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1.
BMC Health Services Research ; 21(780), 2021.
Article in English | CAB Abstracts | ID: covidwho-1840966

ABSTRACT

Background: Vaccines are an important tool to limit the health and economic damage of the Covid-19 pandemic. Several vaccine candidates already provided promising effectiveness data, but it is crucial for an effective vaccination campaign that people are willing and able to get vaccinated as soon as possible. Taking Germany as an example, we provide insights of using a mathematical approach for the planning and location of vaccination sites to optimally administer vaccines against Covid-19.

2.
SAGE Open ; 12(2), 2022.
Article in English | Scopus | ID: covidwho-1840922

ABSTRACT

In the wake of the COVID-19 pandemic, transitions to online L2 learning have rapidly emerged. However, the impacts of these transitions on students’ attitudes toward online language learning are largely unknown. This study investigated how participation in remote EAP instruction impacted the attitudes of Thai university students (n = 263) toward online language learning. The study employed a longitudinal survey design and utilized a questionnaire instrument designed for the study containing 33 Likert scale items. The questionnaire was administered at the beginning and end of students’ first fully remote semester. Within- and between-groups comparisons were made of participants’ mean attitudinal ratings on eight multi-item subscales to measure the extent to which, and in what ways, students’ attitudes changed over time. Statistically significant differences were evident in the subscales of open-mindedness, autonomy, effectiveness of instruction, interactivity, and engagement over time;the general trend was toward a more positive perspective on online learning. Results indicated no statistically significant differences on three of the multi-item subscales (motivation, anxiety, and convenience) over time. The analysis showed a significant interaction between proficiency level and time in ratings for the effectiveness of instruction subscale only. Most subscales were weakly correlated with motivation at the beginning of the term;however, all subscales except interactivity showed a higher correlation at the end of the term. The results of this study will be of interest to educators who are seeking to understand learners’ attitudes toward online language instruction during times of crisis and emergency remote teaching (ERT). © The Author(s) 2022.

3.
Sociological Methods & Research ; : 1, 2022.
Article in English | Academic Search Complete | ID: covidwho-1840762

ABSTRACT

Social scientists increasingly use video data, but large-scale analysis of its content is often constrained by scarce manual coding resources. Upscaling may be possible with the application of automated coding procedures, which are being developed in the field of computer vision. Here, we introduce computer vision to social scientists, review the state-of-the-art in relevant subfields, and provide a working example of how computer vision can be applied in empirical sociological work. Our application involves defining a ground truth by human coders, developing an algorithm for automated coding, testing the performance of the algorithm against the ground truth, and running the algorithm on a large-scale dataset of CCTV images. The working example concerns monitoring social distancing behavior in public space over more than a year of the COVID-19 pandemic. Finally, we discuss prospects for the use of computer vision in empirical social science research and address technical and ethical challenges. [ FROM AUTHOR] Copyright of Sociological Methods & Research is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

4.
5th International Conference on Software Engineering and Information Management, ICSIM 2022 ; : 199-205, 2022.
Article in English | Scopus | ID: covidwho-1840646

ABSTRACT

The situation we are facing right now because of the COVID-19 virus is rapidly changing and gives a noticeable impact. Everything has become an online-based activity, which leads to the increasing number of internet and social media users. On the other hand, business owners need to find new ways to survive in the new normal situation. With that in mind, business owners can take advantage of the internet and social media as a marketplace. Social media is a perfect fit for an O2O (Online-to-Offline or Offline-to-Online) implementation. O2O method strategy is designed to educate more than half of online customers of the product's information. The purpose of this study is to analyze the O2O channel via social media as a marketing place. This research uses the quantitative method and snowball sampling method. The result of this study shows that 98.7% of Generation Z have already shopped online before and are using social media to seek information about product's information. The result of this study can be used to help the business owners to determine the right strategy for their business, either implementing Online-to-Offline or Offline-to-Online business model in this pandemic situation. © 2022 ACM.

