Daily activities during the covid pandemic

Covid-19 has turned the world on its head, causing dramatic alterations in people's lives. The study's major goal is to learn about people's attitudes toward online classes/jobs, their daily routines and Covid measures/precautions they take on a daily basis. All of these conclusions were reached by statistical analysis of original data gathered through a questionnaire and an online survey. The report contains the most comprehensive and small-scale investigation into how people perceive the impact of the COVID-19 issue on many elements of their lives based on daily activities. The results of the investigation were also shown in terms of an individual's eating habits, sleep pattern, physical activity, stress/ anxiety levels, work involvement, social media/ phone usage, and productivity. The information was gathered from a sample of 503 people who completed a structured questionnaire using Google forms utilising a suitable random sampling procedure. The data was analysed and hypotheses were tested using reliable statistical tools such as descriptive statistics, chi-square test, z-test, ANOVA with R programming, and MS-Excel. It was discovered that about half of the people are fairly productive;a small percentage of the population believes they are not productive, and the majority of the populationnever engages in any type of exercise. When people go out, they are cognizant of wearing a mask. The majority of them consume immunity-boosting foods. Most people are least interested in online classes/ related employment due to their health and other issues, and instead prefer to kill their boredom by helping with household tasks. Many people suffer fromstress and anxiety. Many of them are adhering to Covid's safety precautions and lockout rules. This study is unique in that it covers all of the activities that an individual can engage in on a daily basis during the Covid epidemic. © 2023 AIP Publishing LLC.

Knowledge about COVID-19 and its Precautionary Measures among Healthcare Workers at the National Cancer Institute, Cairo University

BACKGROUND: The core of the healthcare system is healthcare workers (HCWs). A skilled and healthy workforce is essential during a health emergency like the coronavirus disease 2019 (COVID-19) epidemic. AIM: This study evaluated knowledge of COVID-19, its preventive measures, and factors affecting it among HCWs at the National Cancer Institute (NCI), Cairo University to determine their desire to obtain the available COVID-19 vaccines in addition to the factors that may affect it. METHOD(S): This descriptive cross-sectional study included 151 HCWs in Egypt using a self-administered questionnaire created by the researchers after examining the information supplied by the Egyptian Ministry of Health and Population, World Health Organization, and the relevant research regarding knowledge assessment. Initially, demographic data were collected. Participants were asked their source of knowledge about COVID-19 and their intent to receive the available vaccines. Knowledge about COVID-19 and preventive measures was compared between different HCWs in NCI. In addition, knowledge score was compared according to different factors to determine factors affecting knowledge. RESULT(S): Physicians represented about 40% of the total number of participants. HCWs' knowledge of COVID-19 was higher among physicians, with a higher percentage of correct answers than the other two groups (nurses and pharmacists and employees and technicians). Younger age groups had a significantly higher median knowledge score than the older groups. Physicians had significantly higher median scores than employee and technician groups. CONCLUSION(S): The overall knowledge about COVID-19 and its preventive measures among HCWs was generally good, especially among physicians more than allied health professionals. However, some of the virus-related knowledge was less advanced than expected for the HCWs position.Copyright © 2023 Nora Atef, Mohamed Bendary, Amira Khater.

Workplace policies and quality of working life (QoWL) during the COVID-19 pandemic in Jordanian hospitals.

BACKGROUND: Many healthcare organizations place a high value on quality of work-life (QoWL). The healthcare system's long-term sustainability and capability to offer high-quality services to patients depend on improving QoWL for their healthcare workers. OBJECTIVE: The study aimed to explore the impact of Jordanian hospitals' workplace policies and measures in three main domains: (I) Infection prevention and control (IPC) measures, (II) Supply of personal protective equipment (PPE), and (III) COVID-19 precautionary measures on the QoWL among healthcare workers during the COVID-19 pandemic. METHODS: A cross-sectional survey was conducted from May to June 2021 through an online self-reported questionnaire (Google Form) targeting hospital healthcare professionals working at Jordanian hospitals (public, private, military, and university). The study used a valid work-related quality of life (WRQoL) scale to study the QoWL. RESULTS: A total of 484 HCWs in Jordanian hospitals participated in the study with a mean age of (34.8 ± 8.28 years). 57.6% of the respondents were females. 66.1% were married, with 61.6% having children at home. An average QoWL among healthcare workers in Jordanian hospitals during the pandemic was observed. The study results also showed a significant positive correlation between workplace policies (IPC measures, supply of PPE, and COVID-19 preventive measures) and the WRQoL among healthcare workers. CONCLUSION: Our findings highlighted the vital need for QoWL and psychological well-being support services for healthcare staff during pandemics. Improved IPC systems and other precautionary measures at the national and hospital management levels are required to help minimize the stress and fear that healthcare workers experience and lower the risk of COVID-19 and future pandemics.

