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The study aims to understand the foremost challenges in the transition to online teaching and learning during the COVID-19 pandemic. The study adopts the PRISMA approach to screening the selection of journal articles and review papers according to the research aims and the inclusion criteria. The journal articles and review papers were extracted and stored in Microsoft Excel and Google Scholar, Academic. Microsoft, Semantic Scholar, Elsevier, and Emerald Insight databases searched relevant documents using formulated keywords. A statistical technique was applied using the M.S. Excel analysis tool (PivotTable and an independent t-Test) to analyze data and determine the differences between teachers and students. The review revealed the evidence that the majority of the studies were primarily focused on the individual developing countries and results from other developing countries were not considered. In addition, the foremost challenges in the transition to online teaching and learning during the COVID-19 pandemic were inadequate skills and training, inadequate Internet/Infrastructure, lack of supporting resources and lack of online student engagement and feedback. Finally, the independent t-test reveals there is no statistically significant difference in challenges in the transition to online teaching and learning during the COVID-19 pandemic. Both teachers and students encounter similar challenges. The systematic review raised concerns that higher learning needs to effectively implement long term strategies and support teachers and students in getting into online teaching and learning.

This paper presents a compilation of personal reflections from 66 contributors on the impact of, and responses to, COVID-19 in accounting education in 45 different countries around the world. It reveals a commonality of issues, and a variability in responses, many positive outcomes, including the creation of opportunities to realign learning and teaching strategies away from the comfort of traditional formats, but many more that are negative, primarily relating to the impact on faculty and student health and well-being, and the accompanying stress. It identifies issues that need to be addressed in the recovery and redesign stages of the management of this crisis, and it sets a new research agenda for studies in accounting education.

At this moment in time, an outbreak of COVID-19 is transmitting from human to human. Different parts have different quality of life (e.g., India compared to Russia), which implies the impact varies in each part of the world. Although clinical vaccines are available to cure, the question is how to minimize the spread without considering the vaccine. In this paper, via a mathematical model, the transmission dynamics of novel coronavirus with quarantine and isolation facilities have been proposed. The examination of the proposed model is set in motion with the boundedness and positivity of the solution, sole disease-free equilibrium, and local stability. Then, the condition for the existence of sole endemic equilibrium and its local stability has established. In addition, the global stability of the endemic equilibrium for a special case has been investigated. Further, it has shown that the system undergoes a transcritical bifurcation. A threshold analysis has also performed to examine the effect of quarantine on transmission dynamics. Lastly, numerical simulations are giving support to theoretical results. [ABSTRACT FROM AUTHOR]

This paper analyses the influence of the Covid-19 coverage by the social media upon the shape of the sovereign yield curves of the five major developing countries, namely Federative Republic of B razil, Russian Federation, Republic of India, People's Republic of China, and the Republic of South Africa (BRICS). The coherenc e between the level, slope, and the curvature of the sovereign yield term structures and the Covid-19 medi a coverage is found to vary between low and high ranges, depending on the phases of the pandemic. The empirical estimations of the yield-curve factors a re performed by means of the Diebold-Li modified version of the Nelson-Siegel model. The intervals of low coherence reveal the capacity of the two latent factors, level and slope, to be used for creating cross-factor diversification strategies, workable under crisis conditions, as evidenced on the example of the ongoing pandemic. Diverse coherence patterns are reported on a per-country basis, highlighting a promising potential of sovereign debt investments for designing cross-country and cross-factor fixed-income strategies, capable of hedging downside risks., Competing Interests: The authors have declared that no competing interests exist.

Within the recent pandemic, scientists and clinicians are engaged in seeking new technology to stop or slow down the COVID-19 pandemic. The benefit of machine learning, as an essential aspect of artificial intelligence, on past epidemics offers a new line to tackle the novel Coronavirus outbreak. Accurate short-term forecasting of COVID-19 spread plays an essential role in improving the management of the overcrowding problem in hospitals and enables appropriate optimization of the available resources (i.e., materials and staff).This paper presents a comparative study of machine learning methods for COVID-19 transmission forecasting. We investigated the performances of deep learning methods, including the hybrid convolutional neural networks-Long short-term memory (LSTM-CNN), the hybrid gated recurrent unit-convolutional neural networks (GAN-GRU), GAN, CNN, LSTM, and Restricted Boltzmann Machine (RBM), as well as baseline machine learning methods, namely logistic regression (LR) and support vector regression (SVR). The employment of hybrid models (i.e., LSTM-CNN and GAN-GRU) is expected to eventually improve the forecasting accuracy of COVID-19 future trends. The performance of the investigated deep learning and machine learning models was tested using confirmed and recovered COVID-19 cases time-series data from seven impacted countries: Brazil, France, India, Mexico, Russia, Saudi Arabia, and the US. The results reveal that hybrid deep learning models can efficiently forecast COVID-19 cases. Also, results confirmed the superior performance of deep learning models compared to the two considered baseline machine learning models. Furthermore, results showed that LSTM-CNN achieved improved performances with an averaged mean absolute percentage error of 3.718%, among others., (Copyright © 2021 Elsevier Inc. All rights reserved.)

