Protection afforded by prior infection, vaccination, and hybrid immunity against symptomatic BA.1 and BA.2 Omicron infections

Background. Protection offered by five different forms of immunity, combining natural and vaccine immunity, was investigated against symptomatic SARS-CoV-2 infection from Omicron BA.1 or BA.2, and severe, critical, or fatal COVID-19 from BA.1 or BA.2, in Qatar, between December 23, 2021 and February 21, 2022. Methods. Six national, matched, test-negative case-control studies were conducted on a sample of 272,861 PCR-positive tests and 669,628 PCR-negative tests to estimate effectiveness of BNT162b2 (Pfizer-BioNTech) vaccine, mRNA-1273 (Moderna) vaccine, natural immunity due to prior infection with pre-Omicron variants, and hybrid immunity from prior infection and vaccination. Results. Effectiveness of prior infection alone against symptomatic BA.2 infection was 46.1% (95% CI: 39.5-51.9%). Effectiveness of two-dose BNT162b2 vaccination alone was negligible at -1.1% (95% CI: -7.1-4.6), but nearly all individuals received their second dose >6 months earlier. Effectiveness of three-dose BNT162b2 vaccination alone was 52.2% (95% CI: 48.1-55.9%). Effectiveness of hybrid immunity of prior infection and two-dose BNT162b2 vaccination was 55.1% (95% CI: 50.9-58.9%). Effectiveness of hybrid immunity of prior infection and three-dose BNT162b2 vaccination was 77.3% (95% CI: 72.4-81.4%). Meanwhile, prior infection, BNT162b2 vaccination, and hybrid immunity all showed strong effectiveness ( >70%) against severe, critical, or fatal COVID-19 due to BA.2. Similar patterns of effectiveness were observed for BA.1 and for the mRNA-1273 vaccine. Conclusion. There are no discernable differences between BA.1 and BA.2 in the effects of prior infection, vaccination, and hybrid immunity. Vaccination enhances the protection of those with a prior infection. Hybrid immunity resulting from prior infection and recent booster vaccination conferred the strongest protection.

Navigating the Kaleidoscope of COVID-19 Misinformation Using Deep Learning

Irrespective of the success of the deep learning-based mixed-domain transfer learning approach for solving various Natural Language Processing tasks, it does not lend a generalizable solution for detecting misinformation from COVID-19 social media data. Due to the inherent complexity of this type of data, caused by its dynamic (context evolves rapidly), nuanced (misinformation types are often ambiguous), and diverse (skewed, fine-grained, and overlapping categories) nature, it is imperative for an effective model to capture both the local and global context of the target domain By conducting a systematic investigation, we show that: (i) the deep Transformer-based pre-trained models, utilized via the mixed-domain transfer learning, are only good at capturing the local context, thus exhibits poor generalization, and (ii) a combination of shallow network-based domain-specific models and convolutional neural networks can efficiently extract local as well as global context directly from the target data in a hierarchical fashion, enabling it to offer a more generalizable solution.

Determinants of regional prevalence and variations in handwashing practices among households in Bangladesh: An application of generalized mixed-effects model

Introduction: Handwashing practice is an effective way to minimize severe infectious diseases such as COVID-19, diarrhea, and pneumonia. The study aimed to explore the prevalence of handwashing behavior and associated determinants in rural and urban areas of Bangladesh. Methods: The research was performed using cross-sectional survey data from the 2019 Bangladesh Multiple Indicator Cluster Survey, and 61,242 household members were the sample for this study. The Chi-square test was applied for the bivariate analysis. A generalized linear mixed-effects model was used to identify the risk factors of practicing handwashing in both urban and rural areas of Bangladesh. Results: Only 65% of the country's households had access to handwashing facilities (place, water, and materials). While urban dwellers were more likely to wash their hands, rural dwellers were only 63% likely to do so. The level of education of household heads, wealth status, division, number of family members, sanitation facilities, and water source were the key factors associated with handwashing behavior. This study revealed that the odds were significantly lower among illiterate respondents compared to those with secondary and above-secondary education in both areas of Bangladesh, and a positive association was found between wealth status and handwashing behavior practiced in both urban and rural areas. In this study, the size of the family was statistically significant for both rural and urban areas of Bangladesh. Conclusion: Handwashing is the most prominent consideration for controlling COVID-19. Policymakers are striving to improve handwashing facilities by increasing awareness-raising programs, especially among rural residents of Bangladesh. © 2022 Asian Journal of Social Health and Behavior ;Published by Wolters Kluwer - Medknow.

How to Churn Deep Contextual Models?