Covid RayScan

The year 2020 was an unprecedented time for all, combating COVID, following precautionary measures and finding a cure for the virus was of utmost importance. As the COVID-19 is here to stay, it is imperative to detect it as early as possible. Our web application (COVID RayScan) is a prediction-based Machine Learning application which can be used by technicians, doctors at hospitals to understand a X-ray or CT-Scan and hence quickly detect if a patient suffers from Covid or not. According to NCBI, it takes 17.4 minutes for a doctor to treat every patient and that metric has increased exponentially with increase in COVID. COVID RayScan with the help of Deep Learning CNN Networks like ResNet50,VGG16,Inception and Xception helps a technician to run the X-Ray/CT-Scan image through our web application to get the desired result which in turn saves the doctor's as well as patients time and make the process much more efficient. © 2022 IEEE.

Coviguard - Intelligent shirt using Arduino for protection against covid

In the current pandemic situation, we need to follow certain precautionary measures to safeguard us from the deadly virus. We have been able to contain the virus to a certain extent through social distancing, by sanitizing ourselves and sterilizing the daily-use items. Monitoring the vitals like body temperature, oxygen saturation, and pulse rate has proven to be effective in diagnosing the fatal disease. In this proposed method, we have come up with a solution to help the user to keep a check on the important parameters mentioned above by incorporating various sensors like MLX90614 non-contact infrared temperature sensor, SpO2 sensor, pulse rate sensor, and ultrasonic sensor in a shirt- CoviGuard. The vitals are displayed on an IOT application called ThingSpeak. A buzzer is used to indicate if the user doesn't maintain the specified distance of 0.5 meters. © 2023 IEEE.

COVID-19 Precautionary Measures and Practices for Delivering Modular Distance Learning

The purpose of this descriptive study is to investigate the safety precautions and delivery methods for the modular distance learning modality during the COVID-19 pandemic. This empirical study employed a quantitative approach and descriptive research design to address the research problem and questions. The respondents for primary data collection were the junior high school teachers who were identified through a referral sampling technique. The frequency count, percentage, ranking, and Chi-square test for homogeneity and independence were the statistical tools employed in the study. The alpha threshold for all inferential statistics was set at 0.05. The outcomes showed that the safety precautions in the modular distance learning delivery provided equitable and inclusive access to favorable learning environments. Teachers' evaluations of the methods employed in the modular distance learning program revealed that parents were urged to play a significant role as home facilitators. The data analysis revealed that there were significant differences in how the modular distance learning was carried out. According to the findings, there is no conclusive link between the prevention measures and the delivery of the distance learning mode. To strengthen this investigation, additional research is required with a larger focus and new factors. © 2023 World Journal of English Language.

Instructors Experience of using Cloud Computing Based Applications in Saudi Arabia

With the recent global spread of the COVID-19 (also known as the corona virus) pandemic, several governments have attempted to control its transmission through preventive and precautionary measures. Education is one of the factors that has been impacted by the pandemic. As a result, to limit the spread of the virus, many countries adopted distance education instead of traditional education to ensure the continuity of the educational process. Cloud computing is a technology that offers numerous advantages in the field of education. The Kingdom of Saudi Arabia was one of the countries that had decided and continues to use various cloud platforms for distance education. In this study, we look at how effective cloud computing platforms are in the learning process in Saudi Arabian schools. The primary goal of this research was to investigate the teacher's ability to access different cloud computing services, as well as their ease of use and utility, by evaluating the effectiveness of these platforms as a mode of teaching before and after the pandemic. A total of 559 male and female schoolteachers' data was collected using self-administered questionnaires in Al-Bahah region and was analyzed using the IBM SPSS Statistics software. The analysis of this study expanded our understanding on the possibility of using educational platforms across schools in the kingdom. The findings also revealed that the use of cloud platforms during the pandemic increased by 28% in the region which have now become integral part of education. Furthermore, the findings revealed that teachers frequently encountered difficulties in implementing cloud-based educational processes, particularly in rural and mountainous areas. © 2023 IEEE.

Trust and COVID precautionary measures during the early days of the COVID-19 pandemic: Evidence from two African countries.

