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Relatively few studies have prospectively examined the effects of known protective factors, such as religion, on pandemic-related outcomes. The aim of this study was to evaluate the pre- and post-pandemic trajectories and psychological effects of religious beliefs and religious attendance. Male and female adults (N = 189) reported their beliefs in religious importance (RI) and their religious attendance (RA) both before (T1) and after (T2) the pandemic's onset. Descriptive and regression analyses were used to track RI and RA from T1 to T2 and to test their effects on psychological outcomes at T1 and T2. The participants who reported a decrease in religious importance and attendance were greater in number than those who reported an increase, with RI (36.5% vs. 5.3%) and RA (34.4% vs. 4.8%). The individuals with decreased RI were less likely to know someone who had died from COVID-19 (O.R. =0.4, p = 0.027). The T1 RI predicted overall social adjustment (p < 0.05) and lower suicidal ideation (p = 0.05). The T2 RI was associated with lower suicidal ideation (p < 0.05). The online RA (T2) was associated with lower depression (p < 0.05) and lower anxiety (p < 0.05). Further research is needed to evaluate the mechanisms driving decreases in religiosity during pandemics. Religious beliefs and online religious attendance were beneficial during the pandemic, which bodes well for the use of telemedicine in therapeutic approaches.
Subject(s)
COVID-19 , Mental Health , Adult , Humans , Male , Female , Prospective Studies , Pandemics , COVID-19/epidemiology , ReligionABSTRACT
BACKGROUND: Prospective studies are needed to assess the influence of pre-pandemic risk factors on mental health outcomes following the COVID-19 pandemic. From direct interviews prior to (T1), and then in the same individuals after the pandemic onset (T2), we assessed the influence of personal psychiatric history on changes in symptoms and wellbeing. METHODS: Two hundred and four (19-69 years/117 female) individuals from a multigenerational family study were followed clinically up to T1. Psychiatric symptom changes (T1-to-T2), their association with lifetime psychiatric history (no, only-past, and recent psychiatric history), and pandemic-specific worries were investigated. RESULTS: At T2 relative to T1, participants with recent psychopathology (in the last 2 years) had significantly fewer depressive (mean, M = 41.7 v. 47.6) and traumatic symptoms (M = 6.6 v. 8.1, p < 0.001), while those with no and only-past psychiatric history had decreased wellbeing (M = 22.6 v. 25.0, p < 0.01). Three pandemic-related worry factors were identified: Illness/death, Financial, and Social isolation. Individuals with recent psychiatric history had greater Illness/death and Financial worries than the no/only-past groups, but these worries were unrelated to depression at T2. Among individuals with no/only-past history, Illness/death worries predicted increased T2 depression [B = 0.6(0.3), p < 0.05]. CONCLUSIONS: As recent psychiatric history was not associated with increased depression or anxiety during the pandemic, new groups of previously unaffected persons might contribute to the increased pandemic-related depression and anxiety rates reported. These individuals likely represent incident cases that are first detected in primary care and other non-specialty clinical settings. Such settings may be useful for monitoring future illness among newly at-risk individuals.
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This research work is focused on the accessible Mobile user application developments to facilitate student and faculty communication through native android applications. Covid'19 and this pandemic brings E-learning systems as majority education levels. Mobile technology has efficient learning systems in many countries like the United States where the students use google paths such as the classroom to extend learning effectively. Limitations in existing apps are that students are not appreciated and monitored for self learning, schedule sharing is only at the end of the course and also knowing the mapping concepts of teaching pedagogy is also less approachable. To overcome these problems mobile technology support is proposed in this work with these three modules such as i) Authentication self learning and performance (ASLP) - Authentication for right users along with improvement of monitoring self learning to analyze performance ii) Syllable Schedule (SS) - prior scheduling on syllable and organization of time table matching based on outcome iii) Authorized facilitator (AF) - Set permission based on designation such that facilitator communicates based on needs. To achieve the above highlighted terms Google API is applied by peer reviews and interactions enabled such that efficient mobile applications development is proved. Also the ion hierarchy is improved when setting the interaction module implies less complexity, less storage. Thus the scope of research work is to use classroom overall performance, interaction, development process to upgrade the results of students. Education level is also enhanced through online E-learning mobile technology (OELMT) that has native applications to develop students' knowledge in a better way. © 2022 IEEE.
