Efficacy of Virtual Cognitive Behavioral Therapy on COVID-19 Pandemic Related Anxiety in a Middle Eastern Country: An Interventional Study

Introduction Emotional stress and anxiety during COVID-19 pandemic has gained a lot of attention. The capacity to withstand from the manipulated thinking and COVID-19 related stress and anxiety depends on the resilience level of an individual. Cognitive behavioral therapy (CBT) has patronizing benefits for people affected with altered mental health. Relieving COVID-19 related anxiety using CBT has beneficial impact on health and improves quality of life of people. Objective Aimed to relieve the anxiety of Omani population during COVID-19 pandemic using CBT. Methods This research utilized a pre-experimental one group pre-test post-test design. A non-probability convenient sampling technique was used to select 96 Omani people who fulfilled the inclusion criteria. The pre-anxiety level was assessed using CAS (Corona virus Anxiety Scale). The participants who scored above nine in the scale were given three sessions of CBT. Post-anxiety level was assessed using CAS after three CBT sessions. Results The study revealed that the level of anxiety reduced during post-test (6.35) after intervention when compared to pre-test (13.22). The CBT intervention was effective in reducing the anxiety in the post-test at p ≤ .000. Conclusion CBT is effective in reducing COVID-19 related anxiety among the Omani population. Therefore, this strategy is highly recommended in people having mental health issues.

Efficacy of Virtual Cognitive Behavioral Therapy on COVID-19 Pandemic Related Anxiety in a Middle Eastern Country: An Interventional Study.

Introduction: Emotional stress and anxiety during COVID-19 pandemic has gained a lot of attention. The capacity to withstand from the manipulated thinking and COVID-19 related stress and anxiety depends on the resilience level of an individual. Cognitive behavioral therapy (CBT) has patronizing benefits for people affected with altered mental health. Relieving COVID-19 related anxiety using CBT has beneficial impact on health and improves quality of life of people. Objective: Aimed to relieve the anxiety of Omani population during COVID-19 pandemic using CBT. Methods: This research utilized a pre-experimental one group pre-test post-test design. A non-probability convenient sampling technique was used to select 96 Omani people who fulfilled the inclusion criteria. The pre-anxiety level was assessed using CAS (Corona virus Anxiety Scale). The participants who scored above nine in the scale were given three sessions of CBT. Post-anxiety level was assessed using CAS after three CBT sessions. Results: The study revealed that the level of anxiety reduced during post-test (6.35) after intervention when compared to pre-test (13.22). The CBT intervention was effective in reducing the anxiety in the post-test at p ≤ .000. Conclusion: CBT is effective in reducing COVID-19 related anxiety among the Omani population. Therefore, this strategy is highly recommended in people having mental health issues.

Reliability Analysis using Bayesian Network for Medical Test of Covid-19

Covid-19 is a corona virus pandemic disease affected by a new corona virus. Maximum people infected by covid-19 will experience symptoms namely mild to moderate respiratory illness and recover without requiring any special treatment. However elderly people and those having underlying medical diseases such as diabetes, cardiovascular diseases, cancer and chronic respiratory disease are more prone to develop serious illness. Reliability analysis for medical test for covid-19 is performed using a Bayesian network. A Bayesian network (BN) is a probabilistic graphical model that represents knowledge about an uncertain domain where each node corresponds to a random variable and each edge represents the corresponding conditional probability. The BN is used to prioritize the factors that influence virus symptoms of covid-19. The BN model is constructed based on a list of general symptoms of covid-19. The marginal probabilities for all states are computed. The comparison of prior and conditional probabilities is determined. Using BN the reliability of medical test for covid-19 is obtained. © 2022 American Institute of Physics Inc.. All rights reserved.

A fractal fractional order vaccination model of COVID-19 pandemic using Adam’s moulton analysis

The pandemic caused by coronaviruses (SARS-COV-2) is a zoonotic disease targeting the respiratory tract of active humans. Few mild symptoms of fever and tiredness get cured without any medicinal aid , whereas some severe symptoms of dry cough with breathing illness led to perceived risk of secondary transmission. This paper studies the effectiveness of vaccination in Covid -19 pandemic disease by modelling three compartments susceptible, vaccinated and infected (SVI) of Atangana Baleanu of Caputo (ABC) type derivatives in non-integer order. The disease dynamics is analysed and its stability is performed. Numerical approximation is derived using Adam’s Moulton method and simulated to forecast the results for controllability of pandemic spread.

ADID-UNET-a segmentation model for COVID-19 infection from lung CT scans.

Currently, the new coronavirus disease (COVID-19) is one of the biggest health crises threatening the world. Automatic detection from computed tomography (CT) scans is a classic method to detect lung infection, but it faces problems such as high variations in intensity, indistinct edges near lung infected region and noise due to data acquisition process. Therefore, this article proposes a new COVID-19 pulmonary infection segmentation depth network referred as the Attention Gate-Dense Network- Improved Dilation Convolution-UNET (ADID-UNET). The dense network replaces convolution and maximum pooling function to enhance feature propagation and solves gradient disappearance problem. An improved dilation convolution is used to increase the receptive field of the encoder output to further obtain more edge features from the small infected regions. The integration of attention gate into the model suppresses the background and improves prediction accuracy. The experimental results show that the ADID-UNET model can accurately segment COVID-19 lung infected areas, with performance measures greater than 80% for metrics like Accuracy, Specificity and Dice Coefficient (DC). Further when compared to other state-of-the-art architectures, the proposed model showed excellent segmentation effects with a high DC and F1 score of 0.8031 and 0.82 respectively.

An integrated feature frame work for automated segmentation of COVID-19 infection from lung CT images.

The novel coronavirus disease (SARS-CoV-2 or COVID-19) is spreading across the world and is affecting public health and the world economy. Artificial Intelligence (AI) can play a key role in enhancing COVID-19 detection. However, lung infection by COVID-19 is not quantifiable due to a lack of studies and the difficulty involved in the collection of large datasets. Segmentation is a preferred technique to quantify and contour the COVID-19 region on the lungs using computed tomography (CT) scan images. To address the dataset problem, we propose a deep neural network (DNN) model trained on a limited dataset where features are selected using a region-specific approach. Specifically, we apply the Zernike moment (ZM) and gray level co-occurrence matrix (GLCM) to extract the unique shape and texture features. The feature vectors computed from these techniques enable segmentation that illustrates the severity of the COVID-19 infection. The proposed algorithm was compared with other existing state-of-the-art deep neural networks using the Radiopedia and COVID-19 CT Segmentation datasets presented specificity, sensitivity, sensitivity, mean absolute error (MAE), enhance-alignment measure (EMφ), and structure measure (S m) of 0.942, 0.701, 0.082, 0.867, and 0.783, respectively. The metrics demonstrate the performance of the model in quantifying the COVID-19 infection with limited datasets.