This study examines how trust was associated with social distancing during the early days of the COVID-19 pandemic in Burkina Faso and Kenya. It fills gaps in previous research on trust and health by 1) simultaneously considering the relationship of individual- and aggregate-level indicators of trust, and 2) evaluating trust in local government and national government separately. Performance Monitoring for Action (PMA) data on COVID-precautionary measures and individual-level trust measures were spatially linked with aggregated trust data from the Afrobarometer to create a multilevel dataset. PMA data show that women in Kenya were generally more likely to report taking COVID-precautionary measures relative to Burkinabé women, although levels of these measures were high in both countries. Hierarchical logistic models for each country show levels of interpersonal trust mattered more in Burkina Faso. Although the association between individual-level trust in government and social distancing was not statistically significant, overall levels of trust in the region where an individual lived were associated with social distancing. We found a significant interaction effect between regional trust in the national government and regional trust in local government: individuals in regions where trust was high in both national and local government were the most likely to socially distance; individuals in regions with low local government trust but high national government trust were less likely to report social distancing. We unpack possible implications of these findings; they point to the importance of a unified government front within African countries in promoting health safety measures during a pandemic.

Factors influencing the adoption of cashless transactions during COVID-19: an extension of enhanced UTAUT with pandemic precautionary measures

This paper aims to extend the enhanced "unified theory of acceptance and use of technology (UTAUT)” model with pandemic precautionary measures (PPM) to examine the variables that influence the adoption of "cashless transactions (CLT)” during pandemic periods such as COVID-19. For this purpose, this research polled 363 people from 15 zones of Chennai City, India. Utilizing the responses, the relationship between components was explored using a PLS-SEM approach in two distinct research models to examine the influence of PPM in the enhanced UTAUT. The results indicate that the model with PPM (model 2) exhibited an improvement over the model without PPM (model 1) in the variance explained of behavioural intention from 77.3 to 82%. Further, in model 1, performance expectation is the most potent predictor of individuals' intentions to use CLT, and in model 2, performance expectations deteriorated slightly in its ability and PPM became the most potent predictor. Furthermore, these findings reveal that the PPM-included enhanced UTAUT has significantly strengthened its ability to explain behavioural intent to adopt CLT. Thus, this research model has the potential to be of great use in investigating the adoption of CLT in any epidemic period. © 2023, The Author(s), under exclusive licence to Springer Nature Limited.

Effective Health Screening and Prompt Vaccination to Counter the Spread of COVID-19 and Minimize Its Adverse Effects

Precautionary measures are the best conceivable ways to impede the spreading of COVID-19 disease. Initial stage detection, proper analysis, suitable confinement, effective therapy and prompt vaccination are the key consideration to inhibit expedite transmission. Precautions are primarily concentrated on effective health screening, efficient treatment, and vaccination on time for each and every individual. Before the invention of COVID-19 vaccine, proper health checking or explorations are of predominant concern from therapeutic viewpoint as avoidance is superior than healing. At present scenario, impressive health screening through RT-PCR test, rapid antigen test, etc. is very imperative to identify Corona-positive cases in early stages even there is no disorders or symptoms of COVID-19 infection. Faster vaccination of most of the people, irrespective of cast, religion, and economic conditions, is indispensable to prohibit the transportation of such growing disorder and minimize its adverse effects. Recent literature reveals that most of the vaccines are safe and effective against coronavirus. A potent and competent vaccine lessens mild to moderate and serious conditions of COVID-19 patients regardless of comorbidities. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

A Technical Analysis on the Current Implementation of Online Examination System and the Factors Influencing the Issue

An examination is an essential aspect of learning. The traditional method of examination is when the exam taker takes an assessment in a room or a huge space with other candidates while being supervised by the exam monitor. Although the term 'Online Examination' has existed for a while, however, it has been taking off recently due to the global nation closing as a precautionary measure to prevent the spreading of the Covid-19 virus. Many institutions have their way of conducting online examinations, which prompt many responses from the exam takers. While it is undeniable that online examination could increase efficiency in conducting an examination, multiple factors could influence the success of conducting a smooth online examination. Hence, this paper will study exam taker's sentiments towards online examinations based on the current online exam implementation in their institutions. A questionnaire survey was conducted throughout 67 exam takers above the age of 18, where their responses are gathered and analyzed in identifying their concerns and obstacles when taking an online examination. The results would be helpful for institutions to formulate a method for creating an online examination method that would be both efficient and convenient for both students, lecturers, and staff of the institution. © 2022 IEEE.