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An impending branch of computer science is artificial intelligence. It plays an important role in the construction of smart machines that are capable of performing sophisticated operations. One of the key characteristics of artificial intelligence is its ability to make decisions on its own and rationalize the solution, helping us to achieve a certain goal. Our human race has faced many threats in the form of epidemics and pandemics, which have proved to be almost incurable in the past. Nevertheless, science and its evolving technologies have given us some hope to fight such threats. One such pandemic that our human race is facing in the current times is COVID-19. This deadly disease is rapidly spreading across the whole world endangering the lives of humans. Amid the chaos, we desperately need to stop the spread, or at least take adequate counter-active measures to detect this virus at its early stage. Deep learning, a subset of artificial intelligence provides many models which helps in the automation of the task of detecting viruses in humans mainly with the help of image processing. In detecting COVID-19, deep learning is a breakthrough, which has helped us in our proposed system. This system makes use of chest radiographs (CXR) to detect the presence of the virus in the human body thereby lowering the risk of spread which is fairly high in manual detection methods. The CXRs are one of the most common imaging tests in the clinical field, which helps in detecting the presence of cold, cough, shortness of breath in the lungs, and so on. The proposed model is very efficient when it comes to detecting problems in the lungs with the help of image processing. We propose an improvised neural network derived from the Convolutional Neural Network which works similar to the human brain structure to detect and process the CXR images efficiently and at faster rates. The neural network mimics the functioning of the brain, where self-learning and decision making are its key features. The image data sets are a collection of CXR images which have a RGB value of 1. This approach is proven to be safer and better than the manual testing methods that are currently deployed. As the traditional methods for detecting COVID-19 virus is tedious, and not fairly accurate, automating this task can help in giving accurate results with reduced risk of spread of disease through physical contact. © 2022 Scrivener Publishing LLC.
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Data Mining is a powerful technology and is used to identify useful and understandable patterns by analyzing large sets of data. It gives a detailed view of various disease predictions. It will be more useful especially in pandemic times. During these days, doctors are in the front line and battling with the COVID-19 virus. It will be hard for people to immediately get medical guidance or appointments. Our proposed system, the smart health application will come in handy at these times. The system allows people to get medical guidance for their health issues. Also, the system is fed with symptoms and the disease-related with it which will give high accuracy for disease prediction. Our model aims to use Stacking Ensemble Classification Algorithms to give high accuracy and correct prediction than Naive Bayes, Random Forest, Support Vector Machine, K - Nearest Neighbor, Decision Tree, Logistic Regression for different types of 149 diseases. The GUI is designed which can be used easily to predict the different types of disease accurately.
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Aim: To assess the perception of stress using the most widely used psychological assessment instrument perceived stress scale (PSS-10) during the lockdown in the second wave of COVID-19 pandemic among the late adolescents in Malaysian population. Materials and methods: Cross sectional study using the google form via WhatsApp among late adolescents in Malaysian population using the snowball sampling technique was used. PSSscoring is 0 = Never, 1 = Almost Never, 2 = Sometimes, 3 = Fairly Often, 4 = Very Often. Scores are obtained by reversing responses to the four positively stated items. Cronbach's alpha was used to measure reliability. Results were analysed by SPSS. Results: Cronbach's alpha value was 0.634, indicates acceptable. Our results showed females have higher scores in the questions related to been upset, unable to control, could not cope and been angered. On the other hand, the males have higher scores compared to felt confident, control irritations. Conclusion: In conclusion, this study found females get higher level of stress symptoms than males to cope up with second wave of pandemic. Therefore, it is suggested to support with psychotherapy techniques during the management of COVID-19 pandemic.
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Infections such as COVID-19 are affecting the entire world and measures such as social distancing can be done so that the contact among people is reduced. IoT devices usage keeps on increasing every day thereby connecting the environments physically. Among the current technologies, machine learning can be employed along with IoT devices. Predicting the risk related with COVID-19, a novel method employing machine learning is proposed. Random forest and Naive Bayes classifier are used for the prediction from the data collected with the help of sensors. Groups of people are recognized and the disease impact can be reduced for the particular group with more population. The accuracy of RF is 97% and for NB it is 99%.
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COVID-19 has been affecting the entire world from the year 2019 and in order to handle this pandemic situation, it is necessary to follow the measures till valid medicine has been found. The proposed approach helps in detecting as well as monitoring the COVID-19 on real time basis. Data is collected with the help of IoT devices for detecting this disease at the early stage. The components of the system are, (i) Collection of symptom data, (ii) Center of Isolation, (iii) Machine Learning approaches for analysis, (iv) Healthcare analysts and (v) Cloud. The three algorithms in machine learning used for detection of the virus are Decision tree, Support vector machine and Neural Network. These three algorithms are tested with the real time dataset and it is observed that all these algorithms have accuracy greater than 91%. Identifying the disease accurately with the three machine learning algorithms, effective results are produced. The response of treatment for every person who gets affected by the virus is then documented. © 2022 IEEE.