This paper searches for optimal ways of employing deep contextual models to solve practical natural language processing tasks. It addresses the diversity in the problem space by utilizing a variety of techniques that are based on the deep contextual BERT (Bidirectional Encoder Representation from Transformer) model. A collection of datasets on COVID-19 social media misinformation is used to capture the challenge in the misinformation detection task that arises from small labeled data, noisy labels, out-of-distribution (OOD) data, fine-grained & nuanced categories, and heavily-skewed class distribution. To address this diversity, both domain-agnostic (DA) and domain-specific (DS) BERT pretrained models (PTMs) for transfer learning are examined via two methods, i.e., fine-tuning (FT) and extracted feature-based (FB) learning. The FB is implemented using two approaches: non-hierarchical (features extracted from a single hidden layer) and hierarchical (features extracted from a subset of hidden layers are first aggregated, then passed to a neural network for further extraction). Results obtained from an extensive set of experiments show that FB is more effective than FT and that hierarchical FB is more generalizable. However, on the OOD data, the deep contextual models are less generalizable. It identifies the condition under which DS PTM is beneficial. Finally, bigger models may only add an incremental benefit and sometimes degrade the performance. © 2021 ACM.

Supervised Method Pursued For Overall Impact of Online Class During Lockdown in Bangladesh

The purpose behind this survey is to know what is the perspective of teachers and students on online education and to predict the future of online education in Bangladesh by collaborating with their perspective. The participants are students and teachers from the school, college and university of Bangladesh. The data for this research has been collected through a google form where there are two separate sections of the question set both for teachers and students. There are already some existing works on this topic but they only focus on the perspective of the students on online education and they only show what students feel about online learning. They don't give us the data what teachers have to say about online learning and they don't also mention the steps we should take to make online learning system suitable both for teachers and students. As we know to have a proper education environment the cooperation between both students and teachers should be satisfactory. So, by our survey we tried to cover the point of view of both students and teachers and by comparing their viewpoint we will give the decision if this online education will be fruitful and if it's not then what steps we should take to make it fruitful. This research can help both students and teachers to know each other's points of view so they can participate in online learning sessions by cooperating with each other and also can help the education sectors to know what they should do to support all the students and teachers with online education. © 2021 IEEE.

People's concerns with the prediction of COVID-19 in Bangladesh: application of Autoregressive integrated moving average model

Introduction: The coronavirus disease 2019 (COVID-19) has become a public health concern, and behavioral adjustments will minimize its spread worldwide by 80%. The main purpose of this research was to examine the factors associated with concerns about COVID-19 and the future direction of the COVID-19 scenario of Bangladesh.

The applicability of blockchain technology in healthcare contexts to contain COVID-19 challenges

Purpose Blockchain technology, a key feature of the fourth industrial revolution, is receiving widespread attention and exploration around the world. Taking the coronavirus pandemic as an example, the purpose of this study to examine the application of blockchain technology from the perspective of epidemic prevention and control. Design/methodology/approach Exploring multiple case studies in the Chinese context at various stages of deployment, this study documents a framework about how some of the major challenges associated with COVID-19 can be alleviated by leveraging blockchain technology. Findings The case studies and framework presented herein show that utilization of blockchain acts as an enabler to facilitate the containment of several COVID-19 challenges. These challenges include the following: complications associated with medical data sharing;breaches of patients' data privacy;absence of real-time monitoring tools;counterfeit medical products and non-credible suppliers;fallacious insurance claims;overly long insurance claim processes;misappropriations of funds;and misinformation, rumors and fake news. Originality/value Blockchain is ushering in a new era of innovation that will lay the foundation for a new paradigm in health care. As there are currently insufficient studies pertaining to real-life case studies of blockchain and COVID-19 interaction, this study adds to the literature on the role of blockchain technology in epidemic control and prevention.

Ultrasensitive detection of pathogenic viruses with electrochemical biosensor: State of the art.

Last few decades, viruses are a real menace to human safety. Therefore, the rapid identification of viruses should be one of the best ways to prevent an outbreak and important implications for medical healthcare. The recent outbreak of coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus which belongs to the single-stranded, positive-strand RNA viruses. The pandemic dimension spread of COVID-19 poses a severe threat to the health and lives of seven billion people worldwide. There is a growing urgency worldwide to establish a point-of-care device for the rapid detection of COVID-19 to prevent subsequent secondary spread. Therefore, the need for sensitive, selective, and rapid diagnostic devices plays a vital role in selecting appropriate treatments and to prevent the epidemics. During the last decade, electrochemical biosensors have emerged as reliable analytical devices and represent a new promising tool for the detection of different pathogenic viruses. This review summarizes the state of the art of different virus detection with currently available electrochemical detection methods. Moreover, this review discusses different fabrication techniques, detection principles, and applications of various virus biosensors. Future research also looks at the use of electrochemical biosensors regarding a potential detection kit for the rapid identification of the COVID-19.