5.
13th International Conference on E-Education, E-Business, E-Management, and E-Learning, IC4E 2022 ; : 197-202, 2022.
Article in English | Scopus | ID: covidwho-1840636

ABSTRACT

The purpose of this study is to explore and understand Saudi students' experiences while learning via an online platform during the Covid-19 crisis. Ten undergraduate students engaged in this study answered in-depth interviews questions. A narrative approach was adopted as a research design to stimulate participants in expressing their points of view concerning their e-learning experiences during the Covid-19 crisis. Thematic analysis of the data provided an explanation of the university students' orientations at the beginning of and during the pandemic. The findings highlight many challenges faced by the students during the crisis, including issues such as technology problems, difficulties in communication with each other's and with some faculty members and, for some of them, the lack of suitable learning environments. The students also reported various affordance factors emerging from their engagement with online learning, including being more organised, a shift in the mode of education towards being more interactive, and having a more democratic relationship with faculty. Finally, this paper presents the students' suggestions concerning their future learning which have emerged from their own online experiences;in particular, to adopt a blend of learning situations. They suggested that online learning would be suitable to deliver theoretical content whereas face-to-face lectures were preferable to deal with the practical aspects of their programme. This would help them to save time and better tackle the heavy workload associated with their course requirements. Finally, the findings of this research are discussed in the light of the relevant literature. © 2022 ACM.

6.
13th International Conference on E-Education, E-Business, E-Management, and E-Learning, IC4E 2022 ; : 90-96, 2022.
Article in English | Scopus | ID: covidwho-1840634

ABSTRACT

The study demonstrates the learning losses in remote online learning during the Covid-19 pandemic and how online teaching can mitigate these losses. The findings were analyzed using descriptive statistics and theme analysis based on online surveys and focus groups collected. Significant learning losses occurred in an online learning environment due to reduced curricular content, a lack of student engagement, and holistic performance assessment. Unique and varied teaching tactics, such as optimizing the use of online tools and platforms, online teacher presence, and tailored evaluations, were discovered to assure optimal student learning. The University's e-readiness contributes to the e-learning success of the University's curriculum delivery. The findings provided in this paper have policy implications for curriculum review, performance assessment and evaluation in online learning, and intervention and development programs for online teachers. © 2022 ACM.

7.
13th International Conference on E-Education, E-Business, E-Management, and E-Learning, IC4E 2022 ; : 203-208, 2022.
Article in English | Scopus | ID: covidwho-1840628

ABSTRACT

Feedback redirects or refocuses the learner's actions to achieve a goal, by aligning effort and activity with an outcome. Feedback can come from a variety of sources. Studies have shown positive effects of feedback from teachers and peers. This study explores students' perceptions towards peer feedback on writing assignments in fundamental English courses which are conducted online during the COVID-19. Qualitative data was collected from email interviews with 30 students who had completed the course. All the interviews transcripts were critically examined to draw out the aspects of peer-to-peer feedback emerging from the participants' viewpoints in relation to their experiences and perceptions as a result of engaging with peers within online discourse, and providing and/or receiving feedback. The results show that the positive perceptions outweigh the negative ones. Specifically, students can benefit from a wide range of feedback, learn from others, boost their confidence and activeness, and create cooperative and collaborative learning. However, there are a few drawbacks regarding linguistic limitation and unwillingness to provide sincere feedback, which needs to be taken into consideration if peer feedback is still applied in the next semesters. © 2022 Association for Computing Machinery. All rights reserved.

8.
2022 International Mobile and Embedded Technology Conference, MECON 2022 ; : 184-188, 2022.
Article in English | Scopus | ID: covidwho-1840284

ABSTRACT

This research paper gives a brief idea of controlling entrance gates of different areas like metro stations, railway stations, airports, corporate offices, restaurants, hotels and home with the face mask detection technology. In this, the camera will capture the real time video of a person using Artificial Intelligence[15],whosoever is entering the gate, processes the video and detects if the concerned person is wearing the mask properly or not. If the person is wearing a mask then the gate will open, if not then the gate will remain closed until the mask has been worn properly. The main motivation for this project comes from the current situation in the world where Covid-19 is spreading at a pace which is being difficult to control. This upcoming technology prototype can fuel in new ideas into different projects which are already ongoing to battle the pandemic. Also, the scope of this technology is not just limited to the face mask detection and has a wider and a more complex use-case in the real world. © 2022 IEEE.

