Assessment of the Psychological Stress Experienced by COVID Warriors during COVID-19 Pandemic in India

Introduction: In the fight against COVID-19, doctors, nurses, administrative staff, police personnel and other supporting staff have been in the frontline providing emergency services. While performing their duties, they are at risk of getting infections and transmitting them to their near and dear ones. This can lead to increased psychological stress levels among them. This study was conducted to assess the level of stress among health care workers and police personnel during the COVID-19 pandemic period in Delhi. Methodology: This is a cross-sectional study among the COVID-19 warriors working in designated COVID-19 hospitals using Google forms. The relationship between various social, demographic, and administrative factors and the level of stress experienced by the study subjects was assessed using Perceptive Stress Scale (PSS-10). Results: The results describe heightened severity of perception of stress among the study cohort. We found at least 10 risk factors that showed statistically significant association with increased TPSS in the studied cohort group. Conclusion: There is an urgent need for screening, proper diagnosis, and management of psychiatric issues among FLCWs, and for expanding mental health services for reducing stress among the target population. © 2021 Indian Society for Malaria and Communicable Diseases. All rights reserved.

Deep stacked ensemble learning model for COVID-19 classification

COVID-19 is a growing problem worldwide with a high mortality rate. As a result, the World Health Organization (WHO) declared it a pandemic. In order to limit the spread of the disease, a fast and accurate diagnosis is required. A reverse transcript polymerase chain reaction (RT-PCR) test is often used to detect the disease. However, since this test is time-consuming, a chest computed tomography (CT) or plain chest X-ray (CXR) is sometimes indicated. The value of automated diagnosis is that it saves time and money by minimizing human effort. Three significant contributions are made by our research. Its initial purpose is to use the essential finetuning methodology to test the action and efficiency of a variety of vision models, ranging from Inception to Neural Architecture Search (NAS) networks. Second, by plotting class activation maps (CAMs) for individual networks and assessing classification efficiency with AUC-ROC curves, the behavior of these models is visually analyzed. Finally, stacked ensembles techniques were used to provide greater generalization by combining finetuned models with six ensemble neural networks. Using stacked ensembles, the generalization of the models improved. Furthermore, the ensemble model created by combining all of the finetuned networks obtained a state-of-the-art COVID-19 accuracy detection score of 99.17%. The precision and recall rates were 99.99% and 89.79%, respectively, highlighting the robustness of stacked ensembles. The proposed ensemble approach performed well in the classification of the COVID-19 lesions on CXR according to the experimental results. © 2022 Tech Science Press. All rights reserved.