Exploring the Dynamics of COVID-19 with a Novel Family of Models

Much effort has recently been expended in developing efficient models that can depict the true picture for COVID-19 mortality data and help scientists choose the best-fit models. As a result, this research intends to provide a new G family for both theoretical and practical scientists that solves the concerns typically encountered in both normal and non-normal random events. The new-G distribution family is able to generate efficient continuous univariate and skewed models that may outperform the baseline model. The analytic properties of the new-G family and its sub-model are investigated and described, as well as a theoretical framework. The parameters were estimated using a classical approach along with an extensive simulation study to assess the behaviour of the parameters. The efficiency of the new-G family is discussed using one of its sub-models on COVID-19 mortality data sets. © 2023 by the authors.

A Unified Decision-Making Technique for Analysing Treatments in Pandemic Context

The COVID-19 pandemic has triggered a global humanitarian disaster that has never been seen before. Medical experts, on the other hand, are undecided on the most valuable treatments of therapy because people ill with this infection exhibit a wide range of illness indications at different phases of infection. Further, this project aims to undertake an experimental investigation to determine which treatments for COVID-19 disease is the most effective and preferable. The research analysis is based on vast data gathered from professionals and research journals, making this study a comprehensive reference. To solve this challenging task, the researchers used the HF AHP-TOPSIS Methodology, which is a well-known and highly effective Multi-Criteria Decision Making (MCDM) technique. The technique assesses the many treatment options identified through various research papers and guidelines proposed by various countries, based on the recommendations of medical practitioners and professionals. The review process begins with a ranking of different treatments based on their effectiveness using the HF-AHP approach and then evaluates the results in five different hospitals chosen by the authors as alternatives. We also perform robustness analysis to validate the conclusions of our analysis. As a result, we obtained highly corroborative results that can be used as a reference. The results suggest that convalescent plasma has the greatest rank and priority in terms of effectiveness and demand, implying that convalescent plasma is the most effective treatment for SARS-CoV-2 in our opinion. Peepli also has the lowest priority in the estimation. © 2022 Tech Science Press. All rights reserved.

An automated real-time face mask detection system using transfer learning with faster-rcnn in the era of the covid-19 pandemic

Today, due to the pandemic of COVID-19 the entire world is facing a serious health crisis. According to the World Health Organization (WHO), people in public places should wear a face mask to control the rapid transmission of COVID-19. The governmental bodies of different countries imposed that wearing a face mask is compulsory in public places. Therefore, it is very difficult to manually monitor people in overcrowded areas. This research focuses on providing a solution to enforce one of the important preventativemeasures of COVID-19 in public places, by presenting an automated system that automatically localizes masked and unmasked human faces within an image or video of an area which assist in this outbreak of COVID-19. This paper demonstrates a transfer learning approach with the Faster-RCNN model to detect faces that are masked or unmasked. The proposed framework is built by fine-tuning the state-of-the-art deep learning model, Faster-RCNN, and has been validated on a publicly available dataset named Face Mask Dataset (FMD) and achieving the highest average precision (AP) of 81% and highest average Recall (AR) of 84%. This shows the strong robustness and capabilities of the Faster-RCNN model to detect individuals with masked and un-masked faces. Moreover, this work applies to real-time and can be implemented in any public service area. © 2022 Tech Science Press. All rights reserved.

Security performance In Online Education systems in Saudi Arabia

Due to the spread of the COVID-19 virus globally and the transformation of traditional education into virtual education to reduce human contact and maintain social distancing, the Ministry of Education in the Kingdom of Saudi Arabia decided to solve the problem through distance education using a blackboard. For universities, colleges and Madrasati platform for all school categories. This search aims to identify weaknesses in e-learning platforms and discuss possible solutions to avoid them;the focus will be on the problem of unauthorized access. The percentage change in the percentage increase in the percentage increase in the area. The central issue of e-learning is that students can learn science courses in addition to sharing data. Viewpoint and contact in IS, study destination, international migration and other destination, network and international network. This virtual learning can help students develop new capabilities. This project explicitly centred around security and protection worries as the significant issues limiting understudy commitment. Due to the spread of the COVID-19 virus globally and the transformation of traditional education into virtual education to reduce human contact and maintain social distancing, the Ministry of Education in the Kingdom of Saudi Arabia decided to solve the problem through distance education using a blackboard. For universities, colleges and Madrasati platform for all school categories. This search aims to identify weaknesses in e-learning platforms and discuss possible solutions to avoid them. The focus will be on the problem of unauthorized access. The percentage change in the percentage increase in the percentage increase in the area. The central issue of e-learning is that students can learn science courses in addition to sharing data. Viewpoint and contact in IS, study destination, international migration and other destination, network and international network. This virtual learning can help students develop new capabilities. This paper explicitly centred around security and protection worries as the significant issues limiting understudy commitment.

