COVID, Volume 2, Issue 2 (February 2022) – 9 articles

Background: To analyze the prevalence and impact of SARS-CoV-2 infection in people with lived experience of mental illness integrated into community-based psychosocial rehabilitation structures in Portugal. Methods: One hundred and thirty-nine people with lived experience of mental illness integrated into community-based psychosocial rehabilitation structures in Portugal answered an online survey that included dimensions related to COVID-19 pandemic prevalence, routine/lifestyle, social support, access to health care, mental health and well-being during the pandemic and confinement, and life satisfaction and postpandemic future expectations. Results: The results point to a low prevalence of SARS-CoV-2 infections in this sample. High levels of resilience and mental well-being were identified in the individuals. We also found that participants were satisfied with the social support during this phase and their routine/lifestyle. Conclusions: The study showed that the COVID-19 pandemic seems not to have had a significant negative impact on people with experience of mental illness integrated into community-based psychosocial rehabilitation structures in Portugal. However, more research in this field should be done in the future. Full article

Facing the worldwide coronavirus disease 2019 (COVID-19) pandemic, a new fitting method (QDF, quasi-distribution fitting) which can be used to analyze the data of COVID-19 is developed based on piecewise quasi-uniform B-spline curves. For any given country or district, it simulates the distribution histogram data which is made from the daily confirmed cases (or the other data including daily recovery cases and daily fatality cases) of COVID-19 with piecewise quasi-uniform B-spline curves. After using the area normalization method, the fitting curves could be regarded as a kind of probability density function (PDF): its mathematical expectation and the variance could be used to analyze the situation of the coronavirus pandemic. Numerical experiments based on the data of certain countries have indicated that the QDF method demonstrates the intrinsic characteristics of COVID-19 data of a given country or district, and because the interval of data used in this paper is over one year (500 days), it reveals the fact that after the multi-wave transmission of the coronavirus, the case fatality rate has obviously declined. These results show that the QDF method is effective and feasible as an appraisal method. Full article

The COVID-19 pandemic led to global healthcare consequences including insomnia. This survey used the Pittsburgh Sleep Quality Index (PSQI) to assess sleep quality at two time points (July 2020 and November 2020) among employees at a healthcare technology and services organization during the COVID-19 pandemic. Of the 1280 eligible employees, 251 complete responses (response rate, RR = 19.6%) in July and 108 (RR = 8.4%) in November were received and analyzed. The overall mean global PSQI scores were 7.3 ± 3.6 in July and 7.7 ± 3.6 in November 2020 (p > 0.05). There was no significant difference in any of the PSQI components or global scores between periods. Our findings indicate poor reported sleep quality among our study participants during the COVID-19 pandemic. Additional studies are needed to assess the longitudinal impact on sleep quality post-COVID-19 pandemic. Full article

Drug repurposing is a more inexpensive and shorter approach than the traditional drug discovery and development process. The concept of identifying a potent molecule from a library of pre-existing molecules or an already approved drug has become a go-to tactic to accelerate the identification of drugs that can prevent COVID-19. This seemingly uncontrollable disease is caused by SARS-CoV-2. It is a novel virus of the Betacoronavirus genus, exhibiting similarities to the previously reported SAR-CoV genome structure and viral pathogenesis. The emergence of SARS-CoV-2 and the rapid outbreak of COVID-19 have resulted in a global pandemic. Researchers are hard-pressed to develop new drugs for total containment of the disease, thus making the cost-effective drug repurposing a much more feasible approach. Therefore, the current review attempts to collate both the experimental and computational drug repurposing strategies that have been utilized against significant drug targets of SARS-CoV-2. Along with the strategies, the available druggable targets shall also be discussed. However, the occurrence of frequent recombination of the viral genome and time-bound primary analysis, resulting in insignificant data, are two major challenges that drug repurposing still faces. Full article