This paper studies the interplay between social distancing and the spread of the COVID-19 disease-a global pandemic that has affected most of the world's population. Our goals are to (1) to observe the correlation between the strictness of social distancing policies and the spread of disease and (2) to determine the optimal adoption level of social distancing policies. The earliest instances of the virus were found in China, and the virus has reached the United States with devastating consequences. Other countries severely affected by the pandemic are Brazil, Russia, the United Kingdom, Spain, India, Italy, and France. Although it is impossible to stop it, it is possible to slow down its spread to reduce its impact on the society and economy. Governments around the world have deployed various policies to reduce the virus spread in response to the pandemic. To assess the effectiveness of these policies, the system's dynamics of the society needs to be analyzed, which is generally not possible with mathematical linear equations or Monte Carlo methods because human society is a complex adaptive system with continuous feedback loops. Because of the challenges with the other methods, we chose agent-based methods to conduct our study. Moreover, recent agent-based modeling studies for the COVID-19 pandemic show significant promise in assisting decision-makers in managing the crisis by applying policies such as social distancing, disease testing, contact tracing, home isolation, emergency hospitalization, and travel prevention to reduce infection rates. Based on modeling studies conducted in Imperial College, increasing levels of interventions could slow the spread of disease and infection. We ran the model with six different percentages of social distancing while keeping the other parameters constant. The results show that social distancing affects the spread of COVID-19 significantly, in turn decreasing the spread of disease and infection rates when implemented at higher levels. We also validated these results by using the behavior space tool with ten experiments with varying social distancing levels. We conclude that applying and increasing social distancing policy levels leads to a significant reduction in infection spread and the number of deaths. Both experiments show that infection rates are reduced drastically when social distancing intervention is implemented between 80% to 100%.

Objective: To provide experience for formulating prevention and control policies, this study analyzed the effectiveness of the Coronavirus disease 2019(COVID-19) prevention and control policies, and evaluated health equity and epidemic cooperation among BRICS countries. Methods: This study summarized the pandemic prevention and control policies in BRICS countries and evaluated the effectiveness of those policies by extracting COVID-19 related data from official websites. Result: As of May 4, 2021, responding to COVID-19. China adopted containment strategies. China's total confirmed cases (102,560) were stable, without a second pandemic peak, and the total deaths per million (3.37) were much lower than others. India and South Africa who adopted intermediate strategies have similar pandemic curves, total confirmed cases in India (20,664,979) surpassed South Africa (1,586,148) as the highest in five countries, but total deaths per million (163.90) lower than South Africa (919.11). Brazil and Russia adopted mitigation strategies. Total confirmed cases in Brazil (14,856,888) and Russia (4,784,497) continued to increase, and Brazil's total deaths per million (1,936.34) is higher than Russia (751.50) and other countries. Conclusion: This study shows BRICS countries implemented different epidemic interventions. Containment strategy is more effective than intermediate strategy and mitigation strategy in limiting the spread of COVID-19. Especially when a strict containment strategy is implemented in an early stage, but premature relaxation of restrictions may lead to rebounding. It is a good choice to combat COVID-19 by improving the inclusiveness of intervention policies, deepening BRICS epidemic cooperation, and increasing health equities. [ABSTRACT FROM AUTHOR]

In the COVID-19 pandemic, patients who are older and residents of long-term care facilities (LTCF) are at greatest risk of worse clinical outcomes. We reviewed discharge criteria for hospitalised COVID-19 patients from 10 countries with the highest incidence of COVID-19 cases as of 26 July 2020. Five countries (Brazil, Mexico, Peru, Chile and Iran) had no discharge criteria; the remaining five (USA, India, Russia, South Africa and the UK) had discharge guidelines with large inter-country variability. India and Russia recommend discharge for a clinically recovered patient with two negative reverse transcription polymerase chain reaction (RT-PCR) tests 24 h apart; the USA offers either a symptom based strategy—clinical recovery and 10 days after symptom onset, or the same test-based strategy. The UK suggests that patients can be discharged when patients have clinically recovered; South Africa recommends discharge 14 days after symptom onset if clinically stable. We recommend a unified, simpler discharge criteria, based on current studies which suggest that most SARS-CoV-2 loses its infectivity by 10 days post-symptom onset. In asymptomatic cases, this can be taken as 10 days after the first positive PCR result. Additional days of isolation beyond this should be left to the discretion of individual clinician. This represents a practical compromise between unnecessarily prolonged admissions and returning highly infectious patients back to their care facilities, and is of particular importance in older patients discharged to LTCFs, residents of which may be at greatest risk of transmission and worse clinical outcomes. [ABSTRACT FROM AUTHOR]