9.
2022 International Mobile and Embedded Technology Conference, MECON 2022 ; : 108-112, 2022.
Article in English | Scopus | ID: covidwho-1840277

ABSTRACT

Coronavirus, also known as covid-19, was a newly found disease in 2019. It is a highly transmissible virus that has a noxious effect on people all over the world. The most familiar indication of covid-19 are wheezing, cold and raised body temperature. Doctors and medical field experts need to assess at topmost priority a patient with symptoms relating to COVID-19. The most Critical task is to diagnose it with low resource settings. To help in detection of COVID-19, we propose the extraction of features from Chest X-ray images, a technology available at most hospitals and classifying them using machine learning. We compiled 3-class dataset of X-ray chest images including COVID-19, Viral pneumonia, normal cases. We labeled datasets then using Vertex AI and Vision AI trained the model. It automatically uses 80% of the dataset for training, 10% for corroborating and 10% for testing whereas Vision AI process the images for training. © 2022 IEEE.

10.
2022 International Mobile and Embedded Technology Conference, MECON 2022 ; : 617-620, 2022.
Article in English | Scopus | ID: covidwho-1840276

ABSTRACT

Coronavirus diseases is a contagious transmissible infectious malady rooted by the SARS-CoV-2 virus and it mostly affects the lungs thereby causing a global health care problem. Coronavirus triggers respiratory tract infection by infecting upper respiratory tract consisting of sinuses, nose, and throat or lower tract of respiratory system that includes windpipe and lungs. WHO proclaimed the COVID-19 outbreak a global epidemic. To control the spreading of novel Coronavirus, early detection and cure is mandatory. Although RT-PCR test is used to detect the infected humans but owing to colossal demand RT-PCR kits are now limited, and its low reliability made way for implementation of radiographic procedures such as X-Rays and Computed Tomography-Scan for symptomatic purposes. These come with a great specificity for diagnosing and detecting Covid-19 instances. This study suggests adopting a Deep Learning technique to automate the diagnosis of COVID19 infection using CT scans of patients for explicit identification of Covid-19. CNN namely Densenet, Inception and Xception networks or architectures are used in a deep learning architecture to distinguish human beings based on whether confirmed positive or not for COVID-19 infection. These networks are then collated on the ground of their accuracy and the outcomes procured from various CNN models are analysed to obtain a robust system. © 2022 IEEE.

11.
4th IEEE Global Conference on Life Sciences and Technologies, LifeTech 2022 ; : 470-474, 2022.
Article in English | Scopus | ID: covidwho-1840265

ABSTRACT

Recent years have witnessed the rapid development of artificial intelligence (AI) in different fields, including biomedical, in which timely detection of anomalies can play a vital role in patients' health monitoring. COVID-19, a contagious disease caused by the Severe Acute Respiratory Syndrome Corona-Virus 2 (SARS-CoV-2), has become a global epidemic. The key to combating this and other epidemics is detecting and isolating the infected patients in time. Therefore, there is an urgent need for a timely, practical detection approach. This paper proposes an AI-enabled pneumonia detection system, AIRBiS, to detect pneumonia (i.e., COVID-19) efficiently. AIRBiS is based on a high-performance Artificial Neural Network and an interactive user interface for effective operation and monitoring. The evaluation results demonstrate that the proposed system achieved 94.4% detection accuracy of pneumonia (i.e., COVID-19) over the collected test data. © 2022 IEEE.