Background: Patients with cardiovascular disease and risk factors for cardiovascular illness are more likely to acquire severe 2019 novel coronavirus (2019-nCoV) infection (COVID-19). COVID-19 infection is more common in patients with cardiovascular illness, and they are more likely to develop severe symptoms. Nevertheless, whether COVID-19 patients are more likely to develop cardiovascular disorders such as acute myocardial infarction (AMI) is still up for debate. Methods: We will follow the preferred reporting items for systematic review and meta-analysis (PRISMA) to report our final study, including a systematic search of the bibliographic database using the appropriate combination of search terms or keywords. The choice of search terms is discussed in more detail later in this paper. The obtained results will be screened, and the data extracted from the studies selected for systematic review will be based on the predefined inclusion and exclusion criteria. Using the obtained data, we will then perform the associated Meta-analysis to generate the forest plot (pooled estimated effect size Hazard Ratio (HR) and 95% Confidence Intervals (CI) values) using the random-effects model. Any publication bias will be assessed using the funnel plot symmetry, Orwin and Classic Fail-Safe N Test and Begg and Mazumdar Rank Correlation Test and Egger’s Test of the intercept. In cases where insufficient data occur, we will also perform a qualitative review. Discussion: This systematic review will explore COVID-19 clinical outcomes, especially survival in patients hospitalised with Acute Myocardial Infarction, by utilising a collection of previously published data on hospitalised COVID-19 patients and Myocardial Infarction. Highlighting these prognostic survival analyses of COVID-19 patients with AMI will have significant clinical implications by allowing for better overall treatment strategies and patient survival estimates by offering clinicians a method of quantitatively analysing the pattern of COVID-19 cardiac complications. Full article

COVID-19 is a life-threatening infectious disease that has become a pandemic. The virus grows within the lower respiratory tract, where early-stage symptoms (such as cough, fever, and sore throat) develop, and then it causes a lung infection (pneumonia). This paper proposes a new artificial testing methodology to determine whether a patient has been infected by COVID-19. We have presented a prediction model based on a convolutional neural network (CNN) and our own developed mathematical equation-based algorithm named SymptomNet. The CNN algorithm classifies lung infections (pneumonia) using frontal chest X-ray images, and the symptom analysis algorithm (SymptomNet) predicts the possibility of COVID-19 infection from the developed symptoms in a patient. By combining the CNN image classifier method and SymptomNet algorithm, we have developed a model that predicts COVID-19 patients with an approximate accuracy of 96%. Ten out of the 13 symptoms were significantly correlated to the COVID-19 disease. Specially, fever, cough, body chills, shortness of breath, muscle pain, and sore throat were shown to be significantly related (r = 0.20; p = 0.001, r = 0.20; p < 0.001, r = 0.22; p < 0.001, r = 0.16; p < 0.001, r = −0.45; p < 0.001, r = −0.35; p < 0.001, respectively). In this model, the CNN classifier has an accuracy of approximately 96% (training loss = 0.1311, training accuracy = 0.9596, validation loss: 0.2754, and validation accuracy of 0.9273, F1-score: 94.16, precision: 91.33), and the SymptomNet algorithm has an accuracy of 97% (485 successful predictions out of 500 samples). This research work obtained promising accuracy while predicting COVID-19-infected patients. The proposed model can be ubiquitously used at a low cost and achieve high accuracy. Full article

The Coronavirus disease 2019 (COVID-19) pandemic has placed unprecedented demands on the whole of society. The public sector human resources (HR) function has played a key role in responding to COVID-19. This paper considers: How did public sector HR organizations in Northwest Florida respond during the COVID-19 pandemic? What are lessons learned from the perspectives of resilience and vulnerability? Interviews were conducted with HR professionals in Northwest Florida in early 2021. Responses suggested many points that show resilience, sensemaking, and adaptive capacity. However, some aspects of responses indicate the presence of vulnerability, as well as concerns with leadership and management. Full article

During the first UK national COVID-19 lockdown, there were fears that increased online gaming and gambling could negatively impact wellbeing. Using a cross-sectional retrospective change survey of 631 UK adult gamers and/or gamblers during the week the UK lockdown was partially lifted (June 2020), we investigated participation in gaming/gambling and relationships with problem gaming, problem gambling and wellbeing (using the following previously validated scales: the Internet Gaming Disorder Short Form; a short-form version of the Problem Gambling Severity Index; a short-form of the Warwick–Edinburgh Mental Well-Being Scale). Results indicated a near-doubling in gaming activity during lockdown and significant increases in problem gaming scores, but not in numbers of disordered gamers. Aggregate changes to gambling participation and problem gambling were negligible: decreases in offline and sports gambling were balanced by increases in online gambling. Wellbeing scores decreased during lockdown across the sample, particularly amongst women, and path analysis revealed moderate correlations between increases in problem gaming and gambling scores and reductions in wellbeing. We conclude that for some, maladaptive gaming/gambling coping strategies during the lockdown may have exacerbated its negative effects. Full article