARS-CoV-2 crisis, we included all inpatients on levodopa. We tested differences between pre-intervention and post-intervention groups using Chi-squared (c2), with post hoc comparisons to examine individual time categories. Results We compared 177 post-intervention administration episodes to 404 in the pre-intervention group. Across all time categories, we found a significant change in administration timings between groups (c2=35.9, p<0.001). This was driven by an increase in levodopa given on time, from 11.4% to 23.2% (p<0.001) and a decrease in levodopa given up to 15 minutes late, from 31.2% to 17.5% (p<0.001). Conclusion Ward-based interventions improve timely levodopa administration. Including non-PD patients altered the study population and may have impacted results. Further work includes surveying staff to identify additional interventions and investigating a PD-only cohort.

"I need to respond to this" - Contributions to group creativity in remote meetings with distractions

Remote meetings have become more prevalent due to the COVID-19 pandemic and technology that facilitates remote work. There is limited research on the effect of remote meetings on group performance and the goal of this study is to identify how distractions affect the individual and group creativity in remote work meetings. A virtual study was conducted where groups of four people participated in divergent and convergent thinking tasks. One group member was assigned an additional non-meeting task while another was assigned as a scribe. Measures of creative performance (e.g., uniqueness of idea) of the distracted members and the group were analyzed. The results show that the distractee contributed (on average) less time and ideas when compared to monotaskers and those assigned as a scribe. The study highlights ways that remote meetings can facilitate creativity. © 2022 ACM.

Pathogenesis of the SARS Coronavirus-2 and Potential Therapeutic Strategies

The recent outbreak of severe acute respiratory syndrome (SARS) belongs to a broad family of viruses known as Coronaviridae. SARS-CoV-2 is an emerging global pandemic with a relatively low mortality rate. The virus has been mutated in a unique manner thus prolonging its search for its vaccine and drug therapy. SARS-CoV-2 is an enveloped virus consisting of many spike (S) proteins, which mediates its fusion to the membrane of the host cell. Its ‘crown-like’ appearance under an electron microscope has led to its name. The clinical symptoms that patients experience would be due to their central immune response to the infection. Pro-inflammatory cytokines play an essential role in cell growth and regulation of the immune system. However, its abundance could contribute to pathological conditions which can cause further injury and possible death. This brief review discusses the pathogenesis of the SARS-CoV-2 along with receptors that can be potentially targeted by therapeutic strategies, inhibiting the membrane fusion, genome replication and immune response. © 2022: Author(s).

LOCAL-SCALE SPATIAL VARIABILITY in SARS-CoV-2 SEROPREVALENCE in AN INDIAN MEGACITY

Background: While the diversity in SARS-CoV-2 transmission across geographies and risk groups is well recognized, there has been limited investigation into spatial heterogeneity at a local scale, that is variability across a single city. Identifying patterns and factors associated with spatial variability requires population representative samples which are challenging to obtain but critical for mitigation strategies including vaccine distribution. Methods: From Jan to May 2021, we sampled 4,828 participants from 2,723 unique households across 100 spatial locations in Chennai, India using a probability proportional to population density sampling approach. All participants provided a blood sample and underwent a household and individual survey. 4,712 samples were tested for antibodies to the Spike protein (anti-Spike IgG) by the Abbott ARCHITECT. SARS-CoV-2 prevalence by spatial location was plotted using splines estimated by generalized additive models. Associations between seroprevalence and spatial attributes (zone, population density), study characteristics (date of sampling), household and individual-level covariates were estimated using Bayesian mixed effects logistic regression accounting for clustering within households and locations. Results: The median age was 38 and 49% self-identified as female. Overall, anti-S IgG prevalence was 61.9% (95% confidence interval [CI]: 60.5-63.3%) but ranged from 41.5% to 73.1% across the 12 zones. Splines indicated statistically significant variation in seroprevalence across the city (Panel A). Mixed effects regression including location and household effects indicated 31% of variance was attributable to location. In adjusted analysis, seroprevalence was significantly associated with population density (OR=1.46 per 100 people/100 sq meter [95%CI: 1.08-1.97];Panel B), age (OR=1.004 [95%CI: 1.0002-1.005]), having an air conditioner (OR=0.65 [95%CI: 0.43-0.98]) and sample timing but not with household crowding (OR=0.97 per person/room [95%CI: 0.75-1.26];Panel C). Significant spatial variation across locations remained after adjustment for these variables, accounting for 28% of variance. Conclusion: We observed substantial spatial heterogeneity of SARS-CoV-2 burden in this high prevalence setting not fully explained by individual, household or population factors. Such local variability in prevalence has implications not only for transmission but for scale-up of vaccines which remain in limited supply in low-and middle-income countries.