12.
6th International Conference on Computing Methodologies and Communication, ICCMC 2022 ; : 1358-1363, 2022.
Article in English | Scopus | ID: covidwho-1840254

ABSTRACT

As the global epidemic of Covid19 progresses, accurate diagnosis of Covid19 patients becomes important. The biggest problem in diagnosing test-positive people is the lack or lack of test kits due to the rapid spread of Covid19 in the community. As an alternative rapid diagnostic method, an automated detection system is needed to prevent Covid 19 from spreading to humans. This article proposes to use a convolutional neural network (CNN) to detect patients infected with coronavirus using computer tomography (CT) images. In addition, the transfer learning of the deep CNN model VGG16 is investigated to detect infections on CT scans. The pretrained VGG16 classifier is used as a classifier, feature extractor, and fine tuner in three different sets of tests. Image augmentation is used to boost the model's generalization capacity, while Bayesian optimization is used to pick optimum values for hyperparameters. In order to fine-tune the models and reduce training time, transfer learning is being researched. Surprisingly, all of the proposed models scored greater than 93% accuracy, which is on par with or better than previous deep learning models. The results show that optimization improved generalization in all models and highlight the efficacy of the proposed strategies. © 2022 IEEE.

13.
6th International Conference on Computing Methodologies and Communication, ICCMC 2022 ; : 1175-1182, 2022.
Article in English | Scopus | ID: covidwho-1840253

ABSTRACT

The corona virus (COVID19) pandemic requires immediate action to avoid adverse effects on local health and the global economy. Due to the effects of COVID19, most of the people lives have been reversed. In the absence of effective antivirals and inadequate medical resources, UN agencies propose a number of measures to regulate infection rates and prevent the limited medical resources from being exhausted. Wearing a face mask is a type of the non-pharmaceutical intervention techniques that can block the primary care of viral droplets ejected by an infected person. According to government basics, it is important for everyone in every country to wear a mask. The government recommends wearing a mask, but many do not. Mask detection is very important in this situation. To contribute to community health, this study aims to develop highly accurate and timely techniques for detecting non-face masks in public and encouraging people to use them. It is said that "Increase the number of people who wear masks correctly and reduce the number of infected people". Starting with MobileNet V2 as a baseline, we used the concept of transfer learning to fuse high levels of linguistic data during mask recognition. For the face detection module, we used Caffe Model in conjunction with OpenCV's DNN module. The anticipated model's remarkable performance makes it ideal for live video police work equipment that detects face masks in real - time. © 2022 IEEE.

14.
6th International Conference on Computing Methodologies and Communication, ICCMC 2022 ; : 1696-1703, 2022.
Article in English | Scopus | ID: covidwho-1840248

ABSTRACT

During this COVID-19 pandemic online class platforms are the only solution to transfer the knowledge in the field of education. Even though the physical classes are being practiced slowly in some countries, still academicians are in the need of online classes. In addition to content delivery, teachers are in the need to concern about throughout the class time whether the students are listening and be active in online classes. Due to more bandwidth consumption of the audio and video streaming, students can't be compelled to unmute the audio and video when the teacher delivers the content. So, there is no option for the teachers to observe the student's activity. With the advancement of technology and enhanced image analysis capacity of deep learning techniques, a system is proposed to compute the student's activity and can report it to the teachers during the class time itself. Drowsiness detection is tested using CNN based segmentation on our own set of 5000 images collected from 1000 students. The observed result shows 90% accuracy in predicting the drowsiness of the student by observing the face pattern of the student without streaming the video to the teacher's device. © 2022 IEEE.

15.
IEEE Access ; 2022.
Article in English | Scopus | ID: covidwho-1840231

ABSTRACT

Nowadays, there are many fragmented records of patient’s health data in different locations like hospitals, clinics, and organizations all around the world. With the arrival of the COVID-19 pandemic, several governments and institutions struggled to have satisfactory, fast, and accurate decision-making in a wide, dispersed, and global environment. In the current literature, we found that the most common related challenges include delay (network latency), software scalability, health data privacy, and global patient identification. We propose to design, implement and evaluate a healthcare software architecture focused on a global vaccination strategy, considering healthcare privacy issues, latency mitigation, support of scalability, and the use of a global identification. We have designed and implemented a prototype of a healthcare software called Fog-Care, evaluating performance metrics like latency, throughput and send rate of a hypothetical scenario where a global integrated vaccination campaign is adopted in wide dispensed locations (Brazil, USA, and United Kingdom), with an approach based on blockchain, unique identity, and fog computing technologies. The evaluation results demonstrate that the minimum latency spends less than 1 second to run, and the average of this metric grows in a linear progression, showing that a decentralized infrastructure integrating blockchain, global unique identification, and fog computing are feasible to make a scalable solution for a global vaccination campaign within other hospitals, clinics, and research institutions around the world and its data-sharing issues of privacy, and identification. Author

