RESUMO
Background: Coronavirus disease 2019 (COVID-19) has imposed a significant impact on populations and healthcare systems. Symptoms of post-COVID syndrome (PCS) persist for at least 12 months following COVID-19 infection leading to significant negative effects on these patients’ cognition, ability to work, physical activity, social interaction, and overall quality of life. Objective: This study aimed to investigate the relation between cognitive deficits, quality of life (QOL) and coping strategies in post COVID-19 survivors. Subjects and Methods: A hundred COVID-19 survivors from both genders participated in this study. Their cognition was evaluated using Montreal Cognitive Assessment (MoCA), the WHO Quality of Life Instrument-Short Form (WHOQOL-BREF) was employed to evaluate patients’ QOL and the Brief Coping Orientation to Problems Experienced (Brief-COPE) was used to assess their coping strategies. Results: A significant positive correlation was found between the scores of MoCA and all HRQOL domains (Physical health, Psychological, Social relationships, Environment, General health and General QOL). Also, a significant negative correlation was noted between scores of MoCA and Brief-COPE (Mal-Adaptive strategies) while no significant correlation was found between MoCA scores and Brief-COPE (Adaptive strategies). Conclusion: There is a relation between cognition deficits, QOL and non-adaptive coping strategies in post COVID-19 survivors, while, there is no relation between cognitive deficits and adaptive coping strategies in PCS patients.
RESUMO
The hybrid conjugate gradient (CG) method is among the efficient variants of CG method for solving optimization problems. This is due to their low memory requirements and nice convergence properties. In this paper, we present an efficient hybrid CG method for solving unconstrained optimization models and show that the method satisfies the sufficient descent condition. The global convergence prove of the proposed method would be established under inexact line search. Application of the proposed method to the famous statistical regression model describing the global outbreak of the novel COVID-19 is presented. The study parameterized the model using the weekly increase/decrease of recorded cases from December 30, 2019 to March 30, 2020. Preliminary numerical results on some unconstrained optimization problems show that the proposed method is efficient and promising. Furthermore, the proposed method produced a good regression equation for COVID-19 confirmed cases globally. © 2021 Institute of Advanced Engineering and Science. All rights reserved.