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Since 2019 the world facing a critical situation known as pandemic and many suggested to go for lockdown. But that didn't work as the period was not ending and still continued. The vaccination could be the only practical solution for ending this pandemic situation. So it is essential that the detailed analysis of vaccination worldwide and it is very important than concentrating on the restrictions and creating awareness by advertising the scenario to the public. This paper analyzes vaccination status and suggests the vaccination distribution methods to be followed to end the lockdown soon by using machine learning techniques. Based on the proposed prophet algorithm, it is predicted that the people vaccinated per 100 would be increased to 97.12% with an accuracy of 98.51% and further lockdowns will not be necessary if the vaccination rate maintained as of now. © 2021 IEEE.
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Severe acute respiratory syndrome (SARS) is a single stranded RNA virus, it infects the epithelial cells within the lungs. . Moreover, these infections can be successfully inactivated by lipid solvents including ether (75%), ethanol, chlorine-containing disinfectant, peroxyacetic corrosive and chloroform with the exception of chlorhexidine. The viability of a few povidone-iodine (PVP-I) items, various other synthetic operators, and different states of being were assessed for their capacity to inactivate the extreme intense respiratory condition coronavirus (SARS-CoV). The stability of SARS coronavirus in human specimens and in environments was studied. The survival abilities on the surfaces of eight different materials and in water were quite comparable, revealing reduction of infectivity after 72 to 96 h exposure. Viruses stayed stable at 4°C.The survival of the virus seems to be relatively strong in humans and environment. Heating and UV irradiation can eliminate the viral infectivity.SARS,to be transmitted through respiratory droplets, fomites or tainted sewage frameworks. Presence of different strains of coronavirus has led to complications in the field of vaccine and medicine.Hence, this study sheds light on the stability of the different strains of virus (SARS CoV) in humans and environment. And also this study emphasizes on the physical and chemical methods of inactivation of SARS CoV. Thus, The necessity and aim of this study is to understand the knowledge about stability of SARS CoV in order to predict the future antiviral treatment and coping methodologies. © 2021, Universitatea de Vest Vasile Goldis din Arad. All rights reserved.
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The world is in a big lockdown due to the coronavirus. It is caused by a novel virus, a modification of SARS-CoV-2 and is spread through human to human transmission and as the days pass by the death rate is increasing rapidly as people are not following the prevention protocol properly and some are still not aware about the serious effects and preventive measures to be taken.It appeared just one month before the spring festival of China, and the massive population flow has brought great challenges for control and prevention of the disease.No specific therapeutic drug has been found for this disease.Hence health education on knowledge for controlling the disease is important.So, this review focuses on decreasing mortality rate and thereby help people return to their normal life.The review was made by collecting information based on the keywords and important points in relation to the topic through search engines like pubmed and google scholar.The present study summarises and gives an overview of COVID-19 that provides an insight to the people on this deadly disease and hence helps the people to stay safe. © 2021, Universitatea de Vest Vasile Goldis din Arad. All rights reserved.
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Aim: The aim of this study is to get knowledge about the crisis and to create awareness about the problems faced by the daily laborers during the lockdown. Introduction: The worldwide COVID-19 pandemic is perpetrating two sorts of shock on nations: a health shock and an eco-nomical shock. The imposition of social distancing, self-isolation at home, closure of institutions, industries, and public facilities, restrictions on mobility can conceivably prompt critical ramifications for economies around the globe, which affects the survival of the daily laborers. Materials and Method: The questionnaire is prepared to comprise 15 questions and the sample size is 100 participants. The results were analyzed using SPSS software. The bar graphs provided depicts the results. Results and Discussion: 87.4% of the participants think that the COVID-19 lockdown led to the loss of the only source of income of the daily laborers .61.2% of the participants think that the daily laborers don’t have enough of their daily wages to survive this lockdown. The results obtained from the population who participated in the study depict the knowledge and awareness of the problems faced by the daily laborers during the lockdown. Conclusion: The study confirms that the public is aware of the economic and food crisis faced by the daily laborers during the lockdown. © IJCRR.
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The aim of the study is to create knowledge and awareness about the Hanta virus and its complications among college students. The study setting is a prospective observational study. The sampling method used is random sampling with a sampling size of 100 participants. A self structured questionnaire was prepared and circulated using the online Google forms link. The study was conducted in the year 2020, and the data was collected during the process of the study in Private Dental College and Hospitals, Chennai. The data was collected and statistically analysed in SPSS. Chi-Square analysis was performed and p<0.05 was considered as statistically significant.According to the results of the survey 68% of the people are aware of Hantavirus, very less number of people think that both coronavirus and Hantavirus are the same and almost 30% people are aware of the knowledge of symptoms of Hantavirus. From the study it has been concluded that around 30 % of the college students are aware about the disease spread and its complications and still a lot more research has to be done in order to create a better knowledge on Hantavirus among the common people.