16.
Industrial Management & Data Systems ; 122(5):1306-1332, 2022.
Article in English | ProQuest Central | ID: covidwho-1840181

ABSTRACT

Purpose>With the increasing use of crowdfunding platforms in raising funds, it has become an important and oft-researched topic to analyze the critical factors associated with successful or failed crowdfunding. However, as a major subject of crowdfunding, medical crowdfunding has received much less scholarly attention. The purpose of this paper is to explore how contingency factors combine and casually connect in determining the success or failure of medical crowdfunding projects based on signal theory.Design/methodology/approach>The paper adopts the crisp-set qualitative comparative analysis to analyze the causal configurations of 200 projects posted on a leading medical crowdfunding platform in China “Tencent Donation.” Five anecdotal conditions that could have an impact on the outcome of medical crowdfunding campions were identified. Three relate to the project (funding duration, number of images and number of updates) and two relate to the funding participants (type of suffer and type of fund-raiser).Findings>The results show that diversified configurations of the aforementioned conditions are found (six configurations for successful medical crowdfunding projects and four configurations for failed ones).Originality/value>Despite the fact that there are a considerably large number of medical crowdfunding projects, relatively few researches have been conducted to investigate configurational paths to medical crowdfunding success and failure. It is found that there are certain combinations of conditions that are clearly superior to other configurations in explaining the observed outcomes.

17.
Embase; 2021.
Preprint in English | EMBASE | ID: ppcovidwho-335896

ABSTRACT

Background: Despite the vaccination process in Germany, a large share of the population is still susceptible to SARS-CoV-2. In addition, we face the spread of novel variants. Until we overcome the pandemic, reasonable mitigation and opening strategies are crucial to balance public health and economic interests. Methods: We model the spread of SARS-CoV-2 over the German counties by a graph-SIR-type, metapopulation model with particular focus on commuter testing. We account for political interventions by varying contact reduction values in private and public locations such as homes, schools, workplaces, and other. We consider different levels of lockdown strictness, commuter testing strategies, or the delay of intervention implementation. We conduct numerical simulations to assess the effectiveness of the different intervention strategies after one month. The virus dynamics in the regions (German counties) are initialized randomly with incidences between 75-150 weekly new cases per 100,000 inhabitants (red zones) or below (green zones) and consider 25 different initial scenarios of randomly distributed red zones (between 2 and 20 % of all counties). To account for uncertainty, we consider an ensemble set of 500 Monte Carlo runs for each scenario. Results: We find that the strength of the lockdown in regions with out of control virus dynamics is most important to avoid the spread into neighboring regions. With very strict lockdowns in red zones, commuter testing rates of twice a week can substantially contribute to the safety of adjacent regions. In contrast, the negative effect of less strict interventions can be overcome by high commuter testing rates. A further key contributor is the potential delay of the intervention implementation. In order to keep the spread of the virus under control, strict regional lockdowns with minimum delay and commuter testing of at least twice a week are advisable. If less strict interventions are in favor, substantially increased testing rates are needed to avoid overall higher infection dynamics. Conclusions: Our results indicate that local containment of outbreaks and maintenance of low overall incidence is possible even in densely populated and highly connected regions such as Germany or Western Europe. While we demonstrate this on data from Germany, similar patterns of mobility likely exist in many countries and our results are, hence, generalizable to a certain extent.