Implementation of Deep Learning Models for Real-Time Face Mask Detection System Using Raspberry Pi

Amidst this COVID-19 pandemic, it is of utmost importance to wear facemasks and follow precautionary and preventive measures to decrease the further spread of this virus. In recent years Convolutional Neural networks (CNN) has impacted tremendously in various fields for classification and detection systems. In this paper we propose a facemask detection system using deep learning algorithms and a comparative study of various metrics for these deep learning algorithms. Algorithms like VGG, Resnet, Inception, Nasnet and Densenet, and its variations have been used. Using these deep learning models as a base and fine tuning the output layers of these models we construct an architecture for deep learning. Hyperparameter tuning and other methods like data augmentation have also helped in achieving better results. Various metrics like precision, recall, F1score, Average precision, accuracy and hamming loss has been evaluated for the models trained. An accuracy of 93.76% and average precision of 90.99% is achieved for the Denset201. Furthermore, we propose a standalone facemask detection system using the Raspberry Pi and a camera by fitting in the Nasnet mobile model into the detection system. Many applications for this system can be foreseen in places like hospitals, malls, restaurants and other places of public interest as an authentication or entry access criteria system. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Impact of Institute Closures and Learning Practices on Education in COVID-19 Pandemic: Omnichannel in Education

Community mitigation is crucial and is part of the preparedness plan of public health planning. Education institutions' closures are a non-pharmaceutical intervention often suggested for mitigating pandemics. Education institute closures could play a major role in the delay of community transmission. An audit is led by past examinations that distinguish the viability of school terminations and social removal techniques during a pandemic. The closure was first executed across China and Hong Kong. Data from the SARS pandemic conclude school closure was not an effective method, but trends of influenza show it may be the right precautionary measure. This article not only debates the need for this precautionary measure but also tells the various available platforms, resources, and solutions to continue the learning and growing process for everyone, regardless of their age group. It also highlights the psychological and mental side effects of the closure and social distancing, concluding with its adverse effects on the economy and welfare of the country. © 2022 IEEE.

Twitter sentiment analysis to understand students' perceptions about online learning during the Covid'19

In 2019, the Covid-19 pandemic led to emergency changes in the educational sector as a precautionary measure to limit the spread of the Covid-19 virus and protect the health and safety of students. Educational institutes couldn't escape this havoc;by April 2020, 189 countries had suspended school, affecting 89 percent of the world's students. Since the epidemic began, online learning has completely taken over the educational industry, leaving students with no choice but to adapt to the brand-new virtual learning environment. Consequently, people turned to social media, such as Twitter, to express their feelings, opinions, and concerns about online learning as an alternative to traditional physical classes. The new online learning platforms, associated technologies, and procedures have been widely discussed on Twitter. In the proposed study, we have presented a systematic approach to analyze the public opinions and perceptions about online learning using Twitter sentiment analysis (TSA) through Twitter's API and term frequency-inverse document frequency (TF-IDF) technique. Further, we classified the sentiments into certain clusters, such as positive, negative, and neutral, using a text mining approach (i.e., lexicons). Moreover, we have uncovered these sentiments and visualized the clusters using visualization techniques such as word clouds and bar charts. Additionally, by using TF-IDF, we measured the strength of words that people use to express their opinions about online schooling and explored to what extent it affects the overall results of our analysis. © 2022 IEEE.

When and How Trust in Government Leads to Compliance towards COVID-19 Precautionary Measures.

Despite the risks of COVID-19, some people ignore the COVID-19 precautionary measures, endangering public health. We aimed to investigate how and in what conditions trust in government and health authorities encourage individuals to comply with COVID-19 precautionary measures. Based on a sample of 664 respondents, we found that an increase in the level of trust in government is associated with higher compliance with COVID-19 precautionary measures. We also found that problem awareness mediates the effect of trust in government on compliance with COVID-19 precautionary measures. In addition, we examined whether individualistic orientation moderates the mediating effect of problem awareness. We found that individualistic orientation mitigates the mediating effect of problem awareness in the relationship between trust in government and compliance behavior. The findings of this study have the potential to inform policy and practice by addressing the ways in which compliance with COVID-19 precautionary measures can be improved.

Robotic Systems Deployed to Combat COVID-19 Pandemic: A Review

The COVID-19 epidemic has been deemed a pandemic by the World Health Organization. It is triggered due to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It originated and spread from Wuhan, China, in December 2019. At present, the entire world is struggling from this virus due to large confirmed positive and death cases of COVID-19. People of every nation have been isolated, and lockdowns are instituted. Despite the introduction of several precautionary measures, the spread of the virus is still increasing at an alarming pace. Although promising development has been made for the development of vaccines for SARS-CoV-2, no vaccines have been reported to cure the infection. Different antiviral therapies have also been attempted but do not seem to be successful for every patient. To deter the dissemination and control the spread of virus, the frontline healthcare staff and police officers deployed numerous autonomous systems for an increased line of protection. Robots are deployed to conduct different operations including decontamination, package delivery, etc. It also acts as a mediator for two-way communication between the doctors and patients. Recent advancement in robotics for its application in healthcare facilities has been found very effective for the healthcare officials to communicate with the virus affected patients, and this literature has addressed it. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Examining relationships between precautionary measures taken for COVID-19 at the destination, service quality, brand equity and behavioral intention: a comparison market segments