Colorectal cancer screening during covid-19 pandemic;A systematic review

The pandemic of COVID 19 has led to the postponement of all elective procedures including screening colonoscopy due to the rising risk of infection with covid-19. Routine use of screening tests for colorectal cancer is not applicable during covid 19 pandemic including colonoscopy, computed tomographic colonography, colon capsule endoscopy, and fecal immunochemical test. Focused reviewing of the impact of covid-19 on the various diagnostic modalities for colorectal cancer screening is the objective of this review. Databases of PubMed, Embase, and Cochrane Library were searched for literature published before June 2020. This narrative review was created from a conscious dissection of different data obtained from the related articles. Careful categorical writing of the recommendations was done in an easy simple manner. The risk of the spread of COVID 19 infection could be higher after using aerosol-generating procedures such as upper and lower GIT endoscopy. Also, CT might raise the risk of infection. Colon capsule endoscopy may be considered a potentially valuable procedure for colorectal cancer screening during the pandemic of COVID 19. During the post-COVID-19 recovery phase, it is expected to have a high demand for colonoscopy services as waiting lists will grow in that time. Colon capsule endoscopy may be considered a valuable diagnostic modality for prioritizing those who will need screening colonoscopy. Colon capsule endoscopy seems to be superior to other modalities for the screening of colorectal cancer during the pandemic of COVID 19 while postponement of colonoscopy service. Colon capsule endoscopy can be used for triaging those requiring further endoscopic procedures. © 2021 Teikyo University School of Medicine. All rights reserved.

Immune monitoring of kidney transplant recipients after SARS-CoV-2 mRNA vaccination

Background: There is limited data on the safety and efficacy of SARS-CoV-2 mRNA vaccines in kidney transplant recipients (KTRs). Methods: We conducted a prospective, multi-center study of 58 adult KTRs receiving mRNA-BNT162b2 or mRNA-1273 vaccines to assess vaccine safety and efficacy. Primary outcome was biopsy-proven rejection within 3 months of vaccination. Secondary outcomes included adverse events, serum creatinine, proteinuria, donor-derived cell-free DNA (ddcfDNA) levels, and antibody and cellular immunity generation against SARSCoV-2. Results: Median age was 62 with 41% females. Median time post-transplantation was 48 months. Only one patient (2%) developed acute cellular rejection though patient had been recently converted to belatacept. There were no severe adverse events or deaths during follow-up. Two patients (3%) developed SARS-CoV-2 infection, one of whom required hospitalization. There was no significant change in serum creatinine, proteinuria or ddcfDNA during the study. Following vaccination, 36%, 25% and 20% of KTRs developed anti-spike, anti-S1 and anti-RBD antibodies. KTRs on mycophenolate-based and steroid-maintenance regimens were less likely to develop an anti-spike antibody response. 100% of KTRs with anti-spike and anti-RBD antibodies had a neutralizing response, compared to 44% in KTRs with anti-spike but without anti-RBD antibodies (RR 2.25, 95% CI 1.08-4.67). There was a significant increase in IFN-gamma spots per 106 PBMCs incubated with S1 peptides following vaccination (p=0.0143). Conclusions: SARS-CoV-2 vaccination in KTRs was safe and associated with the generation of cellular immune response and in a third of patients with anti-spike antibody response. The degree of protection gained by these responses needs to be evaluated in future studies.

Deep learning approach for analysis and characterization of COVID-19

Early diagnosis of a pandemic disease like COVID-19 can help deal with a dire situation and help radiologists and other experts manage human resources more effectively. In a recent pandemic, laboratories perform diagnostics manually, which requires a lot of time and expertise of the laboratorial technicians to yield accurate results. Moreover, the cost of kits is high, and well-equipped labs are needed to perform this test. Therefore, other means of diagnosis is highly desirable. Radiography is one of the existing methods that finds its use in the diagnosis of COVID-19. The radiography observes change in Computed Tomography (CT) chest images of patients, developing a deep learning-based method to extract graphical features which are used for automated diagnosis of the disease ahead of laboratory-based testing. The proposed work suggests an Artificial Intelligence (AI) based technique for rapid diagnosis of COVID-19 from given volumetric chest CT images of patients by extracting its visual features and then using these features in the deep learning module. The proposed convolutional neural network aims to classify the infectious and non-infectious SARS-COV2 subjects. The proposed network utilizes 746 chests scanned CT images of 349 images belonging to COVID-19 positive cases, while 397 belong to negative cases of COVID-19. Our experiment resulted in an accuracy of 98.4%, sensitivity of 98.5%, specificity of 98.3%, precision of 97.1%, and F1-score of 97.8%. The additional parameters of classification error, mean absolute error (MAE), root-mean-square error (RMSE), and Matthew's correlation coefficient (MCC) are used to evaluate our proposed work. The obtained result shows the outstanding performance for the classification of infectious and non-infectious for COVID-19 cases. © 2021 Tech Science Press. All rights reserved.

Impact assessment of covid-19 pandemic through machine learning models

Ever since its outbreak in the Wuhan city of China, COVID-19 pandemic has engulfed more than 211 countries in the world, leaving a trail of unprecedented fatalities. Even more debilitating than the infection itself, were the restrictions like lockdowns and quarantine measures taken to contain the spread of Coronavirus. Such enforced alienation affected both the mental and social condition of people significantly. Social interactions and congregations are not only integral part of work life but also formthe basis of human evolvement. However, COVID-19 brought all such communication to a grinding halt. Digital interactions have failed to enthuse the fervor that one enjoys in face-to-face meets. The pandemic has shoved the entire planet into an unstable state. The main focus and aim of the proposed study is to assess the impact of the pandemic on different aspects of the society in Saudi Arabia. To achieve this objective, the study analyzes two perspectives: The early approach, and the late approach of COVID-19 and the consequent effects on different aspects of the society. We used a Machine Learning based framework for the prediction of the impact of COVID-19 on the key aspects of society. Findings of this research study indicate that financial resources were the worst affected. Several countries are facing economic upheavals due to the pandemic and COVID-19 has had a considerable impact on the lives as well as the livelihoods of people. Yet the damage is not irretrievable and the world's societies can emerge out of this setback through concerted efforts in all facets of life. © 2021 Tech Science Press. All rights reserved.