18.
Embase; 2021.
Preprint in English | EMBASE | ID: ppcovidwho-335610

ABSTRACT

Background: Results of a randomised trial showed the safety and efficacy of GamCOVIDVac against COVID19. However, compared to other vaccines used across the globe, the realworld data on the effectiveness of GamCOVIDVac, especially against the disease caused by Delta variant of concern, was not available. We aimed to assess the effectiveness of vaccination mainly conducted with GamCOVIDVac in St. Petersburg, Russia. Methods: We designed a casecontrol study to assess the vaccine effectiveness (VE) against lung injury and referral to hospital. Selfreported vaccination status was collected for individuals with confirmed SARSCoV2 infection who were referred for initial lowdose computed tomography triage in two outpatient centres in July 3 - August 9, 2021 in St. Petersburg, Russia. We used logistic regression models to estimate the adjusted (for age, sex, and triage centre) VE for complete (>14 days after the second dose) vaccination. We estimated the VE against referral for hospital admission, COVID19related lung injury assessed with LDCT, and decline in oxygen saturation. Results: In the final analysis, 13,893 patients were included, 1,291 (9.3%) of patients met our criteria for complete vaccination status, and 495 (3.6%) were referred to hospital. In the primary analysis, the adjusted VE against referral to hospital was 81% (95% CI: 68-88) for complete vaccination. The VE against referral to hospital was more pronounced in women (84%, 95% CI: 66-92) compared to men (76%, 95% CI: 51-88). Vaccine protective effect increased with increasing lung injury categories, from 54% (95% CI: 48-60) against any sign of lung injury to 76% (95% CI: 59-86) against more than 50% lung involvement. A sharp increase was observed in the probability of hospital admission with age for nonvaccinated patients in relation to an almost flat relationship for the completely vaccinated group. Conclusions: COVID19 vaccination was effective against referral to hospital in patients with symptomatic SARSCoV2 infection in St. Petersburg, Russia. This protection is probably mediated through VE against lung injury associated with COVID19.

19.
Virology ; 570:18-28, 2022.
Article in English | CAB Abstracts | ID: covidwho-1839384

ABSTRACT

The challenge continues globally triggered by the absence of an approved antiviral drug against COVID-19 virus infection necessitating global concerted efforts of scientists. Nature still provides a renewable source for drugs used to solve many health problems. The aim of this work is to provide new candidates from natural origin to overcome COVID-19 pandemic. A virtual screening of the natural compounds database (47,645 compounds) using structure-based pharmacophore model and molecular docking simulations reported eight hits from natural origin against SARS-CoV-2 main proteinase (Mpro) enzyme. The successful candidates were of terpenoidal nature including taxusabietane, Isoadenolin A & C, Xerophilusin B, Excisanin H, Macrocalin B and ponicidin, phytoconstituents isolated from family Lamiaceae and sharing a common ent-kaurane nucleus, were found to be the most successful candidates. This study suggested that the diterpene nucleus has a clear positive contribution which can represent a new opportunity in the development of SARS-CoV-2 main protease inhibitors.

20.
Computers in Biology and Medicine ; 145, 2022.
Article in English | ProQuest Central | ID: covidwho-1838703

ABSTRACT

BackgroundAutomated generation of radiological reports for different imaging modalities is essentially required to smoothen the clinical workflow and alleviate radiologists’ workload. It involves the careful amalgamation of image processing techniques for medical image interpretation and language generation techniques for report generation. This paper presents CADxReport, a coattention and reinforcement learning based technique for generating clinically accurate reports from chest x-ray (CXR) images.MethodCADxReport, uses VGG19 network pre-trained over ImageNet dataset and a multi-label classifier for extracting visual and semantic features from CXR images, respectively. The co-attention mechanism with both the features is used to generate a context vector, which is then passed to HLSTM for radiological report generation. The model is trained using reinforcement learning to maximize CIDEr rewards. OpenI dataset, having 7, 470 CXRs along with 3, 955 associated structured radiological reports, is used for training and testing.ResultsOur proposed model is able to generate clinically accurate reports from CXR images. The quantitative evaluations confirm satisfactory results by achieving the following performance scores: BLEU-1 = 0.577, BLEU-2 = 0.478, BLEU-3 = 0.403, BLEU-4 = 0.346, ROUGE = 0.618 and CIDEr = 0.380.ConclusionsThe evaluation using BLEU, ROUGE, and CIDEr score metrics indicates that the proposed model generates sufficiently accurate CXR reports and outperforms most of the state-of-the-art methods for the given task.

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