ESTIMATION of SARS-CoV-2 CUMULATIVE INCIDENCE: AN APPLICATION of MIXTURE MODELING

Background: With global vaccine scale-up, the utility of the more stable anti-S IgG assay in seroprevalence studies is limited. P population prevalence estimates of anti-N IgG SARS-CoV-2 using alternate targets (eg, anti-N IgG) will be critical for monitoring cumulative SARS-CoV-2 incidence., We demonstrate the utility of a Bayesian approach that accounts for heterogeneities in SARS-CoV-2 seroresponse (eg, must consider mild infections and/or antibody waning) to ensure anti-N IgG prevalence is not underestimated and correlates not misinterpreted. Methods: We sampled 4,828 participants from 2,723 households across 100 unique geospatial locations in Chennai, India, from Jan-May, 2021 when <1% of the general population was vaccinated. All samples were tested for SARS-CoV-2 IgG antibodies to S and N using the Abbott ARCHITECT. We calculated prevalence using manufacturer cut-offs and applied a Bayesian mixture model. In the mixture model, individuals were assigned a probability of being seropositive or seronegative based on their normalized index value, accounting for differential immune response by age and antibody waning. Regression analyses to identify correlates of infection defined seropositivity by manufacturer cut-offs and the mixture model. Results: The raw SARS-CoV-2 seroprevalence using IgG to S (cutoff=50) and N (cutoff=1.4) were 61.9% (95% confidence interval [CI]: 60.5-63.3%) and 13.7% (CI: 12.8-14.7%), respectively with a correlation of 0.33. With the mixture model, anti-N IgG prevalence was 65.4% (95% credible interval [CrI]: 61.8-68.9). Correlates of anti-N IgG positivity differed qualitatively by the two approaches (Table). Using the manufacturer cut-off, income loss during the pandemic, household crowding and lack of air conditioning were associated with significantly lower anti-N prevalence. By contrast, in the mixture model, many measures of lower socioeconomic status were associated with higher prevalence, associations that were comparable when anti-S was the outcome. The age pattern differed between approaches: the mixture model identified that individuals aged >50 had the lowest seroprevalence, but the highest immune response to infection. Conclusion: With global vaccine scale-up, population prevalence estimates of anti-N IgG will be critical for monitoring cumulative SARS-CoV-2 incidence. We demonstrate the utility of a Bayesian approach that accounts for heterogeneities in SARS-CoV-2 seroresponse to improve accuracy of anti-N IgG prevalence estimates and associated correlates.

Level of satisfaction in online teaching programs among medical teachers/supervisors during COVID-19 pandemic

Background The Covid-19 pandemic has triggered a worldwide health catastrophe that has had a significant effect on all of us. During this time, we had to go into quarantine. When all the educational institutions got closed, the students and teachers adopted the online way of learning and teaching. Objective The goal of our study was to find out the level of satisfaction in online teaching programs among medical teachers/supervisors during Covid-19 pandemic. Methods This was descriptive cross-sectional study based on online questionnaire. This study was conducted for duration of six months from July 2020 to December 2020 by including 175 faculty members from different medical and dental colleges in Pakistan. A self-administered questionnaire was circulated via Email, Whatsapp groups and other social media platforms. The variables were represented in frequencies and percentages. Results There were 80 (45.71%) male and 95 (54.29%) female faculty members. According to the designation majority of the respondent 47 (26.86%) were assistant professors. According to the teaching experience more faculty members 44 (25.14%) had teaching experience of 6-10 years while only 5 (2.86%) faculty members had more than 30 years of teaching experience. Majority of the respondents 69 (39.43%) used Webinars for online teaching followed by Zoom 50 (28.57%).Majority of the faculty members were confident, satisfied and felt comfortable with the online education. Conclusion The majorities of medical teachers think that e-learning is a great complement to prevent academic loss and satisfied with the system but it cannot replace in-person education. Studies with greater sample size would further give insight into the teacher's satisfaction regarding online teaching programs.