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Purpose: The purpose of this paper is to apply systems modeling to explore the usability of the online learning platform in the future compared to its usefulness during the pandemic era. Design/methodology/approach: The applied systems research methodology has been used to develop a stock-flow model encompassing enablers and constraints for learning platform usage from the primary data collected through a survey of 163 respondents. Findings: The model simulation observed promising trends over one year for online learning platforms provided the challenges are reduced in seven to eight months. Challenges linked to the Internet and interaction need must be removed for future usage. Research limitations/implications: The results of the survey and model simulation suggest actions for product planning and development of online learning platforms based on customer insights. Product customization and feature enhancement will be required for the continued usability of online learning products. Actions for Internet service providers are to capture the online learner market by removing issues of Internet access bandwidth, and quality of content. Also, there should be sufficient teacher–student interaction in the online learning mode. Originality/value: This is an original study using systems modeling to evaluate factors contributing to students' intention to use online learning conducted at Dayalbagh Educational Institute (Deemed to be University) Dayalbagh Agra, UP, India, 282005. © 2023, Emerald Publishing Limited.
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Background: Corona virus disease is an ongoing pandemic. COVID-19 had put cumbersome mental and physical pressure on the healthcare staff which may lead to burnout in them. Also there is paucity of Indian literature regarding prevalence of burnout in rural health care staff. Objective(s): To study the prevalence of burnout and its association with the determinants among the healthcare workers in rural population of a Ajmer district, Rajasthan. Methodology: A Cross Sectional Questionnaire based study was conducted from January 2021 to June 2021 on 173 healthcare staff of rural population in a Ajmer district, Rajasthan after ethical clearance. Copenhagen Burnout Inventory was used to assess the burnout among Health Care Workers and a semi structured Performa was used to evaluate the demographic and clinical determinants of burnout. Prevalence of burnout was determined and the association of determinants with burnout was assessed. Result(s): The prevalence of personal burnout, work related burnout and pandemic related burnout in health care workers was 42.5%, 33.75% and 49.37%. Pandemic related burnout was significantly greater than personal burnout and work related burnout in health care workers. Doctors, redeployed healthcare staff and staff having covid positive case in family have significantly greater burnout. Supportive work environment and adequate protective measures at workplace significantly reduces the burnout among health care staff. Conclusion(s): Almost half of the rural health care workers are burnout. Protective authoritative and individual measures are needed to prevent burnout in HCWs. Copyright © 2022 Authors. All rights reserved.
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The COVID 19 pandemic has spread rapidly across the globe in the last one year and the global response to it especially from the artificial intelligence community has been tremendous. It was seen from chest CT scans and chest X-rays that the respiratory system is hugely affected by the corona virus. In developing countries like India, a rapid and low-cost diagnostic tool is of prime importance for mass screening of population. Given the recent success of deep learning techniques in solving real-time problems, this paper presents application of deep learning models to detect and classify COVID 19 from chest radiographs. Dataset of 2727 images was constructed for this study and analysis from various open source resources like Kaggle and Github. Several pre-trained models have been used for experimentation. Among all the models, VGG-16 model achieved an impressive classification accuracy of 98.9% and F1-score of 0.984 with high sensitivity and specificity as well.
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IntroductionThe governments of various countries enforced a lockdown to contain the COVID -19 pandemic. As the colleges remain closed, the academic teachings for students was conducted online. The mobile phone remained the main source for academics and entertainment during this period.ObjectivesTo assess patterns of use of mobile phone by Medical Undergraduate students prior to and during the COVID-19 lockdown. To assess Nomophobia among same participants.MethodsThis study was done by an online survey method after obtaining approval from the Institutional Ethics Committee. A validated questionnaire on patterns of mobile phone use and the Nomophobia Questionnaire(NMP-Q) was completed by the medical students (n=187) who consented to participate in the studyResultsPrior to the pandemic lockdown, 52.9% of the participants used the mobile phones for 2-4 hours per day with 78% of the usage in social media. During lockdown, 89.3% of the participants reported an increase in the usage of mobile phones. 35.65% reported an increase in use by 2-4 hours everyday. About 30.5 % used the mobile phone for 6-8 hours per day. 80.2 % reported a maximum usage for social media. 59.45% reported a maximum usage for online academics. 33.7% frequently checked their phones once in 15 minutes. About 60.43% of the participants were in the moderate and 21.4% in the severe category of nomophobia.ConclusionsThere is an increase in mobile phone usage during the lockdown with a significant proportion of students in the moderate and severe category of nomophobia.DisclosureNo significant relationships.
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Italy faced the COVID-19 crisis in the early stages of the pandemic. In the present study, a SEIR compartment mathematical model has been proposed. The model considers four stages of infection: susceptible(S), exposed (E), infected (I) and recovered (R). Basic reproduction number R0 which estimates the transmission potential of a disease has been calculated by the next-generation matrix technique. We have estimated the model parameters using real data for the Coronavirus transmission. To get a dipper insight into the transmission dynamics, we have also studied four of the most pandemic affected regions of Italy. Basic reproduction number stood differently for different regions of Italy i.e. Lombardia (2.1382), Veneto (1.7512), Emilia Romagna (1.6331), Piemonte (1.9099) and for Italy at 2.0683. The sensitivity of R0 corresponding to various disease transmission parameters has also been demonstrated via numerical simulations. Besides, it has been demonstrated with the help of simulations that earlier lockdown and rapid isolation of infective individuals would have been helpful in a dual way;by substantially reducing the number of susceptible people on one hand and preponing the end of the pandemic on the other. This paper also includes complete theoretical analysis of the proposed model including the epidemic feasibility of the model and existence of endemic equilibrium point. We have also derived the conditions under which the disease became endemic. Since the existence of an endemic equilibrium point refers to the possibility of backward bifurcation, we have given a detailed analysis regarding the same. All the theoretical analysis is supported by detailed numerical simulations to understand the transmission dynamics of COVID-19 While analyzing different regions of Italy it was found that Lombardia was the hardest hit and had the highest number of infectives. We have also forecasted the future scenario of the pandemic in Italy. The model predicts that the COVID-19 epidemic shall die out from the worst affected Lombardia region by approximately by November 2020. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Worldwide increasing cases of COVID-19 are putting high pressure on healthcare services. The coronavirus epidemic caused announcing emergency cases in India. The virus started with one infected case by 30th January, 2020, in Kerala, where the first death due to corona in Karnataka and 73 announced cases were reported by 12th March, 2020. 1024 announced cases were reported by 29th March, 2020.This indicates that the number of confirmed cases is increasing rapidly, causing national crises for India. This study aims is to fill a gap between previous studies and the current development of COVID-19 spreading, by extracting a relationship between corona positives as independent and corresponding deaths as a dependent variable. This research statistically analyses the mortality in 10 days of every month. A mathematical model to predict the new deceased cases corresponding infected cases in a practical scenario is proposed. An approximate prediction of mortality corresponding to new predicted cases can be easily performed using the proposed model. As most of the other countries have reached their peaks, confirmed cases start decreasing. By analyzing these countries’ data considering the lockdown, the peak ratio is identified using all countries’ population and the decreasing rate of confirmed cases after the peak has been achieved. The same calculation has been done for death and recovered cases. This average peak ratio is used to identify India’s COVID patients’ peak value. The decreasing rate is also used to define the rate of confirmed cases and mortality after the peak has occurred. The model has also been verified in different countries to identify the significance of the model. © 2020, Novel Carbon Resource Sciences. All rights reserved.