This study examines the relationships between the precautionary measures taken for COVID-19 (PMC) at the destination and destination service quality (DSQ), destination brand equity (DBE), and tourists' behavioral intention, and it also investigates the moderating role of PMC between these variables. In addition, this study also examines whether the relationship between the variables is different for domestic and international visitors, as well as being first-time/repeat visitors. The quantitative research method was implemented to test research hypotheses, and it was exploited the survey for research data. Overall, 423 valid data were collected from tourists who came to the Turkey-Fethiye destination between July and October 2021. The structural equation model was established, and was tested the research hypotheses with the help of path analysis. The findings showed that the PMC had significant impacts on tourists' perceptions of DSQ, DBE, and their behavioral intention. On the other hand, while the DBE and behavioral intention are positively affected by DSQ, the PMC has a moderating role in the relationships between these variables. Finally, the empirical results provide theoretical and practical implications for destination managers for the development of safety measures in tourism services and the design of effective actions to gain a competitive advantage.

Application of Public Health Precautionary Guidelines Before Contracting COVID-19: Lessons Learned from a Qualitative Descriptive Study.

Background: The COVID-19 pandemic has affected the Saudi population despite early efforts taken by officials. This study aimed to describe the precautionary measures taken by patients prior to their diagnosis with COVID-19. Methods: A qualitative descriptive design was employed to collect data from a convenience sample of 26 participants who had COVID-19 and recovered. Data was collected through conducting telephone interviews and was then transcribed and analyzed using inductive content analysis methods adapted from grounded theory. Results: Data analysis yielded six categories of precautionary measures participants reported using to minimize their risk of contracting COVID-19: (1) wearing a mask, (2) staying at home and avoiding crowds, (3) washing hands and wearing gloves, (4) following social distancing, (5) using sanitizers, and (6) not interacting with anyone. Conclusion: The discrepancy found between the participants' reported precautionary measures and their actual behavior, as well as the consistency and specificity with which they implemented some and not all measures raise concerns about existing public health messages. The study findings support the importance of well-structured and detailed communication when designing future public health campaigns and data-driven policies.

US Consumer Behavior during a Pandemic: Precautionary Measures and Compensatory Consumption

This study's purposes were to examine how selected demographic variables affect frequency of use of precautionary measures when shopping for clothing in retail stores;and how uncertainty avoidance/ambiguity intolerance and fashion innovativeness affect (a) precautionary measures used when shopping in retail stores during a pandemic and (b) compensatory consumption. Participants (122 US men;209 US women aged 20 to 64) completed an online questionnaire containing demographic items plus measures of uncertainty avoidance/ambiguity intolerance, compensatory consumption, precautionary measures, and fashion innovativeness. Data analysis included reliability, factor analysis, M/ANOVA and SNK. Older adults, adults with higher education, and married adults more frequently used precautionary measures when shopping in retail stores. Men and women reported similar frequency of use. Fashion innovators and consumers with less tolerance for uncertainty/ambiguity more frequently used precautionary measures. Fashion innovators and consumers higher in uncertainty avoidance/ambiguity intolerance engaged in more compensatory consumption. Generalization of the results is limited because the data are context-specific: country (US), time period (during a pandemic), and sample. Guidelines for the general public regarding precautionary measures came from within organizations, between organizations and experts but the general public was not consulted (public open innovation) perhaps hindering compliance with precautionary measures. © 2022 by the authors.

Designing Smart Restaurant for Reopening During the Relaxation of Lockdown in the Time of Corona Pandemic

Considering the changes that the world is going through, since the emergence of the Coronavirus 'COVID 19' and the following versions of the same virus which has fast spreading behavior among all countries and sometimes worse health effects on the human. It seems that its economic and social repercussions will be significant and influential at the global and national levels, as the outbreak imposed by taking a set of precautionary measures and measures represented in 'isolation' with quarantine or social distancing and restaurant closures. These procedures cause poverty among workers in malls and restaurants and economical losses, so countries start to relax the lockdown. This work aims to propose some procedures in designing restaurants for reopening during the relaxation of lockdown to reduce human contact through designing a waiter robot moves by line follower approach in a restaurant to serve meals to customers and also designing a wireless network to send the customizers orders from the tables to the kitchen. © 2022 IEEE.